Working Memory and Decision-Making in a Frontoparietal Circuit Model — TEI-tagged view

Murray, Jaramillo, Wang · 2017 · neuroscience · doi:10.1523/jneurosci.0343-17.2017 · 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 (html); every evidence string verified as an exact substring of that text before merging. Claim type is recorded as mechanism rather than method because the paper's headline contribution is an explanation (why parietal and prefrontal cortex differ), even though the work also delivers a reusable circuit model; the tradeoff-amelioration claim ("We highlight a tradeoff that exists when optimizing the recurrent structure...") is the model-building side of the same result. The discussion also sets the model against inactivation studies that found no causal role for parietal cortex in perceptual decisions (Erlich et al., 2015; Katz et al., 2016), but the paper attributes the discrepancy to task differences rather than declaring a contradiction, so no contradicts relation is recorded. 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.
6 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
model 4 fields · selection 3 fields · feedback 3 fields · standard deviation 3 fields · task 2 fields · module 2 fields · and 3 more →
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PPC13 PFC6 module3 circuit3 model22 attractor3 recurrent6 distractor2 evidence8 selection7 action3 inhibition17 feedback6 projection2 tradeoff cell type10 LIP4 inactivation7 task13 network24 simulation51

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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
phenomenondistributed working memory and decision-making in the frontoparietal circuit— “Working memory (WM) and decision-making (DM) are fundamental cognitive functions involving a distributed interacting network of brain areas, with the posterior parietal cortex (PPC) and prefrontal cortex (PFC) at the core.”
claim-typemechanism— “The difference in distractor filtering across modules arises because, first, the external input activates the PFC module only indirectly and, second, the PFC module is modeled as a strong attractor network, with increased local structure relative to the PPC.”
method — evidence— “To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits.”
object of study — evidence— “To examine these issues, we developed a distributed circuit model of two reciprocally interacting modules representing PPC and PFC circuits. The circuit architecture includes hierarchical differences in local recurrent structure and implements reciprocal long-range projections. This parsimonious model captures a range of behavioral and…
research question — evidence— “The general similarity of neural activity of PPC and PFC during WM and DM has supported the view that they make comparable contributions to these functions. Important open questions are to identify how PPC and PFC interact during WM and DM and what their specialized roles may be.”
contribution — evidence— “We highlight a tradeoff that exists when optimizing the recurrent structure for robustness against distractors in WM versus slow integration of evidence in DM; the model developed here ameliorates this tradeoff by extension to two modules with a hierarchical increase in recurrent strengths.”
transferfrom dynamical systems: attractor networks, in which strong recurrent excitation and lateral inhibition produce both a stable memory state and winner-take-all competition— “A class of models called attractor networks can perform these functions through strong recurrent synaptic interactions ( Wang, 2008 ). In the attractor network framework, strong synaptic connections among neurons can provide reverberatory excitation, potentially mediated by slow NMDA receptors ( Wang, 1999 ), that maintains a…transfers[0]
transferfrom balanced-network theory: pathway-specific excitation-inhibition balance, used here to produce discrete flow between stages without a gate— “An important feature of our distributed circuit model is the pathway-specific E/I balance in the feedforward projection from selection to action cells. This E/I balance regulates a cascade of activations across functional cell types and can be characterized as discrete flow, since response preparation in action cells begins only after target…transfers[1]
mechanismLateral inhibition and recurrent self-excitation, both set by J_S, jointly resist a distractor by counteracting activation of the distractor population and deactivation of the target population— “The network mechanism for robustness results from the combined effects of lateral inhibition and recurrent excitation set by J S ( Brunel and Wang, 2001 ).”mechanisms[0]
mechanismStructured PFC feedback (same-selectivity excitation plus cross-selectivity inhibition) onto a weak-attractor PPC pushes PPC back into the target memory state after the distractor ends— “Because PPC is a weak attractor network, this combination of same-selectivity excitation and cross-selectivity inhibition from PFC can effectively switch PPC back to the target memory state.”mechanisms[1]
