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Reward Value-Based Gain Control: Divisive Normalization in Parietal Cortex
Open accessAbstract
The representation of value is a critical component of decision making. Rational choice theory assumes that options are assigned absolute values, independent of the value or existence of other alternatives. However, context-dependent choice behavior in both animals and humans violates this assumption, suggesting that biological decision processes rely on comparative evaluation. Here we show that neurons in the monkey lateral intraparietal cortex encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives. Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates. This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation. Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.
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TL;DR
Neurons in monkey lateral intraparietal cortex (LIP) encode saccadic value relative to the values of other available alternatives, not as absolute values. Full divisive normalization best describes this context-dependent modulation, suggesting a canonical cortical gain-control mechanism for value coding.
Key findings
- In a two-target task, LIP activity increased with reward inside the response field (Vin) and decreased with reward outside it (Vout): 12/16 neurons (75.0%) were positively correlated with Vin, and 18/27 (66.7%) were negatively correlated with Vout; population mean correlations were 0.33 and −0.21, respectively.
- In a three-target task, stimulus-driven activity was suppressed by summed extra-RF value: 33/62 neurons (53.2%) were modulated, with 30/33 showing suppression; population mean correlation was −0.155 (p = 2.6×10−6). Activity in the absence of an RF target was also suppressed: 27/62 (43.6%) modulated, 21/27 suppressed; mean correlation −0.087 (p = 0.0019).
- Full divisive normalization best fit individual neurons (MED-RF: 53/62, 85.5%; HIGH-RF: 16/17, 94.1%; LOW-RF: 26/41, 61.9%) and the combined population dataset (R² = 0.961) versus fractional (0.730), difference (0.601), and simple normalization (0.219); it also had the lowest cross-validated mean squared error (0.0393).
- Suppression depended on extra-RF target value but not relative target distance (ANCOVA: value p = 0.004; distance p = 0.058; interaction p = 0.793), and behavioral measures such as fixation breaks, error rates, and reaction times showed no consistent dependence on Vout.
Methods
- Three male rhesus monkeys performed two-target and three-target saccade tasks with water reward magnitudes assigned to targets inside or outside the LIP neuron’s response field. Two-target task: 27 LIP neurons (monkey D n = 17; monkey W n = 10), with a subset (n = 16) also tested with RF value varied. Three-target task: 62 neurons under MED-RF (monkey W n = 31; monkey B n = 31), plus 45 neurons under HIGH-RF or LOW-RF conditions.
- Single-unit electrophysiology in LIP; activity analyzed mainly 0–1000 ms after peripheral target presentation and normalized to peak firing in single-target RF mapping trials.
- Analyses included Pearson correlations, multiple regression with Vout and saccade latency, ANCOVA on a suppression index, and model fitting of fractional, difference, simple normalization, and full normalization models using AIC and leave-one-out cross-validation.
Limitations
- Stated: the circuit mechanisms generating divisive normalization remain unknown, the biophysical implementation of the semisaturation term σ is unknown, and the causal role of normalized value coding in context-dependent choice remains to be explored.
- Stated: visuospatial parameters may influence suppression when alternative targets are very close to the RF (distance effect p = 0.058, not significant here), and attention cannot be fully excluded as an alternative explanation, though the authors argue the parametric value effects are not easily reconciled with simple attentional modulation.
- Evident: findings are restricted to macaque LIP, specific saccade tasks, and water reward magnitudes; some neurons were recorded under multiple value conditions, so across-condition analyses were restricted to population responses rather than individual-neuron comparisons.
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52 Own result · 28 Own interpretation · 13 Not checked yet · 4 Own method · 4 No basis given
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In the paper
“Examination of the AIC values confirmed that the full divisive normalization model was the most likely of these underlying value representations in the vast majority of LIP neurons (MED-RF condition: n = 53/62, 85.5%; HIGH-RF condition: n = 16/17, 94.1%; LOW-RF conditions: n = 26/41, 61.9%).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“As seen in Figure 7, the full normalization equation (Rmax = 3.86, σ = 1541 µl, β = 87.6 µl) characterized LIP responses in both conditions, which is evident when the best-fit normalization model (black lines) is compared to the population responses (red and blue points).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“Examination of AIC values confirmed that the full normalization model best explained the MED-RF condition data (AICfract = −31.8, AICdiff = −26.5, AICnorm_simp = −21.9, AICnorm_full = −70.9).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“In contrast, the average correlation coefficient in RF target-absent trials remained significantly negative.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Consistent with previous reports, many LIP neurons in this task possessed motor-related activity: a presaccadic increase in firing rate selective for saccades into the RF.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“As shown in Figure 5, population activity in the three-target task also depended on the value of the RF target, with progressively higher firing rates at larger levels of Vin.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Across the population of neurons, the average correlation coefficient was significantly negative for most of the cue presentation interval in both RF target-present and RF target-absent conditions (p < 0.05, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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In the paper
“As shown for the example neuron in Figure 2B, activity in trials without a target in the RF also varied as a function of Vout (r = −0.161, p = 0.037).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Finally, reaction times for saccades towards the RF target across the different value contexts did not differ across different Vout conditions in either monkey (Monkey D: p = 0.97; Monkey W: p = 0.20; one-way ANOVA).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
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“We observed that stimulus-driven activity, elicited by the appearance of the target in the RF, was a consistent function of the total amount of reward available, despite the fact that RF target value remained constant.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“However, the simple normalization model failed to predict the suppression below baseline levels observed in the absence of RF stimulation; only the full normalization model fully predicted the modulation of both RF-driven and baseline activity.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“However, examination of the relative target positions (distance from RF target) of small and large value alternative targets showed no discernible difference in the average relative position (distancesmall = 35.43 deg, distancelarge = 35.10 deg; p = 0.77, t-test) or the relative position distributions (p = 0.98, two-sample Kolmogorov-Smirnov test; Figure 4 inset).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“The majority of LIP neurons were modulated by value context, with almost all neurons showing suppression (modulation: n = 33/62, 53.2%; suppression n = 30/33, 90.9%); furthermore, suppression was significant across the population of neurons (mean correlation −0.155; p = 2.6×10−6, t-test) and evident in the mean population activity (Figure 2C).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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In the paper
