Decision Making in Recurrent Neuronal Circuits
Open accessAbstract
Decision making has recently emerged as a central theme in neurophysiological studies of cognition, and experimental and computational work has led to the proposal of a cortical circuit mechanism of elemental decision computations. This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options. Such a circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior. While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule. Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.
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Written from the paper’s full text.
TL;DR
This review proposes that decision making arises from recurrent cortical circuits with slow recurrent excitation balanced by fast feedback inhibition, producing attractor states and long transients for evidence accumulation and categorical choice. It integrates monkey physiology, human imaging, and biophysical modeling to argue that reward-dependent synaptic plasticity and intrinsic stochastic dynamics explain adaptive and probabilistic choice.
Key findings
- A recurrent neural circuit model with strong recurrent excitation and feedback inhibition can instantiate attractor states for categorical choices while using slow transients to accumulate evidence; NMDA-receptor-mediated synapses are proposed as a key substrate, with an example network time constant of τnetwork = τsyn/(|1 − wrec|), where τsyn = 100 ms and wrec = 0.9 gives τnetwork = 1 s.
- In the random-dot motion task, monkey response time increased by ~400 ms from easiest (c′ = 100%) to hardest (c′ = 3.2%), and LIP neurons showed ramping activity whose threshold was independent of c′ and response time; the model reproduces longer RTs in error trials and performance plateaus at long viewing times.
- Decision threshold is a core concept for reaction-time tasks but can be dissociated from a general decision rule; threshold may be implemented downstream in superior colliculus movement neurons and gated by basal ganglia, with cortico-striatal synaptic strength as a candidate locus for adaptive tuning.
- Reward-dependent synaptic plasticity in the decision circuit can produce adaptive choice behavior, including matching law behavior and undermatching in foraging tasks, and can generate quasirandom behavior in competitive games through stochastic synaptic dynamics rather than an explicit random number generator.
Methods
- Design: narrative review and theoretical synthesis, not a primary experimental study.
- Data or sample: synthesizes electrophysiological findings from behaving monkeys (e.g., LIP, VIP, MT, prefrontal, premotor), human EEG/fMRI studies, and prior computational models; no new experimental sample is reported.
- Main analysis: conceptual integration of experimental results with biophysically based spiking recurrent network models, attractor/decision-space analysis, and phase-plane analyses.
Limitations
- The paper states that most ramping-activity recordings are from one cell at a time and usually reported as trial-averaged activity, leaving open whether single-trial spike trains show quasilinear ramps or sudden jumps with variable timing.
- The paper states that it remains unclear whether decision-related parietal activity is generated locally or reflects inputs from elsewhere, and that prefrontal recordings in the reaction-time version of the RDM task are lacking.
- The paper states that cellular and synaptic mechanisms of ramping activity remain to be elucidated experimentally, whether LIP acts as an attractor network has been questioned, and the site of valuation-related synaptic plasticity and learning with long-delayed outcomes are largely unknown; as a review, it also does not provide new primary data or systematic review methodology (evident from the text).
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 169 cited sources checked · 0 supported · 0 flagged
- Findingcitation
In VIP but not MT, neuronal activity was significantly larger than baseline in false-alarm trials.
“In VIP but not MT, neuronal activity was significantly larger than baseline in false-alarm trials, suggesting that the subjective judgment was computed downstream of MT.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cook and Maunsell, 2002 source
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- Findingcitation
In rodents, olfactory discrimination is fast (~300 ms) in rats.
“In rodents, a study reported that olfactory discrimination is fast (~300 ms) in rats.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Uchida and Mainen, 2003
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- Findingcitation
In a visual motion detection task, ventral intraparietal (VIP) neurons exhibited ramping activity correlated with subjective judgment and response time.
“Another physiological study reported that, while monkeys performed a task of detecting the presence of a visual motion stimulus, neurons in ventral intraparietal (VIP) area of the posterior parietal cortex exhibited ramping activity that was correlated with the subjective judgment (a higher activity in hit and false-alarm trials than in miss and correct rejection trials) and response time (ranging”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cook and Maunsell, 2002 source
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- Findingcitation
In a reaction time version of the random-dot motion task, the subject's response time increased by ~400 ms from the easiest (c′ = 100%) to the hardest (c′ = 3.2%).
“Moreover, in a reaction time version of the task (Roitman and Shadlen, 2002; Huk and Shadlen, 2005), the subject’s response time increased by ~400 ms from the easiest (with c′ = 100%) to the hardest (with c′ = 3.2%) (Figures 1A and 1B).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Findingown data
LIP cells exhibited characteristic firing time courses that reflected the monkey's response time and perceptual choice.
“LIP cells exhibited characteristic firing time courses that reflected the monkey’s response time and perceptual choice (Figures 1C and 1D).”
Quote in the paperSubstantial claim0 of 2 sources checked
- Findingcitation
The ramping slope of LIP neurons was larger with a higher motion coherence c′.
“The ramping slope was larger with a higher c′ (a higher quality of sensory information).”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingcitation
Decision-related activity has also been observed in the prefrontal cortex.
“Decision-related activity has also been observed in the prefrontal cortex, a high-level cognitive area that plays a major role in time integration and a gamut of other cognitive functions (Fuster, 2008; Miller and Cohen, 2001; Wang, 2006a).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Fuster, 2008
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- cites Miller and Cohen, 2001 source
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- cites Wang, 2006a
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- Findingcitation
In a fixed-duration version of the random-dot motion direction discrimination task, activity of recorded neurons correlated with decision during motion viewing and with motor response after the delay could be temporally dissociated.
“In contrast to the RT version, now the activity of recorded neurons correlated with decision (during motion viewing) and that with motor response (after the delay) could be temporally dissociated.”
Quote in the paperSubstantial claim0 of 3 sources checked - Findingcitation
MT neural responses were larger in hits and misses.
“MT neural responses were larger in hits (with successful detection) and misses (failure), implying that the strength of signals provided by MT neurons to higher areas was stronger in trials with ultimately successful detection than in failed trials.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cook and Maunsell, 2002 source
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- Findingcitation
Activity of neurons in the lateral intraparietal area (LIP) was correlated with monkey's perceptual choice in both correct and error trials.
“Shadlen and Newsome found that activity of neurons in the lateral intraparietal area (LIP) was correlated with monkey’s perceptual choice in both correct and error trials (Shadlen and Newsome, 1996, 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Shadlen and Newsome, 1996, 2001
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- Findingcitation
From the onset of a random-dot motion stimulus until the monkey produced a choice response, spike activity of LIP neurons selective for a particular saccadic target increased for hundreds of milliseconds.
“From the onset of a random-dot motion stimulus until the time the monkey produced a choice response by a rapid saccadic eye movement, spike activity of LIP neurons selective for a particular saccadic target increased for hundreds of milliseconds.”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingown data
The decision choice was made when the firing rate of LIP neurons selective for that choice response reached a threshold that was independent of c′ and the response time.
“Furthermore, it was observed that the decision choice (as indicated by a saccade) was made when the firing rate of LIP neurons (selective for that choice response) reached a threshold that was independent of c′ and the response time.”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingcitation
In an oculomotor delayed response task, neurons in both LIP and prefrontal cortex display directionally tuned persistent activity.
“For instance, in an oculomotor delayed response task, neurons in both LIP and prefrontal cortex display directionally tuned persistent activity (Gnadt and Andersen, 1988; Funahashi et al., 1989).”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingcitation
In a picture recognition task, areas that exhibited a gradual buildup in activity peaking in correspondence with the time of recognition were the parietal and frontal areas as well as the inferior temporal area.
“In another study (Ploran et al., 2007), as a subject viewed pictures that were revealed from a blank screen gradually in eight steps (2 s each), the areas that exhibited a gradual buildup in activity peaking in correspondence with the time of recognition were the parietal and frontal areas as well as the inferior temporal area.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ploran et al., 2007 source
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- Findingreasoning
At low c′, noise can induce the system's trajectory to cross the boundary between attraction basins, leading the system to evolve to the decision state B in an error trial.
“However, at low c′ the bias is not strong, and noise can induce the system’s trajectory to travel across the boundary of the two attraction basins, in which case the system eventually evolves to the decision state B in an error trial (red).”
Quote in the paperSubstantial claim - Findingown data
With a varying c′, the ramping activity is faster when the quality of sensory data is higher.
“With a varying c′, the ramping activity is faster when the quality of sensory data is higher (Figure 3C).”
Quote in the paperSubstantial claim - Findingreasoning
In the attractor landscape, when sensory evidence favors choice A, attractor A has a larger basin of attraction than attractor B.
“In this example, the sensory evidence is in favor of the choice A, so attractor A has a larger basin of attraction (orange) than that of attractor B (brown).”
Quote in the paperSubstantial claim - Findingreasoning
In a simulation with zero motion strength, the firing rates of the two competing neural populations initially ramp up together for hundreds of milliseconds before diverging when one increases and the other declines.
“At the stimulus onset, the firing rates (rA and rB) of the two competing neural populations initially ramp up together for hundreds of milliseconds before diverging from each other when one increases (by virtue of recurrent excitation within that neural pool) while the other declines (due to winner-take-all competition mediated by feedback inhibition).”
Quote in the paperSubstantial claim - Findingcitation
Similar detection psychometric functions were obtained with premotor cortex microstimulation or mechanical stimulation.
“Similar detection psychometric functions were obtained with premotor cortex miscrostimulation or mechanical stimulation, suggesting that the stimulated frontal site may be causally related to this decision behavior.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites de Lafuente and Romo, 2005
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- Findingnone given
After the stimulus is withdrawn, the network stores the decision choice in working memory as persistent activity that is insensitive to c′.
“Third, after the stimulus is withdrawn, the network stores the decision choice in working memory, in the form of persistent activity that is insensitive to c′.”
Quote in the paperSubstantial claim - Findingcitation
Activity of premotor neurons in the frontal lobe, but not that of primary somatosensory neurons, covaried with trial-by-trial subjective reports of whether a vibrotactile stimulus was present or absent.
“de Lafuente and Romo showed that activity of premotor neurons in the frontal lobe, but not that of primary somatosensory neurons, covaried with trial-by-trial subjective reports (whether a stimulus was present or absent) (de Lafuente and Romo, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites de Lafuente and Romo, 2005
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- Findingown data
In a somatosensory delayed discrimination task, neurons in the inferior convexity of the prefrontal cortex and the premotor cortex showed persistent activity during the delay, with firing rate increasing (a plus cell) or decreasing (a minus cell) monotonically with the stimulus frequency.
“It was found that neurons in the inferior convexity of the prefrontal cortex and the premotor cortex showed persistent activity during the delay, with the firing rate of memory activity increasing (a “plus cell”) or decreasing (a “minus cell”) monotonically with the stimulus frequency (Figures 2C and 2D).”
Quote in the paperSubstantial claim - Findingreasoning
If τsyn = 100 ms for NMDA receptor-mediated synapses and wrec = 0.9, then τnetwork = 1 s.
“For instance, if τsyn = 100 ms for the NMDA receptor-mediated synapses (Hestrin et al., 1990), and wrec = 0.9, then τnetwork = 1 s.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hestrin et al., 1990 source
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- Findingcitation
In the model, the synaptic strengths (cA and cB) are proportional to the returns (the average reward per choice) rather than the incomes (the average reward per trial) of the two targets.
“It turns out that, in the model, the synaptic strengths (cA and cB) are proportional to the returns (the average reward per choice) rather than the incomes (the average reward per trial) of the two targets (Figure 5D).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
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- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Findingcitation
Neural activity of LIP neurons selective for a saccadic response was modulated by a representation of the outcome value, defined behaviorally as a leaky integration of past rewards on that target.
“Moreover, neural activity of LIP neurons selective for a saccadic response was modulated by a representation of the outcome value, which was defined behaviorally as a leaky integration of past rewards on that target (Sugrue et al., 2004).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sugrue et al., 2004 source
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- Findingcitation
The model reproduced salient behavioral observations in monkeys playing matching pennies with a computer opponent.
“The model reproduced salient behavioral observations (Soltani et al., 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani et al., 2006
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- Findingown data
The threshold of cortical firing activity read out by the downstream motion system has the same narrow distribution regardless of ramping speed or reaction times.
“However, the threshold of cortical firing activity that is read out by the downstream motion system has the same narrow distribution (insert in Figure 4C), regardless of the ramping speed or reaction times.”
Quote in the paperSubstantial claim - Findingown data
The extended multiple-circuit model reproduced the monkey's behavioral performance and reaction times quantitatively.
“This model reproduced the monkey’s behavioral performance and reaction times quantitatively (Figure 4D).”
Quote in the paperSubstantial claim - Findingreasoning
The model reproduces the matching behavior: as the reward rate λA/λB varies from one block of trials to the next block, the model's behavior changes quickly, so that the probability of choosing A versus B matches approximately λA/λB.
“The model reproduces the matching behavior: as the reward rate λA/λB varies from one block of trials to the next block, the model’s behavior changes quickly, so that the probability of choosing A versus B matches approximately λA/λB (Figure 5F).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
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- Findingcitation
As observed in LIP, in the model, neurons are modulated by the values of the response options, even though they are not directly responsible for valuation itself.
“As observed in LIP, in the model, neurons are modulated by the values of the response options (Figure 5E), even though they are not directly responsible for valuation itself.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
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- Findingcitation
These behavioral and neurophysiological observations were reproduced in the neural circuit model of decision making endowed with reward-dependent plasticity.
“These behavioral and neurophysiological observations were reproduced in the neural circuit model of decision making endowed with reward-dependent plasticity (Soltani and Wang, 2006) (Figures 5D–5G).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
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- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Findingcitation
Monkey's choice behavior is fit well by a softmax function of the difference in the two incomes.
“Interestingly, the monkey’s choice behavior is fit well by a softmax function of the difference in the two incomes (Corrado et al., 2005) (Figure 5C).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Corrado et al., 2005 source
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- Findingown data
When the task is more difficult, with a lower motion coherence, ramping activity is slower, leading to longer reaction times.
“Moreover, when the task is more difficult (with a lower motion coherence), ramping activity is slower, leading to longer reaction times.”
Quote in the paperSubstantial claim - Findingcitation
Monkey's behavior conformed to the matching law, which states that a subject allocates her or his choices in a proportion which matches the relative reinforcement obtained from these choices.
“The studies found that monkey’s behavior conformed to the matching law, which states that a subject allocates her or his choices in a proportion which matches the relative reinforcement obtained from these choices (Herrnstein et al., 1997) (Figures 5A and 5B).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Sugrue et al., 2004 source
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- cites Lau and Glimcher, 2005, 2008
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- cites Herrnstein et al., 1997
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- Findingcitation
Trial-to-trial stimulus variation had no discernible effect on the trial-to-trial variance of firing activity in MT neurons.
“Interestingly, it was found that the trial-to-trial stimulus variation had no discernible effect on the trial-to-trial variance of firing activity in MT neurons (Britten et al., 1993), nor on the relationship between MT neural responses and behavioral choice (Britten et al., 1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Britten et al., 1993 source
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- Findingcitation
When the opponent is fully interactive according to the rules of matching pennies, the model behavior becomes quasirandom.
“Similar to monkey’s behavior, when the opponent is fully interactive according to the rules of matching pennies, the model behavior becomes quasirandom.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani et al., 2006
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- Findingcitation
In situations when there is a conflict, for instance when the subject has to inhibit a planned saccade by a stop signal introduced with a short delay after the target onset, the behavior becomes probabilistic.
“In situations when there is a conflict, for instance when the subject has to inhibit a planned saccade by a stop signal introduced with a short delay after the target onset, the behavior becomes probabilistic (the saccade is suppressed on some trials, but not on other trials) (Logan and Cowan, 1984; Boucher et al., 2007).”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingcitation
Trial-to-trial stimulus variation had no discernible effect on the relationship between MT neural responses and behavioral choice.
“Interestingly, it was found that the trial-to-trial stimulus variation had no discernible effect on the trial-to-trial variance of firing activity in MT neurons (Britten et al., 1993), nor on the relationship between MT neural responses and behavioral choice (Britten et al., 1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Britten et al., 1996 source
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- Findingcitation
A saccade was initiated when the firing activity reached a threshold level of movement neurons in the FEF.
“In monkey physiological experiments, Hanes and Schall found that a saccade was initiated when the firing activity reached a threshold level of movement neurons in the FEF (Hanes and Schall, 1996; Schall, 2001).”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingcitation
Saccade response time fluctuates considerably from trial to trial.
“However, saccade response time (the time between target onset and saccadic eye movement) fluctuates considerably from trial to trial (Carpenter, 1981).”
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- Findingcitation
There is evidence for a trial-to-trial correlation between the response latency and the preparatory activity of cortical movement neurons, before the target onset, in saccade and other sensory-motor tasks.
“There is also evidence for a trial-to-trial correlation between the response latency and the preparatory activity of cortical movement neurons, before the target onset, in saccade and other sensory-motor tasks (Dorris et al., 1997; Churchland et al., 2006; Nakahara et al., 2006).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationreasoning
The proposed cortical circuit mechanism produces long transients for gradually accumulating evidence in favor of or against alternative options.
“This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Recurrent synaptic excitation is sufficiently strong to generate multiple self-sustained stable states of neural populations (attractor states).
- Interpretationreasoning
The proposed cortical circuit mechanism instantiates attractor states for forming categorical choices.
“This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Recurrent synaptic excitation is sufficiently strong to generate multiple self-sustained stable states of neural populations (attractor states).
- Interpretationnone given
The proposed cortical circuit mechanism of elemental decision computations depends on slow recurrent synaptic excitation balanced by fast feedback inhibition.
“This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Recurrent synaptic excitation is sufficiently strong to generate multiple self-sustained stable states of neural populations (attractor states).
- Rests on an assumption: In the mammalian brain, a decision is not made by single cells, but by the collective dynamics of a neural circuit.
- Rests on an assumption: Decision computations depend on cortical circuits endowed with an abundance of positive and negative feedback loops, the behavior of which is not readily predictable by intuition alone.
- Interpretationnone given
A recurrent circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior.
“Such a circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior.”
Quote in the paperSubstantial claim - Interpretationreasoning
LIP neurons display stochastic ramping to a set level, as expected for a neural integrator.
“Therefore, these LIP neurons display stochastic ramping to a set level, as expected for a neural integrator.”
