How Can Single Sensory Neurons Predict Behavior? — TEI-tagged view

Pitkow, Liu, Angelaki, DeAngelis, Pouget · 2015 · neuroscience · doi:10.1016/j.neuron.2015.06.033 · full text at the source · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. faceted 2026-08 by reading the locally held text (xml); every evidence string verified as an exact substring of that text before merging. method is recorded as electrophysiology because the paper reports single-unit recordings from eight monkeys, but the primary contribution is analytical: closed-form predictions for choice correlations plus simulations of 500-neuron populations used to validate the fitting procedure. Claim type is mechanism rather than existence because the paper's answer to its title question is causal (high choice correlations arise BECAUSE information-limiting correlations restrict what any decoder can extract). The VIP result is a boundary case: choice correlations proportional to but larger than the optimal prediction, which the paper reads as VIP being redundant and not decoded. 2026-08-13 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
Other fields define these differently the same word, defined another way in another discipline
behavior 4 fields · population 3 fields · Akaike Information Criterion 3 fields · standard deviation 3 fields · threshold 2 fields · bootstrap 2 fields · and 2 more →
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choice correlation choice probability2 threshold21 optimal decoding suboptimal decoding correlation-blind information-limiting differential correlations noise correlations4 redundant decoder5 efficiency4 VIP14 MSTd VN/CN LIP4 inactivation7 population15 behavior19 tuning curve4 electrophysiology110

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonchoice correlations between sensory neurons and perceptual decisions— “These have different consequences for choice correlations, the correlations between neural responses and behavioral choices.”
claim-typemechanism— “We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or suboptimal decoding that is blind to correlations.”
method — evidence— “We recorded extracellularly the activity of single neurons in the VN/CN, MSTd, and VIP using epoxy-coated tungsten microelectrodes (FHC, 5–7 MΩ impedance for VN/CN, 1–2 MΩ for MSTd and VIP).”
object of study — evidence— “Single sensory neurons can be surprisingly predictive of behavior in discrimination tasks. We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or suboptimal decoding that is blind to…
research question — evidence— “Thus both suboptimal decoding and information-limiting noise correlations could explain these two puzzling phenomena. Which is the correct explanation? We derive quantitative consequences of each hypothesis in order to understand the nature of neural population codes. We test these consequences in various brain areas that are responsive to…
contribution — evidence— “Single sensory neurons can be surprisingly predictive of behavior in discrimination tasks. We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or suboptimal decoding that is blind to…
transferfrom information theory: THE DATA PROCESSING INEQUALITY, used to rule out a model of cortical coding outright - THE EXTENSIVE INFORMATION MODEL ATTRIBUTES MORE INFORMATION TO THE CORTEX THAN TO THE SENSORS, AND THIS IS PROHIBITED— “However, we presented theoretical arguments in favor of limited information. First, if cortical populations have extensive information then the brain would need to throw away almost all of it to explain behavioral performance. Second, since cortical populations are much larger than the sensory population but not much noisier, the extensive…transfers[0]
transferfrom statistical model selection: the Akaike Information Criterion, used to choose between five predictors of the same measurements - and OUR DATA WAS STRONG ENOUGH TO SIGNIFICANTLY DIFFERENTIATE BETWEEN THE MODELS— “In contrast, the prediction of suboptimal decoding ( Eq. 2 ) was poorly matched to data across all areas ( Figure 7C , middle panel). The best linear combination of predictors using the regression weights above could provide a better explanation than either predictor alone, but the improvement over the parameter-free optimal decoding…transfers[1]
mechanismNoise correlations aligned with the stimulus-induced change in mean firing (differential correlations) are indistinguishable from real signal, so pooling cannot average them away; population information saturates and single-neuron thresholds stay close to the behavioral threshold, producing large choice correlations.— “These information-limiting correlations cannot be averaged away by appropriately pooling many neurons, because these noise fluctuations are indistinguishable from changes in neural activity induced by a real signal.”mechanisms[0]
mechanismA decoder blind to correlations fails to cancel the shared noise fluctuations, so the animal's choice is driven by that irrelevant common noise and every neuron sharing it becomes correlated with the choice.— “In this case, instead of cancelling the large noise fluctuations shared by many neurons, the suboptimal, correlation-blind decoder would preserve them and behavior would be largely driven by this irrelevant noise.”mechanisms[1]
mechanismAn undecoded population inherits information-limiting noise that is partially shared with the decoded population but of larger amplitude, which inflates its choice correlations above the optimal prediction while it contributes nothing to the decision.— “The choice probabilities in population y can be larger than those in the optimally decoded population ( Figure 5C ) precisely when there is information-limiting noise in y that is partially correlated with the information-limiting noise in x , but with larger amplitude.”mechanisms[2]
