The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index; none of the 57 sources its claims cite is checked for support yet.
Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area
Open accessAbstract
This study aimed to identify neural mechanisms that underlie perceptual learning in a visual-discrimination task. We trained two monkeys (Macaca mulatta) to determine the direction of visual motion while we recorded from their middle temporal area (MT), which in trained monkeys represents motion information that is used to solve the task, and lateral intraparietal area (LIP), which represents the transformation of motion information into a saccadic choice. During training, improved behavioral sensitivity to weak motion signals was accompanied by changes in motion-driven responses of neurons in LIP, but not in MT. The time course and magnitude of the changes in LIP correlated with the changes in behavioral sensitivity throughout training. Thus, for this task, perceptual learning does not appear to involve improvements in how sensory information is represented in the brain, but rather how the sensory representation is interpreted to form the decision that guides behavior.
Overview
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Written from the paper’s full text.
TL;DR
In two monkeys trained on a visual motion direction-discrimination task, improved sensitivity to weak motion was accompanied by changes in motion-driven responses in LIP but not MT. The results suggest perceptual learning involved changes in how sensory evidence is interpreted to form a decision, rather than changes in the sensory representation itself.
Key findings
- Discrimination thresholds improved from ~67% to ~15% coherence for monkey C and from ~68% to ~19% coherence for monkey Z, with time constants of 24.6±0.4 and 58.5±2.5 sessions, respectively.
- MT responses did not change significantly with training: neurometric thresholds were ~4% better during versus before training but not significant (monkey C: 25.9% before vs 21.0% during, p=0.1964; monkey Z: 21.1% before vs 18.4% during, p=0.4762).
- LIP responses developed a coherence × time interaction that grew with training and correlated with behavioural threshold (partial correlation r=−0.42, p<10−5 for monkey C; r=−0.35, p=0.0049 for monkey Z).
- MT choice probability increased from 0.493±0.011 in the first 30% of sessions to 0.549±0.018 in the last 30%, and LIP–behaviour model correlations were r=0.59 (p<10−12) for monkey C and r=0.60 (p<10−9) for monkey Z, whereas MT–behaviour correlations were not significant.
Methods
- Two naïve rhesus monkeys performed a one-interval, two-alternative direction-discrimination task using random-dot motion, reporting direction with an eye movement while MT and LIP neurons were recorded during training.
- Behavioural data were fit with a time-dependent cumulative Weibull function to estimate discrimination threshold; MT and LIP responses were normalized and fit with a linear model containing coherence, viewing time, and coherence × time interaction terms.
- Nested models compared behavioural, MT, and LIP data via a common motion-sensitive scaling term; MT neurometric thresholds and choice probabilities were also computed.
Limitations
- The paper states that changes in other extrastriate visual areas carrying motion signals, such as MST, cannot be ruled out.
- The paper states that the exact role of LIP in task performance throughout training is unknown, and multiple brain areas including superior colliculus and FEF show similar sensory-motor responses.
- The text makes evident that only two monkeys were studied, that stimulus configuration varied slightly across sessions because it depended on recorded neuron properties, and that correlated firing among MT pairs was based on a small number of simultaneously recorded pairs (n=8 before, n=9 during training).
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36 Own result · 25 Not checked yet · 9 Own interpretation · 3 Own method
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
“Behavioural sensitivity to weak motion signals improved continuously during training, long after monkeys had acquired the stimulus/response association.”
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In the paper
“During training, improved behavioural sensitivity to weak motion signals was accompanied by changes in motion-driven responses of neurons in LIP but not MT.”
- Also stated in: § Abstract · plain
“This improvement in behavioural sensitivity corresponded to changes in the responses of neurons in LIP but not MT during motion viewing.”
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- Also stated in: § Abstract · plain
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 time course and magnitude of the changes in LIP were correlated with the changes in behavioural sensitivity throughout training.”
