A category-free neural population supports evolving demands during decision-making — TEI-tagged view

Raposo, Kaufman, Churchland · 2014 · neuroscience · doi:10.1038/nn.3865 · 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. Claim type is 'existence' for the positive claim that a single category-free population supports evolving demands; the PAIRS result is itself a null result ('Novel tests revealed that task parameters and temporal response features were distributed randomly across neurons, without evidence of categories'), and a tagger applying rule 4 strictly could code this paper as null-result instead. The paper also builds two new analyses, so method-building is a secondary shape. Inactivation used both muscimol and DREADD, and the causal claim is restricted to visual decisions - auditory decisions were spared. 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.
11 quotations on this page contain a reference number where the paper printed a superscript. This journal style prints citations as raised numerals — parameters12,13 — and the extraction flattened them into the line, so the sentence reads parameters 12 , 13 . The digits are citation markers and not part of what the author wrote. The quotes keep them for the same reason they keep every other extraction fault: a quote is reproduced exactly or it proves nothing.
Other fields define these differently the same word, defined another way in another discipline
principal component analysis 5 fields · selectivity 3 fields · SVM 3 fields · categories 3 fields · clustering 3 fields · population 3 fields · and 4 more →
Tags on this paper every tag the record carries, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
category4 categories5 neuron13 neurons3 PPC13 choice7 modality5 selectivity8 mixed selectivity3 PAIRS clustering12 population15 decoder5 weights random4 noise17 criteria5 pie charts averaging3 dimension3 variance54 movement5 decision6 inactivation7 DREADD2 visual3 auditory3 multisensory6 tolerance2 electrophysiology110

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonpopulation coding in posterior parietal cortex during multisensory decision-making— “Here, we developed a multisensory decision task rich enough to expose the functional organization of a neural population, both at a single moment and over the course of a complex choice with evolving behavioral demands.”
claim-typeexistence— “Our data suggest that in the posterior parietal cortex (PPC), the population is category-free: response features are randomly distributed across neurons.”
method — evidence— “To evaluate whether PPC neurons demonstrate mixed selectivity, we recorded from single, well-isolated neurons in the left PPC of 5 trained rats.”
object of study — evidence— “These multiple behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Novel tests revealed that task…
research question — evidence— “An alternative hypothesis is that neurons reflect random combinations of parameters, leading to neural populations in which neurons’ responses defy categorization. Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly…
contribution — evidence— “These multiple behaviors could rely on distinct categories of neurons specialized to represent particular variables or could rely on a single population of PPC neurons that is leveraged in different ways. To distinguish these possibilities, we evaluated rat PPC neurons recorded during multisensory decisions. Novel tests revealed that task…
transferfrom machine learning: a linear Support Vector Machine, explicitly labelled a standard machine-learning technique, is imported as the decoder for single-trial neural population activity— “We then used a standard machine learning technique, the linear Support Vector Machine (SVM) 30 , to train one classifier for choice and a second for modality.”transfers[0]
transferfrom object recognition in inferotemporal cortex: tolerance, and the reading that a linear decoder working under many conditions shows a representation has been untangled— “Neurons in monkey inferotemporal cortex (IT) are likewise tolerant: many neurons have a preferred stimulus identity that is stable, though modulated, across many retinal positions 29 . Indeed, our task configuration is reminiscent of that used to study object recognition – just as a given object can be viewed from two different angles, a “low…transfers[1]
transferfrom machine learning: a linear support vector machine used as the decoder, and bootstrap aggregation to combine a thousand trained classifiers— “We used a novel test, PAIRS, to demonstrate that task parameters and time-varying response features are distributed randomly across neurons. This configuration does not pose a problem for decoding: a linear SVM could accurately estimate the modality of the stimulus and the rat’s choice based on single-trial responses. A final analysis further…transfers[2]
transferfrom theoretical neuroscience: the prediction that random mixtures let arbitrary groups of neurons be linearly combined, imported as the hypothesis to be tested rather than assumed— “An alternative hypothesis is that neurons reflect random combinations of parameters, leading to neural populations in which neurons’ responses defy categorization. Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly…transfers[3]
transferfrom spatial statistics: nearest-neighbor angles compared against randomly oriented vectors, turned into a clustering test for neural response profiles— “We used these nearest-neighbor angles to compute a statistic indicating whether the population had an excess of small nearest-neighbor angles. We term this statistic “PAIRS”: Projection Angle Index of Response Similarity (Online Methods).”transfers[4]