“As shown in Figure 3, the population of LIP neurons displayed Vout-dependent suppression in both RF target-present and RF target-absent conditions across much of the trial, with peak modulation occurring approximately 200–300 ms after peripheral target onset.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Of the recorded neurons, firing rate was significantly dependent on only value in 29 neurons, on both value and latency in 3 neurons, and on only latency in 1 neuron, suggesting that the observed modulation of LIP activity was primarily driven by the value context.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“In contrast, the difference model predicted suppression even in RF target-absent conditions; however, it could not adequately model both the RF target-driven and RF target-absent effects simultaneously.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“For saccades to the small target, error rates varied by target configuration (Monkey B, χ2(3) = 13.4, p = 0.004; Monkey W, χ2(3) = 17.2, p = 0.0006) but were correlated with total value in only one monkey (Monkey B: r = 0.83, p = 0.167; Monkey W: r = 0.95, p = 0.047) and uncorrelated across the combined data (r = 0.35, p = 0.401).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“This value modulation in the absence of RF stimulation was significant for a large fraction of LIP neurons (modulation n = 27/62, 43.6%; suppression 21/27, 77.8%) and significant across the population (mean correlation −0.087; p = 0.0019, t-test).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Similarly, the rate of incorrect saccadic choices did not vary consistently with Vout; both monkeys exhibited high success rates (> 90%), with error rates independent of Vout condition in Monkey W (χ2(4) = 4.5, p = 0.34) and dependent but not significantly related to Vout in Monkey D (χ2(4) = 14.4, p = 0.006; r = −0.40, p = 0.51).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
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“Despite constant Vin levels, activity in trials with a target in the RF (solid red symbols) was suppressed as Vout increased. Similarly, in trials without a target in the RF (solid blue symbols), activity decreased as a function of alternative target values.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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In the paper
“Both monkeys had low fixation break frequencies during cue presentation (Monkey W: 2.8%; Monkey B: 4.0%) that were uncorrelated with total value in both monkeys individually (Monkey W: r = −0.36, p = 0.42; Monkey B: r = 0.28, p = 0.54) and combined across animals (r = 0.04, p = 0.90).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“consistent with the timepoint analysis, average LIP activity is suppressed by Vout in RF target-absent conditions (mean r = −.0.071, p = 3.7×10−4) and unmodulated in RF target-present (mean r = 0.024, p = 0.302) conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Consistent with the results described above, increasing the value of the target outside the RF in the two-target conditions decreased LIP firing rates (red, orange). Furthermore, activity was also a function of target number, decreasing from single target (black) to double target (red, orange) to triple target (yellow) conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
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“Fixation break frequencies were low in both monkeys (Monkey D: 9.2%; Monkey W: 1.6%); in Monkey W, these frequencies did not differ across Vout conditions (Monkey W: χ2(4) = 5.6, p = 0.23), while in Monkey D, these frequencies differed across conditions (χ2(4) = 20.6, p = 0.0004) but were not significantly correlated with Vout magnitudes (r = −0.87, p = 0.06).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
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“The fractional and simple normalization models failed to account for suppression in the absence of a RF target; because both models incorporate only the quantity Vin in the numerator, they predicted constant levels of activity in all RF-target absent conditions (simple normalization: R = 0; fractional: R = a).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“Across the population, suppression was independent of the relative spatial location of the extra-RF target, supporting a modulation driven primarily by target value rather than spatial location.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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“Targets at these locations did not activate the recorded neuron when presented alone, consistent with a contralateral visual bias in LIP (Ben Hamed et al., 2001; Patel et al., 2010).”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
What the sources say
- Not checked yetcites Ben Hamed et al., 2001
- Not checked yetcites Patel et al., 2010
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“As noted previously, the fractional model failed to account for background suppression in the absence of a RF target, evident as a horizontal line in predicted activity (Figure 6, top); furthermore, the predicted linear responses correspond poorly to the observed nonlinear activity.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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“These results show that the value of targets situated far from the RF can modulate both stimulus-driven activity and activity in the absence of direct feed-forward RF drive.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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“The value of σ derived from the present study is ~1000 µl, a quantity large enough at normal volumes of reward to significantly shift value coding away from a strict fractional representation and towards a richer function of value context.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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“First, we find a monotonic relationship between the value of the target in the RF and LIP activity, as well as a parametric suppression related to the value of extra-RF targets.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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“In the three-target task, extra-RF targets were far from the RF center (average 35 deg) and located primarily in the opposite visual hemifield.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
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“When examined across all data conditions, the full divisive normalization model explained the full dataset (R2 = 0.961) far better than the fractional (R2 = 0.730), difference (R2 = 0.601), or simple normalization (R2 = 0.219) models (Figure 6, right column).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The difference model predicted some RF target-absent suppression, but performed particularly poorly in characterizing the range of responses to different Vin conditions.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While such linear models accurately characterized basic visual neuron responses such as orientation and direction selectivity, they could not explain more complicated, nonlinear phenomena such as contrast saturation and nonspecific suppression.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“More recently, motivated by computational models of two-alternative choice behavior, some researchers have suggested that the difference in option values may better describe LIP activity (Corrado et al., 2005).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Corrado et al., 2005
hedgedcorrelational- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these results suggest that LIP encodes a context-dependent value representation, where the value configuration of the entire choice set controls both stimulus-driven and target-absent activity early in the valuation process.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, while both the fractional and difference models are consistent with LIP value modulation, neither theoretical proposal is directly based on the structure and function of cortical circuits.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Hubel and Wiesel, 1962, 1968
- Not checked yetcites Movshon et al., 1978
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Initial studies of value coding in LIP (Platt and Glimcher, 1999; Sugrue et al., 2004) focused on a fractional representation of value due to its association with classic matching law behavior (Herrnstein, 1961).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Platt and Glimcher, 1999
- Not checked yetcites Sugrue et al., 2004
- Not checked yetcites Herrnstein, 1961
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates.”