Quote in the paperSubstantial claim - Interpretationcitation
The strength of signals provided by MT neurons to higher areas was stronger in trials with ultimately successful detection than in failed trials.
“MT neural responses were larger in hits (with successful detection) and misses (failure), implying that the strength of signals provided by MT neurons to higher areas was stronger in trials with ultimately successful detection than in failed trials.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cook and Maunsell, 2002 source
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- Interpretationnone given
In the unified framework, probabilistic choices result from irregular neuronal activity.
“Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.”
Quote in the paperNot a substantial claim - Premisecitation
Selection among a set of possible actions is a form of choice that can occur quickly, when speed is at a premium.
“Selection among a set of possible actions is a form of choice that can occur quickly, when speed is at a premium.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Real, 1991 source
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- Interpretationcitation
The subjective judgment was computed downstream of MT.
“In VIP but not MT, neuronal activity was significantly larger than baseline in false-alarm trials, suggesting that the subjective judgment was computed downstream of MT.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Cook and Maunsell, 2002 source
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- Premisecitation
There is often a tradeoff between speed and accuracy: performance improves with slower response times.
“There is often a tradeoff between speed and accuracy: performance improves with slower response times (Wickelgren, 1977).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wickelgren, 1977 source
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- Interpretationnone given
Perceptual decisions and value-based economic choices are described within a unified framework.
“Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.”
Quote in the paperSubstantial claim - Premisecitation
The posterior parietal cortex plays a major role in selective visual attention.
“The posterior parietal cortex plays a major role in selective visual attention (Colby and Godberg, 1999; Corbetta and Shulman, 2002; Sereno and Amador, 2006; Ganguli et al., 2008), but it seems unlikely that neural signals observed in these perceptual decision experiments can be solely attributed to attentional effects.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Colby and Godberg, 1999
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- cites Corbetta and Shulman, 2002 source
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- cites Sereno and Amador, 2006
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- Interpretationreasoning
It seems unlikely that neural signals observed in these perceptual decision experiments can be solely attributed to attentional effects.
“The posterior parietal cortex plays a major role in selective visual attention (Colby and Godberg, 1999; Corbetta and Shulman, 2002; Sereno and Amador, 2006; Ganguli et al., 2008), but it seems unlikely that neural signals observed in these perceptual decision experiments can be solely attributed to attentional effects.”
Quote in the paperSubstantial claim - Interpretationcitation
The parietal cortex is part of the brain system underlying accumulation of information and subjective judgment in visual perception.
“This study thus supports the notion that the parietal cortex is part of the brain system underlying accumulation of information and subjective judgment in visual perception.”
Quote in the paperSubstantial claim - Interpretationnone given
In the unified framework, probabilistic choices result from iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.
“Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Attractor states can be created or destroyed by inputs.
“Attractor states can be created or destroyed by inputs; hence, the same network can subserve different functions, such as decision making during stimulus presentation followed by active memory of the choice across a delay in the fixed-duration RDM task.”
Quote in the paperSubstantial claim - Interpretationcitation
Growing evidence from human neuroimaging and monkey single-neuron physiology suggests that the parietal and frontal cortices form a core brain system for temporal accumulation of data and categorical choice formation in perceptual judgments.
“Therefore, growing evidence from human neuroimaging and monkey single-neuron physiology suggests that the parietal and frontal cortices form a core brain system for temporal accumulation of data and categorical choice formation in perceptual judgments.”
Quote in the paperSubstantial claim - Interpretationreasoning
An attractor network is not limited to steady-state behavior but can use long transients to perform computations.
“As illustrated by the above example, an attractor network is not just limited to steady-state behavior but can use long transients to perform interesting computations.”
Quote in the paperSubstantial claim - Premisecitation
A leading candidate mechanism for the generation of persistent activity is strong recurrent excitation in a local cortical circuit that gives rise to stimulus-selective attractor states.
“A leading candidate mechanism for the generation of persistent activity is strong recurrent excitation in a local cortical circuit that gives rise to stimulus-selective attractor states—self-sustained population activity patterns of a neural network (Amit, 1995; Goldman-Rakic, 1995; Wang, 2001).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Amit, 1995 source
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- cites Goldman-Rakic, 1995 source
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- cites Wang, 2001 source
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- The authors state this as an assumption: Recurrent synaptic excitation is sufficiently strong to generate multiple self-sustained stable states of neural populations (attractor states).
- Premisecitation
Within each pyramidal neural group, strong recurrent excitatory connections can sustain persistent activity triggered by a transient preferred stimulus.
“Within each pyramidal neural group, strong recurrent excitatory connections can sustain persistent activity triggered by a transient preferred stimulus.”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisenone given
LIP neural activity is primarily correlated with the decision choice, even in error trials or when the motion strength is zero.
“First, neural activity is primarily correlated with the decision choice (even in error trials or when the motion strength is zero).”
Quote in the paperNot a substantial claim - Interpretationreasoning
Decision making and working memory can be subserved by the same cortical mechanism, presumably residing in the parietofrontal circuit.
“These results suggest that decision making and working memory can be subserved by the same cortical mechanism, presumably residing in the parietofrontal circuit.”
Quote in the paperSubstantial claim - Interpretationcitation
The stimulated frontal site may be causally related to this decision behavior.
“Similar detection psychometric functions were obtained with premotor cortex miscrostimulation or mechanical stimulation, suggesting that the stimulated frontal site may be causally related to this decision behavior.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites de Lafuente and Romo, 2005
Not checked in the free run.
- Interpretationreasoning
The recurrent neural circuit model captures important features of activity of LIP cells recorded from behaving monkeys.
“The model captures important features of activity of LIP cells recorded from behaving monkeys.”
Quote in the paperSubstantial claim - Premisenone given
Decision-related neural activity has been reported in cortical areas that typically exhibit mnemonic persistent activity during working memory maintenance.
“A clue came with the observation that decision-related neural activity has been reported in cortical areas that typically exhibit mnemonic persistent activity during working memory maintenance.”
Quote in the paperSubstantial claim - Interpretationreasoning
The same recurrent network can subserve different functions, such as decision making during stimulus presentation followed by active memory of the choice across a delay in the fixed-duration RDM task.
“Attractor states can be created or destroyed by inputs; hence, the same network can subserve different functions, such as decision making during stimulus presentation followed by active memory of the choice across a delay in the fixed-duration RDM task.”
Quote in the paperSubstantial claim - Interpretationcitation
A decision is likely to involve parallel processing across brain regions rather than proceeding strictly in serial stages.
“Rather than proceeding strictly in serial stages, a decision is likely to involve parallel processing across brain regions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Cisek, 2006 source
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- Premisecitation
In a two-pool version of the recurrent neural circuit model, subpopulations of spiking neurons are selective for two-choice alternatives.
“In a two-pool version of the model, subpopulations of spiking neurons are selective for two-choice alternatives (e.g., A = left motion, B = right motion).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
The two neural groups compete through feedback inhibition from interneurons.
“The two neural groups compete through feedback inhibition from interneurons.”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Miller et al. showed that fine-tuning of connectivity in the somatosensory discrimination model yields a line attractor capable of parametric working memory.
“Miller et al. (Miller et al., 2003) showed that fine-tuning of connectivity in this model yields a line attractor capable of parametric working memory, similar to the one in the gaze-control system (Seung et al., 2000).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationnone given
In the recurrent circuit model, ramping activity eventually stops as an attractor state is reached.
“In the recurrent circuit model, ramping activity eventually stops as an attractor state is reached (Figure 3D).”
Quote in the paperSubstantial claim - Premisecitation
The impact of a brief motion pulse in addition to the random-dot stimulus was greater with an earlier onset time.
“This prediction is also confirmed by the monkey experiment, which showed that the impact of a brief motion pulse in addition to the random-dot stimulus was greater with an earlier onset time (Huk and Shadlen, 2005; Wong et al., 2007).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
The violation of time-shift invariance cannot be accounted for by the inclusion of a leak.
“This violation of time-shift invariance cannot be accounted for by the inclusion of a leak.”
Quote in the paperSubstantial claim - Premisecitation
In the recurrent circuit model, competition between neural pools selective for choice alternatives is instantiated by lateral synaptic inhibition.
“In the recurrent circuit model, competition between neural pools selective for choice alternatives is instantiated by lateral synaptic inhibition (Wang, 2002; Wong and Wang, 2006).”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
In LIP data from the RDM experiment, the dynamics within each of the two selective neural pools is dominated by a slow mode.
“This is consistent with the finding that, in the LIP data recorded from the RDM experiment (Roitman and Shadlen, 2002), the dynamics within each of the two selective neural pools is dominated by a slow mode (Ganguli et al., 2008); thus, the description of two competing neural pools requires two dynamical variables.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
Two neural populations coupled by reciprocal inhibition exhibit persistent activity that is positively and negatively monotonically tuned to the first frequency f1, as observed in prefrontal neurons.
“Moreover, two such neural populations coupled by reciprocal inhibition exhibit persistent activity that is positively and negatively, respectively, monotonically tuned to the first frequency f1, as observed in prefrontal neurons in this task (Romo et al., 1999; Brody et al., 2003; Miller et al., 2003).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Recurrent excitation must be balanced by feedback inhibition.
“Recurrent excitation must be balanced by feedback inhibition (Brunel and Wang, 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Brunel and Wang, 2001
Not checked in the free run.
- Premisecitation
In contrast to the one-dimensional diffusion model, the dynamics of the attractor neural network is inherently two dimensional.
“Interestingly, in contrast to the one-dimensional diffusion model, a “decision-space” analysis (Figure 3D) showed that the dynamics of the attractor neural network is inherently two dimensional (Wong and Wang, 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wong and Wang, 2006 source
Not checked in the free run.
- Interpretationcitation
A two-variable model is needed to explain the observation that LIP neuronal activity displays a biphasic time course, with neurons selective for the two opposite targets first ramping up together before diverging away from each other.
“A two-variable model is needed to explain the observation that LIP neuronal activity displays a biphasic time course, with neurons selective for the two opposite targets first ramping up together before diverging away from each other (Roitman and Shadlen, 2002; Huk and Shadlen, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation
In contrast to the recurrent circuit model, the LCA model predicts that later, not earlier, signals influence more the ultimate decision.
“In fact, in contrast to the recurrent circuit model, the LCA model actually predicts that later, not earlier, signals influence more the ultimate decision, because an earlier pulse is gradually “forgotten” due to the leak and does not affect significantly the decision that occurs much later (Wong et al., 2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wong et al., 2007 source
Not checked in the free run.
- Premisecitation
One candidate cellular substrate for slow reverberation is NMDA receptors at local recurrent excitatory synapses.
“As initially proposed for working memory, one candidate cellular substrate for slow reverberation is the NMDA receptors at local recurrent excitatory synapses (Wang, 1999).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wang, 1999 source
Not checked in the free run.
- Premisecitation
FEF and SC are brain regions essential for selecting, preparing, and initiating saccadic eye movements.
“In the case of oculomotor tasks, a natural candidate is movement neurons in the frontal eye field (FEF) and superior colliculus (SC), which are brain regions essential for selecting, preparing, and initiating saccadic eye movements.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Hanes and Schall, 1996 source
Not checked in the free run.
- cites Munoz and Fecteau, 2002
Not checked in the free run.
- Interpretationcitation
In the attractor network model, the influence of newly arriving inputs diminishes over time as the network converges toward one of the attractor states representing the alternative choices.
“Third, the attractor network model has been shown to be able to subtract negative signals as well as add positive evidence about choice alternatives, but the influence of newly arriving inputs diminishes over time, as the network converges toward one of the attractor states representing the alternative choices (Wang, 2002).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wang, 2002 source
Not checked in the free run.
- Premisecitation
A neural implementation of the diffusion model yields the opposite effect: shorter response times in error trials than in correct trials.
“By contrast, a neural implementation of the diffusion model yields the opposite effect (Mazurek et al., 2003).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Mazurek et al., 2003 source
Not checked in the free run.
- Interpretationreasoning
In the recurrent circuit model, performance plateaus at sufficiently long stimulus-viewing times.
“Consequently performance plateaus at sufficiently long stimulus-viewing times (Wang, 2002).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wang, 2002 source
Not checked in the free run.
- Premisecitation
Machens et al. showed that such a circuit could perform discrimination computation during the comparison period, provided that a switching mechanism was posited for the afferent stimuli.
“Machens et al. (2005) showed that such a circuit could also perform discrimination computation (f1 > f2 or f1 < f2) during the comparison period (Figure 2E), provided that a switching mechanism was posited for the afferent stimuli, such that the second stimulus reached the memory/decision circuit with the opposite sign of tuning to the first stimulus.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Machens et al., 2005 source
Not checked in the free run.
- Premisenone given
The prediction of positive and negative trial-by-trial correlations between neural pairs is confirmed by neural data recorded from the prefrontal cortex.
“This prediction is confirmed by neural data recorded from the prefrontal cortex.”
Quote in the paperSubstantial claim - Premisecitation
The diffusion model assumes that a difference signal about the conflicting inputs is computed at the input level.
“The diffusion model assumes that a difference signal about the conflicting inputs is computed occurs at the input level (Ratcliff, 1978; Mazurek et al., 2003).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
The network time constant τnetwork = τsyn/(|1 − wrec|) is longer than τsyn in the presence of wrec.
“A simple estimate of the network’s time constant is given by τnetwork = τsyn/(|1 − wrec|) (where τsyn is the synaptic time constant, and wrec is the strength of recurrent connections), which is longer than τsyn in the presence of wrec (Seung, 1996; Wang, 2001).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Taken together, these results further support slow reverberating dynamics as a general mechanism for both working memory and decision making.
“Taken together, these results further support slow reverberating dynamics as a general mechanism for both working memory as well as decision making.”
Quote in the paperSubstantial claim - Premisecitation
The recurrent circuit model produces longer response times in error trials than in correct trials.
“First, the recurrent circuit model produces longer response times in error trials than in correct trials (Wong and Wang, 2006), consistent with the monkey experiment (Roitman and Shadlen, 2002).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
As a result of synaptic modifications, the input strengths for the competing neural groups of the decision network vary from trial to trial, leading to adaptive dynamics of choice behavior.
“As a result of synaptic modifications, the input strengths for the competing neural groups of the decision network vary from trial to trial, leading to adaptive dynamics of choice behavior.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Premisecitation
We used binary synapses that undergo a stochastic Hebbian learning rule, namely that synaptic plasticity depends on coactivation of presynaptic and postsynaptic neurons and takes place probabilistically.
“Specifically, we used binary synapses that undergo a stochastic Hebbian learning rule, namely that synaptic plasticity depends on coactivation of presynaptic and postsynaptic neurons and takes place probabilistically (Fusi, 2002; Fusi et al., 2007).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
The variability of reaction times is mostly attributed to the irregular ramping of neural activity itself rather than trial-to-trial variability of the decision bound.
“Therefore, the variability of reaction times is mostly attributed to the irregular ramping of neural activity itself rather than trial-to-trial variability of the decision bound.”
Quote in the paperSubstantial claim - Interpretationcitation
The same type of model is also applicable to competitive games, where several decision makers interact according to a payoff matrix.
“The same type of model is also applicable to competitive games, where several decision makers interact according to a payoff matrix.”
Quote in the paperNot a substantial claim - Interpretationreasoning
If the strengths of the two synaptic connections cA and cB are plastic, then synaptic modifications will alter the network decision behavior over time.
“Suppose that the strengths of the two synaptic connections cA and cB are plastic, then synaptic modifications will alter the network decision behavior over time.”
Quote in the paperSubstantial claim- The authors state this as an assumption: The strengths of the two synaptic connections cA and cB are plastic.
- Interpretationnone given
The recurrent neural network suggests that categorical choice is determined naturally by which of the alternative attractors wins the competition.
“Conceptually, the recurrent neural network suggests that categorical choice is determined naturally by which of the alternative attractors wins the competition.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Categorical choice can be dissociated from a decision threshold.
“Hence, again, categorical choice can be dissociated from a decision threshold.”
Quote in the paperSubstantial claim - Premisecitation
Neural signals correlated with reward expectation have been consistently observed in the striatum, amygdala, orbitofrontal cortex, and anterior cingulate cortex in single-unit recording from behaving animals.
“On the other hand, neural signals correlated with reward expectation have been consistently observed in the striatum, amygdala, orbitofrontal cortex (OFC), and anterior cingulate cortex (ACC) in single-unit recording from behaving animals (reviewed in Rushworth and Behrens, 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Rushworth and Behrens, 2008 source
Not checked in the free run.
- Interpretationreasoning
In a learning process, synapses acquire information about reward outcomes of chosen responses, i.e., action-specific values.
“Therefore, in a learning process, synapses acquire information about reward outcomes of chosen responses, i.e., action-specific values.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Premisecitation
Brain structures activated in decision making and modulated by reward signals include caudate, lateral parietal cortex area LIP, and prefrontal cortex.
“Brain structures activated in decision making and modulated by reward signals include caudate (Samejima et al., 2005; Hikosaka et al., 2006), lateral parietal cortex area LIP (Platt and Glimcher, 1999; Sugrue et al., 2004), and prefrontal cortex (Watanabe, 1996; Roesch and Olson, 2003).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
In reinforcement learning theory, the error signal is postulated to update the reward expectation, which in turn is used to compute the error signal.
“In reinforcement learning theory, the error signal is postulated to update the reward expectation, which in turn is used to compute the error signal (Sutton and Barto, 1998; Bayer and Glimcher, 2005; Rushworth and Behrens, 2008).”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationreasoning
In the extended multiple-circuit model, when input exceeds a well-defined threshold, an all-or-none burst of spikes is triggered in SC movement cells, signaling a particular motor output.
“When this input exceeds a well-defined threshold level, an all-or-none burst of spikes is triggered in the SC movement cells, signaling a particular (A or B) motor output.”
Quote in the paperSubstantial claim - Premisecitation
Phasic activity of dopamine neurons in the ventral tegmental area encodes a reward prediction error.
“A seminal finding was that phasic activity of dopamine neurons in the ventral tegmental area (VTA) encodes a reward prediction error (the difference between the expected and actual reward) (Schultz et al., 1997; Schultz, 1998; Roesch et al., 2007).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationnone given
In the recurrent neural circuit model, integration stops naturally when the system has reached a steady state.
“In contrast, in the recurrent neural circuit model, integration stops naturally when the system has reached a steady state.”