mechanismunder optimal decoding THE CHOICE CORRELATIONS TAKE THE REMARKABLY SIMPLE FORM OF A RATIO OF DISCRIMINATION THRESHOLDS - and this prediction HAS NO FREE PARAMETERS— “As expected from optimal decoding, we found that the ratio of psychophysical to neural thresholds ( Eq. 3 ) did a reasonable job of predicting the measured choice correlations ( Figure 7C , top panel) for neurons in VN/CN and MSTd. We emphasize that this prediction has no free parameters.”mechanisms[3]
mechanismthe information cannot be created downstream: IF INFORMATION IS NOT AVAILABLE AT THE INPUT, THEN IT CANNOT BE CREATED LATER BY ADDING MORE NEURONS - THOSE EXTRA NEURONS MAY APPEAR TO ADD MORE SIGNAL STRENGTH, BUT THEY ALSO INHERIT NOISE WITH THE SAME STRUCTURE AS THE SIGNAL— “However, models involving diverse neural populations might radically overestimate the amount of information in a population because they do not account for how the sensory periphery limits the information provided to a large cortical network. If information is not available at the input, then it cannot be created later by adding more neurons.…mechanisms[4]
mechanismand that noise cannot be pooled away: THESE INFORMATION-LIMITING CORRELATIONS CANNOT BE AVERAGED AWAY BY APPROPRIATELY POOLING MANY NEURONS, BECAUSE THESE NOISE FLUCTUATIONS ARE INDISTINGUISHABLE FROM CHANGES IN NEURAL ACTIVITY INDUCED BY A REAL SIGNAL— “Regardless of their specific form, information-limiting noise correlations may hide essentially unnoticed amongst other noise correlations with higher variance that arise downstream ( Moreno-Bote, Beck et al. 2014 ). These information-limiting correlations cannot be averaged away by appropriately pooling many neurons, because these noise…mechanisms[5]
mechanismONLY NOISE CORRELATIONS THAT MIMIC THE EFFECT OF THE STIMULUS ON POPULATION ACTIVITY LEAD TO SIGNIFICANT CHOICE CORRELATIONS - called DIFFERENTIAL CORRELATIONS BECAUSE THE CORRELATIONS MIMIC THE RELEVANT SIGNAL WHICH IS THE DIFFERENTIAL (DERIVATIVE) OF THE MEAN RESPONSES— “In general, the only noise correlations that lead to significant choice correlations are those that mimic the effect of the stimulus on population activity. For fine discrimination tasks, we have called these differential correlations ( Moreno-Bote, Beck et al. 2014 ) because the correlations mimic the relevant signal which is the…mechanisms[6]
extends Haefner, Gerwinn et al. 2013— “Below we extend the important results of ( Haefner, Gerwinn et al. 2013 ) to analyze choice correlation under conditions of information-limiting correlations, suboptimal decoding, or both.”claim_relations[0]
builds-on Moreno-Bote, Beck et al. 2014 (differential correlations)— “For the fine discrimination task that we examined, the signal is encoded in the change in the mean rate, f ′, so the relevant information-limiting noise covariance is proportional to f ′ f ′ T , which we have described elsewhere as ‘differential correlations’ ( Moreno-Bote, Beck et al. 2014 ).”claim_relations[1]
contradicts Shamir and Sompolinsky 2006; Ecker, Berens et al. 2011 (extensive-information models of population codes)— “The high information content in current models of neural population codes ( Shamir and Sompolinsky 2006 , Ecker, Berens et al. 2011 ) would lead to a huge difference between neural thresholds and behavioral thresholds and immeasurably tiny choice correlations if the brain used all of that information efficiently. Since choice correlations are…claim_relations[2]
qualifies Zohary, Shadlen et al. 1994, that weakly correlated noise explains covariation of single neurons with decisions— “Our study shows that this insight remains essentially true—correlated noise can create significant choice correlations. However, weak positive noise correlations do not always produce large choice correlations; this is only true for the model they consider with homogeneous neurons and homogeneous noise correlations.”claim_relations[3]
contradicts models in which cortical populations carry extensive information that is decoded optimally— “According to Eq. 3 , the choice correlations would then be correspondingly tiny, and most likely not significantly different from zero given typical measurement uncertainty. This argument rules out models in which the brain has extensive information and decodes it optimally.”claim_relations[4]
critiques Haefner, Gerwinn et al. 2013's reconstruction of decoding weights from choice probabilities— “A recent study ( Haefner, Gerwinn et al. 2013 ) demonstrated how to reconstruct the weights that the brain gives to different neurons from choice probability measurements and correlated noise. Despite the elegance of this result, one consequence of our findings is that such a full reconstruction may not be feasible in the presence of…claim_relations[5]
extends Haefner, Gerwinn et al. 2013's analysis of choice probability, extended to information-limiting correlations and suboptimal decoding— “Here, we measure the relationship between neuron and behavior by computing the ‘choice correlation’, the Pearson correlation coefficient C k between the response r k of neuron k and the estimated stimulus ŝ , C k = Corr( ŝ,r k ). This quantity has a simple, nearly affine relationship to choice probability (see Haefner, Gerwinn et al. 2013 and…claim_relations[6]
contradicts the expectation that choice correlations shrink as the decoded population grows— “One common expectation is that choice correlation should be smaller for larger populations since information is distributed across more neurons. However, if the code is redundant and decoded near-optimally, then the choice correlations do not vary with population size.”claim_relations[7]
reconciles large choice correlations in VIP against the null effect of inactivating it— “Data from area VIP in two animals revealed near zero coefficients for the suboptimal predictor, but interestingly showed coefficients on the optimal predictor that were substantially greater than one ( Figure 7A,B ). As described above, this suggests that the information available in VIP is insufficient to account for behavioral performance.…claim_relations[8]