mechanismbecause single neurons are jointly (and randomly) modulated by choice and modality, downstream readouts can weight the same population differently to recover whichever variable is currently needed— “The ability of the same population of neurons to reliably and independently represent information about both choice and modality is a direct consequence of mixed selectivity: the joint modulation of neurons by choice and modality allows them to be combined in different ways to give rise to whatever estimate is needed.”mechanisms[0]
mechanismrandom distribution of parameters across neurons lets an arbitrary subset be linearly combined to estimate a needed parameter, removing the need for pre-patterned connections to downstream targets— “Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly combined to estimate the parameter needed at a given moment”mechanisms[1]
mechanismmoving population activity into different dimensions at different moments lets PPC gate which signals reach which downstream areas— “This could permit PPC to control what signals are routed to different areas at different times.”mechanisms[2]
mechanismPPC inactivation impairs visual choices by lowering the signal-to-noise of the incoming visual evidence, not by changing the animal's decision strategy— “Instead, the analysis indicates that inactivation reduced the signal-to-noise of incoming evidence”mechanisms[3]
mechanismwhen parameters are distributed randomly across neurons, an arbitrary group of them can be linearly combined to estimate the parameter needed at a given moment, which obviates the need for precisely pre-patterned connections and means all information is transmitted— “An alternative hypothesis is that neurons reflect random combinations of parameters, leading to neural populations in which neurons’ responses defy categorization. Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly…mechanisms[4]
mechanismthe average angle between the choice decoder and the modality decoder was 86.5 degrees, ONLY SLIGHTLY LESS THAN A PERFECTLY ORTHOGONAL 90— “This can be verified directly: the average angle between the choice decoder and the modality decoder was 86.5°, only slightly less than a perfectly orthogonal 90°.”mechanisms[5]
mechanismand the neurons retained their choice preference whether the stimulus was auditory, visual or multisensory, which the paper calls tolerance— “Although individual neurons reflected random combinations of task components (as predicted by theory 13 , 14 , 39 ), the observed responses were nonetheless structured. Specifically, we observed that the majority of neurons that were driven by choice had “tolerance” for modality: they retained their choice preference whether the stimulus was…mechanisms[6]
contradicts the widespread assumption of functionally specialised neural categories (refs 1, 5-11, 32)— “Our finding of a category-free neural population challenges these assumptions about the organization of cortical structures.”claim_relations[0]
extends prior work testing for categories defined by response timing (ref 19)— “That work argued against categories, but only tested for categories defined by response sequence.”claim_relations[1]
builds-on theoretical work on random mixed representations (refs 12-14)— “Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly combined to estimate the parameter needed at a given moment 12 - 14 .”claim_relations[2]
builds-on the established rat multisensory rate-discrimination task (refs 20, 21)— “We trained rats on an established decision-making task 20 , 21 in which animals reported a judgment about a 1s series of auditory clicks and/or full-field visual flashes ( Fig. 1a-c ).”claim_relations[3]
critiques the assumption of neural categories, which is built into how neurons are sampled and into how populations are summarized— “Individual neurons are often seen as members of highly specialized categories with response properties making them suitable for particular classes of computations 1 , 2 . This view has been fruitful for understanding early sensory areas where single neurons can be strongly tuned for task parameters, such as direction of motion 3 or disparity…claim_relations[4]
extends earlier work that argued against categories but tested only for categories defined by response sequence— “Other experimental work has probed for the existence of neural categories defined by the timing of a neuron’s response 19 . That work argued against categories, but only tested for categories defined by response sequence. A more general test is thus required.”claim_relations[5]
supports theoretical work predicting that randomly distributed parameters let an arbitrary group of neurons be linearly combined for whatever estimate is needed— “An alternative hypothesis is that neurons reflect random combinations of parameters, leading to neural populations in which neurons’ responses defy categorization. Theoretical work suggests a major advantage for category free populations: when parameters are distributed randomly across neurons, an arbitrary group of them can be linearly…claim_relations[6]
distinguishes-from the primate approach of selecting neurons by tuning and optimising the stimulus per neuron, which finds strong choice signals and leaves subtler ones unexamined— “Second, we recorded from all encountered neurons and used identical stimuli for each. The more traditional approach of using neuron selection criteria and customized stimuli is successful in identifying neurons with strong choice signals, but may leave unexamined neurons with subtler choice signals that nonetheless shape the evolving…claim_relations[7]
