Dopamine Cells Respond to Predicted Events during Classical Conditioning: Evidence for Eligibility Traces in the Reward-Learning Network — TEI-tagged view

Wei‐Xing Pan, Robert Schmidt, Jeffery R. Wickens, Brian I. Hyland · 2005 · neuroscience · doi:10.1523/jneurosci.1478-05.2005 · 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. The paper is a hybrid of in vivo electrophysiology and computational simulation; the single method facet was set to electrophysiology because the modelling is used to interpret recorded data, and the method_evidence sentence names both components. Claim type is 'mechanism' rather than 'critique' because the paper explicitly declines to treat its mismatch with earlier TD models as a refutation of TD, instead locating the discrepancy in parameter choice. The extraction contains PMC site furniture before the article body, but the body itself is clean prose. 2026-08-13 and 2026-08-25 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
Other fields define these differently the same word, defined another way in another discipline
prediction error 6 fields · standard deviation 3 fields · latency 2 fields
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
DA cells prediction error9 temporal difference2 eligibility trace learning rate7 TD(0) TD(1) serial compound state vector discount factor3 conditioned omission2 modulation index5 apomorphine quinpirole substantia nigra pars compacta ventral tegmental area2 supramammillary nucleus random reward cued-reward licking electrophysiology110

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Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenondopamine reward-prediction-error signalling during classical conditioning— “Behavioral conditioning of cue-reward pairing results in a shift of midbrain dopamine (DA) cell activity from responding to the reward to responding to the predictive cue.”
claim-typemechanism— “By exploring the TD parameter space, we demonstrate that the persistent reward responses of DA cells during conditioning are only accurately replicated by a TD model with long-lasting eligibility traces (nonzero values for the parameter λ) and low learning rate (α).”
method — evidence— “Here, we report a combined single-unit recording and temporal difference (TD) modeling approach to this question.”
object of study — evidence— “Behavioral conditioning of cue-reward pairing results in a shift of midbrain dopamine (DA) cell activity from responding to the reward to responding to the predictive cue. However, the precise time course and mechanism underlying this shift remain unclear.”
research question — evidence— “In this study, we first investigated in conscious rats whether DA cells conform to these specific patterns of prediction error signaling by recording from single neurons while animals first learned the association between cues and rewards. The results were not consistent with previous model predictions. We then performed a systematic…
contribution — evidence— “By exploring the TD parameter space, we demonstrate that the persistent reward responses of DA cells during conditioning are only accurately replicated by a TD model with long-lasting eligibility traces (nonzero values for the parameter λ) and low learning rate (α).”
transferfrom machine learning / computer science: The temporal difference algorithm and its prediction-error signal, taken from machine learning, are used as the model of midbrain dopamine cell activity.— “Furthermore, several features of DA cell activity match properties of the prediction error signal of the temporal difference (TD) algorithm for machine learning, leading to the hypothesis that DA cell activity may be providing a teaching signal within a neural analog of a TD learning system in the brain”transfers[0]
transferfrom machine learning: the temporal difference algorithm, imported as a candidate account of what dopamine cells compute— “In the temporal difference algorithm, an estimate of the expected reward at a particular time step t is derived from the difference between the prediction of all future rewards at that time step, P ( t ), and the prediction of all future rewards that was generated at the previous time step, P ( t - 1) ( Sutton, 1988 ). The difference between…transfers[1]
transferfrom classical conditioning theory: the century-old requirement that some trace of the cue survive until the reward arrives, here identified with the eligibility trace— “The concept that for delayed associative conditioning to occur there must be some memory or trace of the antecedent signal at the time of a subsequent reward is as old as the study of classical conditioning itself”transfers[2]
transferfrom reinforcement learning practice: the known trade-off that high lambda with low alpha buys stable early learning, used here as an adaptive argument— “A low learning rate may be considered a disadvantage. However, previous studies in machine learning have found that when λ is set high to enable learning to begin early in training, low values of α improve the stability of learning”transfers[3]
mechanismA long-lasting eligibility trace keeps the weights of an earlier sensory signal open to modification for many time steps, so the reward-driven prediction error can be assigned back to the cue in one step instead of migrating one time step per trial— “In TD(λ), vector weights representing a sensory signal remain eligible for modification by prediction error signals for a variable number of time steps after the signal has occurred.”mechanisms[0]
mechanismA very low learning rate combined with a high eligibility-trace decay makes each trial change the weights only slightly, so responses to the cue and to the predicted reward coexist for a prolonged period before the reward response is suppressed— “Thus, very low α in combination with high λ generates a prolonged period over which both cue and reward responses occur, as seen in the DA cell data.”mechanisms[1]