- Also stated in: § Results · plain
“Moreover, LIP activity was correlated with behavioural threshold across sessions after taking into account the high-coherence errors (Fig. 5, left; partial correlation rth,(k3|λ) = −0.42, H0: r=0 using Fisher’s Z transformation, p<10−5 for monkey C; rth,(k3|λ) =−0.35, p=0.0049 for monkey Z)”
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In the paper
“Behavioural performance improved steadily for both monkeys with training (Fig. 2).”
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In the paper
“Interestingly, this lack of long-term changes across sessions occurred despite short-term changes within sessions, as has been reported previously (geometric mean±SEM ratio of psychometric thresholds in the second 200 versus the first 200 trials = 0.92±0.22 in monkey C and 0.84±0.29 in monkey Z; ratio of neurometric thresholds in MT in the second 200 versus the first 200 trials = 0.87±0.23 in monk”
What the sources say
- Not checked yetcites [30] Zohary, E., Celebrini, S., Britten, K.H. & Newsome, W.T. Neuronal plasticity that underlies improvement in perceptual performance. Science 263, 1289–1292 (1994). source
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In the paper
“There was, however, a slight strengthening of the relationship between the trial-by-trial variability of MT responses and the monkeys’ choices with training.”
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In the paper
“Moreover, none of the three terms changed significantly with training (linear regression versus session, computed separately for each term, each monkey and before and during training, H0: slope=0, p>0.05 for all conditions).”
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In the paper
“This specificity for the axis of motion was mirrored in neural activity in LIP (Fig. 7b, red symbols; monkey C: r=−0.37, p=0.0004; monkey Z: r=−0.29, p=0.0204) but not MT (Fig. 7b, cyan symbols; monkey C: r=0.09, p=0.5439; monkey Z: r=0.22, p=0.2063)”
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In the paper
“However, responses of the most sensitive MT neurons became increasingly predictive of the monkeys’ choices with training.”
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In the paper
“On trials in which the other target was selected, LIP activity tended to decrease gradually during motion viewing and then remain below baseline levels until after the saccade.”
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In the paper
“Thus, both monkeys learned how to perform the task in early sessions and soon could express the visuomotor association for high-coherence stimuli with few or no errors.”
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In the paper
“Throughout training, on trials in which the monkey selected the saccade target in the neuron’s response field LIP activity tended to increase gradually during motion viewing, stay elevated during the subsequent delay period and then increase just prior to saccade onset.”
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“Some of these parameters changed with training, but none were consistently correlated with the changes in LIP responsiveness over the course of training for both monkeys (Fig. 5 and Table S1).”
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In the paper
“Neurometric thresholds were, on average, ~4% better during versus before training, but the differences were not significant (monkey C: geometric mean threshold coherence before training = 25.9%, during training = 21.0%, Mann-Whitney U test p=0.1964; monkey Z: before = 21.1%; during = 18.4%, p=0.4762).”
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“The peri-saccadic activity tended to be smaller in the first ~15 sessions (average activity from −0.5 to −0.1s before saccade onset=18.6 spikes/s from sessions 1–15 and 31.3 spikes/s from sessions 16–160, t-test p=0.0157 for monkey C; 27.0 spikes/s from sessions 1–15 and 48.6 sp/s from sessions 16–130, p=0.0024 for monkey Z) but then remained relatively stable throughout the rest of training (”
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In the paper
“For both monkeys, the value of this motion-sensitive term grew as a function of training session in a similar manner for data from behaviour and LIP but not MT (Fig. 6b,c; monkey C: correlation coefficient between MT and behaviour r=0.03, H0: r=0 using Fisher’s Z transformation, p=0.7874; LIP and behaviour r=0.59, p<10−12; monkey Z: MT and behaviour r=−0.14, p=0.2911; LIP and behaviour r=0.60, p<”
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In the paper
“The pattern of coherence-specific changes in LIP was similar to the pattern of coherence-specific behavioural improvements: both the rate of rise of stimulus-driven LIP responses and discrimination performance increased as a function of training session for most non-zero coherences, with greater increases for higher coherences (H0: slope of a linear regression between session number and either”
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In the paper
“In MT, individual neurons responded similarly to motion stimuli before and throughout training, with, on average, a sensitivity that was similar to that of trained monkeys.”