mechanismCross-inhibition from the active target population onto the distractor population produces the surround suppression that keeps distractor responses smaller than target responses— “This surround suppression is mainly due to the cross-inhibition from the active target population to the distractor population.”mechanisms[2]
mechanismPathway-specific excitation–inhibition balance in the feedforward projection cancels the common-mode drive, so only the difference in activity between the two selection populations reaches the action module— “Therefore, net inputs to the action module reflect the difference of activity between the populations in the selection module.”mechanisms[3]
mechanismSlow synaptic gating variables mean the target population's suppressive effect on the distractor grows as the target-distractor delay lengthens, making later distractors weaker— “Therefore, the suppressive effect from the target population on the distractor responses will slightly increase over time, a consequence of the local network having slow synaptic-gating variables.”mechanisms[4]
mechanismStronger local recurrence in the PFC module widens its basin of attraction, so transient distractor inputs cannot dislodge the target-encoding state— “The stronger recurrence makes target encoding in PFC more robust against transient distractor inputs, which are effectively filtered.”mechanisms[5]
mechanismthe module receiving sensory input is a WEAK ATTRACTOR NETWORK so that it can TRANSIENTLY ENCODE DISTRACTORS and function as a saliency map, and so that INTEGRATION of perceptual evidence has A LONG TIMESCALE TO IMPROVE ACCURACY— “First, Module 1 (PPC or selection cells), which receives sensory input ( Felleman and Van Essen, 1991 ; Buschman and Miller, 2007 ; Ibos et al., 2013 ; Siegel et al., 2015 ), is a weak attractor network. This property is beneficial so that PPC can transiently encode distractors and function as a saliency map, and so that its memory state can…mechanisms[6]
mechanismand the downstream module is a STRONG ATTRACTOR NETWORK, which gives ROBUSTNESS AGAINST DISTRACTORS in memory and lets it RAMP QUICKLY TO THRESHOLD in choice - the same property serving two different functions— “Second, Module 2 (PFC or action cells) is a strong attractor network. In the context of WM, this property is functionally beneficial because PFC can thereby provide robustness against distractors, filtering out the effects of strong distractor responses in PPC. In the context of DM, it is functionally beneficial for action cells to be a…mechanisms[7]
mechanismwith the projections STRUCTURED - NET-EXCITATORY BETWEEN SAME-SELECTIVITY POPULATIONS AND NET-INHIBITORY BETWEEN DIFFERENT-SELECTIVITY POPULATIONS - which is what allows the feedback to SWITCH PPC BACK TO ENCODING THE TARGET after a distractor— “Third, the modules are interconnected via reciprocal projections that are structured: net-excitatory between same-selectivity populations and net-inhibitory between different-selectivity populations. The feedforward Module 1 → Module 2 projection should be structured to propagate signals for both WM and DM. The feedback Module 2 → Module 1…mechanisms[8]
mechanismand a PATHWAY-SPECIFIC E/I BALANCE in the feedforward projection, which regulates a cascade of activations and PROVIDES A MECHANISM FOR DISCRETE FLOW WITHOUT REQUIRING DIRECT GATING of neuronal responses— “An important feature of our distributed circuit model is the pathway-specific E/I balance in the feedforward projection from selection to action cells. This E/I balance regulates a cascade of activations across functional cell types and can be characterized as discrete flow, since response preparation in action cells begins only after target…mechanisms[9]
builds-on Wang (2002) and Wong and Wang (2006) local attractor-network models of working memory and decision-making— “Prior studies have shown that a local attractor network can perform both WM and DM ( Wang, 2002 ; Wong and Wang, 2006 ).”claim_relations[0]
extends Hanks et al. (2015)— “Our model predicts that, due to the interareal differences in local recurrent structure, representations of accumulated evidence are more graded in PPC and more categorical in PFC, as supported by single-neuron recordings ( Hanks et al., 2015 ).”claim_relations[1]
extends Murray et al. (2014b)— “We found that for the given parameter set, the fluctuations timescale of PFC is 3.44 times slower than that of PPC, analogous to the findings of Murray et al. (2014b) , who found a change by a factor of 2.”claim_relations[2]
critiques the view that two areas make comparable contributions because their activity looks similar during the same tasks— “The general similarity of neural activity of PPC and PFC during WM and DM has supported the view that they make comparable contributions to these functions. Important open questions are to identify how PPC and PFC interact during WM and DM and what their specialized roles may be.”claim_relations[3]