senseTHE LARGEST CORRELATION WITH CHOICE SITS IN THE AREA THAT DOES NOT MATTER: REVERSIBLE CHEMICAL INACTIVATION OF VIP DOES NOT IMPAIR HEADING DISCRIMINATION, DESPITE THE FACT THAT VIP NEURONS EXHIBIT LARGE CHOICE CORRELATIONS - and the same holds in another area, ANOTHER RECENT STUDY OF THE PARIETAL CORTEX (AREA LIP) ALSO REPORTS HIGH CHOICE CORRELATIONS, YET INACTIVATING THIS REGION AGAIN HAS NO DISCERNIBLE EFFECT ON VISUAL MOTION DISCRIMINATION— “Data from area VIP in two animals revealed near zero coefficients for the suboptimal predictor, but interestingly showed coefficients on the optimal predictor that were substantially greater than one ( Figure 7A,B ). As described above, this suggests that the information available in VIP is insufficient to account for behavioral performance.…senses[0]
senseand the paper's own summary says the same in its own voice: VIP EXHIBITS HIGH CHOICE PROBABILITIES AND ROBUST NOISE CORRELATIONS MUCH LIKE VN/CN, YET OUR ANALYSIS SUGGESTS THAT VIP IS DECODED SUBOPTIMALLY, IF AT ALL— “VIP exhibits high choice probabilities and robust noise correlations much like VN/CN, yet our analysis suggests that VIP is decoded suboptimally, if at all. However, the consequences of this suboptimality for the animal are modest because, according to our results, VIP contains little information that is not already available in the…senses[1]
senseTHE FIELD'S STANDARD MEASURE OF A NEURON'S INVOLVEMENT IS THEREFORE NOT A MEASURE OF INVOLVEMENT: the brain LOSES VERY LITTLE INFORMATION BY IGNORING VIP, and the choice correlations there are the largest of the three areas— “To summarize, our results indicate that areas MSTd and VN/CN provide redundant codes that are read out near-optimally whereas our data suggest that information in area VIP is not used efficiently for this task. From the pattern and scale of choice correlations in VIP, we infer that VIP must be highly redundant with VN/CN, MSTd, or another…senses[2]
senseTHE PAPER STATES THE RULE THIS PROJECT CALLS WITHIN-RUN COMPARISONS ONLY: IF DIFFERENT BRAIN REGIONS SEEM TO HAVE DIFFERENT CHOICE CORRELATIONS, ONE MUST TAKE CARE TO ACCOUNT FOR DIFFERENCES IN PSYCHOPHYSICAL SENSITIVITY BETWEEN ANIMALS BEFORE CONCLUDING THAT THE DIFFERENCE BETWEEN BRAIN AREAS IS MEANINGFUL - THE SUREST DIAGNOSTIC WOULD BE TO MEASURE CHOICE CORRELATIONS FROM MULTIPLE AREAS WITHIN A SINGLE ANIMAL. The measure has the animal's own threshold in its denominator— “A consequence of optimal decoding that emerges from Eq. 3 is that choice correlations should depend on behavioral performance: neurons with similar stimulus sensitivities should have higher choice correlations in animals that have higher behavioral thresholds. Conversely, if different brain regions seem to have different choice correlations…senses[3]
senseTHE SAME NUMBER MEANS OPTIMAL OR SUBOPTIMAL DEPENDING ON A CONSTANT: SUCH A PATTERN OF CHOICE CORRELATIONS INDICATES OPTIMAL DECODING IF AND ONLY IF THE PROPORTIONALITY CONSTANT EQUALS THE THRESHOLD OF POPULATION ACTIVITY - ALL OTHER VALUES OF THE PROPORTIONALITY CONSTANT REFLECT SUBOPTIMAL DECODING. The pattern alone is not the finding— “This is not sufficient to demonstrate optimality, however: such a pattern of choice correlations indicates optimal decoding if and only if the proportionality constant equals the threshold of population activity, θ , which is the same as the behavioral threshold when decoding is optimal. All other values of the proportionality constant…senses[4]
senseand a scale above one is diagnostic of the opposite of what it looks like: CHOICE CORRELATIONS PROPORTIONAL BUT NOT EQUAL TO THE OPTIMAL PREDICTION OCCUR ONLY WITH SOME DEGREE OF SUBOPTIMAL DECODING - WHENEVER THE CHOICE CORRELATIONS HAVE β ≠1, THE EFFICIENCY IS LESS THAN 1— “An important observation about this scenario is that choice correlations proportional but not equal to the optimal prediction ( Eq. 3 ) occur only with some degree of suboptimal decoding. This can be seen in Figure 5D , which shows the efficiency (color scale) of a decoder that ignores the less informative of the two populations, where…senses[5]
senseTHE COUNTER-INTUITIVE CASE, WORKED OUT: choice correlations LARGER than the optimal prediction arise WHEN BEHAVIOR IS DRIVEN BY ANOTHER, MORE INFORMATIVE, SOURCE OF SENSORY SIGNALS BESIDE THE NEURAL POPULATION UNDER STUDY - a neuron can be perfectly correlated with a choice it plays no part in— “Surprisingly, one can identify circumstances in which the choice correlations have the same pattern as for optimal decoding, that is C k = β C k opt , but with β >1. This arises when behavior is driven by another, more informative, source of sensory signals beside the neural population under study. If responses of this more sensitive…senses[6]
senseTWO HYPOTHESES ARE ONLY DISTINGUISHABLE IF THE SAMPLE IS DIVERSE: THE PREDICTIONS FOR OPTIMAL DECODING COULD BE SATISFIED EVEN FOR A POPULATION OF NEURONS DECODED SUBOPTIMALLY, LEADING US TO MISCHARACTERIZE A SUBOPTIMAL SYSTEM AS OPTIMAL, and THAT NEARLY HAPPENS FOR A HOMOGENEOUS POPULATION. The discriminating power of the test is a property of the sample, not of the theory— “As a consequence, we might worry that the predictions for optimal decoding ( Eq. 3 ) could be satisfied even for a population of neurons decoded suboptimally, leading us to mischaracterize a suboptimal system as optimal. Indeed, that nearly happens for a homogeneous population of neurons that is decoded by the factorial decoder ( Figure 2A–C…senses[7]
senseAND AN EARLIER RESULT HOLDS ONLY UNDER ITS OWN IDEALISATION: WEAK POSITIVE NOISE CORRELATIONS DO NOT ALWAYS PRODUCE LARGE CHOICE CORRELATIONS - THIS IS ONLY TRUE FOR THE MODEL THEY CONSIDER WITH HOMOGENEOUS NEURONS AND HOMOGENEOUS NOISE CORRELATIONS— “Our study shows that this insight remains essentially true—correlated noise can create significant choice correlations. However, weak positive noise correlations do not…senses[8]