qualifies its own null, which covers categories defined by task-parameter selectivity and temporal response features and not those defined by cell type or connectivity— “Our finding of a category-free neural population challenges these assumptions about the organization of cortical structures. As a caveat, we note that neural categories defined by other properties, such as cell type or connectivity, might reveal specialization. Indeed, a few studies have found projection-based categories that are functionally…claim_relations[8]
supports an analogy with inferotemporal cortex, where tolerance is read as evidence that representations have been untangled— “Neurons in monkey inferotemporal cortex (IT) are likewise tolerant: many neurons have a preferred stimulus identity that is stable, though modulated, across many retinal positions 29 . Indeed, our task configuration is reminiscent of that used to study object recognition – just as a given object can be viewed from two different angles, a “low…claim_relations[9]
predicts that future studies test directly for the existence of categories and design their analyses accordingly— “By demonstrating here that cortical areas can lack categories defined by selectivity to task parameters ( Fig. 3a-c ) or by time dependent response features ( Fig. 3d,e ), our findings invite a new approach to interpreting population data. Specifically, future studies can test directly for the existence of categories, and design appropriate…claim_relations[10]
senseTHE TARGET IS THE CATEGORY ASSUMPTION ITSELF: individual neurons are OFTEN SEEN AS MEMBERS OF HIGHLY SPECIALIZED CATEGORIES, and THE ASSUMPTION OF NEURAL CATEGORIES IS REFLECTED IN MANY EXPERIMENTAL DESIGNS AND ANALYSIS METHODS, even far downstream of early sensory areas— “Individual neurons are often seen as members of highly specialized categories with response properties making them suitable for particular classes of computations 1 , 2 . This view has been fruitful for understanding early sensory areas where single neurons can be strongly tuned for task parameters, such as direction of motion 3 or disparity…senses[0]
senseAND THE DEVICES THAT CARRY IT ARE NAMED. In sampling: neurons MUST MEET CERTAIN RESPONSE CRITERIA TO BE INCLUDED FOR STUDY, and IMPLICIT IN THIS APPROACH IS THE IDEA THAT THE CELL'S RESPONSE DURING ONE STIMULUS IDENTIFIES IT AS A MEMBER OF THE CATEGORY BEING EXAMINED— “This assumption can be evident in the way neurons are sampled: sometimes, neurons must meet certain response criteria to be included for study, such as responsiveness to certain stimuli or activity during a delay period 5 - 8 . Implicit in…senses[1]
sensein analysis: PIE CHARTS, A COMMON WAY OF SUMMARIZING POPULATION DATA, EXPLICITLY ASSIGN NEURONS TO CATEGORIES - and AVERAGING OVER MANY NEURONS LIKEWISE REFLECTS THE ASSUMPTION THAT EACH NEURON IS AN EXEMPLAR OF A CATEGORY, DIFFERENT FROM OTHER CATEGORY MEMBERS MAINLY BECAUSE OF NOISEsenses[2]
sensewith the alternative stated as a hypothesis about DISTRIBUTION rather than about types: neurons REFLECT RANDOM COMBINATIONS OF PARAMETERS, leading to populations in which NEURONS' RESPONSES DEFY CATEGORIZATION - and the advantage is that an ARBITRARY GROUP OF THEM CAN BE LINEARLY COMBINED to estimate the parameter needed at a given moment
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[3]
senseand the earlier test is described by what it could not cover: that work ARGUED AGAINST CATEGORIES, BUT ONLY TESTED FOR CATEGORIES DEFINED BY RESPONSE SEQUENCE. A MORE GENERAL TEST IS THUS REQUIRED
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[4]
senseA NEW STATISTIC NAMED, DEFINED, AND BOUNDED BY ITS OWN AUTHORS: PAIRS, PROJECTION ANGLE INDEX OF RESPONSE SIMILARITY - 1 IF ALL NEURONS HAVE AT LEAST K IDENTICAL PARTNERS, AND 0 IF CLUSTERING IS ONLY AS STRONG AS EXPECTED BY CHANCE
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senses[5]
senseand its limits stated in the same paragraph that introduces it: PAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY of the distribution of tuning, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
senses[6]
senseA SIGNIFICANT P-VALUE READ AS SUPPORTING THE NULL: no evidence for clear categories in any animal, with p-values 0.236, 0.253, 0.399, 0.004 - and THE ONE SIGNIFICANT P-VALUE INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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senses[7]
senseTHE NULL VALIDATED BY MANUFACTURING WHAT WAS ABSENT: the lack of categories WAS NOT SIMPLY BECAUSE NEURAL VARIABILITY CAUSED OUR ANALYSIS TO MISS STRUCTURE - when SYNTHETIC CATEGORIES were introduced with noise derived from the real data, A STRIKINGLY DIFFERENT PAIRS DISTRIBUTION WAS EVIDENT
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senses[8]
senseand the manufacture is exact rather than approximate - the faux partners were made by RESAMPLING TRIALS WITH REPLACEMENT from real neurons, so THESE FAUX NEURONS HAD EXACTLY THE SAME AMOUNT OF NOISE AS THEIR ORIGINATING NEURONS
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senses[9]
sensewith the exceptions counted rather than dismissed: relatively close neuron pairs were occasionally observed but DIFFERED FROM CHANCE IN ONLY 1 OF 5 CASES, and THE VAST MAJORITY OF NEURONS EACH REFLECT A UNIQUE COMBINATION OF RESPONSE FEATURES
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senses[10]
senseTHE SCOPE OF THE NULL, STATED BY THE AUTHORS: neural categories defined by OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION - so `PPC is category-free` holds for categories defined by task-parameter selectivity and by time-dependent response features, and for nothing else