Quote in the paperSubstantial claim - Interpretationcitation
Because synapses are potentiated or weakened stochastically over time, they are forgetful and behave like a leaky integrator of past choice outcomes, with a time constant determined by the learning rate as well as the reward statistics in the environment.
“Moreover, because synapses are potentiated or weakened stochastically over time, they are forgetful and behave like a leaky integrator of past choice outcomes, with a time constant determined by the learning rate as well as the reward statistics in the environment (Soltani and Wang, 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
Not checked in the free run.
- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Premisecitation
Neurons in the primate lateral habenula reflect reward-prediction errors with an opposite sign from dopamine neurons.
“Moreover, a recent study found that neurons in the primate lateral habenula reflect reward-prediction errors with an opposite sign from dopamine neurons (exhibiting a strong increase in spiking activity when the actual reward is smaller than the expected outcome), suggesting that the lateral habenula is a source for negative prediction error signal (Matsumoto and Hikosaka, 2007).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Matsumoto and Hikosaka, 2007 source
Not checked in the free run.
- Interpretationcitation
Undermatching is a natural consequence of fluctuating network dynamics.
“A model analysis showed that undermatching is a natural consequence of fluctuating network dynamics (Soltani and Wang, 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
Not checked in the free run.
- Premisecitation
Spiking activity of dopamine neurons increases with both reward magnitude and probability.
“Consistent with a prediction error signal, spiking activity of dopamine neurons increases with both reward magnitude and probability (Fiorillo et al., 2003; Tobler et al., 2005; Roesch et al., 2007).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
Reinforcement learning models suggest that action values are learned at synapses onto neurons in a decision circuit, thereby influencing choice behavior.
“Reinforcement learning models suggest that action values are learned at synapses onto neurons in a decision circuit, thereby influencing choice behavior (Seung, 2003; Wörgötter and Porr, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Seung, 2003 source
Not checked in the free run.
- cites Wörgötter and Porr, 2005
Not checked in the free run.
- Interpretationreasoning
The recurrent neural circuit model offers a way to reconcile the two seemingly contrasting views about the origin of randomness in decision making.
“The recurrent neural circuit model offers a way to reconcile these two seemingly contrasting views.”
Quote in the paperSubstantial claim - Interpretationreasoning
In a foraging task, the serial responses and outcomes lead to changes in the synaptic strengths so that ΔI reflects the difference in the values of the choice options.
“In a foraging task, the serial responses and outcomes lead to changes in the synaptic strengths so that ΔI reflects the difference in the values of the choice options.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Interpretationreasoning
Neural activity in a putative decision network can be interpreted as the DV in both reward-based choice tasks and perceptual tasks.
“We interpret neural activity in a putative decision network as the DV in both reward-based choice tasks and perceptual tasks.”
Quote in the paperSubstantial claim - Interpretationreasoning
The stochasticity inherent in a neural system reveals itself by external uncertainty.
“Therefore, in some sense, the stochasticity inherent in a neural system reveals itself by external uncertainty.”
Quote in the paperNot a substantial claim - Interpretationreasoning
A decision circuit produces random-choice behavior, not necessarily because the system relies on a "random number generator," but because the trial-to-trial interplay between a decision maker with a volatile environment or with other decision makers leads to adaptive and seemingly random decision patterns.
“In this view, a decision circuit produces random-choice behavior, not necessarily because the system relies on a “random number generator,” but because the trial-to-trial interplay between a decision maker with a volatile environment or with other decision makers leads to adaptive and seemingly random decision patterns.”
Quote in the paperSubstantial claim - Interpretationcitation
Reward or value signals modulate neural firing through synaptic inputs, just like sensory stimuli.
“Reward or value signals modulate neural firing through synaptic inputs, just like sensory stimuli, in consonance with the view of Gold and Shadlen (2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gold and Shadlen, 2007 source
Not checked in the free run.
- Interpretationreasoning
The source of stochasticity in reinforcement models is interpreted as internal.
“The source of stochasticity is thus interpreted as internal.”
Quote in the paperNot a substantial claim - Interpretationreasoning
This perspective, emphasizing intrinsic stochastic neural dynamics, also applies to perceptual decisions, where ΔI measures the relative input strength (such as the coherence of an RDM stimulus).
“This perspective, emphasizing intrinsic stochastic neural dynamics, also applies to perceptual decisions, where ΔI measures the relative input strength (such as the coherence of an RDM stimulus).”
Quote in the paperNot a substantial claim - Interpretationcitation
This conclusion is consistent with behavioral studies demonstrating an indispensible role of feedback in producing random patterns of responses.
“This conclusion is consistent with behavioral studies demonstrating an indispensible role of feedback in producing random patterns of responses (Rapoport and Budescu, 1992; Camerer, 2003; Glimcher, 2005).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Rapoport and Budescu, 1992
Not checked in the free run.
- cites Camerer, 2003
Not checked in the free run.
- cites Glimcher, 2005 source
Not checked in the free run.
- Interpretationreasoning
In this model, there is no fundamental distinction between the DV and the decision rule, insofar as the same recurrent neural dynamics instantiate the accrual process as well as categorical choice.
“In this model, there is no fundamental distinction between the DV and the decision rule, insofar as the same recurrent neural dynamics instantiate the accrual process as well as categorical choice.”
Quote in the paperSubstantial claim - Interpretationcitation
When the amount of reward uncertainty is varied, ΔI is adjusted through synaptic plasticity so that the system behaves more, or less, randomly in compliance with the matching law.
“When the amount of reward uncertainty is varied, ΔI is adjusted through synaptic plasticity so that the system behaves more, or less, randomly in compliance with the matching law (Soltani and Wang, 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Soltani and Wang, 2006
Not checked in the free run.
- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
- Interpretationreasoning
A decision maker does not have a goal to play randomly, but simply tries to play at its best, given the environment or other decision agents in an interactive game.
“Therefore, a decision maker does not have a goal to play randomly, but simply tries to play at its best, given the environment or other decision agents in an interactive game (Soltani et al., 2006; Lee and Wang, 2008).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Soltani et al., 2006
Not checked in the free run.
- cites Lee and Wang, 2008
Not checked in the free run.
- Interpretationreasoning
The smaller is the absolute value of ΔI, the more random is the network behavior.
“The smaller is the absolute value of ΔI, the more random is the network behavior.”
Quote in the paperSubstantial claim
- May combine two claims. 38
- Classified as probably not a substantial claim. 14
May combine two claims.
FindingcitationIn rats, olfactory discrimination performance varied from chance level to near 100% correct as task difficulty varied, but reaction times changed only slightly (by ~30 ms).
“Performance varied from chance level to near 100% correct, as the task difficulty was varied by adjusting the relative proportions of two odorants in a binary mixture, but the RTs were changed only slightly (by ~30 ms) (Uchida and Mainen, 2003).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Uchida and Mainen, 2003
Not checked in the free run.
May combine two claims.
FindingcitationIn mice, olfactory discrimination performance was high (~95% correct) regardless of discrimination difficulty, while reaction times increased by ~80 ms from the easiest to the hardest.
“In contrast, another study found that, in mice, olfactory discrimination performance was high (~95% correct), regardless of discrimination difficulty, while RTs increased by ~80 ms from the easiest to the hardest (Abraham et al., 2004), suggesting that in these tasks rodents exhibit speed-accuracy tradeoff on a timescale of less than 100 ms.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Abraham et al., 2004
Not checked in the free run.
May combine two claims.
Findingown dataDuring comparison decision in the somatosensory task, neural activity became binary: a plus neuron showed high firing when the monkey's choice was f1 > f2 and low firing when the monkey's choice was f1 < f2; a minus neuron showed the opposite trend.
“During comparison decision, neural activity became binary: a “plus neuron” showed high firing in trials when the monkey’s choice was f1 > f2, low firing in trials when the monkey’s choice was f1 < f2; a “minus neuron” showed the opposite trend.”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationThe time-to-peak of the P300 component recorded in the parietal cortical area increased with the difficulty of stimulus discriminability and reaction time but was indifferent to stimulus-response compatibility.
“Interestingly, it was found that the time-to-peak of the P300 component recorded in the parietal cortical area increased with the difficulty of stimulus discriminability and RT but was indifferent to the stimulus-response compatibility (Kutas et al., 1977; McCarthy and Donchin, 1981).”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationA free motor decision could be decoded from BOLD signals in the frontal and parietal cortex long (seconds) before it entered awareness.
“Moreover, a recent study showed that a free motor decision could be decoded from BOLD signals in the frontal and parietal cortex long (seconds) before it entered awareness (Soon et al., 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Soon et al., 2008 source
Not checked in the free run.
May combine two claims.
FindingcitationIn a speech sound identification task, BOLD fMRI of the auditory cortex reflected the signal-to-noise ratio (the quality of sensory information), whereas that of the inferior frontal cortex increased linearly with the reaction time.
“In one study (Binder et al., 2004), a subject was asked to identify a speech sound in a noisy auditory stimulus, blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) of the auditory cortex was found to reflect the signal-to-noise ratio (the quality of sensory information), whereas that of the inferior frontal cortex increased linearly with the reaction time (an index of”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Binder et al., 2004 source
Not checked in the free run.
May combine two claims.
FindingcitationNeurons in lateral prefrontal cortex and LIP showed a similar activity pattern: their activity reflected the monkey's choice, was a graded function of motion strength c′ during motion viewing, and persistent activity during the delay became insensitive to c′.
“Neurons in the lateral prefrontal cortex (Kim and Shadlen, 1999) and LIP (Shadlen and Newsome, 1996, 2001; Roitman and Shadlen, 2002) showed a similar activity pattern: their activity reflected the monkey’s choice, but, while it was a graded function of the motion strength c′ during motion viewing, persistent activity during the delay became insensitive to c′.”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Findingown dataThe rate of ramping activity fluctuates from trial to trial due to stochastic firing dynamics in the cortex and is inversely related to decision time on a trial-by-trial basis.
“The rate of ramping activity fluctuates from trial to trial, as a result of stochastic firing dynamics in the cortex, and is inversely related to the decision time (as defined by the time when a burst is triggered in the SC) on a trial-by-trial basis (Figure 4C).”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationTrial-to-trial variability of saccade response time was inversely correlated with the slope of the buildup activity of movement-related neurons, whereas the threshold level remained constant independent of the response time.
“Trial-to-trial variability of saccade response time was shown to be inversely correlated with the slope of the buildup activity of movement-related neurons, whereas the threshold level remained constant independent of the response time (Hanes and Schall, 1996).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hanes and Schall, 1996 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn human studies, mean reaction times typically range from tens of milliseconds to about a second in simple perceptual tasks.
“In human studies, mean RTs typically range from tens of milliseconds to about a second in simple perceptual tasks (Luce, 1986; Usher and McClelland, 2001).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Luce, 1986
Not checked in the free run.
- cites Usher and McClelland, 2001 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenDecision threshold is a core concept for reaction time tasks.
“While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenElectrophysiological studies with behaving monkeys have revealed that reaction times can be related to neural activity at the single-cell level.
“Recently, electrophysiological studies with behaving monkeys have revealed that reaction times can be related to neural activity at the single-cell level.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationWe typically take a longer time to ponder a more difficult decision, when information provided by the external world is conflicting or when there are numerous options to consider.
“Moreover, we typically take a longer time to ponder a more difficult decision, when information provided by the external world is conflicting or when there are numerous options to consider (Hick, 1952; Vickers, 1970).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Hick, 1952 source
Not checked in the free run.
- cites Vickers, 1970
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe diffusion model is popular because of its simplicity yet proven success with fitting behavioral data in numerous human studies.
“The diffusion model is popular because of its simplicity yet proven success with fitting behavioral data in numerous human studies (Ratcliff, 1978; Busemeyer and Townsend, 1993; Smith and Ratcliff, 2004).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenDecision threshold can be dissociated from a general decision rule.
“While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn the random-dot motion direction discrimination task, direction-sensitive neurons in area MT encode the motion stimulus, while the decision process itself occurs downstream of MT, potentially in posterior parietal cortex and/or prefrontal cortex.
“Extensive physiological and microstimulation studies have shown that while direction-sensititve neurons in the area MT encode the motion stimulus (Newsome et al., 1989; Britten et al., 1992, 1993, 1996), the decision process itself occurs downstream of MT, potentially in the posterior parietal cortex and/or prefrontal cortex.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Newsome et al., 1989 source
Not checked in the free run.
- cites Britten et al., 1992, 1993, 1996
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationPrefrontal neurons often display ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation.
“Prefrontal neurons often display ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation (Fuster, 2008; Chafee and Goldman-Rakic, 1998; Quintana and Fuster, 1999; Brody et al., 2003; Miller et al., 2003; Watanabe and Funahashi, 2007).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Fuster, 2008
Not checked in the free run.
- cites Chafee and Goldman-Rakic, 1998
Not checked in the free run.
- cites Quintana and Fuster, 1999
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe crossing of a boundary between attraction basins is slow, which explains why reaction times are longer in error trials than in correct trials.
“The crossing of a boundary between attraction basins is slow, which explains why the reaction times are longer in error trials than in correct trials, as was observed in the monkey experiment (Roitman and Shadlen, 2002).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Roitman and Shadlen, 2002 source
Not checked in the free run.
May combine two claims.
Interpretationown dataThe neural decision process proceeds in two steps: sensory data are first integrated over time in a graded fashion, followed by winner-take-all competition leading to a binary choice.
“Second, the neural decision process proceeds in two steps: sensory data are first integrated over time in a graded fashion, followed by winner-take-all competition leading to a binary choice.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe subject made the decision during stimulus viewing and maintained actively the binary choice across the delay to guide a later behavioral response.
“The implication was that the subject made the decision during stimulus viewing and maintained actively the binary choice across the delay to guide a later behavioral response.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataIn the somatosensory task, working memory precedes decision making, but the same circuit is engaged in both processes.
“In this task, working memory precedes decision making, but again the same circuit is engaged in both processes.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationcitationThe common occurrence of decision-related activity and mnemonic persistent activity suggests a common circuit mechanism underlying decision making and working memory.
“Motivated by this observation, it has been proposed that this is not a mere coincidence, but suggests a common circuit mechanism underlying decision making and working memory (Wang, 2002).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Wang, 2002 source
Not checked in the free run.
May combine two claims.
InterpretationcitationThese areas may provide top-down signals to sensory neurons, whose spiking activity commonly displays weak trial-to-trial correlated variability with monkeys' choices.
“These areas may provide top-down signals to sensory neurons, whose spiking activity commonly displays weak trial-to-trial correlated variability with monkeys’ choices (Britten et al., 1996; Shadlen and Newsome, 1996; Parker and Krug, 2003; Law and Gold, 2008).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Britten et al., 1996 source
Not checked in the free run.
- cites Shadlen and Newsome, 1996
Not checked in the free run.
- cites Parker and Krug, 2003
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe slow-reverberation mechanism relies on the slow kinetics of NMDA receptor-mediated channels and emphasizes the importance of NMDA receptors for online cognition.
“Note that this mechanism relies on the slow kinetics of NMDA receptor-mediated channel and emphasizes the importance of NMDA receptors for online cognition (rather than its well-known role in long-term synaptic plasticity).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationIn an RT task, decision time can be deduced from measured reaction time minus motor response latency, which has been shown to be correlated with threshold crossing of LIP neuronal activity.
“In an RT task, a decision time can be deduced from measured reaction time (minus motor response latency), which has been shown to be correlated with threshold crossing of LIP neuronal activity (Roitman and Shadlen, 2002).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Roitman and Shadlen, 2002 source
Not checked in the free run.
May combine two claims.
InterpretationcitationThe feedback mechanism of lateral synaptic inhibition, not feed-forward subtraction, is supported by the observation that micro-stimulation of one neural pool in LIP sped up decisions in its preferred direction and slowed down decisions in the antipreferred direction.
“This feedback mechanism, not the feed-forward subtraction, is supported by the observation that micro-stimulation of one neural pool in LIP not only sped up the decisions in its preferred direction but also slowed down the decisions in the antipreferred direction (Hanks et al., 2006).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hanks et al., 2006 source
Not checked in the free run.
May combine two claims.
InterpretationcitationThe attractor network model can subtract negative signals as well as add positive evidence about choice alternatives.
“Third, the attractor network model has been shown to be able to subtract negative signals as well as add positive evidence about choice alternatives, but the influence of newly arriving inputs diminishes over time, as the network converges toward one of the attractor states representing the alternative choices (Wang, 2002).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wang, 2002 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe model predicts positive trial-by-trial correlation between neural pairs of the same tuning type, and negative correlation between neural pairs of opposite tuning.
“The model predicts positive trial-by-trial correlation between neural pairs of the same (positively or negatively monotonic) tuning type, and negative correlation between neural pairs of opposite tuning.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationHuman studies commonly report skewed RT distributions with a long right tail, which is well captured by the diffusion model but not the existing neural circuit model.
“On the other hand, human studies commonly report skewed RT distributions with a long right tail, which is well captured by the diffusion model (Ratcliff, 1978; Luce, 1986; Ratcliff and Rouder, 1998; Usher and McClelland, 2001; Sigman and Dehaene, 2005) but not the existing neural circuit model.”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Premisenone givenA distinguishing feature of the nonlinear attractor model is strong recurrent excitation, which is absent in linear accumulator models.
“A distinguishing feature of the nonlinear attractor model is strong recurrent excitation, which is absent in linear accumulator models.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThe network displays transient dynamics of ramping activity on a timescale of up to 1 s, and this ability critically depends on NMDA receptors.
“Thus, the network displays transient dynamics of ramping activity on a timescale of up to 1 s, and this ability critically depends on the NMDA receptors (Wang, 2002; Wong and Wang, 2006).”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationThe recurrent neural circuit model produces decision-time distributions that do not show pronounced right tails and are similar to those observed in the monkey experiment.
“This is not the case in the recurrent neural circuit model, which produces decision-time distributions that do not show pronounced right tails and are similar to those observed in the monkey experiment (X.-J. Wang, 2006, Soc. Neurosci., abstract).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites X.-J. Wang, 2006, Soc. Neurosci., abstract
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe positive-feedback mechanism ultimately leads to an acceleration of the ramping neural activity toward a decision bound, preventing excessively long decision times.
“The positive-feedback mechanism ultimately leads to an acceleration of the ramping neural activity toward a decision bound, preventing excessively long decision times.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningMatching implies that the two returns are equalized, thus encoding reward values in terms of returns is especially suited for matching computation.