senseA MODEL RULED OUT BY A MATHEMATICAL LAW RATHER THAN BY DATA: SINCE CORTICAL POPULATIONS ARE MUCH LARGER THAN THE SENSORY POPULATION BUT NOT MUCH NOISIER, THE EXTENSIVE INFORMATION MODEL ATTRIBUTES MORE INFORMATION TO THE CORTEX THAN TO THE SENSORS - AND THIS IS PROHIBITED BY THE DATA PROCESSING INEQUALITY
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senses[9]
senseand one hypothesis killed by an order-of-magnitude argument: THE CHOICE CORRELATIONS WOULD THEN BE CORRESPONDINGLY TINY, AND MOST LIKELY NOT SIGNIFICANTLY DIFFERENT FROM ZERO GIVEN TYPICAL MEASUREMENT UNCERTAINTY - THIS ARGUMENT RULES OUT MODELS IN WHICH THE BRAIN HAS EXTENSIVE INFORMATION AND DECODES IT OPTIMALLY
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senses[10]
senseTWO CONVENTIONAL EXPECTATIONS REVERSED. First: CHOICE CORRELATION SHOULD BE SMALLER FOR LARGER POPULATIONS SINCE INFORMATION IS DISTRIBUTED ACROSS MORE NEURONS - HOWEVER, IF THE CODE IS REDUNDANT AND DECODED NEAR-OPTIMALLY, THEN THE CHOICE CORRELATIONS DO NOT VARY WITH POPULATION SIZE
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senses[11]
senseSecond, and it can invert: CHOICE CORRELATIONS SHOULD BE GREATER WHEN NOISE CORRELATIONS ARE LARGER - THIS IS TRUE ONLY FOR A SPECIFIC PATTERN OF CORRELATIONS, OR FOR CERTAIN TYPES OF DECODERS, and CHOICE CORRELATIONS COULD POTENTIALLY RISE EVEN WHILE THE TOTAL NOISE CORRELATION FALLS
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senses[12]
senseOPTIMALITY IS CHEAP AND THE PAPER SAYS SO: ACHIEVING NEAR-OPTIMAL PERFORMANCE MAY NOT BE SUCH AN IMPRESSIVE FEAT, AS OUR RESULTS IMPLY THAT THE INFORMATION ENCODED IN NEURAL POPULATIONS IS HIGHLY REDUNDANT AND THEREFORE ROBUST TO SUBOPTIMAL DECODING - OPTIMALITY MEASURES THE QUALITY OF DECODING, RATHER THAN THE PARTICULAR NETWORK STRUCTURE USED TO DECODE
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senses[13]
senseso the label does not identify the mechanism: A BROAD RANGE OF DECODERS MAY ALL PRODUCE SIMILAR NEAR-OPTIMAL PERFORMANCE, and THE TRUE DECODING WEIGHTS COULD RESEMBLE THOSE OF A CORRELATION-BLIND DECODER, OR MANY OTHER DECODERS, AND YET BE NEAR-OPTIMAL
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senses[14]
senseTHE QUANTITY THE ARGUMENT TURNS ON CANNOT BE MEASURED DIRECTLY: INFORMATION-LIMITING CORRELATIONS ARE DIFFICULT TO ESTIMATE DIRECTLY, THE INFORMATION-LIMITING COMPONENT CAN BE VERY SMALL YET HAVE ENORMOUS EFFECTS ON POPULATION INFORMATION, and MISTAKES CAN RADICALLY CHANGE THE ESTIMATED INFORMATION CONTENT OF A NEURAL POPULATION - so it is inferred from an INDIRECT CONSEQUENCE observable with single neurons
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senses[15]
senseand it is invisible by construction: INFORMATION-LIMITING NOISE CORRELATIONS MAY HIDE ESSENTIALLY UNNOTICED AMONGST OTHER NOISE CORRELATIONS WITH HIGHER VARIANCE THAT ARISE DOWNSTREAM, because THESE NOISE FLUCTUATIONS ARE INDISTINGUISHABLE FROM CHANGES IN NEURAL ACTIVITY INDUCED BY A REAL SIGNAL
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senses[16]
senseA PREDICTION WITH NO FREE PARAMETERS, AND THE AUTHORS SAY SO: THE RATIO OF PSYCHOPHYSICAL TO NEURAL THRESHOLDS DID A REASONABLE JOB OF PREDICTING THE MEASURED CHOICE CORRELATIONS - WE EMPHASIZE THAT THIS PREDICTION HAS NO FREE PARAMETERS
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senses[17]
senseAN ADMISSION THAT THE MODEL CANNOT BE FORCED TO FAIL: WE CAN ALWAYS CONCOCT SOME COMBINATION OF CORRELATIONS AND SUBOPTIMAL DECODERS THAT WOULD BE CONSISTENT WITH FINE DETAILS OF OUR MEASUREMENTS - HOWEVER, THIS WOULD REQUIRE UNPALATABLE FINE-TUNING OF THE MODEL. The conclusion is a parsimony judgment and is labelled as one
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senses[18]
senseTHE INFORMATION CANNOT BE MANUFACTURED DOWNSTREAM: IF INFORMATION IS NOT AVAILABLE AT THE INPUT, THEN IT CANNOT BE CREATED LATER BY ADDING MORE NEURONS - THOSE EXTRA NEURONS MAY APPEAR TO ADD MORE SIGNAL STRENGTH, BUT THEY ALSO INHERIT NOISE WITH THE SAME STRUCTURE AS THE SIGNAL
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senses[19]
senseand the empirical answer: THE RESULTANT REGRESSION COEFFICIENTS ON THE SUBOPTIMAL PREDICTORS WERE NEAR ZERO, WHEREAS THOSE ON OPTIMAL PREDICTORS WERE CLOSE TO ONE - THE INFORMATION AVAILABLE IN THE VESTIBULAR AND CEREBELLAR NUCLEI IS USED NEAR-OPTIMALLY, AND BEHAVIORAL PERFORMANCE IS LIMITED PRIMARILY BY CORRELATED NOISE, NOT SUBOPTIMAL DECODING
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senses[20]
controlthe whole discrimination procedure run first on simulated populations whose decoder was known — held
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— rules out the method not being able to tell the two hypotheses apart at all - IN ALL FOUR MODEL SYSTEMS, THIS METHOD WAS ABLE TO RECOVER THE TRUE VALUES OF BOTH COEFFICIENTS WITHIN THE SIMULATED EXPERIMENTAL UNCERTAINTY
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controls[0]
controlthree further optimality signatures checked, each of which could have failed — held — rules out optimality being read off one fit - uninformative neurons had choice correlations NO MORE THAN EXPECTED BY CHANCE, NO NEURONS HAVE A THRESHOLD LOWER THAN THE ANIMAL'S BEHAVIORAL THRESHOLD, and negative choice correlations excluded zero for ONLY 4/339 RECORDED CELLS
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controls[1]
controla neuron-antineuron correction applied to the neural thresholds that an earlier study omitted — held — rules out NO NEURONS HAVE A THRESHOLD LOWER THAN THE ANIMAL'S BEHAVIORAL THRESHOLD, AS LONG AS NEURAL THRESHOLDS ARE PROPERLY CORRECTED FOR USE OF A NEURON-ANTINEURON PAIR - A CORRECTION THAT WAS NOT APPLIED IN ( COHEN AND NEWSOME 2009 )