mechanismSustained firing in reverberating circuits, or biochemical processes at the synapse, could hold the seconds-long eligibility trace the model requires— “Potential mechanisms include sustained firing in reverberating circuits and biochemical mechanisms acting at the synaptic level ( Houk et al., 1995 ).”mechanisms[2]
mechanisman eligibility trace keeps earlier time steps modifiable, so a cue several steps back can be updated in one trial instead of waiting for the error to migrate— “The eligibility trace enables weights associated with previous time steps to be altered by prediction error at time step t and was calculated recursively ( Sutton, 1988 ) by the following: e l ( t + 1) = λ e l ( t ) + x l ( t ) , where λ is the eligibility trace decay parameter.”mechanisms[3]
mechanismwith a low learning rate each trial moves the weights only slightly, which is what lets cue and reward responses coexist for hundreds of trials— “Otherwise, the value for α, given a suitable setting for λ, determined the number of trials needed for growth of the cue response and for abolition of the reward response and therefore the number of trials over which these responses coexisted.”mechanisms[4]
mechanismunder TD(0) the error must arrive one step earlier per trial, so reaching a cue k steps back takes at least k trials— “A physiological interpretation of TD(0) is that the system can only hold a memory of an event from one moment to the next. Our findings suggest that this limitation cannot apply to learning in the brain reward pathways.”mechanisms[5]
mechanismcandidate substrates for a seconds-long trace are reverberating circuits and synaptic biochemistry— “The existence of eligibility traces lasting several seconds in neural circuits is open to experimental verification. Potential mechanisms include sustained firing in reverberating circuits and biochemical mechanisms acting at the synaptic level”mechanisms[6]
mechanismlong traces with slow learning is the combination machine learning already found to give early but stable learning, which may be why biology uses it— “Thus, combining prolonged eligibility for change with low rates for learning rate offers considerable practical advantages that may…mechanisms[7]
builds-on Montague et al. (1996) temporal-difference model of dopamine activityclaim_relations[0]
contradicts previous TD models predicting a gradual stepwise latency shift (Montague et al., 1996; Schultz et al., 1997)claim_relations[1]
extends Miller et al., 1981; Kosobud et al., 1994; Kiyatkin and Rebec, 2001; Hyland et al., 2002 (rat DA cell responses to reward-predicting cues)
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claim_relations[2]
contradicts the stepwise latency migration predicted by TD(0) models of dopamine activity
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claim_relations[3]
contradicts the prediction that a redundant intermediate cue draws no response
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claim_relations[4]
contradicts the prediction that reward responses vanish before cue responses appear
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claim_relations[5]
reconciles the temporal difference framework with the recorded time course, by relocating the disagreement to parameter choice
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claim_relations[6]
critiques published primate datasets, which on re-inspection never supported gradual migration either
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claim_relations[7]
confirms earlier reports that reward responses are diminished in highly trained animals
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claim_relations[8]
extends rat studies showing dopamine responses to reward-predicting cues, by showing they arise during acquisition
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claim_relations[9]
sensepresumed DA cells are a doubly filtered category: rate below 10 Hz and spike at least 1 ms, THEN more than 50% inhibition by a dopamine agonist. Cell identity is pharmacological here, not genetic or anatomical
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senses[0]
senseTD(0), TD(1) and TD(lambda) are three different models, not one - the value of lambda changes which weights an error can touch, and the published DA models were TD(0)
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senses[1]
senseprediction error here is the scalar difference between actual reward and the temporal difference of successive predictions, with the NEGATIVE side deliberately clipped to match dopamine cells' floor
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senses[2]
senseearly and late training are defined by block count - 6 or fewer against 10 or more - and the paper's central result holds only early
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senses[3]
sensethe recorded population pools substantia nigra pars compacta, ventral tegmental area and supramammillary nucleus, justified by a null on regional differences
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senses[4]
controlelectrode track positions confirmed histologically after the experiments were completed — held — rules out attributing the responses to dopamine cells that were not where the recordings were thought to be - the confirmation comes after, from the tissue, not from the coordinates
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controls[0]
differs inprevious TD models predicting a gradual stepwise latency shift (Montague et al., 1996; Schultz et al., 1997) — the time course of the latency shift: previous TD models PREDICT A GRADUAL STEPWISE SHIFT IN LATENCY WITH RESPONSES TO REWARDS LOST BEFORE RESPONSES DEVELOP TO THE CONDITIONED CUE