“Note that matching implies that the two returns are equalized, thus encoding reward values in terms of returns is especially suited for matching computation.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenThe response time is interpreted in terms of the time at which neural signals from a decision circuit are read out by the motor system, which can be flexibly adjusted and differently controlled in a reaction-time task or a fixed-duration task.
“The response time is interpreted in terms of the time at which neural signals from a decision circuit are read out by the motor system, which can be flexibly adjusted and differently controlled in a reaction-time task or a fixed-duration task.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationWithout neural variability, decision behavior tends to get stuck with the more rewarding alternative; stochastic spiking activity renders the network more exploratory and produces undermatching as a consequence.
“Without neural variability, decision behavior tends to get stuck with the more rewarding alternative; stochastic spiking activity renders the network more exploratory and produces undermatching as a consequence.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningThe decision behavior is influenced by past rewards harvested locally in time, in agreement with the observed monkey's behavior.
“Hence, the decision behavior is influenced by past rewards harvested locally in time, in agreement with the observed monkey’s behavior (Sugrue et al., 2004; Lau and Glimcher, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checkedClassified as probably not a substantial claim.
Premisenone givenThe network behavior is described by a softmax decision criterion: the probability of choosing A versus B is a sigmoid function of the difference in the inputs to the two competing neural pools.
“Recall that the network behavior is described by a softmax decision criterion, that is, the probability of choosing A versus B is a sigmoid function of the difference in the inputs (ΔI) to the two competing neural pools (Figure 4D, upper panel).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningReward expectation and prediction error depend on each other and must be computed iteratively, possibly in different brain regions connected in a reciprocal loop.
“Thus, the reward expectation and the prediction error depend on each other and must be computed iteratively, possibly in different brain regions connected in a reciprocal loop.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningIn a competitive game, the interplay with the opponent induces reward-dependent synaptic plasticity that forces ΔI to be close to zero, resulting in random behavior.
“In a competitive game, the interplay with the opponent induces reward-dependent synaptic plasticity that forces ΔI to be close to zero, resulting in random behavior.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
Classified as probably not a substantial claim.
PremisecitationIn reinforcement models for reward-dependent choice, the DV is defined by values of response options which are deterministically updated according to past rewards, whereas the choice is generated by a probabilistic decision rule (e.g., a softmax criterion) based on the DV.
“On the other hand, in reinforcement models for reward-dependent choice (Barraclough et al., 2004; Sugrue et al., 2004, 2005; Lau and Glimcher, 2005), the DV is defined by values of response options which are deterministically updated according to past rewards, whereas the choice is generated by a probabilistic decision rule (e.g., a softmax criterion) based on the DV.”
Quote in the paperSubstantial claim0 of 3 sources checkedClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningAlthough activity of decision neurons depends on values of response options, valuation may occur elsewhere, perhaps at the synaptic level.
“Therefore, although activity of decision neurons depends on values of response options, valuation may occur elsewhere, perhaps at the synaptic level.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIntrinsic indeterminacy may be essential for unpredictable behavior.
“Glimcher (2005) argued that intrinsic indeterminacy may be essential for unpredictable behavior.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Glimcher, 2005 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenThe computational benefit of time integration is understood in terms of the signal-to-noise ratio, which increases over time (~√t in the diffusion model).
“The computational benefit of time integration is understood in terms of the signal-to-noise ratio, which increases over time (~√t in the diffusion model).”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThis softmax is simply a description of behavioral statistics, not the decision criterion used to produce individual choices in single trials.
“This softmax is simply a description of behavioral statistics, not the decision criterion used to produce individual choices in single trials.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationcitationIf initially cA is larger than cB, and the system chooses target A more frequently, it would be exploited by the opponent, and the unrewarded outcomes from choosing A induce depression of the synapses to the neural pool A, so that the difference cA − cB decreases over time, and the system gradually chooses B more frequently.
“For instance, if initially cA is larger than cB, and the system chooses target A more frequently, it would be exploited by the opponent, and the unrewarded outcomes from choosing A induce depression of the synapses to the neural pool A, so that the difference cA − cB decreases over time, and the system gradually chooses B more frequently (Soltani et al., 2006; Lee and Wang, 2008).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Soltani et al., 2006
Not checked in the free run.
- cites Lee and Wang, 2008
Not checked in the free run.
- The authors state this as an assumption: Synaptic learning depends on reward signals.
- The authors state this as an assumption: Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
May combine two claims.
InterpretationreasoningThe neural dynamics give rise to stochastic decisions, with the aggregate behavior characterized by a softmax function of the difference ΔI in the inputs to the competing decision neurons.
“The neural dynamics give rise to stochastic decisions, with the aggregate behavior characterized by a softmax function of the difference ΔI in the inputs to the competing decision neurons.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationIn accumulator models of perceptual decision, randomness originates from noise in the external input, so the DV is stochastic but the decision rule (the bound) is fixed.
“Thus, in accumulator models of perceptual decision, randomness originates from noise in the external input, so the DV is stochastic but the decision rule (the bound) is fixed.”
Quote in the paperNot a substantial claim
The authors state this as an assumption.
Recurrent synaptic excitation is sufficiently strong to generate multiple self-sustained stable states of neural populations (attractor states).
“Recurrent synaptic excitation is assumed to be sufficiently strong to generate multiple self-sustained stable states of neural populations, which are mathematically referred to as “attractor states.””
The paper relies on this without support.
In the mammalian brain, a decision is not made by single cells, but by the collective dynamics of a neural circuit.
“Yet, in the mammalian brain, a decision is not made by single cells, but by the collective dynamics of a neural circuit.”
The paper relies on this without support.
Decision computations depend on cortical circuits endowed with an abundance of positive and negative feedback loops, the behavior of which is not readily predictable by intuition alone.
“Moreover, decision computations depend on cortical circuits endowed with an abundance of positive and negative feedback loops, the behavior of which is not readily predictable by intuition alone.”
The authors state this as an assumption.
Synaptic learning depends on reward signals.
“In addition, it is assumed that synaptic learning depends on reward signals, based on the observation that dopamine signal is known to gate”
“In addition, it is assumed that synaptic learning depends on reward signals, based on the observation that dopamine signal is known to gate synaptic plasticity in the striatum (Wickens et al., 2003; Shen et al., 2008) and prefrontal cortex (Otani et al., 2003; Matsuda et al., 2006).”
The authors state this as an assumption.
The strengths of the two synaptic connections cA and cB are plastic.
“Suppose that the strengths of the two synaptic connections cA and cB are plastic, then synaptic modifications will alter the network decision behavior over time.”
The authors state this as an assumption.
Synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.
“For instance, synapses for inputs to decision neurons are potentiated only if the choice is rewarded, and depressed otherwise.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Neural correlates of reward valuation have been observed in different studies.
“Neural correlates of reward valuation have been observed in different studies1-3”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [3]
Prefrontal cortex plays a major role in dynamic decision processes encoding and updating values.
“Prefrontal cortex plays a major role in dynamic decision processes encoding and updating values1-4.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [3]
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BackgroundMidbrain dopamine neurons encode a quantitative reward prediction error signal
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MethodIntentional Maps in Posterior Parietal Cortex
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“ramping activity is motor intention, as LIP is specifically involved with planning saccades (Andersen and Buneo, 2002).”
BackgroundLateral habenula as a source of negative reward signals in dopamine neurons
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“…reward-prediction errors with an opposite sign from dopamine neurons (exhibiting a strong increase in spiking activity when the actual reward is smaller than the expected outcome), suggesting that the lateral habenula is a source for negative prediction error signal (Matsumoto and Hikosaka, 2007).”
BackgroundRole of the Basal Ganglia in the Control of Purposive Saccadic Eye Movements
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BackgroundInfluentialAdaptive Coding of Reward Value by Dopamine Neurons
Tobler, P. N., Fiorillo, C. D., & Schultz, W. (2005) · Science · doi:10.1126/science.1105370 · Cited by 1,276 · cited 1× · see citing works
“Consistent with a prediction error signal, spiking activity of dopamine neurons increases with both reward magnitude and probability (Fiorillo et al., 2003; Tobler et al., 2005; Roesch et al., 2007).”
BackgroundProbabilistic decision making by slow reverberation in cortical circuits
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Self-citationNoise, neural codes and cortical organization
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BackgroundDichotomous dopaminergic control of striatal synaptic plasticity
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“…1998) and that long-term potentiation and depression of the cortico-striatal synapses depend on the dopamine signals (Reynolds et al., 2001; Shen et al., 2008), our work suggests that dopamine-dependent plasticity of corticostriatal synapses represents a candidate neural locus for adaptive…”
BackgroundNeuronal correlates of a perceptual decision
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“Extensive physiological and microstimulation studies have shown that while direction-sensititve neurons in the area MT encode the motion stimulus (Newsome et al., 1989; Britten et al., 1992, 1993, 1996), the decision process itself occurs downstream of MT, potentially in the posterior parietal cortex and/or prefrontal…”
BackgroundA Metric for Thought: A Comparison of P300 Latency and Reaction Time
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“Interestingly, it was found that the time-to-peak of the P300 component recorded in the parietal cortical area increased with the difficulty of stimulus discriminability and RT but was indifferent to the stimulus-response compatibility (Kutas et al., 1977; McCarthy and Donchin, 1981).”
BackgroundNeural Control of Voluntary Movement Initiation
Hanes, D. P., & Schall, J. D. (1996) · Science · doi:10.1126/science.274.5286.427 · Cited by 1,145 · cited 8× · see citing works
“…(Shadlen and Newsome, 1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007), economic choice behavior (Platt and Glimcher,…”
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“Both for models of perceptual decisions and value-based choice behavior, intrinsic stochasticity arises from highly irregular neural firing (Amit and Brunel, 1997; Brunel and Wang, 2001), a characteristic of cortical neuronal firing (Softky and Koch, 1993; Shadlen and Newsome, 1994).”
BackgroundSynaptic reverberation underlying mnemonic persistent activity
Wang, X.-J. (2001) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01868-3 · Cited by 1,059 · cited 5× · see citing works
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BackgroundMethodInfluentialSelf-citationPsychology and neurobiology of simple decisions
Smith, P. L., & Ratcliff, R. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.01.006 · Cited by 1,051 · cited 2× · see citing works
“…for two-alternative forced choices, an accumulator adds evidence in favor of one alternative and subtracts evidence in favor of the other; a decision is made when it reaches either a positive threshold or a negative threshold (Stone, 1960; Laming, 1968; Ratcliff, 1978; Smith and Ratcliff, 2004).”
BackgroundMethodA relationship between behavioral choice and the visual responses of neurons in macaque MT
Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996) · Visual Neuroscience · doi:10.1017/s095252380000715x · Cited by 1,049 · cited 5× · see citing works
“sensory neurons, whose spiking activity commonly displays weak trial-to-trial correlated variability with monkeys’ choices (Britten et al., 1996; Shadlen and Newsome, 1996; Parker and Krug, 2003; Law and Gold, 2008).”
BackgroundInfluentialMatching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030 · cited 7× · see citing works
“(A) is reproduced with permission from Sugrue et al. (2004), (B) and (C) from Corrado et al. (2005), and (D)–(G) from Soltani and Wang (2006). of fluctuating network dynamics (Soltani and Wang, 2006).”
BackgroundResultInfluentialA Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029 · cited 6× · see citing works
“Thus, the network displays transient dynamics of ramping activity on a timescale of up to 1 s, and this ability critically depends on the NMDA receptors (Wang, 2002; Wong and Wang, 2006).”
BackgroundMethodResultInfluentialSelf-citationNeuroeconomics: decision making and the brain
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Cisek, P., & Kalaska, J. F. (2005) · Neuron · doi:10.1016/j.neuron.2005.01.027 · Cited by 1,019 · cited 1× · see citing works
“…Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007), economic choice behavior (Platt and Glimcher, 1999; Sugrue et al., 2004, 2005;…”
BackgroundMemory related motor planning activity in posterior parietal cortex of macaque
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“For instance, in an oculomotor delayed response task, neurons in both LIP and prefrontal cortex display directionally tuned persistent activity (Gnadt and Andersen, 1988; Funahashi et al., 1989).”
BackgroundInformation theory of choice-reaction times
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Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque
Kim, J.-N., & Shadlen, M. N. (1999) · Nature Neuroscience · doi:10.1038/5739 · Cited by 950 · cited 1× · see citing works
“Neurons in the lateral prefrontal cortex (Kim and Shadlen, 1999) and LIP (Shadlen and Newsome, 1996, 2001; Roitman and Shadlen, 2002) showed a similar activity pattern: their activity reflected the monkey’s choice, but, while it was a graded function of the motion strength c0 during motion viewing,…”
BackgroundOptimal decision making and the anterior cingulate cortex
Kennerley, S. W., Walton, M. E., Behrens, T. E. J., Buckley, M. J., & Rushworth, M. F. S. (2006) · Nature Neuroscience · doi:10.1038/nn1724 · Cited by 948 · cited 1× · see citing works
“For instance, neural firing in ACC of behaving monkeys has been described as a temporal filter of past rewards, on a timescale of several trials (or tens of seconds) (Kennerley et al., 2006; Seo and Lee, 2007).”
BackgroundRepresentation of Action-Specific Reward Values in the Striatum
Samejima, K., Ueda, Y., Doya, K., & Kimura, M. (2005) · Science · doi:10.1126/science.1115270 · Cited by 939 · cited 1× · see citing works
“Brain structures activated in decision making and modulated by reward signals include caudate (Samejima et al., 2005; Hikosaka et al., 2006), lateral parietal cortex area LIP (Platt and Glimcher, 1999; Sugrue et al., 2004), and prefrontal cortex (Watanabe, 1996; Roesch and Olson, 2003).”
BackgroundPre- and poststimulus activation of response channels: A psychophysiological analysis
Gratton, G., Coles, M. G. H., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.14.3.331 · Cited by 895 · cited 1× · see citing works
“Human studies on the physiological correlates of RTs in perceptual discrimination tasks began in the 1980s, with the development of event-related potential measurements (Gratton et al., 1988).”
BackgroundPre- and poststimulus activation of response channels: A psychophysiological analysis.
Gratton, G., COLES, M. G. H., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.14.3.331 · Cited by 891 · see citing works
Neural computations that underlie decisions about sensory stimuli
Gold, J. I., & Shadlen, M. N. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01567-9 · Cited by 881 · cited 2× · see citing works
“Applied to the RDM discrimination experiment, ramping spiking activity of LIP neurons has been interpreted as a temporal summation of the logarithm of the likelihood ratio (Gold and Shadlen, 2001; Jazayeri and Movshon, 2006), or of the posteriors (that combine evidence with prior information) (Ma et al.”
MethodA cellular mechanism of reward-related learning
Reynolds, J. N. J., Hyland, B. I., & Wickens, J. R. (2001) · Nature · doi:10.1038/35092560 · Cited by 879 · cited 1× · see citing works
“…errors (Schultz, 1998) and that long-term potentiation and depression of the cortico-striatal synapses depend on the dopamine signals (Reynolds et al., 2001; Shen et al., 2008), our work suggests that dopamine-dependent plasticity of corticostriatal synapses represents a candidate…”
BackgroundOn the ability to inhibit thought and action: General and special theories of an act of control.
Logan, G. D., Zandt, T. V., Verbruggen, F., & Wagenmakers, E. (2014) · Psychological Review · doi:10.1037/a0035230 · Cited by 878 · see citing works
The neural systems that mediate human perceptual decision making
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2374 · Cited by 877 · cited 1× · see citing works
“…tasks, including perceptual discrimination (Shadlen and Newsome, 1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007),…”
BackgroundChoice, uncertainty and value in prefrontal and cingulate cortex
Rushworth, M. F. S., & Behrens, T. E. J. (2008) · Nature Neuroscience · doi:10.1038/nn2066 · Cited by 866 · cited 6× · see citing works
“On the other hand, neural signals correlated with reward expectation have been consistently observed in the striatum, amygdala, orbitofrontal cortex (OFC), and anterior cingulate cortex (ACC) in single-unit recording from behaving animals (reviewed in Rushworth and Behrens, 2008).”
BackgroundMethodInfluentialSomogyi, P., Tamás, G., Luján, R., & Buhl, E. H. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(97)00061-1 · Cited by 860 · cited 1× · see citing works
“…the time is ripe to investigate the underlying mechanisms in terms of the biophysics of single neurons (Llinás, 1988; Magee et al., 1998), the dynamics of synaptic connections (Abbott and Regehr, 2004), and the microcircuit wiring connectivity (Somogyi et al., 1998; Douglas and Martin, 2004).”
BackgroundSynaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory
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“As initially proposed for working memory, one candidate cellular substrate for slow reverberation is the NMDA receptors at local recurrent excitatory synapses (Wang, 1999).”
BackgroundSelf-citationNeuronal correlates of parametric working memory in the prefrontal cortex
Romo, R., Brody, C. D., Hernández, A., & Lemus, L. (1999) · Nature · doi:10.1038/20939 · Cited by 822 · cited 1× · see citing works
“…two such neural populations coupled by reciprocal inhibition exhibit persistent activity that is positively and negatively, respectively, monotonically tuned to the first frequency f1, as observed in prefrontal neurons in this task (Romo et al., 1999; Brody et al., 2003; Miller et al., 2003).”
BackgroundNeural correlates, computation and behavioural impact of decision confidence
Kepecs, A., Uchida, N., Zariwala, H. A., & Mainen, Z. F. (2008) · Nature · doi:10.1038/nature07200 · Cited by 809 · cited 1× · see citing works
“Yet others examined estimation of confi- dence about a probabilistic decision (Grinband et al., 2006; Kepecs et al., 2008).”
BackgroundA general mechanism for perceptual decision-making in the human brain
Heekeren, H. R., Marrett, S., Bandettini, P. A., & Ungerleider, L. G. (2004) · Nature · doi:10.1038/nature02966 · Cited by 741 · cited 2× · see citing works
“…in which the task was to discriminate whether an ambiguous image is a face or a house, and the BOLD signal in the dorsolateral prefrontal cortex was found to covary with the difference signal between the face- and house-selective regions in the ventral temporal cortex (Heekeren et al., 2004).”
BackgroundEye Movements: Cognition and Visual Perception
Biederman, I., Fisher, D. F., Monty, R. A., & Senders, J. W. (1982) · The American Journal of Psychology · doi:10.2307/1422144 · Cited by 740 · see citing works
Motion perception: seeing and deciding.