qualifies inactivation results showing that parietal cortex is not causally required for perceptual decisions— “Recent inactivation studies found that even though PPC shows DM-related signals, it does not play a causal role in perceptual DM ( Erlich et al., 2015 ; Katz et al., 2016 ). The random-dot motion and auditory accumulation tasks where such deficits were observed are different from the visuospatial attention task we modeled in Figures 7 and 8 .…claim_relations[4]
supports single-neuron recordings showing more graded evidence representations in parietal and more categorical ones in prefrontal cortex— “Our model predicts that, due to the interareal differences in local recurrent structure, representations of accumulated evidence are more graded in PPC and more categorical in PFC, as supported by single-neuron recordings ( Hanks et al., 2015 ).”claim_relations[5]
extends local attractor-network models of working memory and decision-making, by asking what changes when two such circuits are coupled— “A class of models called attractor networks can perform these functions through strong recurrent synaptic interactions ( Wang, 2008 ). In the attractor network framework, strong synaptic connections among neurons can provide reverberatory excitation, potentially mediated by slow NMDA receptors ( Wang, 1999 ), that maintains a…claim_relations[6]
predicts that inactivating prefrontal cortex should disrupt the top-down modulation of surround suppression in parietal cortex— “Our model predicts that PFC inactivation should disrupt this modulation of surround suppression in PPC. Furthermore, if PFC is inactivated or the PFC–PPC feedback projection is very weak, the system can operate in a 'remember-last' regime ( Fig. 5 D ), whereas PFC engagement can switch the system into a 'remember-first'…claim_relations[7]
supports combined stimulation-imaging work in humans showing a role for prefrontal feedback in distractor filtering— “Along these lines, we suggest that the effective strength of the lateral inhibition in PPC is not a purely local property and can be flexibly controlled by top-down (i.e., feedback) prefrontal engagement ( Falkner et al., 2010 , 2013 ). Recording in LIP during visuospatial WM, Falkner et al. (2010 , 2013 ) found that surround suppression of…claim_relations[8]
senseONE CIRCUIT CANNOT BE OPTIMAL FOR BOTH FUNCTIONS: A SINGLE LOCAL CIRCUIT FACES A TRADEOFF BETWEEN OPTIMIZATION FOR WM VERSUS DM FUNCTION - and the distributed model ameliorates it, capturing the observations WITH A SINGLE SET OF NETWORK PARAMETERS— “We found that a single local circuit faces a tradeoff between optimization for WM versus DM function. This performance tradeoff can be ameliorated in the distributed circuit, whose network properties are functionally desirable for both WM and DM. With a single set of network parameters, the distributed model can capture salient empirical…senses[0]
sensewith the tradeoff named on its two axes: optimizing the recurrent structure FOR ROBUSTNESS AGAINST DISTRACTORS IN WM VERSUS SLOW INTEGRATION OF EVIDENCE IN DM - ameliorated BY EXTENSION TO TWO MODULES WITH A HIERARCHICAL INCREASE IN RECURRENT STRENGTHS— “We highlight a tradeoff that exists when optimizing the recurrent structure for robustness against distractors in WM versus slow integration of evidence in DM; the model developed here ameliorates this tradeoff by extension to two modules with a hierarchical increase in recurrent strengths.”senses[1]
senseTHE MODULES ARE labeled PPC AND PFC THROUGHOUT, AND THEN MAY NOT BE AREAS AT ALL: BOTH MODULES COULD REPRESENT NEURAL POPULATIONS THAT ARE DISTRIBUTED ACROSS MULTIPLE AREAS - visual cells in LIP, FEF and SC, movement cells in FEF and SC. The anatomical names are a reading of the model, not a claim of it— “Our results suggest that both modules could represent neural populations that are distributed across multiple areas (e.g., visual cells in LIP/FEF/SC, movement cells in FEF/SC).”senses[2]
senseTHE CAUSAL EVIDENCE AGAINST THE modeled AREA IS CITED, AND ANSWERED BY TASK: RECENT INACTIVATION STUDIES FOUND THAT EVEN THOUGH PPC SHOWS DM-RELATED SIGNALS, IT DOES NOT PLAY A CAUSAL ROLE IN PERCEPTUAL DM - and the reply is that THE RANDOM-DOT MOTION AND AUDITORY ACCUMULATION TASKS WHERE SUCH DEFICITS WERE OBSERVED ARE DIFFERENT FROM THE VISUOSPATIAL ATTENTION TASK WE MODELED, with rat PPC inactivation disrupting VISUAL BUT NOT AUDITORY evidence— “Recent inactivation studies found that even though PPC shows DM-related signals, it does not play a causal role in perceptual DM ( Erlich et al., 2015 ; Katz et al., 2016 ). The random-dot motion and auditory accumulation tasks where such deficits were observed are different from the visuospatial attention task we modeled in Figures 7 and 8 .…senses[3]