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claim_relations[1]
differs inthe stepwise latency migration predicted by TD(0) models of dopamine activity — the latency profile of the new response: NEW CONDITIONED RESPONSES MADE THEIR FIRST APPEARANCE AT A CONSTANT, SHORT LATENCY AFTER PREDICTIVE CUES, and DEVELOPED RAPIDLY, WITHIN A FEW TRIALS - not the gradual migration the models expect
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claim_relations[3]
differs inthe temporal difference framework with the recorded time course, by relocating the disagreement to parameter choice — the model's free parameters: THESE APPARENT DISPARITIES BETWEEN TD MODEL PREDICTION ERRORS AND DA CELL ACTIVITY ARE ATTRIBUTABLE TO THE PARTICULAR CHOICE OF PARAMETERS USED IN THE MODEL - the settings for ALPHA and LAMBDA, not the framework
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claim_relations[6]
differs inpublished primate datasets, which on re-inspection never supported gradual migration either — gradual migration against simultaneous responding, and the stage of learning: the existing primate data PROVIDES NO SUPPORT FOR THE GRADUAL MIGRATION OF A RESPONSE DURING LEARNING AND SUGGESTS THAT DA CELLS SHOW SIMULTANEOUS RESPONDING TO BOTH CUE AND REWARD, AT LEAST EARLY IN LEARNING
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claim_relations[7]
auditprediction error —operationalised-as→ DA cells — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says DA cells' baseline spiking can be equated to zero prediction error; operationalised-as is loose.
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concept_edges[0]
noteprediction error —operationalised-as→ DA cells — the identification is asymmetric by construction: firing can rise to 100 Hz but only fall to zero from a 5 Hz baseline
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concept_edges[0]
auditTD(0) —is-a→ temporal difference — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'TD(0) model'; 'temporal difference' is an abbreviation variant.
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concept_edges[2]
auditTD(1) —is-a→ temporal difference — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'TD(1) model'; 'temporal difference' is an abbreviation variant.
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concept_edges[3]
auditapomorphine —operationalised-as→ DA cells — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but apomorphine inhibition was an inclusion criterion for DA cells; operationalised-as is loose.
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concept_edges[5]
noteapomorphine —operationalised-as→ DA cells — direction is test-to-category: more than 50% inhibition by the agonist is what makes a recorded cell count as dopaminergic
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concept_edges[5]
edgeserial compound —operationalised-as→ state vector
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concept_edges[8]
auditomission —operationalised-as→ prediction error — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but responses to cue omission were analysed; operationalised-as 'prediction error' is interpretive.
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concept_edges[9]
noteomission —operationalised-as→ prediction error — the negative side of the signal is measured by withholding what was predicted
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concept_edges[9]
weightomission —operationalised-as→ prediction error — 0.8
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concept_edges[9]
outcome (stated by the tagger, no quotation)control — heldcontrols[0]
for which uselimitation — the fitted parameter values: OUR RESULTS SHOULD NOT BE UNDERSTOOD AS PROVIDING EXPLICIT FIXED VALUES FOR NEURAL TD LEARNING PARAMETERS, BECAUSE THE SPECIFIC VALUES ... MAY BE DIFFERENT IF DETAILS OF THE MODEL STRUCTURE WERE CHANGED
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limitations[0]
for which uselimitation — the timing predictions in particular: IT IS NOT KNOWN HOW SENSORY STIMULI ARE REPRESENTED IN THE BRAIN FOR REWARD LEARNING
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limitations[1]
for which uselimitation — the same scope as limitations[0] - the values are a property of this model structure
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limitations[2]
for which uselimitation — the same scope as limitations[1], with the convention named: WE FOLLOWED MANY PREVIOUS STUDIES IN USING A SERIAL COMPOUND REPRESENTATION OF CONDITIONED STIMULI
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limitations[3]
for which uselimitation — any reading of the click as a neutral cue: ANIMALS WERE THEREFORE EXPOSED TO HUNDREDS OF SOLENOID-REWARD PAIRINGS BY THE TIME THE RECORDINGS WERE MADE
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limitations[4]
for which uselimitation — the negative prediction-error signal: THE AMPLITUDE OF INHIBITIONS WAS SMALL, CONSISTENT WITH THE FACT THAT INHIBITORY RESPONSES CAN BE DIFFICULT TO DETECT AND QUANTIFY IN NEURONS THAT ALREADY HAVE A LOW BASELINE RATE
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limitations[5]
for which uselimitation — what a 'dopamine neuron' means here, and in every study that skips the drug test: FROM 45 NEURONS THAT WERE POSSIBLY DOPAMINERGIC ON ELECTROPHYSIOLOGICAL CRITERIA, 24 ... WERE CONFIRMED DA NEURONS BASED ON HAVING A CLEAR INHIBITORY RESPONSE TO DOPAMINE AGONIST DRUGS
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limitations[6]
replicationuntested
limitationThe fitted parameter values are not claimed to be fixed neural constants, since a different model structure could yield different best-fitting values.