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“In RDM discrimination experiments, recorded LIP neurons were preselected using the criterion that they displayed directionally tuned mnemonic activity in a delayed oculomotor response task (Shadlen and Newsome, 1996, 2001; Roitman and Shadlen, 2002; Huk and Shadlen, 2005).”
BackgroundMethodInfluentialNeural Signatures of Economic Preferences for Risk and Ambiguity
Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T., & Platt, M. L. (2006) · Neuron · doi:10.1016/j.neuron.2006.01.024 · Cited by 713 · cited 1× · see citing works
“Some studies aimed at identifying distinct brain systems that are recruited by different types of uncertainty (Yu and Dayan, 2005; Hsu et al., 2005; Huettel et al., 2006; Behrens et al., 2007).”
BackgroundModels for choice-reaction time
Stone, M. (1960) · Psychometrika · doi:10.1007/bf02289729 · Cited by 696 · cited 2× · see citing works
“…for two-alternative forced choices, an accumulator adds evidence in favor of one alternative and subtracts evidence in favor of the other; a decision is made when it reaches either a positive threshold or a negative threshold (Stone, 1960; Laming, 1968; Ratcliff, 1978; Smith and Ratcliff, 2004).”
BackgroundSpeed and accuracy of olfactory discrimination in the rat
Uchida, N., & Mainen, Z. F. (2003) · Nature Neuroscience · doi:10.1038/nn1142 · Cited by 687 · cited 1× · see citing works
“Performance varied from chance level to near 100% correct, as the task difficulty was varied by adjusting the relative proportions of two odorants in a binary mixture, but the RTs were changed only slightly (by 30 ms) (Uchida and Mainen, 2003).”
BackgroundReward expectancy in primate prefrental neurons
Watanabe, M. (1996) · Nature · doi:10.1038/382629a0 · Cited by 684 · cited 1× · see citing works
“Brain structures activated in decision making and modulated by reward signals include caudate (Samejima et al., 2005; Hikosaka et al., 2006), lateral parietal cortex area LIP (Platt and Glimcher, 1999; Sugrue et al., 2004), and prefrontal cortex (Watanabe, 1996; Roesch and Olson, 2003).”
BackgroundWorking memory in primate sensory systems
Pasternak, T., & Greenlee, M. W. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1603 · Cited by 678 · cited 1× · see citing works
“Perceptual discrimination (Pasternak and Greenlee, 2005) and action selection (Tanji and Hoshi, 2008) tasks often also depend working memory, in order to retain information useful for a future decision or to remember a choice made previously.”
BackgroundPrefrontal cortex and decision making in a mixed-strategy game
Barraclough, D. J., Conroy, M. L., & Lee, D. (2004) · Nature Neuroscience · doi:10.1038/nn1209 · Cited by 678 · cited 5× · see citing works
“Specifically, model simulations have been car- 226 Neuron 60, October 23, 2008 ª2008 Elsevier Inc. ried out for the experiment of Barraclough et al. (2004), in which monkeys played matching pennies with a computer opponent.”
BackgroundInfluentialDecision-making with multiple alternatives
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008) · Nature Neuroscience · doi:10.1038/nn.2123 · Cited by 671 · cited 1× · see citing works
“This model is easily extended to decisions with multiple alternatives (Usher and McClelland, 2001; McMillen and Holmes, 2006; Bogacz et al., 2007), which is not straightforward for the diffusion model (Niwa and Ditterich, 2008; Churchland et al., 2008).”
MethodNeuroeconomics: Decision Making and the Brain
Rushworth, M. F. S., & Walton, M. E. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.005 · Cited by 669 · see citing works
Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling
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Continuous attraction toward phonological competitors
Spivey, M. J., Grosjean, M., & Knoblich, G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0503903102 · Cited by 659 · cited 1× · see citing works
“Even in spoken-language processing, there is evidence that a spoken word elicits multiple lexical representations, and spoken-word recognition proceeds from real-time integration of information sources to categorical choice among phonological competitors (Spivey et al., 2005).”
Chafee, M. V., & Goldman, P. S. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.6.2919 · Cited by 655 · cited 1× · see citing works
“…ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation (Fuster, 2008; Chafee and Goldman-Rakic, 1998; Quintana and Fuster, 1999; Brody et al., 2003; Miller et al., 2003; Watanabe and Funahashi, 2007).”
BackgroundA role for neural integrators in perceptual decision making
Mazurek, M. E., Roitman, J. D., Ditterich, J., & Shadlen, M. N. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg097 · Cited by 620 · cited 2× · see citing works
“By contrast, a neural implementation of the diffusion model yields the opposite effect (Mazurek et al., 2003).”
BackgroundBrunel, N., & Wang, X.-J. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1011204814320 · Cited by 620 · see citing works
Self-citationRoesch, M. R., Calu, D. J., & Schoenbaum, G. (2007) · Nature Neuroscience · doi:10.1038/nn2013 · Cited by 597 · cited 2× · see citing works
“Consistent with a prediction error signal, spiking activity of dopamine neurons increases with both reward magnitude and probability (Fiorillo et al., 2003; Tobler et al., 2005; Roesch et al., 2007).”
BackgroundResponses of neurons in macaque MT to stochastic motion signals
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993) · Visual Neuroscience · doi:10.1017/s0952523800010269 · Cited by 594 · cited 2× · see citing works
“Interestingly, it was found that the trial-to-trial stimulus variation had no discernible effect on the trial-to-trial variance of firing activity in MT neurons (Britten et al., 1993), nor on the relationship between MT neural responses and behavioral choice (Britten et al., 1996).”
BackgroundNeuronal activity in monkey superior colliculus related to the initiation of saccadic eye movements
Dorris, M. C., Paré, M., & Munoz, D. P. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-21-08566.1997 · Cited by 593 · cited 1× · see citing works
“There is also evidence for a trial-to-trial correlation between the response latency and the preparatory activity of cortical movement neurons, before the target onset, in saccade and other sensorymotor tasks (Dorris et al., 1997; Churchland et al., 2006; Nakahara et al., 2006).”
BackgroundDurstewitz, D., & Seamans, J. K. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.05.015 · Cited by 586 · cited 1× · see citing works
“Conversely, relatively modest reductions of recurrent excitation and inhibition could produce marked impairments of cognitive functions (Wang, 2006b; Durstewitz and Seamans, 2008; Rolls et al., 2008).”
BackgroundFrank, M. J., & Claus, E. D. (2006) · Psychological Review · doi:10.1037/0033-295x.113.2.300 · Cited by 578 · see citing works
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Gold, J. I., & Shadlen, M. N. (2002) · Neuron · doi:10.1016/s0896-6273(02)00971-6 · Cited by 547 · see citing works
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“This prediction is also confirmed by the monkey experiment, which showed that the impact of a brief motion pulse in addition to the random-dot stimulus was greater with an earlier onset time (Huk and Shadlen, 2005; Wong et al., 2007).”
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Self-citationChoosing the greater of two goods: neural currencies for valuation and decision making
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1666 · Cited by 536 · cited 4× · see citing works
“One challenge is thus toelucidate how various dimensions of valuation are represented in the brain (Sugrue et al., 2005; Rushworth and Behrens, 2008).”
BackgroundInfluentialFlexible control of mutual inhibition: a neural model of two-interval discrimination
Machens, C. K., Romo, R., & Brody, C. D. (2005) · Science · doi:10.1126/science.1104171 · Cited by 535 · cited 3× · see citing works
“Reproduced with permission from Machens et al. (2005).”
BackgroundInfluentialHow the brain keeps the eyes still
Seung, H. S. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.23.13339 · Cited by 534 · cited 1× · see citing works
“A simple estimate of the network’s time constant is given by tnetwork = tsyn/(j1 wrecj) (where tsyn is the synaptic time constant, and wrec is the strength of recurrent connections), which is longer than tsyn in the presence of wrec (Seung, 1996; Wang, 2001).”
BackgroundOptimal representation of sensory information by neural populations
Jazayeri, M., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1691 · Cited by 530 · cited 4× · see citing works
“Applied to the RDM discrimination experiment, ramping spiking activity of LIP neurons has been interpreted as a temporal summation of the logarithm of the likelihood ratio (Gold and Shadlen, 2001; Jazayeri and Movshon, 2006), or of the posteriors (that combine evidence with prior information) (Ma et al.”
BackgroundMethodInfluentialNeural basis of deciding, choosing and acting
Schall, J. D. (2001) · Nature reviews. Neuroscience · doi:10.1038/35049054 · Cited by 528 · cited 5× · see citing works
“…1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007), economic choice behavior (Platt and Glimcher, 1999; Sugrue et al.,…”
BackgroundInfluentialNeural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area
Law, C.-T., & Gold, J. I. (2008) · Nature Neuroscience · doi:10.1038/nn2070 · Cited by 516 · cited 1× · see citing works
“Neuron 60, October 23, 2008 ª2008 Elsevier Inc. 219 sensory neurons, whose spiking activity commonly displays weak trial-to-trial correlated variability with monkeys’ choices (Britten et al., 1996; Shadlen and Newsome, 1996; Parker and Krug, 2003; Law and Gold, 2008).”
BackgroundCortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks
Lo, C.-C., & Wang, X.-J. (2006) · Nature Neuroscience · doi:10.1038/nn1722 · Cited by 505 · cited 3× · see citing works
“To test this scenario for a decision threshold, we considered an extended, multiple-circuit model (Lo and Wang, 2006).”
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“This model prediction is confirmed by a recent monkey experiment (Kiani et al., 2008).”
BackgroundResultCohen, J. D., Braver, T. S., & O´Reilly, R. (1996) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1996.0138 · Cited by 487 · cited 1× · see citing works
“Candidate loci include the corticostriatal connections in the basal ganglia or synaptic pathways within the orbitofrontal cortex, which have been explored in connectionist neural network models (Cohen et al., 1996; Frank and Claus, 2006) and reinforcement learning models (Samejima and Doya, 2007).”
BackgroundDYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS
Lau, B., & Glimcher, P. W. (2005) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2005.110-04 · Cited by 485 · see citing works
Inhibitory control in mind and brain: An interactive race model of countermanding saccades.
Boucher, L., Palmeri, T. J., Logan, G. D., & Schall, J. D. (2007) · Psychological Review · doi:10.1037/0033-295x.114.2.376 · Cited by 485 · cited 5× · see citing works
“In situations when there is a conflict, for instance when the subject has to inhibit a planned saccade by a stop signal introduced with a short delay after the target onset, the behavior becomes probabilistic (the saccade is suppressed on some trials, but not on other trials) (Logan and Cowan, 1984; Boucher et al., 20…”
BackgroundInfluentialNeural correlates of sensory and decision processes in auditory object identification
Binder, J. R., Liebenthal, E., Possing, E. T., Medler, D. A., & Ward, B. D. (2004) · Nature Neuroscience · doi:10.1038/nn1198 · Cited by 482 · cited 2× · see citing works
“In one study (Binder et al., 2004), a subject was asked to identify a speech sound in a noisy auditory stimulus, blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) of the auditory cortex was found to reflect the signal-to-noise ratio (the quality of sensory…”
BackgroundFree choice activates a decision circuit between frontal and parietal cortex
Pesaran, B., Nelson, M., & Andersen, R. (2008) · Nature · doi:10.1038/nature06849 · Cited by 479 · cited 1× · see citing works
“One promising approach to this outstanding issue is to examine interareal interactions by recording simultaneously single units and local field potentials from two or more areas in behaving animals (Pesaran et al., 2008).”
BackgroundReward Value Coding Distinct From Risk Attitude-Related Uncertainty Coding in Human Reward Systems
Tobler, P. N., O'Doherty, J. P., Dolan, R. J., & Schultz, W. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00745.2006 · Cited by 472 · cited 1× · see citing works
“Other studies quantified uncertainty-correlated neural signals in terms of probabilities (Fiorillo et al., 2003) or the variance of a probability distribution (Tobler et al., 2007).”
BackgroundRole of the Lateral Prefrontal Cortex in Executive Behavioral Control
Tanji, J., & Hoshi, E. (2008) · Physiological Reviews · doi:10.1152/physrev.00014.2007 · Cited by 451 · cited 1× · see citing works
“Perceptual discrimination (Pasternak and Greenlee, 2005) and action selection (Tanji and Hoshi, 2008) tasks often also depend working memory, in order to retain information useful for a future decision or to remember a choice made previously.”
BackgroundDorris, M. C., & Glimcher, P. W. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.009 · Cited by 428 · cited 2× · see citing works
“, 2004, 2005; PadoaSchioppa and Assad, 2006), and competitive games (Barraclough et al., 2004; Dorris and Glimcher, 2004; Glimcher, 2003; Lee, 2008).”
BackgroundDecisions uncertainty. and the brain: The science of neuroeconomics
(2003) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(03)90161-1 · Cited by 425 · see citing works
Value Representations in the Primate Striatum during Matching Behavior
Lau, B., & Glimcher, P. W. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.021 · Cited by 411 · cited 1× · see citing works
“In a monkey experiment (Sugrue et al., 2004; Lau and Glimcher, 2005, 2008), rewards were delivered to two (A and B) response options stochastically at baiting rates lA and lB, respectively, according to a concurrent variableinterval reinforcement schedule, in which choice targets are baited with…”
BackgroundA central source of movement variability
Churchland, M. M., Afshar, A., & Shenoy, K. V. (2006) · Neuron · doi:10.1016/j.neuron.2006.10.034 · Cited by 393 · cited 1× · see citing works
“There is also evidence for a trial-to-trial correlation between the response latency and the preparatory activity of cortical movement neurons, before the target onset, in saccade and other sensorymotor tasks (Dorris et al., 1997; Churchland et al., 2006; Nakahara et al., 2006).”
BackgroundHestrin, S., Sah, P., & Nicoll, R. A. (1990) · Neuron · doi:10.1016/0896-6273(90)90162-9 · Cited by 392 · cited 2× · see citing works
“For instance, if tsyn = 100 ms for the NMDA receptor-mediated synapses (Hestrin et al., 1990), and wrec = 0.”
BackgroundInfluentialThe Hebbian paradigm reintegrated: local reverberations as internal representations
Amit, D. J. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00040164 · Cited by 388 · see citing works
Neuronal correlates of decision-making in secondary somatosensory cortex
Romo, R., Hernández, A., Zainos, A., Lemus, L., & Brody, C. D. (2002) · Nature Neuroscience · doi:10.1038/nn950 · Cited by 387 · cited 1× · see citing works
“Recurrent circuit models have also been developed for the somatosensory discrimination experiment (Romo et al., 2002, 2004).”
BackgroundComputational models of schizophrenia and dopamine modulation in the prefrontal cortex
Rolls, E. T., Loh, M., Deco, G., & Winterer, G. (2008) · Nature reviews. Neuroscience · doi:10.1038/nrn2462 · Cited by 380 · cited 1× · see citing works
“Conversely, relatively modest reductions of recurrent excitation and inhibition could produce marked impairments of cognitive functions (Wang, 2006b; Durstewitz and Seamans, 2008; Rolls et al., 2008).”
BackgroundAnimal choice behavior and the evolution of cognitive architecture
Real, L. A. (1991) · Science · doi:10.1126/science.1887231 · Cited by 377 · cited 1× · see citing works
“This is illustrated by examples from simple organisms (Real, 1991): a toad produces a prey-catching or an avoidance behavior, depending on whether an ambiguous moving object is perceived as prey or a predator (Ewert, 1997); or an archerfish, by watching a prey’s initial condition, quickly (within…”
BackgroundBasal ganglia orient eyes to reward
Hikosaka, O., Nakamura, K., & Nakahara, H. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00458.2005 · Cited by 377 · cited 1× · see citing works
“Brain structures activated in decision making and modulated by reward signals include caudate (Samejima et al., 2005; Hikosaka et al., 2006), lateral parietal cortex area LIP (Platt and Glimcher, 1999; Sugrue et al., 2004), and prefrontal cortex (Watanabe, 1996; Roesch and Olson, 2003).”
BackgroundEvidence for an Accumulator Model of Psychophysical Discrimination
VICKERS, D. (1970) · Ergonomics · doi:10.1080/00140137008931117 · Cited by 369 · cited 1× · see citing works
“Moreover, we typically take a longer time to ponder a more difficult decision, when information provided by the external world is conflicting or when there are numerous options to consider (Hick, 1952; Vickers, 1970).”
BackgroundFrom Perception to Action: Temporal Integrative Functions of Prefrontal and Parietal Neurons
Quintana, J. (1999) · Cerebral Cortex · doi:10.1093/cercor/9.3.213 · Cited by 369 · cited 2× · see citing works
“…ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation (Fuster, 2008; Chafee and Goldman-Rakic, 1998; Quintana and Fuster, 1999; Brody et al., 2003; Miller et al., 2003; Watanabe and Funahashi, 2007).”
BackgroundMicrostimulation of macaque area LIP affects decision-making in a motion discrimination task
Hanks, T. D., Ditterich, J., & Shadlen, M. N. (2006) · Nature Neuroscience · doi:10.1038/nn1683 · Cited by 360 · cited 1× · see citing works
“This feedback mechanism, not the feedforward subtraction, is supported by the observation that microstimulation of one neural pool in LIP not only sped up the decisions in its preferred direction but also slowed down the decisions in the antipreferred direction (Hanks et al., 2006).”
BackgroundTiming and Neural Encoding of Somatosensory Parametric Working Memory in Macaque Prefrontal Cortex
Brody, C. D. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg100 · Cited by 350 · cited 2× · see citing works
“…ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation (Fuster, 2008; Chafee and Goldman-Rakic, 1998; Quintana and Fuster, 1999; Brody et al., 2003; Miller et al., 2003; Watanabe and Funahashi, 2007).”
BackgroundSaccade Target Selection in the Superior Colliculus During a Visual Search Task
McPeek, R. M., & Keller, E. L. (2002) · Journal of Neurophysiology · doi:10.1152/jn.2002.88.4.2019 · Cited by 346 · cited 1× · see citing works
“…is ultimately produced, is potently controlled by inhibitory processes, such as suppressive gating of movement neurons by ‘‘holding’’ neurons ( Hanes and Schall, 1996; McPeek and Keller, 2002; Narayanan and Laubach, 2006; Boucher et al., 2007), and by the basal ganglia (Hikosaka et al., 2000).”