§ Value-based gain control: divisive normalization in parietal cortex > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably, activity positively related to Vout was absent in the two-target task (Figure 1C), suggesting that such activity may be a salience effect related to the instructional offset of the peripheral targets which only occurred in the three-target task.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Three-alternative neural data
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This epoch represents the entire period of target array presentation prior to the signal identifying the saccade target; the general results presented here are not sensitive to the exact definition of this epoch.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Data analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, activity was both an increasing function of Vin and a decreasing function of Vout, suggesting that LIP value representation is relative rather than absolute; we explore the specific algorithm underlying this relative representation below.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Two-alternative neural data
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The value of a given target is equated with the associated reward magnitude.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, normalization-style models yield efficient coding of natural signal statistics in both visual and auditory modalities (Schwartz and Simoncelli, 2001; Valerio and Navarro, 2003) and may play a role in attentional processing (Reynolds et al., 1999; Reynolds and Heeger, 2009), suggesting that divisive normalization may serve as a canonical computational algorithm in cortical circuits.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Schwartz and Simoncelli, 2001
- Not checked yetcites Valerio and Navarro, 2003
- Not checked yetcites Reynolds and Heeger, 2009
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In sensory cortices, stimuli outside the classical receptive field can nonetheless significantly modulate neuronal activity. Many of these extra-classical effects are well-described by models where response is specified by the sensory properties of the stimulus inside the receptive field, divided by the weighted sum of the sensory properties of stimuli both outside and inside the receptive field (”
§ Value-based gain control: divisive normalization in parietal cortex > INTRODUCTION
What the sources say
- Not checked yetcites Heeger, 1992
- Not checked yetcites Carandini et al., 1997
- Not checked yetcites Cavanaugh et al., 2002
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the relatively small number of neurons recorded in multiple Vin conditions, we restricted analyses across all value conditions to the population responses.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Electrophysiological recording
plaindirectn = 18- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To address these issues, Heeger proposed that visual cortical function involves a normalization stage, where a given cell’s response is divided by a quantity representing the pooled activity of a large number of other similar neurons (Heeger, 1992).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Heeger, 1992
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“This divisive normalization model explains a number of nonlinear phenomena in striate and extrastriate cortex (Carandini et al., 1997; Britten and Heuer, 1999; Cavanaugh et al., 2002; Heuer and Britten, 2002) as well as responses in the ventral visual stream (Zoccolan et al., 2005).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
What the sources say
- Not checked yetcites Carandini et al., 1997
- Not checked yetcites Britten and Heuer, 1999
- Not checked yetcites Cavanaugh et al., 2002
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For both tasks, the initial ten trials in a block were discarded to avoid learning effects.”
§ Value-based gain control: divisive normalization in parietal cortex > MATERIALS AND METHODS > Data analysis
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, these results indicate that full divisive normalization outperformed the alternative models in terms of both response characterization and predictive power.”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to nonlinearity introduced by the semisaturation term (σ), the divisive normalization representation can produce responses on a continuum between absolute and relative value coding; this form of adaptive gain control depends on the relative balance between total value and σ.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, at intermediate total values (V ~ σ) the denominator in the normalization model depends on both total value and σ, and activity grows increasingly sensitive to choice context, similar to a fractional model (angled shading and angled contour lines).”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results provide the first example of normalization outside a strictly sensory area, and the most anterior extension of divisive normalization coding in the dorsal stream.”
§ Value-based gain control: divisive normalization in parietal cortex > DISCUSSION
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The neural basis of context-dependent valuation is unknown.
“However, the neural basis of such context-dependent valuation is unknown.”
The paper relies on this without support.
The specific form of value representation in LIP and how it arises out of cortical circuit interactions remains largely unexplored.
“Current evidence suggests that parietal neurons are modulated by the value of their associated saccade, but the specific form of value representation and how it arises out of cortical circuit interactions remains largely unexplored.”
The paper relies on this without support.
LIP neurons have a circumscribed response field and many have a similarly tuned response to upcoming saccade amplitude and direction.
“LIP neurons, like those in visual cortex, respond to stimulus presentation in a circumscribed region of visual space termed the response field (RF); many of these neurons also possess a similarly tuned response to upcoming saccade amplitude and direction.”
The authors state this as an assumption.
The value of a given target is equated with the associated reward magnitude.
“The value of a given target was equated with the associated reward magnitude.”
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?
In the citing paper
“Similar results are obtained in tasks that manipulate the desirability of a target using different methods, for example by controlling the relative magnitude, probability or delay of its expected reward (Bernacchia et al., 2011; Louie et al., 2011; Sugrue et al., 2004; Yang and Shadlen, 2007).”