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controls[2]
controlmodel quality scored by AIC against a null model and three alternatives — held — rules out a better fit bought with free parameters - and OUR DATA WAS STRONG ENOUGH TO SIGNIFICANTLY DIFFERENTIATE BETWEEN THE MODELS
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controls[3]
differs inZohary, Shadlen et al. 1994, that weakly correlated noise explains covariation of single neurons with decisions — the homogeneity of the model - WEAK POSITIVE NOISE CORRELATIONS DO NOT ALWAYS PRODUCE LARGE CHOICE CORRELATIONS - THIS IS ONLY TRUE FOR THE MODEL THEY CONSIDER WITH HOMOGENEOUS NEURONS AND HOMOGENEOUS NOISE CORRELATIONS
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claim_relations[3]
differs inmodels in which cortical populations carry extensive information that is decoded optimally — what the choice correlations would then have to be - CORRESPONDINGLY TINY, AND MOST LIKELY NOT SIGNIFICANTLY DIFFERENT FROM ZERO GIVEN TYPICAL MEASUREMENT UNCERTAINTY
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claim_relations[4]
differs inHaefner, Gerwinn et al. 2013's reconstruction of decoding weights from choice probabilities — whether information-limiting correlations are present - MANY DIFFERENT DECODING WEIGHT PROFILES CAN EXTRACT NEARLY ALL OF THE AVAILABLE INFORMATION
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claim_relations[5]
differs inthe expectation that choice correlations shrink as the decoded population grows — whether the code is redundant - IF THE CODE IS REDUNDANT AND DECODED NEAR-OPTIMALLY, THEN THE CHOICE CORRELATIONS DO NOT VARY WITH POPULATION SIZE
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claim_relations[7]
differs inlarge choice correlations in VIP against the null effect of inactivating it — whether the area is decoded - VIP IS NOT DECODED FOR HEADING DISCRIMINATION BUT NONETHELESS CONTAINS INFORMATION THAT IS CORRELATED WITH ANOTHER AREA THAT IS DECODED NEAR-OPTIMALLY
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claim_relations[8]
evidential statuslarge choice correlations in VIP against the null effect of inactivating it — the paper's own words: preliminary results
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claim_relations[8]
differs inVIP —contrasted-with→ MSTd — THE AXIS IS WHETHER THE AVAILABLE INFORMATION IS ACTUALLY USED. 'areas MSTd and VN/CN provide redundant codes that are read out near-optimally whereas our data suggest that information in area VIP is not used efficiently for this task.' The areas are not distinguished by what they encode but by what the animal does with it - and 'for this task' bounds it, so the same area could be read efficiently elsewhere.
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concept_edges[2]
noteVIP —contrasted-with→ MSTd — AREAS MSTd AND VN/CN PROVIDE REDUNDANT CODES THAT ARE READ OUT NEAR-OPTIMALLY WHEREAS OUR DATA SUGGEST THAT INFORMATION IN AREA VIP IS NOT USED EFFICIENTLY FOR THIS TASK
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concept_edges[2]
notechoice correlation —operationalised-as→ threshold — FOR OPTIMAL COMPUTATION, THE CHOICE CORRELATIONS TAKE THE REMARKABLY SIMPLE FORM OF A RATIO OF DISCRIMINATION THRESHOLDS
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concept_edges[3]
noteoptimal decoding —contrasted-with→ suboptimal decoding — BOTH SUBOPTIMAL DECODING AND INFORMATION-LIMITING NOISE CORRELATIONS COULD EXPLAIN THESE TWO PUZZLING PHENOMENA. WHICH IS THE CORRECT EXPLANATION?
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concept_edges[5]
auditdecoder —operationalised-as→ correlation-blind — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but correlation-blind decoders are the class considered; operationalised-as is loose.
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concept_edges[7]
notedecoder —operationalised-as→ correlation-blind — THE PARTICULAR CLASS OF SUBOPTIMAL DECODERS WE CONSIDERED... NAMELY THE CORRELATION-BLIND DECODERS, which is QUITE GENERAL, ALTHOUGH NOT ALL-ENCOMPASSING
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concept_edges[7]
weightdecoder —operationalised-as→ correlation-blind — 0.8
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concept_edges[7]
noteinformation-limiting —part-of→ noise correlations — INFORMATION-LIMITING NOISE CORRELATIONS MAY HIDE ESSENTIALLY UNNOTICED AMONGST OTHER NOISE CORRELATIONS WITH HIGHER VARIANCE THAT ARISE DOWNSTREAM
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concept_edges[8]
weightinformation-limiting —part-of→ noise correlations — 0.3
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concept_edges[8]
notedifferential correlations —causes→ choice correlation — THE ONLY NOISE CORRELATIONS THAT LEAD TO SIGNIFICANT CHOICE CORRELATIONS ARE THOSE THAT MIMIC THE EFFECT OF THE STIMULUS ON POPULATION ACTIVITY
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concept_edges[9]
auditnoise correlations —contrasted-with→ choice correlation — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but choice correlations rise with noise correlations only for certain patterns or decoders; contrasted-with is loose.
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concept_edges[10]
differs innoise correlations —contrasted-with→ choice correlation — THE AXIS IS THE DECODING REGIME. 'choice correlations should be greater when noise correlations are larger ... This is true only for A specific pattern of correlations, or for certain types of decoders. For SUBOPTIMAL DECODING of highly informative populations, choice correlations DO increase with the overall scale of correlated noise. However, our results instead favor near-optimal decoding, for which most correlations play little role.' The expected relationship is not denied - it is confined to a decoding assumption, and the assumption is not a property of the data.