senseand the question is left to be settled by design rather than by argument: it SHOULD BE ADDRESSED BY FUTURE EXPERIMENTS THAT CAREFULLY CONSIDER BEHAVIORAL PERFORMANCE, TASK DESIGN, SPECIES-SPECIFIC DIFFERENCES, AND THE PRESENCE OF EVIDENCE ACCUMULATION IN MULTIPLE SIMULTANEOUSLY RECORDED AREAS— “Furthermore, it is unclear whether these results can be accounted for via compensatory mechanisms and/or distributed processing within a broader region in PPC that includes LIP. The issue of LIP involvement in decision-making tasks should be addressed by future experiments that carefully consider behavioral performance, task design…senses[4]
senseA MODEL RESULT THAT DOES NOT MATCH THE DATA, WITH THE MISMATCH USED RATHER THAN HIDDEN: input to one module only RESULTED IN HIGHLY ATTENUATED RESPONSES IN THE PFC, TO A GREATER EXTENT THAN THE FINDINGS it is compared with - THUS, IN OUR MODEL WE EFFECTIVELY INVESTIGATED A SUBCIRCUIT— “In modeling responses to intervening distractors ( Figs. 4 , 5 ) we considered input to the PPC module only, which resulted in highly attenuated responses in the PFC—to a greater extent than the findings by Suzuki and Gottlieb (2013) . Thus, in our model we effectively investigated a subcircuit in the PFC that strongly filters distractors and…senses[5]
senseTHE PREMISE BEING ARGUED AGAINST IS AN INFERENCE FROM RESEMBLANCE: THE GENERAL SIMILARITY OF NEURAL ACTIVITY OF PPC AND PFC DURING WM AND DM HAS SUPPORTED THE VIEW THAT THEY MAKE COMPARABLE CONTRIBUTIONS TO THESE FUNCTIONS— “The general similarity of neural activity of PPC and PFC during WM and DM has supported the view that they make comparable contributions to these functions. Important open questions are to identify how PPC and PFC interact during WM and DM and what their specialized roles may be.”senses[6]
senseand the alternative readings of the same signal are both live: persistent activity MAY BE A LOCALLY GENERATED PHENOMENON OR, ALTERNATIVELY, THE RESULT OF DISTRIBUTED INTERAREAL INTERACTIONS - and IT IS UNCLEAR TO WHAT DEGREE the specialization may be due to INTRINSIC DIFFERENCES IN LOCAL MICROCIRCUITRY— “For instance, WM-related persistent activity in these areas may be a locally generated phenomenon or, alternatively, the result of distributed interareal interactions ( Christophel et al., 2017 ). Despite general…senses[7]
sensewith the unit of description itself in question: recordings have revealed A DIVERSITY OF FUNCTIONALLY DEFINED CELL TYPES, raising QUESTIONS OF THE DIVISION OF LABOR AMONG BRAIN AREAS, OR FUNCTIONAL CELL TYPES - areas or cell types, and the paper does not choosesenses[8]
senseTHE SAME PROPERTY IS GOOD FOR TWO DIFFERENT REASONS, WHICH IS WHY THE ARCHITECTURE WORKS: the strong attractor gives ROBUSTNESS AGAINST DISTRACTORS in memory and lets the module RAMP QUICKLY TO THRESHOLD in choice - and it PREDICTS A DIFFERENCE IN THE REPRESENTATION OF ACCUMULATED VALUE, MORE GRADED IN MODULE 1 AND MORE CATEGORICAL IN MODULE 2
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[9]
senseand the weak attractor is chosen for what it fails to hold: it can TRANSIENTLY ENCODE DISTRACTORS, its memory state CAN BE EFFECTIVELY CONTROLLED BY WEAK FEEDBACK, and it supports INTEGRATION WITH A LONG TIMESCALE TO IMPROVE ACCURACY
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senses[10]
sensewith the long-range projections STRUCTURED rather than uniform - NET-EXCITATORY BETWEEN SAME-SELECTIVITY POPULATIONS AND NET-INHIBITORY BETWEEN DIFFERENT-SELECTIVITY ones, which is what lets feedback SWITCH PPC BACK TO ENCODING THE TARGET
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senses[11]
senseA PROPERTY DECLARED NOT TO BE LOCAL: the effective strength of the lateral inhibition in PPC IS NOT A PURELY LOCAL PROPERTY AND CAN BE FLEXIBLY CONTROLLED BY TOP-DOWN PREFRONTAL ENGAGEMENT - so a measurement of that circuit is a measurement of its inputs too
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senses[12]
senseand the model is made to say what would falsify it: OUR MODEL PREDICTS THAT PFC INACTIVATION SHOULD DISRUPT THIS MODULATION of surround suppression in PPC, and that weak feedback puts the system in a REMEMBER-LAST regime while engagement switches it to REMEMBER-FIRST
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senses[13]
sensewith a second prediction already matched: representations of accumulated evidence are MORE GRADED IN PPC AND MORE CATEGORICAL IN PFC, AS SUPPORTED BY SINGLE-NEURON RECORDINGS
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senses[14]
senseBREAKING THE MECHANISM PRODUCES A DISTINCT ERROR RATHER THAN A DEGRADED ONE: a STRONGLY IMBALANCED CIRCUIT PRODUCED A DISTINCT TYPE OF ERROR - ACTION CELLS STARTED RAMPING BEFORE THE POPULATIONS OF SELECTION CELLS HAD DIVERGED, that is BEFORE SELECTION WAS ACCOMPLISHED - and the modulation gave A SMOOTH TRADEOFF BETWEEN SPEED AND ACCURACY, A PLAUSIBLE MECHANISM AMONG OTHERS
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senses[15]
senseand the balance is what removes the need for a separate gate: PATHWAY-SPECIFIC BALANCE THEREBY PROVIDES A MECHANISM FOR DISCRETE FLOW WITHOUT REQUIRING DIRECT GATING OF NEURONAL RESPONSES