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senses[11]
senseand the recommendation that follows is procedural, not factual: FUTURE STUDIES CAN TEST DIRECTLY FOR THE EXISTENCE OF CATEGORIES, AND DESIGN APPROPRIATE ANALYSES IF NEURONS ARE SHOWN TO REFLECT RANDOM COMBINATIONS of task parameters
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senses[12]
senseTHE SAMPLING CHOICE IS ITSELF THE ARGUMENT: WE RECORDED FROM ALL ENCOUNTERED NEURONS AND USED IDENTICAL STIMULI FOR EACH - the traditional approach of selection criteria and customized stimuli IS SUCCESSFUL IN IDENTIFYING NEURONS WITH STRONG CHOICE SIGNALS, BUT MAY LEAVE UNEXAMINED NEURONS WITH SUBTLER CHOICE SIGNALS THAT NONETHELESS SHAPE THE EVOLVING DECISION
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senses[13]
sensestated again as a difference from the primate literature - the stimulus WAS NOT OPTIMIZED FOR EACH NEURON'S PREFERENCE and neurons WERE NOT SELECTED BASED ON TUNING PROPERTIES
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senses[14]
senseMIXED SELECTIVITY MAKES TWO CONDITIONS INDISTINGUISHABLE IN ONE CELL: rare neurons had PURE choice or PURE modality selectivity, but for the majority THE MIXING SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS
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senses[15]
senseand the absence of correlation is explicitly refused as proof: LACK OF CORRELATION DOES NOT CONCLUSIVELY RULE OUT THE EXISTENCE OF FUNCTIONAL CATEGORIES - selectivity for task features could still define categories, if the points formed an X or clusters symmetric about the origin
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senses[16]
sensewith every member of the population contributing: the neural weights WERE NONZERO FOR ALL NEURONS, and the distribution of weights DID NOT DIFFER FROM THOSE THAT WOULD BE EXPECTED BY CHANCE if neurons reflected random weightings
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senses[17]
senseAN INACTIVATION WHOSE SPARED CONDITION IS THE CONTROL: impairment was SPECIFIC TO VISUAL TRIALS with NO CONSISTENT EFFECT ON AUDITORY DECISIONS, and THE SPARING OF AUDITORY DECISIONS REVEALS THAT INACTIVATION DID NOT REDUCE THE ANIMAL'S MOTIVATION, OR INTRODUCE CONFUSION about the stimulus-response contingency
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senses[18]
senseand a confound answered by the wrong direction of its own effect: THE MORE POSTERIOR EXPRESSION WAS APPARENTLY NOT THE SOURCE OF THE IMPAIRMENT BECAUSE THE RAT WITH MORE EXPRESSION POSTERIOR TO PPC HAD WEAKER VISUAL IMPAIRMENT
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senses[19]
sensewith the surprise stated rather than smoothed: our inactivation results WERE SURPRISING IN THAT AUDITORY DECISIONS WERE SPARED DESPITE A CLEAR SIGNATURE OF THE DEVELOPING CHOICE IN PPC NEURONS - a signal that is present, reliable, and not necessary
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senses[20]
senseTHE NETWORK EXPLORES DIFFERENT DIMENSIONS AT DIFFERENT MOMENTS: dimensions that CAPTURED CONSIDERABLE VARIANCE DURING THE MOVEMENT CAPTURED FAR LESS VARIANCE DURING DECISION FORMATION, arguing AGAINST ALIGNED STATE SPACES
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senses[21]
senseand the null is protected by a POSITIVE CONTROL - the same analysis on visual against multisensory trials revealed STRONG ALIGNMENT, which PROVIDES REASSURANCE THAT THE MISALIGNMENT of decision and movement INDICATES A REAL DIFFERENCE IN NEURAL COVARIANCE
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senses[22]
senseand it is not two groups of cells taking turns: modulation during the stimulus was STRONGLY CORRELATED with modulation during movement, so THE MISALIGNMENT IS A POPULATION-LEVEL PHENOMENON, NOT DUE TO HAVING SEPARATE GROUPS OF NEURONS
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senses[23]
sensevisible in single cells as a reversal - one neuron has an ELEVATED response before a leftwards choice and is THEN SUPPRESSED DURING THE LEFTWARDS MOVEMENT used to report that same choice
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senses[24]
senseA CONTROL ANIMAL BORROWED FROM ANOTHER STUDY, WITH ITS OWN NULL RECORDED: one animal had stimulating fibers implanted and laser stimulation on half its trials - THE ANIMAL EXPRESSED NO LIGHT-ACTIVATED ION CHANNELS IN ITS BRAIN, AND LASER STIMULATION HAD NO EFFECT ON NEURAL ACTIVITY OR BEHAVIOR
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senses[25]
senseand the structure that survives the randomness is named TOLERANCE - although individual neurons reflected RANDOM COMBINATIONS of task components, THE OBSERVED RESPONSES WERE NONETHELESS STRUCTURED, retaining their choice preference across modalities
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senses[26]
controlsynthetic categories introduced into the population, with noise derived from the real neural data — held — rules out the lack of categories being a failure of the analysis to see structure - with faux neurons carrying exactly the same amount of noise as their originals, a strikingly different PAIRS distribution was evident
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controls[0]
controlthe distribution of decoder weights compared with random weightings — held — rules out the decode resting on rare neurons with pure selectivity - the weights were nonzero for all neurons and did not differ from what would be expected by chance
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controls[1]