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In the paper
“Indeed, throughout training, performance improved significantly for all non-zero coherences for monkey C and for 12.8–99.9% coherence for monkey Z (linear regression of percent correct per coherence with training session, H0: slope=0, p<0.05).”
plaincorrelationaln = 2 ·- Asserted: passes — The authors put it forward as true.
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In the paper
“However, a small number of simultaneously recorded pairs of MT neurons provide preliminary evidence that the degree of correlation is similar before (mean±SEM correlation coefficient r=0.16±0.06, n=8) and during (r=0.18±0.04, n=9) training and does not increase over the course of training (linear regression, H0: slope=0, p=0.5728).”
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In the paper
“we showed that both behaviour and LIP responses continued to evolve well after the visuomotor association was established, together reflecting improved sensitivity to weak motion.”
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In the paper
“To further quantify improvements in performance with training, we used a time-dependent cumulative Weibull function (Eq. 1 and solid lines in Fig. 2a,b) to estimate the discrimination threshold, which is the motion strength at which the monkey achieved ~82% correct for a 1-s viewing duration, for each session. This function takes into account associative (high-coherence) errors (λ in Eq. 1) and”
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In the paper
“In trained monkeys, MT and LIP are thought to play different but complementary roles in forming the direction decision: MT (possibly in tandem with other motion-sensitive areas like the middle superior temporal area, or MST) provides sensory evidence used to form the decision, whereas LIP (possibly in tandem with other sensory-oculomotor areas like the FEF and superior colliculus) accumulates and”
What the sources say
- Not checked yetcites [23] Gold, J.I. & Shadlen, M.N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007). source
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In the paper
“A straightforward interpretation of this result is that training does not affect the representation of sensory evidence in MT directly but effectively scales its output, providing increasingly sensitive evidence to the decision variable in LIP used to guide behaviour.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Within this constraint, we sought to minimize changes in the stimulus configuration across sessions by selecting neurons with consistent tuning properties, including receptive field (RF) location and direction preference of MT neurons and response field location of LIP neurons (Fig. S1).”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“which is consistent with the idea that perceptual learning can be specific by virtue of what is learned (in this case, the LIP decision variable) and need not involve changes in the sensory representation (direction-selective responses in area MT)3.”
What the sources say
- Not checked yetcites [3] Mollon, J.D. & Danilova, M.V. Three remarks on perceptual learning. Spat. Vis. 10, 51–58 (1996). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Perceptual learning is typically specific to the stimulus attributes used during training, including motion direction in a direction-discrimination task33.”
What the sources say
- Not checked yetcites [33] Ball, K. & Sekuler, R. A specific and enduring improvement in visual motion discrimination. Science 218, 697–698 (1982). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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
“Because these neurons were selected based on spatially tuned pre-saccadic activity (see Methods, Fig. S5), this result implies that training both established and then shaped the sensory-driven responses in these sensory-motor neurons.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Training occurred only while the responses of at least one MT or LIP neuron were isolated and recorded, allowing us to make direct, session-by-session comparisons of behavioural and neuronal performance as training progressed.”