From Attention, learning, and the value of information (2012) · cites this paper as Louie et al., 2011
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentχ²(4) = 5.6reported p = .23 · recomputed p = .231
“χ2(4) = 5.6, p = .23”
- Consistentχ²(4) = 20.6reported p = .0004 · recomputed p < .001
“χ2(4) = 20.6, p = .0004”
- Consistentχ²(4) = 4.5reported p = .34 · recomputed p = .343
“χ2(4) = 4.5, p = .34”
- Consistentχ²(4) = 14.4reported p = .006 · recomputed p = .006
“χ2(4) = 14.4, p = .006”
- Consistentχ²(3) = 13.4reported p = .004 · recomputed p = .004
“χ2(3) = 13.4, p = .004”
- Consistentχ²(3) = 17.2reported p = .0006 · recomputed p < .001
“χ2(3) = 17.2, p = .0006”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“LOW-RF conditions: n = 26/41, 61.9%”
§ Value-based gain control: divisive normalization in parietal cortex > RESULTS > Divisive normalization and value coding
Reported 61.9 · recomputed 63.414634
Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · cited 1×
“To quantify the relative performance of the different models, we calculated the AIC for each neuron under every model; the AIC provides an information theoretic basis for model comparison that considers both goodness of fit and parsimony (Akaike, 1974).”
MethodTheory of games and economic behavior
Garcia, S. R., & Miller, S. J. (2019) · American Mathematical Society eBooks · doi:10.1090/mbk/121/32 · Cited by 16,777
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · cited 1×
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
BackgroundReceptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685 · cited 1×
“Early models of striate cortex described visual neuron function as a linear operation, producing responses that are weighted sums of relevant inputs (Hubel and Wiesel, 1962, 1968; Movshon et al., 1978).”
BackgroundTheory of Games and Economic Behavior
N., E., von Neumann, J., & Morgenstern, O. (1945) · The Journal of Philosophy · doi:10.2307/2019327 · Cited by 4,095
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878 · cited 1×
“Under larger choice sets, human decision makers report greater difficulty choosing and lower outcome satisfaction, and they often choose the default option or opt not to choose at all (Iyengar and Lepper, 2000).”
Luce, R. (1959) · Econometrica · doi:10.2307/1911299 · Cited by 2,603 · cited 2×
“Reward Value-Based Gain Control: Divisive Normalization in”
BackgroundRELATIVE AND ABSOLUTE STRENGTH OF RESPONSE AS A FUNCTION OF FREQUENCY OF REINFORCEMENT 1, 2
Herrnstein, R. J. (1961) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1961.4-267 · Cited by 2,281 · cited 2×
“Initial studies of value coding in LIP (Platt and Glimcher, 1999; Sugrue et al., 2004) focused on a fractional representation of value because of its association with classic matching law behavior (Herrnstein, 1961).”
BackgroundShadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178 · cited 1×
“Cortical neurons exhibit considerable variability in their spiking activity, and information is likely represented by distributions of neurons and spiking rates (Tolhurst et al., 1983; Shadlen and Newsome, 1998; Pouget et al., 2003).”
BackgroundNormalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835 · cited 4×
“…well described by models in which response is specified by the sensory properties of the stimulus inside the receptive field, divided by the weighted sum of the sensory properties of stimuli both outside and inside the receptive field (Heeger, 1992; Carandini et al., 1997; Cavanaugh et al., 2002).”
BackgroundInfluential
Research works printed but not linked: 1
- Stephens DW Krebs JR Foraging theory 1986 Princeton, NJ Princeton University Press
Cited by
Sorted by citations
Bartra, O., McGuire, J. T., & Kable, J. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.063 · Cited by 1,927
“We have assumed for simplicity that zero corresponds to no outcome at all, but there is evidence that decision makers actually evaluate outcomes relative to a reference point based either on predictions (Montague et al., 1996) or concurrent context (Louie et al., 2011).”
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
Top-down versus bottom-up attentional control: a failed theoretical dichotomy
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.06.010 · Cited by 1,557
Spectral 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
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126
“Divisive normalization from sensory physiology describes well the adaptations to changes in individual values of choice options in reward neurons of parietal cortex (332).”
BackgroundWhat the orbitofrontal cortex does not do
Stalnaker, T. A., Cooch, N. K., & Schoenbaum, G. (2015) · Nature Neuroscience · doi:10.1038/nn.3982 · Cited by 531
Sterling, P., & Laughlin, S. (2015) · doi:10.7551/mitpress/9780262028707.001.0001 · Cited by 482
Selection history: How reward modulates selectivity of visual attention.
Failing, M., & Theeuwes, J. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1380-y · Cited by 345
Normalization is a general neural mechanism for context-dependent decision making
Louie, K., Khaw, M. W., & Glimcher, P. W. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1217854110 · Cited by 340
“We have recently demonstrated that relative value representation in parietal cortex is specifically implemented by the classic divisive normalization algorithm (17), in which each option value is transformed into a mean firing rate:”
BackgroundMethodInfluentialSelf-citationFetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3503 · Cited by 322
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
“Finally, the lateral intraparietal cortex (LIP) is associated with choice processes, but it does not appear to represent values (Leathers and Olson, 2012) and does not show value comparison signals (Louie et al., 2011).”
BackgroundDopamine prediction error responses integrate subjective value from different reward dimensions
Lak, A., Stauffer, W. R., & Schultz, W. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321596111 · Cited by 257
Attention, learning, and the value of information
Gottlieb, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.034 · Cited by 241
“Similar results are obtained in tasks that manipulate the desirability of a target using different methods, for example by controlling the relative magnitude, probability or delay of its expected reward (Bernacchia et al., 2011; Louie et al., 2011; Sugrue et al., 2004; Yang and Shadlen, 2007).”