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concept_edges[10]
notenoise correlations —contrasted-with→ choice correlation — CHOICE CORRELATIONS COULD POTENTIALLY RISE EVEN WHILE THE TOTAL NOISE CORRELATION FALLS
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concept_edges[10]
weightnoise correlations —contrasted-with→ choice correlation — 0.4
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concept_edges[10]
auditpopulation —contrasted-with→ choice correlation — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span discusses how choice correlation scales with population size; contrasted-with is loose.
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concept_edges[11]
notepopulation —contrasted-with→ choice correlation — IF THE CODE IS REDUNDANT AND DECODED NEAR-OPTIMALLY, THEN THE CHOICE CORRELATIONS DO NOT VARY WITH POPULATION SIZE
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concept_edges[11]
weightpopulation —contrasted-with→ choice correlation — 0.3
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concept_edges[11]
auditchoice probability —same-as→ choice correlation — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but choice correlation has 'a simple, NEARLY AFFINE relationship to' choice probability; same-as is loose.
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concept_edges[14]
notechoice probability —same-as→ choice correlation — THIS QUANTITY HAS A SIMPLE, NEARLY AFFINE RELATIONSHIP TO CHOICE PROBABILITY... BUT IT IS CONCEPTUAL SIMPLER AND MATHEMATICALLY MORE CONVENIENT
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concept_edges[14]
weightchoice probability —same-as→ choice correlation — 0.8
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concept_edges[14]
outcomecontrol — held
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controls[0]
outcome (stated by the tagger, no quotation)control — heldcontrols[1]
outcome (stated by the tagger, no quotation)control — heldcontrols[2]
outcome (stated by the tagger, no quotation)control — heldcontrols[3]
for which uselimitation — the decoder conclusion: IT REMAINS POSSIBLE THAT NEURAL PROCESSING IS EVEN BETTER DESCRIBED BY SOME OTHER CLASS OF SUBOPTIMAL DECODERS THAT WE DID NOT CONSIDER
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limitations[0]
for which uselimitation — the same scope as limitations[0]: THESE RESULTS DEPEND ON THE PARTICULAR CLASS OF SUBOPTIMAL DECODERS WE CONSIDERED ... NAMELY THE CORRELATION-BLIND DECODERS
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limitations[1]
for which uselimitation — the noisiest-mode argument specifically: WE CANNOT DEFINITIVELY RULE IT OUT
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limitations[2]
for which uselimitation — the optimality test itself: IDEALLY, WE WOULD LIKE TO COMPUTE NEURAL THRESHOLDS FROM THE SAME NEURAL SIGNALS THAT DRIVE THE BEHAVIOR, BUT WE DON'T KNOW PRECISELY WHAT THOSE SIGNALS ARE
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limitations[3]
for which uselimitation — any claim about the information content of a population built from pairwise correlation measurements
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limitations[4]
for which uselimitation — the suboptimality conclusion, which is relative to one family: THESE RESULTS DEPEND ON THE PARTICULAR CLASS OF SUBOPTIMAL DECODERS WE CONSIDERED ... NAMELY THE CORRELATION-BLIND DECODERS
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limitations[5]
duplicate oflimitation — limitations[0]
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limitations[6]
for which uselimitation — the same scope as limitations[0] - DUE TO MEASUREMENT NOISE IN THE DATA, IT REMAINS POSSIBLE THAT NEURAL PROCESSING IS EVEN BETTER DESCRIBED BY SOME OTHER class of decoder
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limitations[6]
for which uselimitation — treating the conclusion as an exclusion rather than as a parsimony judgment
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limitations[7]
for which uselimitation — applying the test in a preparation whose tuning curves are not heterogeneous
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limitations[8]
for which uselimitation — the MSTd evidence specifically: CHOICE CORRELATIONS FOR MSTd IN THE OTHER MONKEY WERE INCONCLUSIVE
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limitations[9]
for which uselimitation — the earlier weight-reconstruction method, which this result bears on: DESPITE THE ELEGANCE OF THIS RESULT, ONE CONSEQUENCE OF OUR FI[ndings is that] SUCH A FULL RECONSTRUCTION MAY NOT BE [possible]
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limitations[10]
replicationuntested
no type for thisnoise fluctuations → real signal — PROVED INDISTINGUISHABLE - the FORMAL form / Not 'we could not tell them apart' but 'they cannot be told apart'. AND IT SURVIVES THE OBVIOUS REMEDY: 'These information-limiting correlations cannot be averaged away by appropriately pooling many neurons, because these noise fluctuations are indistinguishable from changes in neural activity induced by a real signal'. The indistinguishability is the REASON pooling fails, so it is load-bearing for the paper's conclusion rather than a caveat on it.