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In the paper
“larger effects have been found in V1 of human subjects using fMRI36”
What the sources say
- Not checked yetcites [36] Furmanski, C.S., Schluppeck, D. & Engel, S.A. Learning strengthens the response of primary visual cortex to simple patterns. Curr. Biol. 14, 573–578 (2004). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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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
“the established role of MT in task performance16–19”
What the sources say
- Not checked yetcites [16] Britten, K.H., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745–4765 (1992). source
- Not checked yetcites [17] Britten, K.H., Newsome, W.T., Shadlen, M.N., Celebrini, S. & Movshon, J.A. A relationship between behavioral choice and the visual responses of neurons in macaque MT. Vis. Neurosci. 13, 87–100 (1996). source
- Not checked yetcites [18] Newsome, W.T. & Pare, E.B. A selective impairment of motion perception following lesions of the middle temporal visual area (MT). J. Neurosci. 8, 2201–2211 (1988). source
- Not checked yetcites [19] Salzman, C.D., Britten, K.H. & Newsome, W.T. Cortical microstimulation influences perceptual judgments of motion direction. Nature 346, 174–177 (1990). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Of the learning-induced changes that have been found previously in V1, attentional modulation appears to play a primary role8.”
What the sources say
- Not checked yetcites [8] Crist, R.E., Li, W. & Gilbert, C.D. Learning to see: experience and attention in primary visual cortex. Nat. Neurosci. 4, 519–525 (2001). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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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
“Our results also further distinguish early visual cortical areas from primary somatosensory and auditory cortices, which in both monkeys and humans can show dramatic changes with perceptual learning5, 6, 37–39.”
What the sources say
- Not checked yetcites [5] Recanzone, G.H., Schreiner, C.E. & Merzenich, M.M. Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J. Neurosci. 13, 87–103 (1993). source
- Not checked yetcites [6] Recanzone, G.H., Merzenich, M.M., Jenkins, W.M., Grajski, K.A. & Dinse, H.R. Topographic reorganization of the hand representation in cortical area 3b owl monkeys trained in a frequency-discrimination task. J. Neurophysiol. 67, 1031–1056 (1992). source
- Not checked yetcites [37] Pleger, B. et al. Functional imaging of perceptual learning in human primary and secondary somatosensory cortex. Neuron 40, 643–653 (2003). source
- Not checked yetcites [38] Bao, S., Chang, E.F., Woods, J. & Merzenich, M.M. Temporal plasticity in the primary auditory cortex induced by operant perceptual learning. Nat. Neurosci. 7, 974–981 (2004). source
- Not checked yetcites [39] Blake, D.T., Heiser, M.A., Caywood, M. & Merzenich, M.M. Experience-dependent adult cortical plasticity requires cognitive association between sensation and reward. Neuron 52, 371–381 (2006). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“LIP plays a multitude of cognitive and sensory-motor roles that could, in principle, change during training and account for the changes in LIP responses during motion viewing 20–22.”
What the sources say
- Not checked yetcites [20] Colby, C.L. & Goldberg, M.E. Space and attention in parietal cortex. Annu. Rev. Neurosci. 22, 319–349 (1999). source
- Not checked yetcites [21] Platt, M.L. & Glimcher, P.W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999). source
- Not checked yetcites [22] Snyder, L.H., Batista, A.P. & Andersen, R.A. Intention-related activity in the posterior parietal cortex: a review. Vision Res. 40, 1433–1441 (2000). source
hedged- Asserted: passes — The authors put it forward as true.
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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
“In trained monkeys, multiple brain areas including LIP, the superior colliculus and parts of the prefrontal cortex including the FEF exhibit similar sensory-motor responses during performance of the discrimination task24, 25, 27, 28.”
What the sources say
- Not checked yetcites [24] Shadlen, M.N. & Newsome, W.T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001). source
- Not checked yetcites [25] Roitman, J.D. & Shadlen, M.N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002). source
- Not checked yetcites [27] Horwitz, G.D. & Newsome, W.T. Separate signals for target selection and movement specification in the superior colliculus. Science 284, 1158–1161 (1999). source
- Not checked yetcites [28] Kim, J.N. & Shadlen, M.N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999). source
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- 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.
The paper relies on this without support.
Our understanding of the neural plasticity underlying perceptual learning is incomplete.
“Despite the prevalence of this phenomenon, called perceptual learning, our understanding of the underlying neural plasticity is incomplete.”
The paper relies on this without support.
Changes in higher stages of processing that contribute to decision-making and attention have not been identified directly in the brain.