ResultA distributed, hierarchical and recurrent framework for reward-based choice
Hunt, L. T., & Hayden, B. Y. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.7 · Cited by 245
Bayesian 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 235
Salience driven value integration explains decision biases and preference reversal
Tsetsos, K., Chater, N., & Usher, M. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1119569109 · Cited by 223
Value-based attention but not divisive normalization influences decisions with multiple alternatives
Gluth, S., Kern, N., Kortmann, M., & Vitali, C. L. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0822-0 · Cited by 188
A Range-Normalization Model of Context-Dependent Choice: A New Model and Evidence
Soltani, A., Martino, B. D., & Camerer, C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002607 · Cited by 177
“This prediction is in contrast with experimental evidence showing that discriminability between items decreases with the increase of the data set [57], and that neural representation of option values decrease as the number of alternatives increase [52].”
BackgroundResultLieder, F., Griffiths, T. L., & Hsu, M. (2017) · Psychological Review · doi:10.1037/rev0000074 · Cited by 182
“Neurophysiological data from animal studies (Louie et al., 2011) and psychophysical data from humans (Louie et al.”
Resource-rational decision making
Bhui, R., Lai, L., & Gershman, S. J. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.015 · Cited by 168
Noonan, M. P., Chau, B. K. H., Rushworth, M. F. S., & Fellows, L. K. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.0692-17.2017 · Cited by 167
“Without the mOFC/vmPFC, animals are less likely to choose the best in a pair when another valuable alternative was present, as if they rely more on a decision mechanism subject to divisive normalization, such as that identified in the intraparietal sulcus (Louie et al., 2011; Chau et al., 2014).”
BackgroundDecision Making: From Neuroscience to Psychiatry
Lee, D. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.008 · Cited by 178
“, 2005), posterior parietal cortex (Platt and Glimcher, 1999; Dorris and Glimcher, 2004; Sugrue et al., 2004; Seo et al., 2009; Louie et al., 2011), premotor cortex (Pastor-Bernier and Cisek, 2011), and medial prefrontal cortex (Sohn and Lee, 2007; Seo and Lee, 2009; So and Stuphorn, 2010).”
Efficient coding and the neural representation of value
Louie, K., & Glimcher, P. W. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2012.06496.x · Cited by 171
Self-citationA unified selection signal for attention and reward in primary visual cortex
Stănişor, L., van der Togt, C., Pennartz, C. M. A., & Roelfsema, P. R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1300117110 · Cited by 158
“Comparable effects have previously been observed in the lateral intraparietal area (area LIP) (21, 24, 36, 41) and in premotor cortex (42), where the relative value of a stimulus has a strong effect but overall reward expectancy does not.”
BackgroundResultInfluentialMeister, M. L. R., Hennig, J. A., & Huk, A. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2984-12.2013 · Cited by 143
“LIP responses have been interpreted as reflecting the sum of visual, cognitive, and motor components, modulated by a wide-field response normalization (Dorris and Glimcher, 2004; Sugrue et al., 2004; Ipata et al., 2009; Falkner et al., 2010; Louie et al., 2011).”
BackgroundValue normalization in decision making: theory and evidence
Rangel, A., & Clithero, J. A. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.07.011 · Cited by 165
Neural dynamics and circuit mechanisms of decision-making
Wang, X. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.08.006 · Cited by 140
“Contrary to [45], the violation was seen only in subjects with lesion of the lateral orbitofrontal cortex.”
Taking stock of value in the orbitofrontal cortex
Knudsen, E. B., & Wallis, J. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00589-2 · Cited by 148
In Monkeys Making Value-Based Decisions, LIP Neurons Encode Cue Salience and Not Action Value
Leathers, M. L., & Olson, C. R. (2012) · Science · doi:10.1126/science.1226405 · Cited by 133
A neural mechanism underlying failure of optimal choice with multiple alternatives
Chau, B. K. H., Kolling, N., Hunt, L. T., Walton, M. E., & Rushworth, M. F. S. (2014) · Nature Neuroscience · doi:10.1038/nn.3649 · Cited by 128
Hippocampal Neurons Construct a Map of an Abstract Value Space
Knudsen, E. B., & Wallis, J. D. (2021) · Cell · doi:10.1016/j.cell.2021.07.010 · Cited by 130
Bushong, B., Rabin, M., & Schwartzstein, J. (2020) · The Review of Economic Studies · doi:10.1093/restud/rdaa055 · Cited by 126
Normalized value coding explains dynamic adaptation in the human valuation process
Khaw, M. W., Glimcher, P. W., & Louie, K. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715293114 · Cited by 119
“Recent studies show that normalization extends beyond early sensory processing to higher order cognitive functions, including attention, multisensory integration, and decision making (18, 19, 23, 51, 52).”
BackgroundSelf-citationThe Ordinal Nature of Emotions: An Emerging Approach
Yannakakis, G. N., Cowie, R., & Busso, C. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2879512 · Cited by 128
“Further evidence show that neurons in the monkey lateral intraparietal cortex (LIP) encode a relative form of saccadic value which is explicitly dependent on the values of the other available alternatives [51].”
BackgroundThe primate amygdala combines information about space and value
Peck, C. J., Lau, B., & Salzman, C. D. (2013) · Nature Neuroscience · doi:10.1038/nn.3328 · Cited by 115
Models, movements, and minds: bridging the gap between decision making and action
Wispinski, N. J., Gallivan, J. P., & Chapman, C. S. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13973 · Cited by 112
Decision by sampling implements efficient coding of psychoeconomic functions.