controlauditory decisions measured alongside the impaired visual ones — held — rules out reduced motivation or confusion about the stimulus-response contingency, since the sparing of auditory decisions rules both out
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controls[2]
controla second inactivation method that permits visualization of the agent to determine its spread — held — rules out the impairment coming from spread into neighboring visual areas - and the rat with more expression posterior to PPC had WEAKER visual impairment
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controls[3]
controlthe Variance Alignment analysis repeated on visual against multisensory trials within the decision epoch — held — rules out noise making two aligned state spaces look misaligned - this positive control gave strong alignment, which is the reassurance that the decision-movement misalignment is real
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controls[4]
controlmodulation during the stimulus compared against modulation during movement, neuron by neuron — held — rules out the misalignment being two separate groups of active neurons - the two were strongly correlated, so it is a population-level phenomenon
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controls[5]
controlthe excess rate analysis over sliding windows of the stimulus — held
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— rules out snap judgments or evidence leak as the explanation for the impairment - the analysis indicates inactivation reduced the signal-to-noise of incoming evidence instead
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controls[6]
differs inthe assumption of neural categories, which is built into how neurons are sampled and into how populations are summarized — pie charts and averaging, which assign neurons to categories and treat each neuron as an exemplar differing from others mainly because of noise
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[4]
differs inthe primate approach of selecting neurons by tuning and optimising the stimulus per neuron, which finds strong choice signals and leaves subtler ones unexamined — THE AXIS IS HOW THE NEURONS AND STIMULI WERE CHOSEN. 'we recorded from all encountered neurons and used identical stimuli for each. The more traditional approach of using neuron selection criteria and customized stimuli is successful in identifying neurons with strong choice signals, but may leave unexamined neurons with subtler choice signals.' The older method is granted its successes and denied its denominator - it finds what it selects for, so a literature built on it cannot report what fraction of the population is involved.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[7]
differs inits own null, which covers categories defined by task-parameter selectivity and temporal response features and not those defined by cell type or connectivity — THE AXIS IS WHICH PROPERTY DEFINES THE CATEGORY. 'As a caveat, we note that neural categories defined by other properties, such as cell type or connectivity, might reveal specialization.' The paper's headline null - no categories - is bounded by the definition of category it used, IN THE SENTENCE AFTER THE CLAIM. Its [4] criticises pie charts and averaging for assigning neurons to categories; here it says which categories its own null covers.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
claim_relations[8]
auditclustering —contrasted-with→ PAIRS — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says PAIRS also tests non-uniformity beyond clustering; contrasted-with is loose.
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concept_edges[8]
noteclustering —contrasted-with→ PAIRS — its limits stated where it is introduced - NOT ONLY A TEST FOR CLUSTERING BUT MORE GENERALLY FOR NON-UNIFORMITY, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER
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concept_edges[8]
auditchoice —distinct-from→ modality — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says pure choice or modality selectivity is rare and most neurons mix both; distinct-from is loose.
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concept_edges[16]
notechoice —distinct-from→ modality — MIXED SELECTIVITY MAKES TWO CONDITIONS INDISTINGUISHABLE IN ONE CELL - the mixing SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS
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concept_edges[16]
outcome (stated by the tagger, no quotation)control — heldcontrols[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]
outcome (stated by the tagger, no quotation)control — heldcontrols[4]
outcome (stated by the tagger, no quotation)control — heldcontrols[5]
outcomecontrol — held
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
controls[6]
for which uselimitation — the category-free claim, which is relative to the features tested: NEURAL CATEGORIES DEFINED BY OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION
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limitations[0]
for which uselimitation — the strength of the clustering null: IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER, though the test is described as reasonably general
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limitations[1]
for which uselimitation — the same scope as limitations[0]: THIS LEAVES OPEN THE POSSIBILITY THAT THERE ARE CATEGORIES DEFINED BY OTHER FEATURES OF THE DATA
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limitations[2]