BackgroundMeyer, D. E., Osman, A. M., Irwin, D. E., & Yantis, S. (1988) · Biological Psychology · doi:10.1016/0301-0511(88)90013-0 · Cited by 344 · cited 1× · see citing works
“At the behavioral level, reaction time (RT) measurements have provided a powerful tool for probing time integration in perception, memory, and cognitive processes (Donders, 1969; Posner, 1978; Luce, 1986; Meyer et al., 1988).”
BackgroundCisek, P. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.5605-05.2006 · Cited by 344 · cited 2× · see citing works
“Indeed, a recurrent circuit approach has been used to build models for action selection and movement preparation (Wilimzig et al., 2006; Cisek, 2006; Heinzle et al., 2007).”
BackgroundMethodLoewenstein, G. F., Rick, S. I., & Cohen, J. D. (2007) · Annual Review of Psychology · doi:10.1146/annurev.psych.59.103006.093710 · Cited by 342 · see citing works
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
Seung, H. S. (2003) · Neuron · doi:10.1016/s0896-6273(03)00761-x · Cited by 340 · cited 4× · see citing works
“It remains to be seen how such a learning rule works when the outcome (reward or not) is revealed only long after the behavioral response, by incorporating either persistent neural activity that bridges the temporal gap between the two events or an ‘‘eligibility trace’’ in the synapses (Sutton and Barto, 1998; Seung,…”
BackgroundInfluentialNeuronal Correlates of a Perceptual Decision in Ventral Premotor Cortex
Romo, R., Hernández, A., & Zainos, A. (2004) · Neuron · doi:10.1016/s0896-6273(03)00817-1 · Cited by 340 · cited 3× · see citing works
“…variety of decision tasks, including perceptual discrimination (Shadlen and Newsome, 1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and…”
BackgroundStability of the memory of eye position in a recurrent network of conductance-based model neurons
Seung, H. S., Lee, D. D., Reis, B. Y., & Tank, D. W. (2000) · Neuron · doi:10.1016/s0896-6273(00)81155-1 · Cited by 339 · cited 1× · see citing works
“Miller et al. (Miller et al., 2003) showed that fine-tuning of connectivity in this model yields a line attractor capable of parametric working memory, similar to the one in the gazecontrol system (Seung et al., 2000).”
ResultMaintaining Accuracy at the Expense of Speed
Abraham, N. M., Spors, H., Carleton, A., Margrie, T. W., Kuner, T., & Schaefer, A. T. (2004) · Neuron · doi:10.1016/j.neuron.2004.11.017 · Cited by 338 · cited 1× · see citing works
“…olfactory discrimination performance was high ( 95% correct), regardless of discrimination difficulty, while RTs increased by 80 ms from the easiest to the hardest (Abraham et al., 2004), suggesting that in these tasks rodents exhibit speed-accuracy tradeoff on a timescale of less than 100 ms.”
BackgroundA Neural Representation of Categorization Uncertainty in the Human Brain
Grinband, J., Hirsch, J., & Ferrera, V. P. (2006) · Neuron · doi:10.1016/j.neuron.2006.01.032 · Cited by 330 · cited 1× · see citing works
“Yet others examined estimation of confi- dence about a probabilistic decision (Grinband et al., 2006; Kepecs et al., 2008).”
BackgroundSeo, H., & Lee, D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2369-07.2007 · Cited by 324 · cited 1× · see citing works
“For instance, neural firing in ACC of behaving monkeys has been described as a temporal filter of past rewards, on a timescale of several trials (or tens of seconds) (Kennerley et al., 2006; Seo and Lee, 2007).”
BackgroundPersistent neural activity: prevalence and mechanisms
Major, G., & Tank, D. (2004) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2004.10.017 · Cited by 320 · cited 1× · see citing works
“Other slow biophysical mechanisms, such as shortterm synaptic facilitation (Abbott and Regehr, 2004) or calciumdependent processes in single cells (Major and Tank, 2004), may also contribute to time integration.”
BackgroundNeuronal correlates of subjective sensory experience
de Lafuente, V., & Romo, R. (2005) · Nature Neuroscience · doi:10.1038/nn1587 · Cited by 319 · cited 2× · see citing works
“The near-threshold detection experiment (de Lafuente and Romo, 2005) has also been modeled by a similar attractor network model (Deco et al., 2007a).”
BackgroundMethodTop-Down Control of Motor Cortex Ensembles by Dorsomedial Prefrontal Cortex
Narayanan, N. S., & Laubach, M. (2006) · Neuron · doi:10.1016/j.neuron.2006.10.021 · Cited by 311 · see citing works
Roesch, M. R., & Olson, C. R. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00019.2003 · Cited by 309 · cited 1× · see citing works
“Brain structures activated in decision making and modulated by reward signals include caudate (Samejima et al., 2005; Hikosaka et al., 2006), lateral parietal cortex area LIP (Platt and Glimcher, 1999; Sugrue et al., 2004), and prefrontal cortex (Watanabe, 1996; Roesch and Olson, 2003).”
BackgroundGame theory and neural basis of social decision making
Lee, D. (2008) · Nature Neuroscience · doi:10.1038/nn2065 · Cited by 296 · cited 2× · see citing works
“, 2004, 2005; PadoaSchioppa and Assad, 2006), and competitive games (Barraclough et al., 2004; Dorris and Glimcher, 2004; Glimcher, 2003; Lee, 2008).”
Background(1997) · Medical Entomology and Zoology · Cited by 289 · see citing works
Dynamics of neuronal responses in macaque MT and VIP during motion detection
Cook, E. P., & Maunsell, J. H. R. (2002) · Nature Neuroscience · doi:10.1038/nn924 · Cited by 289 · cited 1× · see citing works
“The posterior parietal cortex plays a major role in selective visual attention (Colby and Godberg, 1999; Corbetta and Shulman, 2002; Sereno and Amador, 2006; Ganguli et al., 2008), but it seems unlikely that neural signals observed in these perceptual decision experiments can be solely attributed…”
BackgroundELECTRICAL AND CALCIUM SIGNALING IN DENDRITES OF HIPPOCAMPAL PYRAMIDAL NEURONS
Magee, J. C., Hoffman, D. A., Colbert, C. M., & Johnston, D. (1998) · Annual Review of Physiology · doi:10.1146/annurev.physiol.60.1.327 · Cited by 284 · cited 1× · see citing works
“…and monkeys to human subjects, the time is ripe to investigate the underlying mechanisms in terms of the biophysics of single neurons (Llinás, 1988; Magee et al., 1998), the dynamics of synaptic connections (Abbott and Regehr, 2004), and the microcircuit wiring connectivity (Somogyi et al., 1998;…”
BackgroundNeural mechanisms of reward-related motor learning
Wickens, J. R., Reynolds, J. N., & Hyland, B. I. (2003) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2003.10.013 · Cited by 284 · cited 2× · see citing works
“In addition, it is assumed that synaptic learning depends on reward signals, based on the observation that dopamine signal is known to gate synaptic plasticity in the striatum (Wickens et al., 2003; Shen et al., 2008) and prefrontal cortex (Otani et al.”
BackgroundPloran, E. J., Nelson, S. M., Velanova, K., Donaldson, D. I., Petersen, S. E., & Wheeler, M. E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.3522-07.2007 · Cited by 282 · cited 2× · see citing works
“In another study (Ploran et al., 2007), as a subject viewed pictures that were revealed from a blank screen gradually in eight steps (2 s each), the areas that exhibited a gradual buildup in activity peaking in correspondence with the time of recognition were the parietal and frontal areas as well…”
BackgroundThe Cognitive Neuroscience of Human Decision Making: A Review and Conceptual Framework
Fellows, L. K. (2004) · Behavioral and Cognitive Neuroscience Reviews · doi:10.1177/1534582304273251 · Cited by 267 · cited 1× · see citing works
“Decision making has recently attracted increasing attention not only in the neurobiological studies of cognition but also in psychiatry with the recognition that impaired decision making is prominently associated with various mental disorders (Fellows, 2004; Sachdev and Malhi, 2005).”
BackgroundTouch and Go: Decision-Making Mechanisms in Somatosensation
Romo, R., & Salinas, E. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.107 · Cited by 259 · cited 1× · see citing works
“…studies of behaving nonhuman primates in a variety of decision tasks, including perceptual discrimination (Shadlen and Newsome, 1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001,…”
BackgroundCompte, A., Constantinidis, C., Tegner, J. N., Raghavachari, S., Chafee, M. V., Goldman, P. S., & Wang, X. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00949.2002 · Cited by 256 · cited 1× · see citing works
“For instance, Poisson-like statistics is a characteristic of delay-period persistent activity of prefrontal neurons recorded from behaving monkeys, even in the absence of external stimulus during working memory (Compte et al., 2003).”
BackgroundSelf-citationTemporal Evolution of a Decision-Making Process in Medial Premotor Cortex
Hernández, A., Zainos, A., & Romo, R. (2002) · Neuron · doi:10.1016/s0896-6273(02)00613-x · Cited by 253 · cited 1× · see citing works
“…neural activity during the same task across a number of cortical areas (primary and secondary somatosensory areas, premotor and lateral prefrontal areas), which yielded a rich picture of neural dynamics in these areas as the process unfolds in time (Hernández et al., 2002; Romo et al., 2004).”
BackgroundParsing a Cognitive Task: A Characterization of the Mind's Bottleneck
Sigman, M., & Dehaene, S. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030037 · Cited by 235 · cited 1× · see citing works
“On the other hand, human studies commonly report skewed RT distributions with a long right tail, which is well captured by the diffusion model (Ratcliff, 1978; Luce, 1986; Ratcliff and Rouder, 1998; Usher and McClelland, 2001; Sigman and Dehaene, 2005) but not the existing neural circuit model.”
BackgroundSeeing at a glance, smelling in a whiff: rapid forms of perceptual decision making
Uchida, N., Kepecs, Á., & Mainen, Z. F. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1933 · Cited by 231 · cited 1× · see citing works
“Although the concept of time integration is appealing, it is not obvious what types of choice behavior engage such accumulation process (a characteristic of deliberate decision making) and on what timescale (Uchida et al., 2006).”
BackgroundEvidence for time‐variant decision making
Ditterich, J. (2006) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2006.05221.x · Cited by 229 · cited 1× · see citing works
“In another relevant analysis, Ditterich (2006) found that a diffusion model produced reaction time histograms with long right tails (reflecting unusually long RTs), inconsistent with the monkey ex- 222 Neuron 60, October 23, 2008 ª2008 Elsevier Inc. periment.”
ResultGeneration of random series in two-person strictly competitive games.
Rapoport, A., & Budescu, D. V. (1992) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.121.3.352 · Cited by 220 · cited 1× · see citing works
“This conclusion is consistent with behavioral studies demonstrating an indispensible role of feedback in producing random patterns of responses (Rapoport and Budescu, 1992; Camerer, 2003; Glimcher, 2005).”
ResultIndeterminacy in brain and behavior
Glimcher, P. W. (2005) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.141429 · Cited by 206 · cited 7× · see citing works
“On the other hand, in reinforcement models for reward-dependent choice (Barraclough et al., 2004; Sugrue et al., 2004, 2005; Lau and Glimcher, 2005), the DV is defined by values of response options which are deterministically updated according to past rewards, whereas the choice is generated by a…”
BackgroundMethodResultInfluentialLINEAR-NONLINEAR-POISSON MODELS OF PRIMATE CHOICE DYNAMICS
Corrado, G. S., Sugrue, L. P., Seung, H. S., & Newsome, W. T. (2005) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2005.23-05 · Cited by 206 · see citing works
Abraham, N. M., Spors, H., Carleton, A., Margrie, T. W., Kuner, T., & Schaefer, A. T. (2004) · Neuron · doi:10.1016/s0896-6273(04)00753-6 · Cited by 205 · see citing works
Wong, K.-F. (2007) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.006.2007 · Cited by 204 · cited 4× · see citing works
“This prediction is also confirmed by the monkey experiment, which showed that the impact of a brief motion pulse in addition to the random-dot stimulus was greater with an earlier onset time (Huk and Shadlen, 2005; Wong et al., 2007).”
BackgroundMethodResultInfluentialWörgötter, F., & Porr, B. (2005) · Neural Computation · doi:10.1162/0899766053011555 · Cited by 203 · cited 2× · see citing works
“Reinforcement learning models suggest that action values are learned at synapses onto neurons in a decision circuit, thereby influencing choice behavior (Seung, 2003; Wörgötter and Porr, 2005).”
BackgroundFusi, S., Asaad, W. F., Miller, E. K., & Wang, X.-J. (2007) · Neuron · doi:10.1016/j.neuron.2007.03.017 · Cited by 199 · cited 1× · see citing works
“Specifically, we used binary synapses that undergo a stochastic Hebbian learning rule, namely that synaptic plasticity depends on coactivation of presynaptic and postsynaptic neurons and takes place probabilistically (Fusi, 2002; Fusi et al., 2007).”
MethodSelf-citationDopaminergic Modulation of Long-term Synaptic Plasticity in Rat Prefrontal Neurons
Otani, S. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg092 · Cited by 193 · cited 1× · see citing works
“In addition, it is assumed that synaptic learning depends on reward signals, based on the observation that dopa- mine signal is known to gate synaptic plasticity in the striatum (Wickens et al., 2003; Shen et al., 2008) and prefrontal cortex (Otani et al., 2003; Matsuda et al., 2006).”
BackgroundBogacz, R., Usher, M., Zhang, J., & McClelland, J. L. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2059 · Cited by 182 · see citing works
On Building a Bridge Between Brain and Behavior
Schall, J. D. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.141907 · Cited by 182 · cited 1× · see citing works
“…1996, 2001; Romo and Salinas, 2001; Roitman and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007), economic choice behavior (Platt and Glimcher, 1999; Sugrue et al.,…”
BackgroundDecision‐making and Weber's law: a neurophysiological model
Deco, G., & Rolls, E. T. (2006) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2006.04940.x · Cited by 179 · cited 2× · see citing works
“…showed that the intrinsically sto- 228 Neuron 60, October 23, 2008 ª2008 Elsevier Inc. chastic decision circuit dynamics could account for Weber’s law, which states that the ratio of just-noticeable input difference to absolute input intensity is constant (Deco and Rolls, 2006; Deco et al., 2007b).”
BackgroundSolving the distal reward problem through linkage of STDP and dopamine signaling
Izhikevich, E. M. (2007) · BMC Neuroscience · doi:10.1186/1471-2202-8-s2-s15 · Cited by 168 · cited 1× · see citing works
“…works when the outcome (reward or not) is revealed only long after the behavioral response, by incorporating either persistent neural activity that bridges the temporal gap between the two events or an ‘‘eligibility trace’’ in the synapses (Sutton and Barto, 1998; Seung, 2003; Izhikevich, 2007).”
BackgroundMean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks
Renart, A., Moreno‐Bote, R., Wang, X., & Parga, N. (2006) · Neural Computation · doi:10.1162/neco.2007.19.1.1 · Cited by 157 · cited 2× · see citing works
“Theoretically proposed mechanisms for highly irregular neural activity in the cortex posit recurrent cortical circuit dynamics endowed with balanced synaptic excitation and inhibition (van Vreeswijk and Sompolinsky, 1996; Amit and Brunel, 1997; Mattia and Del Giudice, 2004; Renart et al., 2007; Barbieri and Brunel, 20…”
BackgroundSelf-citationThe dynamics of choice among multiple alternatives
McMillen, T., & Holmes, P. (2006) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2005.10.003 · Cited by 153 · cited 2× · see citing works
“This model is easily extended to decisions with multiple alternatives (Usher and McClelland, 2001; McMillen and Holmes, 2006; Bogacz et al., 2007), which is not straightforward for the diffusion model (Niwa and Ditterich, 2008; Churchland et al.”
MethodOne-dimensional dynamics of attention and decision making in LIP
Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.038 · Cited by 152 · cited 5× · see citing works
“consistent with the finding that, in the LIP data recorded from the RDM experiment (Roitman and Shadlen, 2002), the dynamics within each of the two selective neural pools is dominated by a slow mode (Ganguli et al., 2008); thus, the description of two competing neural pools requires two dynamical variables.”
BackgroundResultInfluentialTarget Selection Signals for Arm Reaching in the Posterior Parietal Cortex
Scherberger, H., & Andersen, R. A. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.4274-06.2007 · Cited by 148 · cited 2× · see citing works
“…and Shadlen, 2002; Romo et al., 2004; Heekeren et al., 2008), target selection (Hanes and Schall, 1996; Schall, 2001, 2004; Cisek and Kalaska, 2005; Scherberger and Andersen, 2007), economic choice behavior (Platt and Glimcher, 1999; Sugrue et al., 2004, 2005; PadoaSchioppa and Assad, 2006), and…”
BackgroundResultA recurrent network model of somatosensory parametric working memory in the prefrontal cortex
Miller, P. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg101 · Cited by 144 · cited 6× · see citing works
“(Miller et al., 2003) showed that fine-tuning of connectivity in this model yields a line attractor capable of parametric working memory, similar to the one in the gazecontrol system (Seung et al.”
BackgroundResultInfluentialObsessive–Compulsive Behaviour: A Disorder of Decision-Making
Sachdev, P. S., & Malhi, G. S. (2005) · Australian & New Zealand Journal of Psychiatry · doi:10.1080/j.1440-1614.2005.01680.x · Cited by 141 · see citing works
Munoz, D. P., & Fecteau, J. H. (2002) · Progress in brain research · doi:10.1016/s0079-6123(02)40039-8 · Cited by 138 · cited 1× · see citing works
“These neurons are selective for saccade amplitude and direction and fire a stereotypical burst of spikes immediately before a saccade is initiated (Hanes and Schall, 1996; Munoz and Fecteau, 2002).”
BackgroundRoxin, A., & Ledberg, A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000046 · Cited by 135 · cited 1× · see citing works
“This conclusion is supported by other recent modeling studies of the RDM experiment (Roxin and Ledberg, 2008; Grossberg and Pilly, 2008).”
ResultHebbian spike-driven synaptic plasticity for learning patterns of mean firing rates
Fusi, S. (2002) · Biological Cybernetics · doi:10.1007/s00422-002-0356-8 · Cited by 131 · cited 1× · see citing works
“Specifically, we used binary synapses that undergo a stochastic Hebbian learning rule, namely that synaptic plasticity depends on coactivation of presynaptic and postsynaptic neurons and takes place probabilistically (Fusi, 2002; Fusi et al., 2007).”