Bhui, R., & Gershman, S. J. (2018) · Psychological Review · doi:10.1037/rev0000123 · Cited by 105
On event-based optical flow detection
Brosch, T., Tschechne, S., & Heiko Neumann, B. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00137 · Cited by 100
“…inhibition of the surround activity (also used here) that has been suggested to explain findings ranging from gain control (Ayaz and Chance, 2009; Louie et al., 2011) over attention effects (Reynolds and Heeger, 2009; Lee and Maun-sell, 2009; Montijn et al., 2012) to normalization in…”
MethodOptimal policy for multi-alternative decisions
Tajima, S., Drugowitsch, J., Patel, N., & Pouget, A. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0453-9 · Cited by 107
The mechanistic foundation of Weber’s law
Pardo‐Vázquez, J. L., de Saa, J. R. C., Valente, M., Damião, Í. I. C., Costa, T., Vicente, M. I., Mendonça, A. G., Mainen, Z. F., & Renart, A. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0439-7 · Cited by 104
Adaptive neural coding: from biological to behavioral decision-making
Louie, K., Glimcher, P. W., & Webb, R. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.08.008 · Cited by 110
“Rather than an absolute representation of individual action values, however, new studies suggest that LIP and other similar regions employ a relative form of valuation [22,34 ,35,36].”
BackgroundSelf-citationOculomotor capture by stimuli that signal the availability of reward
Failing, M., Nissens, T., Pearson, D., Pelley, M. E. L., & Theeuwes, J. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00441.2015 · Cited by 107
The role of reward prediction in the control of attention.
Sali, A. W., Anderson, B. A., & Yantis, S. (2014) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0037267 · Cited by 101
Organization and evolution of parieto-frontal processing streams in macaque monkeys and humans
Caminiti, R., Innocenti, G. M., & Battaglia‐Mayer, A. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.06.014 · Cited by 97
Absolutely relative or relatively absolute: violations of value invariance in human decision making
Teodorescu, A. R., Moran, R., & Usher, M. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0858-8 · Cited by 90
“In a recent study, normalization was used to explain relative effects of value manipulations on accuracy (Louie et al. 2011).”
Peng, Y., Tang, X., Zhou, Y., Li, J., Qi, Y., Liu, L., & Lin, H. Y. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3225239 · Cited by 94
“Generally speaking, to make the training of DQL faster and more stable, the values of above parameters (i.e., C þ ser , C (cid:4) mig and C (cid:4) res ) can be set to a constant within ½(cid:4) 1 ; 1 (cid:7) [37], [38].”
BackgroundLi, H.-H., & Ma, W. J. (2020) · Nature Communications · doi:10.1038/s41467-020-15581-6 · Cited by 92
Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits
Louie, K., LoFaro, T., Webb, R., & Glimcher, P. W. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2851-14.2014 · Cited by 85
“In monkey parietal cortex, LIP neurons encode a relative representation of value (Rorie et al., 2010; Louie et al., 2011) consistent with a normalization computation:”
BackgroundMethodInfluentialSelf-citationAversive stimuli and loss in the mesocorticolimbic dopamine system
Brooks, A. M., & Berns, G. S. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.04.001 · Cited by 85
Parietal neurons encode expected gains in instrumental information
Foley, N. C., Kelly, S. P., Mhatre, H., Lopes, M. C., & Gottlieb, J. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1613844114 · Cited by 76
Linear reinforcement learning in planning, grid fields, and cognitive control
Piray, P., & Daw, N. D. (2021) · Nature Communications · doi:10.1038/s41467-021-25123-3 · Cited by 90
Evidence for the speed–value trade-off: Human and monkey decision making is magnitude sensitive.
Pirrone, A., Azab, H., Hayden, B. Y., Stafford, T., & Marshall, J. A. R. (2017) · Decision · doi:10.1037/dec0000075 · Cited by 75
Parietal neurons encode information sampling based on decision uncertainty
Horan, M., Daddaoua, N., & Gottlieb, J. P. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0440-1 · Cited by 71
Marsh, B. T., Tarigoppula, V. S. A., Chen, C., & Francis, J. T. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1802-14.2015 · Cited by 80
“…and noncortical regions, many of which connect to M1 (Platt and Glimcher, 1999; Roesch and Olson, 2003; Musallam et al., 2004; Tanaka et al., 2004; Campos et al., 2005; Shuler and Bear, 2006; Louie et al., 2011; Mizuhiki et al., 2012), and dopamine receptors exist in M1 (Richfield et al., 1989).”
BackgroundCaminiti, R., Borra, E., Visco‐Comandini, F., Battaglia‐Mayer, A., Averbeck, B. B., & Luppino, G. (2017) · eNeuro · doi:10.1523/eneuro.0306-16.2017 · Cited by 77
“It has also been proposed that LIP decision-related activity is the result of integrative mechanisms encoding action value (Louie and Glimcher, 2010) in relation to alternative options (Louie et al., 2011, 2014).”
BackgroundOhshiro, T., Angelaki, D. E., & DeAngelis, G. C. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.043 · Cited by 81
Free choice shapes normalized value signals in medial orbitofrontal cortex
Yamada, H., Louie, K., Tymula, A. A., & Glimcher, P. W. (2018) · Nature Communications · doi:10.1038/s41467-017-02614-w · Cited by 68
Self-citationReward expectation differentially modulates attentional behavior and activity in visual area V4
Baruni, J., Lau, B., & Salzman, C. D. (2015) · Nature Neuroscience · doi:10.1038/nn.4141 · Cited by 69
Webb, R., Glimcher, P. W., & Louie, K. (2014) · Management Science · doi:10.1287/mnsc.2019.3536 · Cited by 95
“For example, Louie et al. (2011) examined the activity of neurons Source.”
InfluentialSelf-citationA recurrent circuit implements normalization, simulating the dynamics of V1 activity
Heeger, D. J., & Zemlianova, K. O. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005417117 · Cited by 72
New advances in understanding decisions among multiple alternatives
Churchland, A. K., & Ditterich, J. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.04.009 · Cited by 72
Value-based attentional capture influences context-dependent decision-making
Itthipuripat, S., Cha, K., Rangsipat, N., & Serences, J. T. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00343.2015 · Cited by 70
Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback
Brosch, T., & Heiko Neumann, B. (2014) · Neural Computation · doi:10.1162/neco_a_00675 · Cited by 64
“, 2011), attention mechanisms (Reynolds & Heeger, 2009; Treue, 2001), and decision making (Louie et al., 2011; Louie & Glimcher, 2012).”