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unhoused_relations[0]
limitationMeasurement noise leaves open other classes of suboptimal decoder that were not tested
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limitations[0]
limitationThe conclusions depend on the specific class of suboptimal (correlation-blind) decoders considered
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limitations[1]
limitationA decoder that removes only the noisiest mode cannot be definitively excluded
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limitations[2]
limitationThe optimality test depends on measuring neural thresholds from signals that are not known to be the ones driving behavior
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limitations[3]
limitationthe quantity the whole account turns on is not directly measurable - INFORMATION-LIMITING CORRELATIONS ARE DIFFICULT TO ESTIMATE DIRECTLY, REQUIRING LARGE SIMULTANEOUS RECORDINGS WITH MANY TRIALS, and EXTRAPOLATING THE FULL NOISE CORRELATIONS FROM A SPARSE SUBSET OF PAIRWISE CORRELATION MEASUREMENTS IS EXTREMELY DIFFICULT, AND MISTAKES CAN RADICALLY CHANGE THE ESTIMATED INFORMATION CONTENT
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limitations[4]
limitationthe conclusion is relative to one family of alternatives - THESE RESULTS DEPEND ON THE PARTICULAR CLASS OF SUBOPTIMAL DECODERS WE CONSIDERED IN THE EXTENSIVE INFORMATION CASE, NAMELY THE CORRELATION-BLIND DECODERS, which is QUITE GENERAL, ALTHOUGH NOT ALL-ENCOMPASSING
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limitations[5]
limitationDUE TO MEASUREMENT NOISE IN THE DATA, IT REMAINS POSSIBLE THAT NEURAL PROCESSING IS EVEN BETTER DESCRIBED BY SOME OTHER CLASS OF SUBOPTIMAL DECODERS THAT WE DID NOT CONSIDER
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limitations[6]
limitationand the authors say the model cannot be forced to fail - WE CAN ALWAYS CONCOCT SOME COMBINATION OF CORRELATIONS AND SUBOPTIMAL DECODERS THAT WOULD BE CONSISTENT WITH FINE DETAILS OF OUR MEASUREMENTS, rejected on the ground that it WOULD REQUIRE UNPALATABLE FINE-TUNING OF THE MODEL
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limitations[7]
limitationthe two hypotheses are only separable if the sample is diverse enough - THE PREDICTIONS FOR OPTIMAL DECODING COULD BE SATISFIED EVEN FOR A POPULATION OF NEURONS DECODED SUBOPTIMALLY, LEADING US TO MISCHARACTERIZE A SUBOPTIMAL SYSTEM AS OPTIMAL, and THAT NEARLY HAPPENS FOR A HOMOGENEOUS POPULATION
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limitations[8]
limitationone monkey's data did not decide anything - CHOICE CORRELATIONS FOR MSTd IN THE OTHER MONKEY WERE INCONCLUSIVE
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limitations[9]
limitationa result of this paper is that an elegant earlier method may not work - SUCH A FULL RECONSTRUCTION MAY NOT BE FEASIBLE IN THE PRESENCE OF INFORMATION-LIMITING CORRELATIONS, and IT MAY NOT BE POSSIBLE TO USE CHOICE PROBABILITIES TO COMPUTE DECODING WEIGHTS FROM EXPERIMENTAL DATA
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limitations[10]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingMore informative neurons - those with lower discrimination thresholds - correlate more strongly with the animal's choice.
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content_tags.findings[0]
findingThe two competing explanations are separable in the data because optimal decoding predicts choice correlations that scale with each neuron's threshold and ignore its preferred stimulus, while correlation-blind suboptimal decoding predicts the reverse.
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content_tags.findings[1]
findingFOR VN/CN DATA OBTAINED FROM 4 ANIMALS, THE RESULTANT REGRESSION COEFFICIENTS ON THE SUBOPTIMAL PREDICTORS WERE NEAR ZERO, WHEREAS THOSE ON OPTIMAL PREDICTORS WERE CLOSE TO ONE
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content_tags.findings[2]
findingDATA FROM AREA VIP IN TWO ANIMALS REVEALED NEAR ZERO COEFFICIENTS FOR THE SUBOPTIMAL PREDICTOR, BUT INTERESTINGLY SHOWED COEFFICIENTS ON THE OPTIMAL PREDICTOR THAT WERE SUBSTANTIALLY GREATER THAN ONE
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content_tags.findings[3]
findingMSTd and VN/CN carry redundant heading codes read out near-optimally, whereas the data suggest VIP's information is not used efficiently for this task.
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content_tags.findings[4]
findingHowever the population is decoded, information-limiting noise keeps the variance of any unbiased linear estimator from falling below ε.
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content_tags.findings[5]
findingBecause information-limited population codes are highly redundant, a wide range of suboptimal decoders performs almost as well as the optimal one.
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content_tags.findings[6]
findingConfidence intervals excluded zero from below for only 4 of 339 recorded cells, a proportion that is not significant.
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content_tags.findings[7]
findingNo recorded neuron had a discrimination threshold better than the animal's behavioral threshold once thresholds were corrected for the neuron-antineuron pairing, as optimal computation requires.
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content_tags.findings[8]
findingContrary to the common expectation that choice correlations shrink as the pool of neurons grows, they are independent of population size when the code is redundant and read out near-optimally.
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content_tags.findings[9]
findingREVERSIBLE CHEMICAL INACTIVATION OF VIP DOES NOT IMPAIR HEADING DISCRIMINATION, DESPITE THE FACT THAT VIP NEURONS EXHIBIT LARGE CHOICE CORRELATIONS
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content_tags.findings[10]
findingANOTHER RECENT STUDY OF THE PARIETAL CORTEX (AREA LIP) ALSO REPORTS HIGH CHOICE CORRELATIONS, YET INACTIVATING THIS REGION AGAIN HAS NO DISCERNIBLE EFFECT ON VISUAL MOTION DISCRIMINATION
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content_tags.findings[11]
findingTHE RESULTANT REGRESSION COEFFICIENTS ON THE SUBOPTIMAL PREDICTORS WERE NEAR ZERO, WHEREAS THOSE ON OPTIMAL PREDICTORS WERE CLOSE TO ONE
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content_tags.findings[12]
findingTHE PREDICTION OF SUBOPTIMAL DECODING WAS POORLY MATCHED TO DATA ACROSS ALL AREAS
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content_tags.findings[13]
findingNO NEURONS HAVE A THRESHOLD LOWER THAN THE ANIMAL'S BEHAVIORAL THRESHOLD, AS LONG AS NEURAL THRESHOLDS ARE PROPERLY CORRECTED FOR USE OF A NEURON-ANTINEURON PAIR
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content_tags.findings[14]
findingthe negative choice correlations' 95% CONFIDENCE INTERVALS EXCLUDE ZERO FROM BELOW FOR ONLY 4/339 RECORDED CELLS, A PROPORTION WHICH IS NOT SIGNIFICANT
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content_tags.findings[15]
findingCHOICE CORRELATIONS PROPORTIONAL BUT NOT EQUAL TO THE OPTIMAL PREDICTION OCCUR ONLY WITH SOME DEGREE OF SUBOPTIMAL DECODING
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content_tags.findings[16]
findingIF THE CODE IS REDUNDANT AND DECODED NEAR-OPTIMALLY, THEN THE CHOICE CORRELATIONS DO NOT VARY WITH POPULATION SIZE
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content_tags.findings[17]
findingCHOICE CORRELATIONS COULD POTENTIALLY RISE EVEN WHILE THE TOTAL NOISE CORRELATION FALLS
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content_tags.findings[18]
findingWEAK POSITIVE NOISE CORRELATIONS DO NOT ALWAYS PRODUCE LARGE CHOICE CORRELATIONS - THIS IS ONLY TRUE FOR THE MODEL THEY CONSIDER WITH HOMOGENEOUS NEURONS AND HOMOGENEOUS NOISE CORRELATIONS
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content_tags.findings[19]
quantityr =.26 — correlation between the optimal and correlation-blind predicted choice correlations in simulation, showing the two predictions are nearly independent
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content_tags.quantities[0]