MethodA Biophysically Based Neural Model of Matching Law Behavior: Melioration by Stochastic Synapses
Soltani, A., & Wang, X. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5159-05.2006 · Cited by 127 · cited 4× · see citing works
“(A) is reproduced with permission from Sugrue et al. (2004), (B) and (C) from Corrado et al. (2005), and (D)–(G) from Soltani and Wang (2006). of fluctuating network dynamics (Soltani and Wang, 2006).”
BackgroundInfluentialSelf-citationMultiple Representations of Belief States and Action Values in Corticobasal Ganglia Loops
Samejima, K., & Doya, K. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1390.024 · Cited by 124 · cited 2× · see citing works
“Candidate loci include the corticostriatal connections in the basal ganglia or synaptic pathways within the orbitofrontal cortex, which have been explored in connectionist neural network models (Cohen et al., 1996; Frank and Claus, 2006) and reinforcement learning models (Samejima and Doya, 2007).”
BackgroundMatsuda, Y., Marzo, A., & Otani, S. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.5312-05.2006 · Cited by 119 · cited 1× · see citing works
“In addition, it is assumed that synaptic learning depends on reward signals, based on the observation that dopa- mine signal is known to gate synaptic plasticity in the striatum (Wickens et al., 2003; Shen et al., 2008) and prefrontal cortex (Otani et al., 2003; Matsuda et al., 2006).”
BackgroundThe time course of saccadic decision making: Dynamic field theory
Wilimzig, C., Schneider, S., & Schöner, G. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.03.003 · Cited by 104 · cited 1× · see citing works
“Indeed, a recurrent circuit approach has been used to build models for action selection and movement preparation (Wilimzig et al., 2006; Cisek, 2006; Heinzle et al., 2007).”
MethodPerceptual Decisions between Multiple Directions of Visual Motion
Niwa, M., & Ditterich, J. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5564-07.2008 · Cited by 98 · cited 1× · see citing works
“This model is easily extended to decisions with multiple alternatives (Usher and McClelland, 2001; McMillen and Holmes, 2006; Bogacz et al., 2007), which is not straightforward for the diffusion model (Niwa and Ditterich, 2008; Churchland et al., 2008).”
MethodMiller, P. C., & Wang, X. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0508072103 · Cited by 88 · see citing works
Self-citationA Microcircuit Model of the Frontal Eye Fields
Heinzle, J., Hepp, K., & Martin, K. A. C. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.0974-07.2007 · Cited by 85 · cited 1× · see citing works
“Indeed, a recurrent circuit approach has been used to build models for action selection and movement preparation (Wilimzig et al., 2006; Cisek, 2006; Heinzle et al., 2007).”
MethodSereno, A. B., & Amador, S. C. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00431.2005 · Cited by 83 · cited 1× · see citing works
“The posterior parietal cortex plays a major role in selective visual attention (Colby and Godberg, 1999; Corbetta and Shulman, 2002; Sereno and Amador, 2006; Ganguli et al., 2008), but it seems unlikely that neural signals observed in these perceptual decision experiments can be solely attributed…”
BackgroundFinite-size dynamics of inhibitory and excitatory interacting spiking neurons
Mattia, M., & Giudice, P. D. (2004) · Physical Review E · doi:10.1103/physreve.70.052903 · Cited by 82 · cited 1× · see citing works
“…for highly irregular neural activity in the cortex posit recurrent cortical circuit dynamics endowed with balanced synaptic excitation and inhibition (van Vreeswijk and Sompolinsky, 1996; Amit and Brunel, 1997; Mattia and Del Giudice, 2004; Renart et al., 2007; Barbieri and Brunel, 2008).”
BackgroundEwert, J. ‐. (1997) · Trends in Neurosciences · doi:10.1016/s0166-2236(96)01042-9 · Cited by 78 · see citing works
Deco, G., Scarano, L., & Soto‐Faraco, S. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1072-07.2007 · Cited by 77 · cited 2× · see citing works
“The near-threshold detection experiment (de Lafuente and Romo, 2005) has also been modeled by a similar attractor network model (Deco et al., 2007a).”
BackgroundMethodNeural mechanism for stochastic behaviour during a competitive game
Soltani, A., Lee, D., & Wang, X.-J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.044 · Cited by 77 · cited 3× · see citing works
“Therefore, a decision maker does not have a goal to play randomly, but simply tries to play at its best, given the environment or other decision agents in an interactive game (Soltani et al., 2006; Lee and Wang, 2008).”
BackgroundInfluentialSelf-citationSmall Circuits for Large Tasks: High-Speed Decision-Making in Archerfish
Schlegel, T., & Schuster, S. (2008) · Science · doi:10.1126/science.1149265 · Cited by 73 · cited 1× · see citing works
“…or an avoidance behavior, depending on whether an ambiguous moving object is perceived as prey or a predator (Ewert, 1997); or an archerfish, by watching a prey’s initial condition, quickly (within 100 ms) decides on the course of action in order to catch the prey (Schlegel and Schuster, 2008).”
BackgroundTemporal dynamics of decision-making during motion perception in the visual cortex
Grossberg, S., & Pilly, P. K. (2008) · Vision Research · doi:10.1016/j.visres.2008.02.019 · Cited by 65 · cited 1× · see citing works
“This conclusion is supported by other recent modeling studies of the RDM experiment (Roxin and Ledberg, 2008; Grossberg and Pilly, 2008).”
ResultNeural correlates of a postponed decision report
Lemus, L., Hernández, A., Luna, R., Zainos, A., Nácher, V., & Romo, R. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707961104 · Cited by 62 · cited 1× · see citing works
“…comparison period, and medial premotor neurons were found to retain the monkey’s choice and past sensory information in the form of persistent activity across the second delay (Lemus et al., 2007), reinforcing the point that the same circuit is involved in both working memory and decision making.”
BackgroundBarbieri, F. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.003.2008 · Cited by 60 · cited 1× · see citing works
“…for highly irregular neural activity in the cortex posit recurrent cortical circuit dynamics endowed with balanced synaptic excitation and inhibition (van Vreeswijk and Sompolinsky, 1996; Amit and Brunel, 1997; Mattia and Del Giudice, 2004; Renart et al., 2007; Barbieri and Brunel, 2008).”
BackgroundObsessive-compulsive behaviour: a disorder of decision-making
Sachdev, P. S., & Malhi, G. S. (2005) · Australian & New Zealand Journal of Psychiatry · doi:10.1111/j.1440-1614.2005.01680.x · Cited by 54 · cited 1× · see citing works
“Decision making has recently attracted increasing attention not only in the neurobiological studies of cognition but also in psychiatry with the recognition that impaired decision making is prominently associated with various mental disorders (Fellows, 2004; Sachdev and Malhi, 2005).”
BackgroundWatanabe, K., & Funahashi, S. (2007) · Cerebral Cortex · doi:10.1093/cercor/bhm102 · Cited by 51 · cited 1× · see citing works
“…ramping activity during a mnemonic delay period, but this could reflect anticipation and timing of an upcoming response rather than decision computation (Fuster, 2008; Chafee and Goldman-Rakic, 1998; Quintana and Fuster, 1999; Brody et al., 2003; Miller et al., 2003; Watanabe and Funahashi, 2007).”
BackgroundExtended LATER model can account for trial-by-trial variability of both pre- and post-processes
Nakahara, H., Nakamura, K., & Hikosaka, O. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.07.001 · Cited by 45 · cited 1× · see citing works
“There is also evidence for a trial-to-trial correlation between the response latency and the preparatory activity of cortical movement neurons, before the target onset, in saccade and other sensorymotor tasks (Dorris et al., 1997; Churchland et al., 2006; Nakahara et al., 2006).”
BackgroundNeuronal mechanisms for the perception of ambiguous stimuli
Parker, A. J., & Krug, K. (2003) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(03)00099-0 · Cited by 43 · cited 1× · see citing works
“Neuron 60, October 23, 2008 ª2008 Elsevier Inc. 219 sensory neurons, whose spiking activity commonly displays weak trial-to-trial correlated variability with monkeys’ choices (Britten et al., 1996; Shadlen and Newsome, 1996; Parker and Krug, 2003; Law and Gold, 2008).”
BackgroundDeco, G., Pérez‐Sanagustín, M., de Lafuente, V., & Romo, R. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709794104 · Cited by 39 · cited 2× · see citing works
“The near-threshold detection experiment (de Lafuente and Romo, 2005) has also been modeled by a similar attractor network model (Deco et al., 2007a).”
BackgroundMethod
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- Ewert, 1997, Neural correlates of key stimulus and releasing mechanism: a case study and two concepts, Trends Neurosci., 20, 332
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Sorted by citations
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition
Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939 · see citing works
“Third, NMDA receptor-mediated excitatory reverberation may be critical for slow time integration of information underlying decision making computations (1032, 1034, 1071).”
BackgroundSelf-citationSpectral fingerprints of large-scale neuronal interactions
Siegel, M., Donner, T. H., & Engel, A. K. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3137 · Cited by 1,295 · see citing works
“One possible interpretation of this result is that the large-scale, frontoparietal beta-band oscillations that occur during decision formation reflect reverberant network interactions that actively maintain past sensory evidenc”
BackgroundNeuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126 · see citing works
“[From Wang (623), with permission from Elsevier.”
BackgroundMethodInfluentialStimulus onset quenches neural variability: A widespread cortical phenomenon
Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2501 · Cited by 1,084 · see citing works
The speed-accuracy tradeoff: history, physiology, methodology, and behavior
Heitz, R. P. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00150 · Cited by 952 · see citing works
“Wald’s procedure, which became known as the sequential probability ratio test (Wald, 1947), allows one to approach a known (acceptable) error rate with a potentially enormous savings in time and resources.”
BackgroundThe Neurobiology of Decision: Consensus and Controversy
Kable, J. W., & Glimcher, P. W. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.003 · Cited by 928 · see citing works
“Wang and colleagues (Lo and Wang, 2006; Wang, 2008; Wong and Wang, 2006) have recently shown that a single collicular or parietal circuit can be designed that performs both winner-take-all and thresholding operations in a stochastic fashion that depends on the inhibitory tone of the network.”
Choice-specific sequences in parietal cortex during a virtual-navigation decision task
Harvey, C. D., Coen, P., & Tank, D. W. (2012) · Nature · doi:10.1038/nature10918 · Cited by 916 · see citing works
Value, pleasure and choice in the ventral prefrontal cortex
Grabenhorst, F., & Rolls, E. T. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.12.004 · Cited by 766 · see citing works
Distributed coding of choice, action and engagement across the mouse brain
Steinmetz, N. A., Zatka-Haas, P., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1787-x · Cited by 758 · see citing works
Intention, Action Planning, and Decision Making in Parietal-Frontal Circuits
Andersen, R. A., & Cui, H. (2009) · Neuron · doi:10.1016/j.neuron.2009.08.028 · Cited by 707 · see citing works
“For instance, it is believed that the apparent stability of the world during eye movements, which sweep the visual scene across the retinas, is achieved by a forward model that makes use of feedback of eye movement commands (VonHolst and Mittelstaedt, 1950; von Helmoltz, 1866; Haarmeier et al., 2001).”
BackgroundSequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions
Forstmann, B. U., Ratcliff, R., & Wagenmakers, E. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033645 · Cited by 613 · see citing works
“FITTING THE DIFFUSION DECISION MODEL TO DATA (Vandekerckhove & Tuerlinckx 2007, 2008), fast-dm (Voss & Voss 2007, 2008), and the nonhierarchical HDDM (Wiecki et al. 2013).”
A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex
Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H., & Wang, X. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.008 · Cited by 582 · see citing works
Self-citationMaintenance of persistent activity in a frontal thalamocortical loop
Guo, Z. V., Inagaki, H. K., Daie, K., Druckmann, S., Gerfen, C. R., & Svoboda, K. (2017) · Nature · doi:10.1038/nature22324 · Cited by 529 · see citing works
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
Sussillo, D., & Barak, O. (2013) · Neural Computation · doi:10.1162/neco_a_00409 · Cited by 575 · see citing works
“This is in contrast to network models that are explicitly constructed to implement a specific known mechanism (see Wang, 2008; Hopfield, 1982).”
ResultNeural computations associated with goal-directed choice
Rangel, A., & Hare, T. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.03.001 · Cited by 532 · see citing works
Neural Basis of Reinforcement Learning and Decision Making
Lee, D., Seo, H., & Jung, M. W. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150512 · Cited by 542 · see citing works
“Converging evidence from a number of recent studies suggests that many of these computations are carried out in multiple interconnected regions in the frontal cortex and basal ganglia.”
BackgroundCognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535 · see citing works
“However, recurrent neural networks, which reprocess the outputs of their units and generate complex dynamics, have brought additional engineering advances(106) and better capture the recurrent signaling in brains.(107,108,48,109) Whereas feedforward networks are universal function approximators, recurrent networks are…”
BackgroundRobust neuronal dynamics in premotor cortex during motor planning
Li, N., Daie, K., Svoboda, K., & Druckmann, S. (2016) · Nature · doi:10.1038/nature17643 · Cited by 492 · see citing works
The Connectome of a Decision-Making Neural Network
Jarrell, T. A., Wang, Y., Bloniarz, A. E., Brittin, C. A., Xu, M., Thomson, J. N., Albertson, D. G., Hall, D. H., & Emmons, S. W. (2012) · Science · doi:10.1126/science.1221762 · Cited by 474 · see citing works
Toroidal topology of population activity in grid cells
Gardner, R. J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N. A., Dunn, B. A., Moser, M.-B., & Moser, E. I. (2022) · Nature · doi:10.1038/s41586-021-04268-7 · Cited by 428 · see citing works
Cortical information flow during flexible sensorimotor decisions
Siegel, M., Buschman, T. J., & Miller, E. K. (2015) · Science · doi:10.1126/science.aab0551 · Cited by 434 · see citing works
“Similar dynamics in PFC and LIP could indicate that accumulation of sensory evidence depends on their recurrent interactions (24, 25).”
BackgroundA framework for local cortical oscillation patterns
Donner, T. H., & Siegel, M. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.03.007 · Cited by 433 · see citing works
Value, search, persistence and model updating in anterior cingulate cortex
Kolling, N., Wittmann, M. K., Behrens, T. E. J., Boorman, E. D., Mars, R. B., & Rushworth, M. F. S. (2016) · Nature Neuroscience · doi:10.1038/nn.4382 · Cited by 439 · see citing works
The threshold for conscious report: Signal loss and response bias in visual and frontal cortex
van Vugt, B., Dagnino, B., Vartak, D., Safaai, H., Panzeri, S., Dehaene, S., & Roelfsema, P. R. (2018) · Science · doi:10.1126/science.aar7186 · Cited by 394 · see citing works
Rapid Neocortical Dynamics: Cellular and Network Mechanisms
Haider, B., & McCormick, D. A. (2009) · Neuron · doi:10.1016/j.neuron.2009.04.008 · Cited by 441 · see citing works
Limbic systems for emotion and for memory, but no single limbic system
Rolls, E. T. (2013) · Cortex · doi:10.1016/j.cortex.2013.12.005 · Cited by 428 · see citing works
The inner sense of time: how the brain creates a representation of duration
Wittmann, M. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3452 · Cited by 397 · see citing works
Artificial Neural Networks for Neuroscientists: A Primer
Yang, G. R., & Wang, X. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.005 · Cited by 386 · see citing works
“Progress was also made in developing models of basic cognitive functions such as simple decision-making [Gold and Shadlen, 2007, Wang, 2008].”
MethodSelf-citationDecision-related pupil dilation reflects upcoming choice and individual bias
de Gee, J. W., Knapen, T. H. J., & Donner, T. H. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317557111 · Cited by 381 · see citing works
Introduction to spiking neural networks: Information processing, learning and applications
Ponulak, F., & Kasiński, A. (2011) · Acta Neurobiologiae Experimentalis · doi:10.55782/ane-2011-1862 · Cited by 369 · see citing works
“…with lateral inhibitory interactions are often used for signal decorrelation (Linster and Cleland 2010, Tkacik et al. 2010) or to implement competition between neurons or neural populations (Lumer 2000, Jin and Seung 2002), and thus are considered in the context of decision making (Wang 2008).”
BackgroundThiele, A., & Bellgrove, M. A. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.008 · Cited by 350 · see citing works
“When multiple decisions are possible but competing, the interaction between the representations converges toward a single (winning) choice (Reynolds et al., 1999; Wang, 2008).”
BackgroundWang, X., & Krystal, J. H. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.018 · Cited by 309 · see citing works
Self-citationAttractor and integrator networks in the brain
Khona, M., & Fiete, I. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00642-0 · Cited by 314 · see citing works
“Decision making If, instead of a velocity signal, the input to an integrator network consisted of temporally varying positive and negative evidence in support of two options [63], Fig.”
BackgroundDecision-making in sensorimotor control
Gallivan, J. P., Chapman, C. S., Wolpert, D. M., & Flanagan, J. R. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0045-9 · Cited by 305 · see citing works
Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture
Gao, R., van den Brink, R. L., Pfeffer, T. A., & Voytek, B. (2020) · eLife · doi:10.7554/elife.61277 · Cited by 293 · see citing works
“…thought to result from neural attractor states (Wang, 2002; Wimmer et al., 2014) shaped by NMDA-mediated recurrent excitation and fast feedback inhibition (Wang, 1999, 2008), with contributions from other synaptic and cell-intrinsic properties (Duarte and Morrison, 2019; Gjorgjieva et al., 2016).”
Reward value comparison via mutual inhibition in ventromedial prefrontal cortex
Strait, C. E., Blanchard, T. C., & Hayden, B. Y. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.032 · Cited by 294 · see citing works
“While we do not show directly that vmPFC neurons engage in mutual inhibition, these results are consistent with the theory that value comparison reflects a competition for control of vmPFC responses through mutual inhibition (Cisek, 2012; Hunt et al., 2012; Jocham et al., 2012; Wang, 2008).”
BackgroundMethodResultA reservoir of time constants for memory traces in cortical neurons
Bernacchia, A., Seo, H., Lee, D., & Wang, X.-J. (2011) · Nature Neuroscience · doi:10.1038/nn.2752 · Cited by 292 · see citing works
“The filter f considered in equation (3) is replaced by g(k)·ex(t).”