BackgroundA neuronal theory of sequential economic choice
Hayden, B. Y., & Moreno‐Bote, R. (2018) · Brain and Neuroscience Advances · doi:10.1177/2398212818766675 · Cited by 67
Multiple timescales of normalized value coding underlie adaptive choice behavior
Zimmermann, J., Glimcher, P. W., & Louie, K. (2018) · Nature Communications · doi:10.1038/s41467-018-05507-8 · Cited by 60
Self-citationThe neuroscience of active learning and direct instruction
Dubinsky, J. M., & Hamid, A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105737 · Cited by 121
“In line with its role in recognition memory, the inferior parietal lobule cumulatively calculates and compares the relative expected value of information regarding choices in the current context (Louie et al., 2011).”
BackgroundOver a Decade of Neuroeconomics: What Have We Learned?
Konovalov, A., & Krajbich, I. M. (2016) · Organizational Research Methods · doi:10.1177/1094428116644502 · Cited by 55
Dorsal premotor cortex is involved in switching motor plans
Pastor‐Bernier, A., Tremblay, E., & Cisek, P. (2012) · Frontiers in Neuroengineering · doi:10.3389/fneng.2012.00005 · Cited by 53
McKenzie, S., Keene, C. S., Farovik, A., Bladon, J. H., Place, R., Komorowski, R. W., & Eichenbaum, H. (2015) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2015.12.008 · Cited by 57
Frömer, R., & Shenhav, A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.12.006 · Cited by 57
Vankdothu, R., Hameed, M. A., Ameen, A., & Unnisa, R. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108196 · Cited by 54
Neural processes mediating contextual influences on human choice behaviour
Rigoli, F., Friston, K. J., & Dolan, R. J. (2016) · Nature Communications · doi:10.1038/ncomms12416 · Cited by 52
“This effect is seen in brain regions involved in processing expected value (EV) and reward prediction error (RPE), including ventral striatum 12,13 , ventral tegmental area/substantia nigra (VTA/SN) 6,14 , orbitofrontal cortex 12,15–18 , amygdala 19 and parietal cortex 20 .”
Imagined speech influences perceived loudness of sound
Tian, X., Ding, N., Teng, X., Bai, F., & Poeppel, D. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0305-8 · Cited by 56
Symbol addition by monkeys provides evidence for normalized quantity coding
Livingstone, M. S., Pettine, W. W., Srihasam, K., Moore, B., Mórocz, I. Á., & Lee, D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1404208111 · Cited by 53
“That is, the monkeys’ subjective valuation of each symbol was context dependent, as has been previously described for value coding in midbrain dopamine neurons (23), orbitofrontal cortex neurons (24, 25), LIP neurons (26), and for monkey behavioral value choices (27).”
BackgroundEfficiently irrational: deciphering the riddle of human choice
Glimcher, P. W. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.04.007 · Cited by 59
Self-citationSpike-field activity in parietal area LIP during coordinated reach and saccade movements
Hagan, M. A., Dean, H. L., & Pesaran, B. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00867.2011 · Cited by 52
“…economic decision-making (Churchland et al. 2011; Dorris and Glimcher 2004; Gold and Shadlen 2007; Kiani and Shadlen 2009; Louie and Glimcher 2010; Louie et al. 2011; Platt and Glimcher 1999; Roitman and Shadlen 2002; Seo et al. 2009; Shadlen and Newsome 2001; Sugrue et al. 2004), and the…”
Rational Attention and Adaptive Coding: A Puzzle and a Solution
Padoa‐Schioppa, C., & Rustichini, A. (2014) · American Economic Review · doi:10.1257/aer.104.5.507 · Cited by 48
“…Università degli Studi di Trento for support in experimental research. ues and choices (Tremblay and Schultz (1999), Tobler, Fiorillo and Schultz (2005), PadoaSchioppa (2009), Kobayashi, Pinto de Carvalho and Schultz (2010), Louie, Grattan, and Glimcher (2011), Cai and Padoa-Schioppa (2012)).”
BackgroundPapageorgiou, G., Sallet, J., Wittmann, M. K., Chau, B. K. H., Schüffelgen, U., Buckley, M. J., & Rushworth, M. F. S. (2017) · Nature Communications · doi:10.1038/s41467-017-01833-5 · Cited by 49
Adaptive coding of reward prediction errors is gated by striatal coupling
Park, S. Q., Kahnt, T., Talmi, D., Rieskamp, J., Dolan, R. J., & Heekeren, H. R. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1119969109 · Cited by 51
“This context dependency is precisely predicted by the divisive normalization mechanism (29).”
BackgroundShushruth, S., Mazurek, M. E., & Shadlen, M. N. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0668-18.2018 · Cited by 45
Role of expected reward in frontal eye field during natural scene search
Glaser, J. I., Wood, D. K., Lawlor, P. N., Ramkumar, P., Kording, K. P., & Segraves, M. A. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00119.2016 · Cited by 43
“For example, expected reward modulates visuospatial working memory (Kennerley and Wallis 2009) and spatial attention (Louie et al. 2011; Maunsell 2004; Peck et al. 2009).”
Pairwise normalization: A neuroeconomic theory of multi-attribute choice
Landry, P., & Webb, R. (2021) · Journal of Economic Theory · doi:10.1016/j.jet.2021.105221 · Cited by 64
Context, not conflict, drives cognitive control.