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senses[16]
senseTHE FRAMEWORK IS FLEXIBLE ENOUGH TO DO BOTH JOBS, WHICH IS THE PROBLEM IT CREATES: attractor networks CONSTITUTE A FLEXIBLE TYPE OF COGNITIVE CIRCUIT CAPABLE OF PERFORMING BOTH WM AND DM - so the same architecture explains both, and the specialisation has to come from somewhere else
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senses[17]
senseand the claim is made for design principles rather than for the brain: the study SUGGESTS IMPORTANT DESIGN PRINCIPLES FOR CONSTRUCTING MULTIREGIONAL NEURAL CIRCUIT MODELS, and the model MAY THEREFORE INSTANTIATE FEATURES OF A CANONICAL COGNITIVE CIRCUIT USEFUL FOR STUDYING distributed computation
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senses[18]
sensewith the division of labor stated as the finding: PPC TRANSIENTLY ENCODES DISTRACTORS WHILE PFC FILTERS DISTRACTORS AND SUPPORTS WM ROBUSTNESS, and the PPC module generates GRADED representations of accumulated evidence while PFC generates MORE CATEGORICAL responses
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senses[19]
senseand the limits are listed as extensions: a QUASI-CONTINUOUS network for distractor-target similarity, MORE THAN TWO POPULATIONS for set-size effects, and A SPIKING CIRCUIT MODEL for synchronisation between the areas
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senses[20]
sensewith the hardest one named separately: how to include SUFFICIENT HETEROGENEITY to account for THE DYNAMIC RESPONSES OF INDIVIDUAL CELLS BUT STABLE REPRESENTATIONS AT THE POPULATION LEVEL
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senses[21]
controlone parameter set held fixed across every working-memory and decision-making paradigm tested — held — rules out a fit obtained by retuning per phenomenon - WITH A SINGLE SET OF NETWORK PARAMETERS the model captures salient empirical observations across both functions
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controls[0]
controlthe feedforward inhibition onto the action cells decreased systematically to break the balance — held — rules out the balance being incidental - a STRONGLY IMBALANCED CIRCUIT PRODUCED A DISTINCT TYPE OF ERROR, with action cells ramping BEFORE SELECTION WAS ACCOMPLISHED
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controls[1]
controlthe same two modules run under both task families rather than one — held — rules out an architecture tuned to one function - the tradeoff only appears because both are asked of the same circuit
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controls[2]
differs inthe view that two areas make comparable contributions because their activity looks similar during the same tasks — the local recurrent strength, which differs hierarchically between the modules
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claim_relations[3]
differs ininactivation results showing that parietal cortex is not causally required for perceptual decisions — the task - random-dot motion and auditory accumulation against the visuospatial attention task modeled here
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claim_relations[4]
auditcircuit —causes→ tradeoff — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says a single circuit faces a tradeoff; causes is loose.
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concept_edges[0]
notecircuit —causes→ tradeoff — A SINGLE LOCAL CIRCUIT FACES A TRADEOFF BETWEEN OPTIMIZATION FOR WM VERSUS DM FUNCTION - and the distributed model captures the observations WITH A SINGLE SET OF NETWORK PARAMETERS
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concept_edges[0]
auditcell type —part-of→ circuit — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says frontoparietal circuits contain diverse cell types; part-of is loose.
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concept_edges[11]
notecell type —part-of→ circuit — A DIVERSITY OF FUNCTIONALLY DEFINED CELL TYPES, raising QUESTIONS OF THE DIVISION OF LABOR AMONG BRAIN AREAS, OR FUNCTIONAL CELL TYPES - the unit of description is left open
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concept_edges[11]
auditattractor —operationalised-as→ recurrent — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but attractor networks work through strong recurrent interactions; operationalised-as is loose.
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concept_edges[12]
noteattractor —operationalised-as→ recurrent — attractor networks CONSTITUTE A FLEXIBLE TYPE OF COGNITIVE CIRCUIT CAPABLE OF PERFORMING BOTH WM AND DM - which is why specialisation must come from elsewhere
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concept_edges[12]
auditfeedback —causes→ inhibition — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span suggests lateral inhibition can be controlled by top-down feedback; causes is loose.