for which uselimitation — the decoding accuracy: DECODING WAS PERFORMED ON A SAMPLE OF TENS OF NEURONS OUT OF MANY THOUSANDS, ON A TASK WHERE THE ANIMAL'S PERFORMANCE IS LIKELY NOISE LIMITED
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limitations[3]
for which uselimitation — the inactivation result: ANIMALS RETAINED SOME ABILITY TO MAKE VISUAL JUDGMENTS DESPITE THE INACTIVATION, SUGGESTING EITHER THAT INACTIVATION WAS INCOMPLETE, OR THAT STRUCTURES IN ADDITION TO PPC SUPPORT THE TASK
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limitations[4]
for which uselimitation — categories defined by selectivity to task parameters or by time dependent response features, which is what this test covers
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Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
limitations[5]
for which uselimitation — the category-free conclusion, whose test is broader than its name: PAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY OF THE DISTRIBUTION OF TUNING ACROSS NEURONS
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limitations[6]
for which uselimitation — the same scope as limitations[4], with the effect size: SHALLOWER PSYCHOMETRIC FUNCTIONS ON INACTIVATION DAYS (sigma_saline = 2.64 +/- 0.39; sigma_muscimol = 4.67 +/- 0.67)
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limitations[7]
for which uselimitation — the no-clustering claim, per animal: PAIRS INDEX FOR RATS 1-5: -0.011, 0.108, -0.038, 0.011, -0.007; p-values 0.621, 0.001, 0.209, 0.491, 0.857 - one of five deviates
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limitations[8]
for which uselimitation — the negative inactivation result for audition: AUDITORY RESPONSES IN PPC, THOUGH APPARENTLY NOT NECESSARY FOR THIS TASK, MAY BE INVOKED BY OTHER DECISION TASKS
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limitations[9]
replicationuntested
limitationCategories defined by cell type or connectivity rather than response features were not tested
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limitations[0]
limitationNo clustering test can cover every conceivable way the data might cluster
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limitations[1]
limitationThe choice/modality test leaves categories defined by other features unexamined
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limitations[2]
limitationDecoding was from a small sample of neurons in a noise-limited task
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limitations[3]
limitationResidual visual performance means the inactivation may have been incomplete or other structures may contribute
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limitations[4]
limitationneural categories defined by other properties, such as cell type or connectivity, might reveal specialization
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limitations[5]
limitationPAIRS is not only a test for clustering but more generally for non-uniformity of the distribution of tuning, and it is probably not possible to detect all conceivable ways in which the data might cluster
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limitations[6]
limitationanimals retained some ability to make visual judgments despite the inactivation, suggesting either that inactivation was incomplete, or that structures in addition to PPC support the task
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limitations[7]
limitationrelatively close neuron pairs were occasionally observed and differed from chance in 1 of 5 cases, so a small fraction of neurons do have pairs in feature space
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limitations[8]
limitationauditory responses in PPC, though apparently not necessary for this task, may be invoked by other decision tasks
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limitations[9]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingPPC INACTIVATION RELIABLY IMPAIRED VISUAL DECISIONS, by Mann-Whitney U test at p < 0.001 pooled across animals
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content_tags.findings[0]
findingIMPAIRMENT WAS SPECIFIC TO VISUAL TRIALS AND HAD NO CONSISTENT EFFECT ON AUDITORY DECISIONS
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content_tags.findings[1]
findingMost PPC neurons carried joint information about both stimulus modality and the animal's choice rather than either one alone.
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content_tags.findings[2]
findingThe nearest-neighbor angle distribution of neurons' feature vectors was statistically indistinguishable from that of randomly oriented vectors.
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content_tags.findings[3]
findingA linear decoder read the animal's choice from single-trial population responses significantly above chance in every rat.
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content_tags.findings[4]
findingThe same choice decoder did not estimate stimulus modality better than chance.
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content_tags.findings[5]
findingTHE NEURAL WEIGHTS WERE NONZERO FOR ALL NEURONS, INDICATING THAT EVERY MEMBER OF THE POPULATION CONTRIBUTED
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content_tags.findings[6]
findingThe choice and modality decoder vectors were nearly orthogonal, meaning the two parameters occupy essentially independent dimensions of population activity.
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content_tags.findings[7]
findingDimensions that captured most variance during movement captured little variance during decision formation, so the network occupies different covariance patterns in the two epochs.