Schlaghecken, F., & Martini, P. (2011) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0025791 · Cited by 49
Klink, P. C., Jeurissen, D., Theeuwes, J., Denys, D. A. J. P., & Roelfsema, P. R. (2017) · Scientific Reports · doi:10.1038/s41598-017-08608-4 · Cited by 50
Prefrontal Neurons of Opposite Spatial Preference Display Distinct Target Selection Dynamics
Lennert, T., & Martínez-Trujillo, J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.5156-12.2013 · Cited by 49
“This result strongly suggests that the distracter triggered competitive interactions similar to the ones seen in normalization circuits in visual areas during attentional tasks (Reynolds et al., 1999; Khayat et al., 2010) as well as in area LIP (Louie et al., 2011).”
BackgroundResultAbnormal visual gain control in a Parkinson's disease model
Afsari, F., Christensen, K. V., Smith, G. P., Hentzer, M., Nippe, O. M., Elliott, C. J. H., & Wade, A. R. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu159 · Cited by 46
Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles
Blanchard, T. C., Piantadosi, S. T., & Hayden, B. Y. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00808.2017 · Cited by 44
Divisive normalization does influence decisions with multiple alternatives
Webb, R., Glimcher, P. W., & Louie, K. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00941-5 · Cited by 44
Self-citationExpected Subjective Value Theory (ESVT): A Representation of Decision Under Risk and Certainty
Glimcher, P. W., & Tymula, A. A. (2016) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.12.013 · Cited by 60
Self-citationHeitz, R. P., & Schall, J. D. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0071 · Cited by 45
Testosterone, Cortisol and Financial Risk-Taking
Herbert, J. (2018) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2018.00101 · Cited by 47
“…the brain areas that respond to, anticipate, or evaluate reward in the ventral striatum, its dopaminergic innervation, or the orbital (OFC), anterior cingulate or parietal cortex (Schultz, 2004; Hsu et al., 2009; Kang et al., 2009; Kahnt et al., 2010; Louie et al., 2011; Soutschek et al., 2017).”
BackgroundMechanisms of competitive selection: A canonical neural circuit framework
Mysore, S. P., & Kothari, N. B. (2020) · eLife · doi:10.7554/elife.51473 · Cited by 43
“In value-based decision-making, a model of LIP activity during choice has used an arg-max operation for unitary choice generation, which is consistent with both the WTA as well as the A2T formulations, but the circuit details are as yet unclear ( Louie et al., 2011 ).”
BackgroundMethodInfluentialContext-sensitive valuation and learning
Hunter, L. E., & Daw, N. D. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.05.001 · Cited by 44
“in eye control regions such as lateral intraparietal area LIP) has shown that the response for some option a is modulated not only by Q(a) but also affected by the values of rewards concurrently offered at other options a0 [51].”
BackgroundReward alters the perception of time
Failing, M., & Theeuwes, J. (2015) · Cognition · doi:10.1016/j.cognition.2015.12.005 · Cited by 40
Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges
Dennison, J. B., Sazhin, D., & Smith, D. V. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1589 · Cited by 50
Interrogating the mouse thalamus to correct human neurodevelopmental disorders
Schmitt, L. I., & Halassa, M. M. (2016) · Molecular Psychiatry · doi:10.1038/mp.2016.183 · Cited by 41
Neuroanatomical Substrates for Risk Behavior
Levy, I. (2016) · The Neuroscientist · doi:10.1177/1073858416672414 · Cited by 43
Consistent patterns of distractor effects during decision making
Chau, B. K. H., Law, C.-K., Lopez‐Persem, A., Klein-Flügge, M. C., & Rushworth, M. F. S. (2020) · eLife · doi:10.7554/elife.53850 · Cited by 36
“…neural mechanisms underlying choice representation and decision making and, as a result, subtly but significantly, alter the choices people and animals take (Chau et al., 2014; Louie et al., 2015; Louie et al., 2011; Louie et al., 2013; Louie et al., 2014; Noonan et al., 2017; Noonan et al., 2010).”
BackgroundInfluentialTschechne, S., & Heiko Neumann, B. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00093 · Cited by 40
“…to account for various non-linearities at different stages in cortical processing, e.g., for context related contour responses in V1 (Carandini and Heeger, 1994; Carandini et al., 1999), attention selection (Carandini and Heeger, 2012), and higher level cognitive functions (Louie et al., 2011).”
BackgroundChoice-theoretic foundations of the divisive normalization model
Steverson, K., Brandenburger, A. M., & Glimcher, P. W. (2019) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2019.05.026 · Cited by 39
Self-citation
Metadata
- Authors
- Louie, K., Grattan, L. E., & Glimcher, P. W.
- Affiliations
- New York University (US)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-07-20
- Volume
- 31
- Issue
- 29
- Pages
- 10627-10639
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3285508
- OpenAlex
- W2069815761
- Semantic Scholar
- 2c68a342adf5dd25439ef7f3f553afd1e9a0d1d4
- Cited by
- 308
- References
- 60
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 20 · 2023: 22 · 2022: 18 · 2021: 29 · 2020: 19 · 2019: 25 · 2018: 23 · 2017: 22 · 2016: 16 · 2015: 24 · 2014: 23 · 2013: 24 · 2012: 19
- FWCI
- 11.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (R01EY010536)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- divisive normalization, gain control, context-dependent choice, comparative analysis, decision-making, monkey visual cortex
- MeSH terms
- Action Potentials, Analysis of Variance, Animals, Choice Behavior, Macaca mulatta, Male, Models, Neurological, Neurons, Parietal Lobe, Photic Stimulation, Predictive Value of Tests, Reference Values, Reward, Time Factors, Chi-Square Distribution
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