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concept_edges[16]
notefeedback —causes→ inhibition — A PROPERTY DECLARED NOT TO BE LOCAL - the effective strength of the lateral inhibition in PPC IS NOT A PURELY LOCAL PROPERTY AND CAN BE FLEXIBLY CONTROLLED BY TOP-DOWN PREFRONTAL ENGAGEMENT
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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]
for which uselimitation — the distractor-resistance result specifically: WE CONSIDERED INPUT TO THE PPC MODULE ONLY, WHICH RESULTED IN HIGHLY ATTENUATED RESPONSES IN THE PFC - the modelling choice produces the attenuation being reported
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limitations[0]
for which uselimitation — the generality of that result: THE EFFECTS OF SIMULTANEOUS EXTERNAL INPUTS TO BOTH MODULES ... REMAINS A TOPIC FOR FUTURE COMPUTATIONAL STUDIES
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limitations[1]
for which uselimitation — any comparison with single-cell data: the model uses homogeneous populations, and HOW TO INCLUDE SUFFICIENT HETEROGENEITY ... TO ACCOUNT FOR THE DYNAMIC RESPONSES OF INDIVIDUAL CELLS is an open question
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limitations[2]
for which uselimitation — responses to simultaneous external inputs to both modules, which remains a topic for future computational studies
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limitations[3]
for which uselimitation — random-dot motion and auditory accumulation tasks, which are different from the visuospatial attention task modeled here
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limitations[4]
for which uselimitation — the LIP conclusion: IT IS UNCLEAR WHETHER THESE RESULTS CAN BE ACCOUNTED FOR VIA COMPENSATORY MECHANISMS AND/OR DISTRIBUTED PROCESSING WITHIN A BROADER REGION IN PPC THAT INCLUDES LIP
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limitations[5]
for which uselimitation — any effect that depends on graded tuning similarity: the model is a TWO-POPULATION DISCRETE NETWORK rather than a quasi-continuous one, so such effects cannot appear in it
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limitations[6]
for which uselimitation — the same scope as limitations[2] - how to include sufficient heterogeneity to account for dynamic single-cell responses under a stable population representation is unresolved
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limitations[7]
replicationuntested
limitationDistractor simulations fed stimulus input to the parietal module only, which made the prefrontal responses more attenuated than the empirical data.
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limitations[0]
limitationSimultaneous input to both modules, and how it would interact with the hierarchical recurrence, was not modeled.
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limitations[1]
limitationThe model uses homogeneous populations and cannot yet account for heterogeneous single-cell dynamics under stable population-level coding.
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limitations[2]
limitationmodeling distractors used INPUT TO THE PPC MODULE ONLY, WHICH RESULTED IN HIGHLY ATTENUATED RESPONSES IN THE PFC - TO A GREATER EXTENT THAN THE FINDINGS it is compared against, so what was investigated was EFFECTIVELY A SUBCIRCUIT
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limitations[3]
limitationrecent INACTIVATION STUDIES FOUND THAT EVEN THOUGH PPC SHOWS DM-RELATED SIGNALS, IT DOES NOT PLAY A CAUSAL ROLE in perceptual decision-making - the tasks differ from the one modeled here
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limitations[4]
limitationIT IS UNCLEAR WHETHER THESE RESULTS CAN BE ACCOUNTED FOR via compensatory mechanisms and/or distributed processing within a broader region, and the issue SHOULD BE ADDRESSED BY FUTURE EXPERIMENTS that consider task design and species-specific differences
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limitations[5]
limitationthe model is TWO POPULATIONS rather than a quasi-continuous network, so effects that depend on THE SIMILARITY AND DISTANCE BETWEEN DISTRACTORS AND TARGETS cannot be explored, and generalisation beyond two would be needed for set-size effects
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limitations[6]
limitationand HOW TO INCLUDE SUFFICIENT HETEROGENEITY to account for THE DYNAMIC RESPONSES OF INDIVIDUAL CELLS BUT STABLE REPRESENTATIONS AT THE POPULATION LEVEL is named as an open question
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limitations[7]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingRecurrent structure must pass a threshold for winner-take-all selection, but increasing it further limits the gradual accumulation of sensory evidence.
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content_tags.findings[0]
findinga TRADEOFF EXISTS WHEN OPTIMIZING THE RECURRENT STRUCTURE FOR ROBUSTNESS AGAINST DISTRACTORS IN WM VERSUS SLOW INTEGRATION OF EVIDENCE IN DM, and the model AMELIORATES THIS TRADEOFF BY EXTENSION TO TWO MODULES with a hierarchical increase in recurrent strengths
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content_tags.findings[1]
findingBoth modules hold persistent activity during working memory, but the PPC module encodes intervening distractors transiently while the PFC module filters them out and keeps the memory robust.
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content_tags.findings[2]
findingDuring perceptual decision-making the PPC module represents accumulated evidence in a graded fashion while the PFC module produces more categorical, choice-related responses.
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content_tags.findings[3]
findingObserving persistent activity in one area of a distributed circuit does not necessarily imply that the area is independently capable of multistability.
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content_tags.findings[4]
findingFeedback projections from the PFC module are what switch the PPC module back to encoding the target after a distractor is withdrawn.
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content_tags.findings[5]
findingWITHOUT FEEDBACK FROM PFC, THE DISTRACTOR STIMULUS SWITCHES THE NETWORK TO ENCODING THE DISTRACTOR, WHICH IT CONTINUES TO ENCODE THROUGH THE SUBSEQUENT DELAY
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content_tags.findings[6]
findingFor the given parameters, the PFC module's intrinsic fluctuation timescale is 3.44 times slower than the PPC module's.