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content_tags.findings[8]
findingthis analysis, BY CONTRAST, REVEALED STRONG ALIGNMENT - index 0.840 at p < 10 to the minus 4, PRESENT IN ALL RATS TESTED
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content_tags.findings[9]
findingTHIS WAS NOT THE CASE: INSTEAD, WE OBSERVED A STRONG CORRELATION BETWEEN MODULATION DURING STIMULUS AND MODULATION DURING MOVEMENT
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content_tags.findings[10]
finding80.1% of neurons, 218 of 272 units, HAD A MULTISENSORY RESPONSE THAT WAS BETTER PREDICTED BY THE AUDITORY AND VISUAL RESPONSES THAN BY THE MULTISENSORY MEAN, assessed on left-out data
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content_tags.findings[11]
findingTHE ASSUMPTION OF NEURAL CATEGORIES IS REFLECTED IN MANY EXPERIMENTAL DESIGNS AND ANALYSIS METHODS, even those focusing on structures far downstream of early sensory areas
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content_tags.findings[12]
findingneurons MUST MEET CERTAIN RESPONSE CRITERIA TO BE INCLUDED FOR STUDY, and IMPLICIT IN THIS APPROACH IS THE IDEA THAT THE CELL'S RESPONSE DURING ONE STIMULUS IDENTIFIES IT AS A MEMBER OF THE CATEGORY BEING EXAMINED
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content_tags.findings[13]
findingPIE CHARTS, A COMMON WAY OF SUMMARIZING POPULATION DATA, EXPLICITLY ASSIGN NEURONS TO CATEGORIES, and AVERAGING OVER MANY NEURONS LIKEWISE REFLECTS THE ASSUMPTION THAT EACH NEURON IS AN EXEMPLAR OF A CATEGORY
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content_tags.findings[14]
findingearlier work ARGUED AGAINST CATEGORIES, BUT ONLY TESTED FOR CATEGORIES DEFINED BY RESPONSE SEQUENCE. A MORE GENERAL TEST IS THUS REQUIRED
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content_tags.findings[15]
findingPAIRS is 1 IF ALL NEURONS HAVE AT LEAST K IDENTICAL PARTNERS, AND 0 IF CLUSTERING IS ONLY AS STRONG AS EXPECTED BY CHANCE
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content_tags.findings[16]
findingPAIRS IS NOT ONLY A TEST FOR CLUSTERING, BUT ALSO MORE GENERALLY FOR NON-UNIFORMITY of the distribution of tuning, and IT IS PROBABLY NOT POSSIBLE TO DETECT ALL CONCEIVABLE WAYS IN WHICH THE DATA MIGHT CLUSTER
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content_tags.findings[17]
findingno evidence for clear categories was present in any animal, and THE ONE SIGNIFICANT P-VALUE INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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content_tags.findings[18]
findingthe lack of categories WAS NOT SIMPLY BECAUSE NEURAL VARIABILITY CAUSED OUR ANALYSIS TO MISS STRUCTURE - with SYNTHETIC CATEGORIES introduced, A STRIKINGLY DIFFERENT PAIRS DISTRIBUTION WAS EVIDENT
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content_tags.findings[19]
findingTHESE FAUX NEURONS HAD EXACTLY THE SAME AMOUNT OF NOISE AS THEIR ORIGINATING NEURONS
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content_tags.findings[20]
findingclose neuron pairs DIFFERED FROM CHANCE IN ONLY 1 OF 5 CASES, and THE VAST MAJORITY OF NEURONS EACH REFLECT A UNIQUE COMBINATION OF RESPONSE FEATURES
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content_tags.findings[21]
findingneural categories defined by OTHER PROPERTIES, SUCH AS CELL TYPE OR CONNECTIVITY, MIGHT REVEAL SPECIALIZATION
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content_tags.findings[22]
findingWE RECORDED FROM ALL ENCOUNTERED NEURONS AND USED IDENTICAL STIMULI FOR EACH, where the traditional approach MAY LEAVE UNEXAMINED NEURONS WITH SUBTLER CHOICE SIGNALS THAT NONETHELESS SHAPE THE EVOLVING DECISION
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content_tags.findings[23]
findingthe mixing SOMETIMES RESULTED IN IDENTICAL FIRING RATES FOR DIFFERENT CONDITIONS
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content_tags.findings[24]
findingLACK OF CORRELATION DOES NOT CONCLUSIVELY RULE OUT THE EXISTENCE OF FUNCTIONAL CATEGORIES
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content_tags.findings[25]
findingthe neural weights were NONZERO FOR ALL NEURONS and did not differ from those EXPECTED BY CHANCE if neurons reflected random weightings
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content_tags.findings[26]
findingTHE SPARING OF AUDITORY DECISIONS REVEALS THAT INACTIVATION DID NOT REDUCE THE ANIMAL'S MOTIVATION, OR INTRODUCE CONFUSION about the stimulus-response contingency
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content_tags.findings[27]
findingTHE RAT WITH MORE EXPRESSION POSTERIOR TO PPC HAD WEAKER VISUAL IMPAIRMENT compared to the other rat
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content_tags.findings[28]
findingdimensions that CAPTURED CONSIDERABLE VARIANCE DURING THE MOVEMENT CAPTURED FAR LESS VARIANCE DURING DECISION FORMATION
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content_tags.findings[29]
findingthe positive control revealed STRONG ALIGNMENT, which PROVIDES REASSURANCE THAT THE MISALIGNMENT of decision and movement INDICATES A REAL DIFFERENCE IN NEURAL COVARIANCE
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content_tags.findings[30]
findingTHE MISALIGNMENT IS A POPULATION-LEVEL PHENOMENON, NOT DUE TO HAVING SEPARATE GROUPS OF NEURONS
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content_tags.findings[31]
findingTHE ANIMAL EXPRESSED NO LIGHT-ACTIVATED ION CHANNELS IN ITS BRAIN, AND LASER STIMULATION HAD NO EFFECT ON NEURAL ACTIVITY OR BEHAVIOR
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content_tags.findings[32]
quantity80.1% (218 of 272 units) — neurons whose multisensory response was better predicted by a linear combination of auditory and visual responses than by the multisensory mean
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content_tags.quantities[0]
quantity68.2% — median share of multisensory response variance accounted for by a linear combination of the unisensory responses
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content_tags.quantities[1]
quantity33.8% — proportion of neurons with statistically significant modality preference
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content_tags.quantities[2]
quantity86.5° — average angle between the choice decoder and the modality decoder, near the orthogonal 90°
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content_tags.quantities[3]
quantity68.9%, 61.2%, 59.4%, 80.2%, 70.1% — choice-decoding accuracy in the five rats at 700-800 ms after stimulus onset, outside the training window
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content_tags.quantities[4]