BackgroundInfluentialSelf-citationTaste, olfactory, and food reward value processing in the brain
Rolls, E. T. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.03.002 · Cited by 287 · see citing works
“With our current computational understanding of how decisions are made in attractor neural networks (Rolls, 2008a, 2014; Rolls and Deco, 2010; Wang, 2008) (see Section 4), it is”
BackgroundMacroscopic gradients of synaptic excitation and inhibition in the neocortex
Wang, X. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0262-x · Cited by 277 · see citing works
Self-citationThe neuronal encoding of information in the brain
Rolls, E. T., & Treves, A. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.08.002 · Cited by 284 · see citing works
Encoding and decoding in parietal cortex during sensorimotor decision-making
Park, I. M., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3800 · Cited by 264 · see citing works
Predictive Coding of Dynamical Variables in Balanced Spiking Networks
Martin, B., Machens, C. K., & Denève, S. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003258 · Cited by 257 · see citing works
Prefrontal Cortex and Impulsive Decision Making
Kim, S., & Lee, D. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.07.005 · Cited by 276 · see citing works
“Wang X-J (2008): Decision making in recurrent neuronal circuits.”
BackgroundInfluentialAnticevic, A., Gancsos, M., Murray, J. D., Repovs, G., Driesen, N. R., Ennis, D. J., Niciu, M. J., Morgan, P. T., Surti, T. S., Bloch, M. H., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208494109 · Cited by 243 · see citing works
Self-citationThe population doctrine in cognitive neuroscience
Ebitz, R. B., & Hayden, B. Y. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.011 · Cited by 273 · see citing works
Medial and orbital frontal cortex in decision-making and flexible behavior
Klein-Flügge, M. C., Bongioanni, A., & Rushworth, M. F. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.05.022 · Cited by 244 · see citing works
Multiple signals in anterior cingulate cortex
Kolling, N., Behrens, T., Wittmann, M., & Rushworth, M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.007 · Cited by 249 · see citing works
The structures and functions of correlations in neural population codes
Panzeri, S., Moroni, M., Safaai, H., & Harvey, C. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00606-4 · Cited by 244 · see citing works
“Computational models have proposed that persistent activity reflects the instantiation of attractor states for short-term memory or categorical choices, and that ramping activity reflects the accumulation of evidence towards a decision threshold 78 .”
BackgroundTraining deep neural density estimators to identify mechanistic models of neural dynamics
Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., Vogels, T. P., et al. (2020) · eLife · doi:10.7554/elife.56261 · Cited by 252 · see citing works
Recurrent neural networks as versatile tools of neuroscience research
Barak, O. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.06.003 · Cited by 238 · see citing works
Hoopfer, E. D., Jung, Y., Inagaki, H., Rubin, G. M., & Anderson, D. J. (2015) · eLife · doi:10.7554/elife.11346 · Cited by 235 · see citing works
“Potential mechanisms include the release of a long-lasting neuromodulator (Flavell et al., 2013), a recurrent excitatory circuit (Wang, 2001, 2008), or a cell-autonomous mechanism (Major and Tank, 2004).”
BackgroundBayesian Computation through Cortical Latent Dynamics
Sohn, H., Narain, D., Meirhaeghe, N., & Jazayeri, M. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.012 · Cited by 226 · see citing works
Competitive Disinhibition Mediates Behavioral Choice and Sequences in Drosophila
Jovanic, T., Schneider-Mizell, C. M., Shao, M., Masson, J., Denisov, G., Fetter, R. D., Mensh, B. D., Truman, J. W., Cardona, A., & Zlatic, M. (2016) · Cell · doi:10.1016/j.cell.2016.09.009 · Cited by 212 · see citing works
Attractor Dynamics of Spatially Correlated Neural Activity in the Limbic System
Knierim, J. J., & Zhang, K. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150351 · Cited by 222 · see citing works
“…has also been used to model other neural systems, such as orientation tuning in visual cortex (Ben-Yishai et al. 1995, Somers et al. 1995, see also Ferster & Miller 2000), binocular rivalry (Laing & Chow 2002), and perceptual decision making (Brody et al. 2003, Machens et al. 2005, Wang 2008).”
MethodOptimal policy for value-based decision-making
Tajima, S., Drugowitsch, J., & Pouget, A. (2016) · Nature Communications · doi:10.1038/ncomms12400 · Cited by 209 · see citing works
Balanced cortical microcircuitry for maintaining information in working memory
Lim, S., & Goldman, M. S. (2013) · Nature Neuroscience · doi:10.1038/nn.3492 · Cited by 208 · see citing works
“Note that the strength of sij lm j , , denoted by Jij in equation (9), corresponds to the integrated area under a single postsynaptic potential, and not the height of a single postsynaptic potential.”
BackgroundInfluentialGoard, M. J., Pho, G. N., Woodson, J., & Sur, M. (2016) · eLife · doi:10.7554/elife.13764 · Cited by 207 · see citing works
“Building on this work, theoretical studies have yielded biologically-plausible models describing how the sustained neural activity can be generated and maintained to support short-term memory (Compte et al., 2000; Goldman et al., 2003; Wang, 2008).”
BackgroundLaws of concatenated perception: Vision goes for novelty, decisions for perseverance
Pascucci, D., Mancuso, G., Santandrea, E., Libera, C. D., Plomp, G., & Chelazzi, L. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000144 · Cited by 204 · see citing works
Working Memory and Decision-Making in a Frontoparietal Circuit Model
Murray, J. D., Jaramillo, J., & Wang, X. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0343-17.2017 · Cited by 199 · see citing works
“…to a quasi-continuous network in which neurons exhibit smoothly varying tuning of a parametric stimulus variable (Compte et al., 2000; Furman and Wang, 2008) to explore effects that depend on the similarity and distance between distractors and targets held in WM (Suzuki and Gottlieb, 2013;…”
BackgroundSelf-citationOlfactory pattern classification by discrete neuronal network states
Niessing, J., & Friedrich, R. W. (2010) · Nature · doi:10.1038/nature08961 · Cited by 205 · see citing works
Discrete Neocortical Dynamics Predict Behavioral Categorization of Sounds
Bathellier, B., Ushakova, L., & Rumpel, S. (2012) · Neuron · doi:10.1016/j.neuron.2012.07.008 · Cited by 194 · see citing works
“…work of J. Hopfield on attractors in recurrent neuronal networks (Hopfield, 1982), modeling studies suggested that cortical-like network architectures are prone to generate highly nonlinear population dynamics (Amit and Brunel, 1997; Maass et al., 2007; Mongillo et al., 2008; Wang, 2008).”
Brain mechanisms for perceptual and reward-related decision-making
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.01.010 · Cited by 196 · see citing works
“Consequently, the network's behavior is not just dominated by its steady states, it also exhibits prolonged responses to momentary sensory inputs, with a characteristic time constant of up to a second, during which the model effectively integrates the incoming inputs (Wang, 2008).”
BackgroundMethodInfluentialRorie, A. E., Gao, J., McClelland, J. L., & Newsome, W. T. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009308 · Cited by 195 · see citing works
The effect of acute physical exercise on cognitive function during development
Ellemberg, D., & St-Louis-Deschênes, M. (2009) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2009.09.006 · Cited by 193 · see citing works
“It is also considered as a general indicator of functional integrity of the central nervous system (Wang, 2008).”
BackgroundBeyond working memory: the role of persistent activity in decision making
Curtis, C. E., & Lee, D. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.03.006 · Cited by 191 · see citing works
Rigotti, M. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00024 · Cited by 184 · see citing works
Taste, olfactory and food texture reward processing in the brain and obesity
Rolls, E. T. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.155 · Cited by 186 · see citing works
The storage and recall of memories in the hippocampo-cortical system
Rolls, E. T. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2744-3 · Cited by 183 · see citing works
“The operation of such systems has been described in The Noisy Brain: Stochastic Dynamics as a Principle of Brain Science (Rolls and Deco 2010) in the context of decisionmaking (Wang 2008) but the approach applies equally to memory recall in an autoassociation memory, as the network architecture and operation for memor…”
MethodTheory and Simulation in Neuroscience
Gerstner, W., Sprekeler, H., & Deco, G. (2012) · Science · doi:10.1126/science.1227356 · Cited by 178 · see citing works
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“…and potentially compete until a “threshold” is reached, at which point a response is selected (Beck et al., 2008; Bogacz, 2007; Cisek, 2007; Cisek, Puskas, & El-Murr, 2009; Gold & Shadlen, 2002; Krajbich, Armel, & Rangel, 2010; Ratcliff & McKoon, 2008; Usher & McClelland, 2001; Wang, 2008).”
BackgroundConversion of sensory signals into perceptual decisions
Romo, R., & de Lafuente, V. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.03.007 · Cited by 181 · see citing works
Reward-based training of recurrent neural networks for cognitive and value-based tasks
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“We consider this sampling as an abstract representation of the downstream action selection mechanisms present in the brain, including the role of noise in implicitly realizing stochastic choices with deterministic outputs ( Wang, 2002 , 2008 ).”
BackgroundSelf-citationAdaptive History Biases Result from Confidence-Weighted Accumulation of past Choices
Braun, A., Urai, A. E., & Donner, T. H. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2189-17.2017 · Cited by 157 · see citing works
Cerebral cortex : principles of operation
Rolls, E. T. (2016) · doi:10.82444/warw.33712699 · Cited by 187 · see citing works
From fixed points to chaos: Three models of delayed discrimination
Barak, O., Sussillo, D., Romo, R., Tsodyks, M. V., & Abbott, L. F. (2013) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.02.002 · Cited by 172 · see citing works
“Maintaining the information about stimulus attributes with stationary persistent activity appears to be a natural and robust mechanism of working memory (see e.g. Wang, 2008).”
Confirmation Bias through Selective Overweighting of Choice-Consistent Evidence
Talluri, B. C., Urai, A. E., Tsetsos, K., Usher, M., & Donner, T. H. (2018) · Current Biology · doi:10.1016/j.cub.2018.07.052 · Cited by 168 · see citing works
“The accumulation of fluctuating sensory evidence toward binary choices is well characterized at a neurophysiological level [6, 8, 50, 51].”
BackgroundDecision Making: From Neuroscience to Psychiatry
Lee, D. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.008 · Cited by 169 · see citing works
“Second, and more importantly for the purpose of this review, researchers have begun to elucidate a number of important core mechanisms in the brain responsible for various computational steps of decision making and reinforcement learning (Wang, 2008; Kable and Glimcher, 2009; Lee et al., 2012).”
Disentangling decision models: From independence to competition.
Teodorescu, A. R., & Usher, M. (2013) · Psychological Review · doi:10.1037/a0030776 · Cited by 155 · see citing works
“The mechanism that enables humans to make such decisions has been studied extensively over the last few decades both in psychology (Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006; Bogacz, Usher, Zhang, & McClelland, 2007; Brown & Heathcote, 2008; Krajbich & Rangel (2011); Laming, 1968; Link & Heath, 1975; Ratcliff & Mc…”
Within-group synchronization in the prefrontal cortex associates with intergroup conflict
Yang, J., Zhang, H., Ni, J., De Dreu, C. K. W., & Ma, Y. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0630-x · Cited by 161 · see citing works
Boorman, E. D., Rushworth, M. F. S., & Behrens, T. E. J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3022-12.2013 · Cited by 165 · see citing works
Inferring decoding strategies from choice probabilities in the presence of correlated variability
Haefner, R. M., Gerwinn, S., Macke, J. H., & Bethge, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3309 · Cited by 161 · see citing works
Neuronal Origins of Choice Variability in Economic Decisions
Padoa‐Schioppa, C. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.013 · Cited by 157 · see citing works
“It has often been hypothesized that the neural systems governing economic and perceptual decisions share fundamental principles and core mechanisms (Gold and Shadlen, 2007; Shadlen et al., 2008; Summerfield and Tsetsos, 2012; Wang, 2008).”
Cortical Network Dynamics of Perceptual Decision-Making in the Human Brain
Siegel, M., Engel, A. K., & Donner, T. H. (2011) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2011.00021 · Cited by 159 · see citing works
“However, the actual cortical circuits implementing this process likely engage in highly recurrent interactions (Lamme and Roelfsema, 2000; Wang, 2008).”
BackgroundDecisions are expedited through multiple neural adjustments spanning the sensorimotor hierarchy
Steinemann, N. A., O’Connell, R. G., & Kelly, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-06117-0 · Cited by 160 · see citing works
Mean-Driven and Fluctuation-Driven Persistent Activity in Recurrent Networks
Renart, A., Moreno‐Bote, R., Wang, X., & Parga, N. (2006) · Neural Computation · doi:10.1162/neco.2007.19.1.1 · Cited by 157 · see citing works
Self-citationOscillatory multiplexing of neural population codes for interval timing and working memory
Gu, B.-M., van Rijn, H., & Meck, W. H. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.10.008 · Cited by 155 · see citing works
Selective Theta-Synchronization of Choice-Relevant Information Subserves Goal-Directed Behavior
Womelsdorf, T., Vinck, M. A., Leung, L.-W. S., & Everling, S. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00210 · Cited by 157 · see citing works
Gallivan, J. P., McLean, D. A., Flanagan, J. R., & Culham, J. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0541-12.2013 · Cited by 149 · see citing works
“…explaining neural activity in a variety of cortical (Kalaska and Crammond, 1995; Snyder et al., 1997; Cisek and Kalaska, 2005) and subcortical structures (Basso and Wurtz, 1998) and may provide a neurophysiological basis for decision-making processes (Cisek, 2006; Beck et al., 2008; Wang, 2008).”
BackgroundChoice, difficulty, and confidence in the brain
Rolls, E. T., Grabenhorst, F., & Deco, G. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.073 · Cited by 148 · see citing works
“…that receives the inputs for one of the decisions, and where the choice made is probabilistic because of the noise contributed to by the random spiking times of the neurons for a given firing rate (Deco and Rolls, 2006; Deco et al., 2009; Rolls, 2008; Rolls and Deco, 2010; Wang, 2002, 2008).”
Taste, olfactory and food texture reward processing in the brain and the control of appetite
Rolls, E. T. (2012) · Proceedings of The Nutrition Society · doi:10.1017/s0029665112000821 · Cited by 149 · see citing works
“The result in this non-linear system is that one attractor wins, and this implements a mechanism for decision-making with one winner((10,11,85,91)).”
BackgroundSimilarity Effect and Optimal Control of Multiple-Choice Decision Making
Furman, M., & Wang, X. (2008) · Neuron · doi:10.1016/j.neuron.2008.12.003 · Cited by 143 · see citing works
“Previous work from our lab (Wang, 2002; Wong and Wang, 2006; Wong et al., 2007; Wang, 2008) has shown that attractor dynamics in a biophysically based model is not only suitable for winner-take-all competition underlying categorical choice, but is also compatible with slow transients required for…”
BackgroundInfluentialSelf-citationHierarchical competitions subserving multi-attribute choice
Hunt, L. T., Dolan, R. J., & Behrens, T. E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3836 · Cited by 132 · see citing works
Distributed Winner-Take-All in Dynamic Networks
Li, S., Zhou, M., Luo, X., & You, Z. (2016) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2016.2578645 · Cited by 145 · see citing works
“Wang [25] summarizes a class of WTA algorithms into a unified mechanism depending on slow recurrent synaptic excitation balanced by fast feedback inhibition and interprets this mechanism from the perspective of cognitive science.”
BackgroundCerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level
Lin, Q., Manley, J., Helmreich, M., Schlumm, F., Li, J. M., Robson, D. N., Engert, F., Schier, A., Nöbauer, T., & Vaziri, A. (2020) · Cell · doi:10.1016/j.cell.2019.12.018 · Cited by 122 · see citing works
Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains
de Gee, J. W., Tsetsos, K., Schwabe, L., Urai, A. E., McCormick, D. A., McGinley, M. J., & Donner, T. H. (2020) · eLife · doi:10.7554/elife.54014 · Cited by 125 · see citing works
“…evidence is encoded in sensory cortex, and downstream regions of association and motor cortices accumulate the fluctuating sensory response over time into a decision variable that forms the basis of behavioral choice (Bogacz et al., 2006; Shadlen and Kiani, 2013; Siegel et al., 2011; Wang, 2008).”
BackgroundReverse engineering the mouse brain
O’Connor, D. H., Huber, D., & Svoboda, K. (2009) · Nature · doi:10.1038/nature08539 · Cited by 137 · see citing works
“Perhaps as a consequence, current network models of cortical activity can afford to ignore information about cortical cell types and connectivity and represent all cortical neurons by a few simple cell types, connected in generic way”
BackgroundDark control: The default mode network as a reinforcement learning agent
Dohmatob, E., Dumas, G., & Bzdok, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25019 · Cited by 121 · see citing works
“Those attractors can then constrain both the number of factors taken into account by decision processes and the possible behaviors of the agent (Wang, 2008).”
BackgroundNeural dynamics and circuit mechanisms of decision-making
Wang, X. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.08.006 · Cited by 136 · see citing works
“Adapted from [76] for (b), from [77] for (c) and from [33] for (d).”
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“Being situated at the junction point of the frontoparietal attention (Wang, 2008) and the default mode networks, the posterior parietal cortex has been suggested to play a role in processes involving both cognition and attention, such as time-keeping (Buhusi andMeck, 2005).”
Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior
Pillai, A. S., & Jirsa, V. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.013 · Cited by 125 · see citing works
- Authors
- Wang, X.-J.
- Affiliations
- Yale University (US)
- Corresponding author
- Wang, Xiao-Jing
- ORCID
- Wang, Xiao-Jing
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2008-10
- Volume
- 60
- Issue
- 2
- Pages
- 215-234
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2710297
- OpenAlex
- W2169519447
- Semantic Scholar
- b1845b04420d64b283e65b67457df04ceb93dd6e
- Cited by
- 679
- References
- 213
- Citations by year
- 2026: 32 · 2025: 40 · 2024: 42 · 2023: 48 · 2022: 43 · 2021: 38 · 2020: 49 · 2019: 37 · 2018: 33 · 2017: 32 · 2016: 39 · 2015: 35 · 2014: 41 · 2013: 51 · 2012: 48
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1750 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Kavli Foundation
- National Institutes of Health (2-R01-MH062349)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- decision-making, attractor states, evidence accumulation, product inhibition, synaptic excitation, decision threshold, reaction time, perceptual decision making, probabilistic choice, social decision-making, choice behavior
- MeSH terms
- Animals, Brain, Decision Making, Humans, Nerve Net, Neural Inhibition, Neural Pathways, Synaptic Transmission, Neuronal Plasticity, Neurons, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
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