“However, such changes have not been identified directly in the brain.”
The paper relies on this without support.
Learning a stimulus-response association establishes functional connectivity from neurons that represent the sensory stimulus to neurons that control the motor response.
“Learning a stimulus-response association establishes functional connectivity from neurons that represent the sensory stimulus to neurons that control the motor response.”
The authors state this as an assumption.
Sc and Si are normally distributed with variances that scale with their mean.
“We assume that Sc and Si are normally distributed with variances that scale with their mean”
The authors state this as an assumption.
Sc and Si are independent.
“If Sc and Si are independent and normally distributed”
The authors state this as an assumption.
LIP neurons pool from an unknown number of MT neurons.
“from individual LIP neurons that were assumed to pool from an unknown number of MT neurons”
The authors state this as an assumption.
Behavioural data reflect both MT and LIP contributions.
“from behavioural data assumed to reflect both MT and LIP contributions”
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
“This accumulation is encoded in the firing rates of LIP neurons with response fields aligned to the choice targets representing the direction alternatives (18, 55–57).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [57]
Citations
Cites
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Self-citationThe analysis of visual motion: a comparison of neuronal and psychophysical performance
Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Neural correlates of decision variables in parietal cortex
Platt, M. L., & Glimcher, P. W. (1999) · Nature · doi:10.1038/22268 · Cited by 1,721
SPACE AND ATTENTION IN PARIETAL CORTEX
Colby, C. L., & Goldberg, M. E. (1999) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.22.1.319 · Cited by 1,710
Newsome, W., & Pare, E. (1988) · The Journal of Neuroscience · doi:10.1523/jneurosci.08-06-02201.1988 · Cited by 1,615
Roitman, J. D., & Shadlen, M. N. (2002) · The Journal of Neuroscience · doi:10.1523/jneurosci.22-21-09475.2002 · Cited by 1,589
Maunsell, J. H. R., & Essen, D. C. V. (1983) · Journal of Neurophysiology · doi:10.1152/jn.1983.49.5.1127 · Cited by 1,462
Recanzone, G., Schreiner, C., & Merzenich, M. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00087.1993 · Cited by 1,266
Cited by
Sorted by citations
Gläscher, J., Daw, N., Dayan, P., & O'Doherty, J. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.016 · Cited by 1,166
Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027
Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679
“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).”
BackgroundPerceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation
Shibata, K., Watanabe, T., Sasaki, Y., & Kawato, M. (2011) · Science · doi:10.1126/science.1212003 · Cited by 456
Perceptual learning in Vision Research
Sagi, D. (2010) · Vision Research · doi:10.1016/j.visres.2010.10.019 · Cited by 461
Metadata
- Authors
- Law, C.-T., & Gold, J. I.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Gold, Joshua I
- ORCID
- Gold, Joshua I
- Venue
- Nature Neuroscience
- Published
- 2008-03-09
- Volume
- 11
- Issue
- 4
- Pages
- 505-513
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC2424192
- OpenAlex
- W2103314569
- Semantic Scholar
- 9320464b98ae6d580af8a38d93a505c68c71179d
- Cited by
- 516
- References
- 54
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 25 · 2023: 15 · 2022: 20 · 2021: 20 · 2020: 24 · 2019: 31 · 2018: 29 · 2017: 31 · 2016: 29 · 2015: 36 · 2014: 39 · 2013: 29 · 2012: 43
- FWCI
- 16.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Wellcome Trust
- National Eye Institute (R01EY015260, T32EY007035)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- perceptual learning, middle temporal area, lateral intraparietal area, saccade selection, motion-sensitive neurons, neuroplasticity, sensorimotor integration, decision-making, macaque
- MeSH terms
- Algorithms, Animals, Brain Mapping, Discrimination Learning, Evoked Potentials, Visual, Macaca mulatta, Motion Perception, Parietal Lobe, Psychomotor Performance, Temporal Lobe
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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