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content_tags.findings[7]
findingrelative to PPC, the surround suppression of the distractor population in PFC is STRONGER, toward a lower baseline activity, and MORE RAPID, with a lower time constant
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content_tags.findings[8]
findingWeakening feedforward inhibition in the selection-to-action pathway makes responses faster but less accurate, i.e. it implements a speed–accuracy tradeoff.
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content_tags.findings[9]
findingThe tradeoff is ameliorated when local recurrent structure increases hierarchically from the PPC/selection module to the PFC/action module.
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content_tags.findings[10]
findingBreaking pathway-specific excitation–inhibition balance creates a new error type in which action cells commit to a quasi-random choice before the selection cells have discriminated the target.
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content_tags.findings[11]
findingWhen the feedforward pathway is balanced, every simulated error comes from the selection cells mis-selecting the distractor rather than the target.
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content_tags.findings[12]
findingA SINGLE LOCAL CIRCUIT FACES A TRADEOFF BETWEEN OPTIMIZATION FOR WM VERSUS DM FUNCTION
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content_tags.findings[13]
findingthe tradeoff is between ROBUSTNESS AGAINST DISTRACTORS IN WM VERSUS SLOW INTEGRATION OF EVIDENCE IN DM, ameliorated BY EXTENSION TO TWO MODULES WITH A HIERARCHICAL INCREASE IN RECURRENT STRENGTHS
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content_tags.findings[14]
findingBOTH MODULES COULD REPRESENT NEURAL POPULATIONS THAT ARE DISTRIBUTED ACROSS MULTIPLE AREAS
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content_tags.findings[15]
findingRECENT INACTIVATION STUDIES FOUND THAT EVEN THOUGH PPC SHOWS DM-RELATED SIGNALS, IT DOES NOT PLAY A CAUSAL ROLE IN PERCEPTUAL DM, and IN RATS, INACTIVATION OF PPC DISRUPTS THE ACCUMULATION OF VISUAL BUT NOT AUDITORY EVIDENCE
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content_tags.findings[16]
findinginput to the PPC module only RESULTED IN HIGHLY ATTENUATED RESPONSES IN THE PFC, TO A GREATER EXTENT THAN THE FINDINGS compared against - THUS, IN OUR MODEL WE EFFECTIVELY INVESTIGATED A SUBCIRCUIT
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content_tags.findings[17]
findingTHE GENERAL SIMILARITY OF NEURAL ACTIVITY OF PPC AND PFC DURING WM AND DM HAS SUPPORTED THE VIEW THAT THEY MAKE COMPARABLE CONTRIBUTIONS TO THESE FUNCTIONS
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content_tags.findings[18]
findingWM-related persistent activity MAY BE A LOCALLY GENERATED PHENOMENON OR, ALTERNATIVELY, THE RESULT OF DISTRIBUTED INTERAREAL INTERACTIONS
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content_tags.findings[19]
findingsingle-neuron recordings have revealed A DIVERSITY OF FUNCTIONALLY DEFINED CELL TYPES, raising QUESTIONS OF THE DIVISION OF LABOR AMONG BRAIN AREAS, OR FUNCTIONAL CELL TYPES
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content_tags.findings[20]
findingrepresentations of accumulated evidence are MORE GRADED IN PPC AND MORE CATEGORICAL IN PFC, AS SUPPORTED BY SINGLE-NEURON RECORDINGS
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content_tags.findings[21]
findingthe authors suggest that the effective strength of lateral inhibition in PPC IS NOT A PURELY LOCAL PROPERTY AND CAN BE FLEXIBLY CONTROLLED BY TOP-DOWN PREFRONTAL ENGAGEMENT
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content_tags.findings[22]
findingOUR MODEL PREDICTS THAT PFC INACTIVATION SHOULD DISRUPT THIS MODULATION OF SURROUND SUPPRESSION IN PPC
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content_tags.findings[23]
findingPATHWAY-SPECIFIC BALANCE PROVIDES A MECHANISM FOR DISCRETE FLOW WITHOUT REQUIRING DIRECT GATING OF NEURONAL RESPONSES
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content_tags.findings[24]
findingA STRONGLY IMBALANCED CIRCUIT PRODUCED A DISTINCT TYPE OF ERROR - action cells STARTED RAMPING BEFORE THE POPULATIONS OF SELECTION CELLS HAD DIVERGED IN ACTIVITY
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content_tags.findings[25]
findingattractor networks CONSTITUTE A FLEXIBLE TYPE OF COGNITIVE CIRCUIT CAPABLE OF PERFORMING BOTH WM AND DM
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content_tags.findings[26]
findingPPC TRANSIENTLY ENCODES DISTRACTORS WHILE PFC FILTERS DISTRACTORS AND SUPPORTS WM ROBUSTNESS
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content_tags.findings[27]
findingthe model MAY INSTANTIATE FEATURES OF A CANONICAL COGNITIVE CIRCUIT USEFUL FOR STUDYING DISTRIBUTED COMPUTATION IN THE BRAIN
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content_tags.findings[28]
findinghow to include SUFFICIENT HETEROGENEITY to account for THE DYNAMIC RESPONSES OF INDIVIDUAL CELLS BUT STABLE REPRESENTATIONS AT THE POPULATION LEVEL is an open question
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content_tags.findings[29]
quantity3.44 times — how much slower the modeled PFC module's spontaneous fluctuation timescale is than the PPC module's
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content_tags.quantities[0]
quantity127 ms, 438 ms — fluctuation timescales extracted from the autocorrelation of spontaneous firing in the PPC and PFC modules respectively
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content_tags.quantities[1]
quantity0.13 and 0.19 — THE SLOPES AT ZERO CROSSING ARE 0.13 AND 0.19 FOR PPC AND PFC, RESPECTIVELY
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content_tags.quantities[2]
quantity81.6% — the accuracy level used to define the discrimination threshold in the simulated two-alternative forced choice task
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content_tags.quantities[3]