quantity4/339 — recorded cells whose choice correlation was significantly negative, a proportion no greater than chance
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content_tags.quantities[1]
quantityp =.10 — test of whether uninformative (very high threshold) neurons had non-zero choice correlations more often than chance
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content_tags.quantities[2]
quantity77% (75/97) — share of VN/CN neurons held long enough for at least 20 repetitions per stimulus (with 95% in MSTd and 69% in VIP)
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content_tags.quantities[3]
quantity30-fold — how much better than a single neuron behavior should be for 1000 optimally decoded neurons with extensive information — far better than observed
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content_tags.quantities[4]
quantity80% — efficiency of a decoder that reads out only the more informative of two correlated populations in the illustrated covariance case
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content_tags.quantities[5]
quantityα =0.9 — simulation setting where 90% of the stimulus-estimate variance came from information-limiting correlations
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content_tags.quantities[6]
quantity0.1 to 0.5 — typical empirical range of c0, the proportionality between signal and noise correlations
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content_tags.quantities[7]
quantity500 — number of neurons in each simulated population
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content_tags.quantities[8]
quantityEight — rhesus monkeys implanted and recorded from
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content_tags.quantities[9]
quantity400 msec — spike-count window around peak movement velocity assumed to carry the decision-relevant signal
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content_tags.quantities[10]
quantity(50) ... (30 repetitions) — neurons and trials per stimulus used in the finite-data simulation, matched to the real experiments
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content_tags.quantities[11]
quantity4/339 — recorded cells whose negative choice correlation excluded zero - NOT SIGNIFICANT
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content_tags.quantities[12]
quantityclose to one — the regression coefficient on the optimal predictor in VN/CN, with the suboptimal coefficient near zero
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content_tags.quantities[13]
variableheading direction (independent)
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content_tags.variables[0]
variabledecoding strategy (independent)
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content_tags.variables[1]
variablechoice correlations, neural thresholds and behavioral thresholds (dependent)
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content_tags.variables[2]
variablefixation (control)
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content_tags.variables[4]
variablechoice correlation
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content_tags.variables[5]
variablethreshold
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content_tags.variables[6]
definitionchoice correlation
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content_tags.definitions[0]
definitionchoice probability
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content_tags.definitions[1]
definitiondifferential correlations
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content_tags.definitions[2]
definitiondiscrimination threshold
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content_tags.definitions[3]
definitionfactorial decoder
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content_tags.definitions[4]
definitioncorrelation-blind decoder
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content_tags.definitions[5]
definitionpsychophysical threshold
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content_tags.definitions[6]
definitiondecoder efficiency
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content_tags.definitions[7]
definitionchoice probability
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content_tags.definitions[8]
definitionthreshold
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content_tags.definitions[9]
definitiondifferential correlations
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content_tags.definitions[10]
key-termschoice correlation, choice probability, optimal decoding, suboptimal decoding, correlation-blind, information-limiting, differential correlations, noise correlations, threshold, redundant, efficiency, VIP, MSTd, VN/CN, LIP, inactivation
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptchoice probability — the probability that a neural response associated with one behavioral choice is greater than a neural response associated with the other
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concepts[1]
conceptthreshold — not defined in this paper's own words; THE SIGNAL CHANGE REQUIRED TO EXCEED ONE STANDARD DEVIATION OF NOISE, defined the same way for a neuron and for behavior
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concepts[2]
conceptefficiency — not defined in this paper's own words; the fraction of the available information actually extracted, which β reports on
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concepts[11]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
regression as written: Bayesian multiple linear regression with heteroskedastic errors in variables
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content_tags.statistics[0]
Akaike Information Criterion AICc as written: corrected Akaike Information Criterion (AICc)
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content_tags.statistics[1]
bootstrap as written: 1000-fold bootstrapping
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content_tags.statistics[2]
permutation test as written: permutation test (1000 permutations)
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content_tags.statistics[3]
t-test t
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content_tags.statistics[4]
correlation r as written: Pearson correlation test
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content_tags.statistics[5]
ROC analysis
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content_tags.statistics[6]
maximum likelihood estimation as written: maximum likelihood estimation assuming Poisson statistics
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content_tags.statistics[7]
confidence interval as written: 95% confidence intervals
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content_tags.statistics[8]
standard deviation σ as written: standard deviation of the fitted Gaussian
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content_tags.statistics[9]
Akaike Information Criterion
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content_tags.statistics[10]
linear regression
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content_tags.statistics[11]
t -test
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content_tags.statistics[12]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typemechanism — a second reading reaching the same value; recorded: mechanism
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alternatives.claim_type
methodderive the choice-correlation pattern each hypothesis implies, verify on simulated populations that the two can be told apart, then fit both predictors to recorded neurons by linear regression and compare models by AIC — a second reading DIFFERING from the recorded value; recorded: electrophysiology
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alternatives.method
phenomenona correlation between a single neuron's firing and the animal's choice, which is large and common where the theory of the day says it should be immeasurably small — a second reading DIFFERING from the recorded value; recorded: choice correlations between sensory neurons and perceptual decisions
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alternatives.phenomenon

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