quantityPAIRS index = −0.052; p = 0.632 — clustering statistic for the example rat's feature vectors, indistinguishable from chance (no categories)
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content_tags.quantities[5]
quantityindex = 0.840, p < 10 −4 — Variance Alignment index for visual versus multisensory decision-epoch activity, the aligned positive control
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content_tags.quantities[6]
quantityr = 0.709, r = 0.655, r = 0.577, r = 0.666, r = 0.763 — per-rat correlation between each neuron's stimulus-epoch and movement-epoch modulation depth
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content_tags.quantities[7]
quantity86.5° — the average angle between the choice decoder and the modality decoder, ONLY SLIGHTLY LESS THAN A PERFECTLY ORTHOGONAL 90
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content_tags.quantities[8]
quantity0.004 — the one significant PAIRS p-value, which INDICATED LESS CLUSTERING THAN EXPECTED BY CHANCE
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content_tags.quantities[9]
quantity1 of 5 cases — the number of animals in which close neuron pairs differed from chance at all
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content_tags.quantities[10]
quantity3.3 — the signal-to-noise criterion a neuron had to exceed to be included - firing-rate range divided by maximal s.e.m.
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content_tags.quantities[11]
variablePPC inactivation (independent)
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content_tags.variables[0]
variablecontrol sessions (control)
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content_tags.variables[1]
variableselectivity
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content_tags.variables[7]
variableweights
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content_tags.variables[8]
variablevariance
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content_tags.variables[9]
definitionProjection Angle Index of Response Similarity
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content_tags.definitions[0]
definitionVariance Alignment
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content_tags.definitions[1]
definitionchoice divergence
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content_tags.definitions[2]
definitionchoice preference
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content_tags.definitions[3]
definitionexcess rate
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content_tags.definitions[4]
definitionmixed selectivity
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content_tags.definitions[5]
definitiondecision formation
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content_tags.definitions[6]
definitionDREADD (Designer Receptor Exclusively Activated by Designer Drug)
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content_tags.definitions[7]
definitionimpairment ratio
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content_tags.definitions[8]
definitionPAIRS
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content_tags.definitions[9]
definitionclustering
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content_tags.definitions[10]
definitiontolerance
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content_tags.definitions[11]
definitioncategories
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content_tags.definitions[12]
key-termscategory, categories, neuron, neurons, PPC, choice, modality, selectivity, mixed selectivity, PAIRS, clustering, population, decoder, weights, random, noise, criteria, pie charts, averaging, dimension, variance, movement, decision, inactivation, DREADD, visual, auditory, multisensory, tolerance
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.
Mann-Whitney U test U as written: non-parametric tests (Mann-Whitney U )
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[0]
bootstrap as written: bootstrap analysis of the choice data (2000 resamples)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]
Monte Carlo simulation as written: Monte Carlo simulations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[2]
ROC analysis / area under the curve AUC as written: the area under the ROC curve (AUC)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[3]
principal component analysis PCA as written: principal component analysis (PCA)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[4]
support vector machine classifier SVM as written: the linear Support Vector Machine (SVM)
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[5]
generalized linear model regression β as written: Generalized Linear Model regression (GLM regression
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[6]
sign test p as written: two-sided sign test
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[7]
kurtosis as written: the kurtosis of the distribution of weights
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[8]
maximum likelihood estimation (psychometric function fitting) σ as written: following the maximum likelihood methods described by Wichmann & Hill
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[9]
confidence interval as written: Wilson binomial confidence interval
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[10]
bootstrap aggregation (bagging) as written: a standard technique called bootstrap aggregation
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[11]
PAIRS
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[12]
SVM
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[13]
p-values from Monte Carlo simulations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[14]
Second readings
a facet read a second time, independently, from its own sentence — and whether the two readings agree
claim typenull-result — a second reading DIFFERING from the recorded value; recorded: existence
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.claim_type
methoda statistic on the angles between neurons' response vectors and their nearest neighbours, compared against randomly oriented vectors - PAIRS, Projection Angle Index of Response Similarity — a second reading DIFFERING from the recorded value; recorded: electrophysiology
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.method
phenomenonmixed selectivity - individual neurons modulated by more than one task parameter at once, so that different conditions can give identical firing rates — a second reading DIFFERING from the recorded value; recorded: population coding in posterior parietal cortex during multisensory decision-making
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
alternatives.phenomenon

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

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