quantity∼560 ms — median decision time of the moderate-structure network for a zero-contrast stimulus (slow integration)
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content_tags.quantities[4]
quantity∼430 ms — median decision time of the higher-structure network for the same zero-contrast stimulus (curtailed integration)
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content_tags.quantities[5]
quantity40, 12, and 7 Hz — firing-rate thresholds used to read out reaction time, target discrimination time and action-cell onset time
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content_tags.quantities[6]
quantityJ S (2→2) = 0.4182 nA — the local recurrent structure assigned to Module 2 (PFC), higher than Module 1's 0.35 nA
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content_tags.quantities[7]
quantitya single set of network parameters — the constraint the model holds itself to across every paradigm tested, which is what makes the fit informative rather than fitted
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content_tags.quantities[8]
variablerecurrent structure (independent)
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content_tags.variables[0]
variabletarget-distractor onset asynchrony (TDOA) (independent)
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content_tags.variables[1]
variablestrength of feedforward inhibition (independent)
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content_tags.variables[2]
variablePFC → PPC feedback structure (independent)
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content_tags.variables[3]
variablestrength of feedforward excitation (control)
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content_tags.variables[4]
variablefeedforward PPC → PFC structure (control)
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content_tags.variables[5]
variablereaction time (dependent)
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content_tags.variables[7]
variableerror rate (dependent)
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content_tags.variables[8]
variablediscrimination threshold and robust stimulus range (dependent)
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content_tags.variables[9]
variablerecurrent
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content_tags.variables[10]
variableinhibition
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content_tags.variables[11]
definitionstructure of the network
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content_tags.definitions[0]
definitiontone of the network
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content_tags.definitions[1]
definitionmodule
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content_tags.definitions[2]
definitionrobust stimulus range
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content_tags.definitions[3]
definitiondiscrimination threshold
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content_tags.definitions[4]
definitionintegration time constant
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content_tags.definitions[5]
definitionerror trial
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content_tags.definitions[6]
definitiontarget-distractor onset asynchrony (TDOA)
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content_tags.definitions[7]
definitionattractor
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content_tags.definitions[8]
definitionmodule
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content_tags.definitions[9]
key-termsPPC, PFC, module, circuit, model, attractor, recurrent, distractor, evidence, selection, action, inhibition, feedback, projection, tradeoff, cell type, LIP, inactivation, task, network
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.
curve fitting (exponential) as written: fitted the time courses of the firing rates ... with an exponential function
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content_tags.statistics[0]
autocorrelation as written: performed an autocorrelation on the firing rate
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content_tags.statistics[1]
standard deviation SD as written: a Gaussian distribution with a mean of 0.09 nA and an SD of 0.04 nA
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content_tags.statistics[2]
curve fitting (sigmoid) as written: we fit the fraction of correct trials versus contrast relation ... with a sigmoid
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content_tags.statistics[3]
mean as written: calculated the mean firing rate for each bin
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content_tags.statistics[4]
quantile binning as written: split into quintiles
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content_tags.statistics[5]
correlation as written: correlate with reaction times, both across and within contrast conditions
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content_tags.statistics[6]
psychometric function fitting P as written: the equation commonly used to fit psychometric curves
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content_tags.statistics[7]
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
methoda parsimonious two-module attractor model with hierarchical differences in recurrent strength and structured long-range projections, tested with a single parameter set — a second reading DIFFERING from the recorded value; recorded: simulation
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alternatives.method
phenomenonstimulus-selective persistent activity and ramping evidence accumulation, which appear in both areas and have been read as comparable contributions — a second reading DIFFERING from the recorded value; recorded: distributed working memory and decision-making in the frontoparietal circuit
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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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