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limitations[0]
limitationThe stimulus representation assumed by the model (serial compound) is not known to be how the brain represents stimuli for reward learning.
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limitations[1]
limitationthe paper refuses the strong reading of its own result: these are not fixed values for neural parameters, because a different model structure would shift them
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limitations[2]
limitationhow the brain represents stimuli in time is unknown; the serial compound representation is a convention inherited from earlier models
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limitations[3]
limitationthe animals had already had hundreds of solenoid-reward pairings before recording, so the click was not a neutral event at the start of conditioning
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limitations[4]
limitationthe negative prediction error signal is small and hard to measure against an already low baseline rate
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limitations[5]
limitation21 of 45 electrophysiologically plausible cells failed the pharmacological test and were discarded
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limitations[6]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingDopamine cells kept responding to reward long after they developed responses to the predictive cue.
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content_tags.findings[0]
findingNew conditioned responses appeared straight away at their final short latency after the cue, with no gradual backward drift of latency from reward to cue.
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content_tags.findings[1]
findingEvery cell trained with two cues in sequence responded clearly to the second, seemingly redundant cue, and that response persisted for the whole experiment.
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content_tags.findings[2]
findingCells recorded after many sessions of cue-reward pairing no longer responded to a predictable reward, unlike the cells recorded early in training.
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content_tags.findings[3]
findingAcquiring a cue response and losing the reward response run on different time courses rather than being two sides of one shift.
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content_tags.findings[4]
findingOmitting an expected reward significantly reduced firing rate.
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content_tags.findings[5]
findingOmitting the second cue restored reward responses to the level seen with random reward.
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content_tags.findings[6]
findingWith the chosen parameter values, the TD model's prediction error reproduced key elements of the recorded dopamine pattern.
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content_tags.findings[7]
findingA high eligibility-trace decay parameter was the one setting that let the model learn cue significance without stepwise trial-by-trial migration of the response from reward to cue.
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content_tags.findings[8]
findingOnce the eligibility-trace decay fell below about 0.6, stepwise migration of the prediction error reappeared no matter what learning rate was used.
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content_tags.findings[9]
findingconditioned responses appeared at their final short latency within a few trials, with no gradual migration
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content_tags.findings[10]
findingthe redundant intermediate cue drew a clear response in every two-cue cell, which persisted
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content_tags.findings[11]
findingresponses to predicted reward persisted after cue responses had developed, in every cell recorded early in training
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content_tags.findings[12]
findingresponses to predicted reward are lost completely only after training beyond what first establishes cue responses, so the two changes have different time courses
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content_tags.findings[13]
findingthose late-training cells still responded robustly to reward delivered without cues
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content_tags.findings[14]
findingomitting the second cue restored the reward response to its uncued level, so the redundant cue was doing predictive work
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content_tags.findings[15]
findingTD with eligibility zero and a fast learning rate reproduced the published pattern, with no second-cue response and no overlap
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content_tags.findings[16]
findingTD with lambda 0.9 and alpha 0.005 reproduced the recorded pattern, including the coexistence and the intermediate cue response
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content_tags.findings[17]
findinglambda below about 0.6 brought stepwise migration back for every value of alpha, making it the critical parameter
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content_tags.findings[18]
findingomitted rewards produced a significant firing decrease but omitted cues did not
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content_tags.findings[19]
findingCONTROL NULL: no regional difference in response pattern, which is what licensed pooling the three midbrain sites
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content_tags.findings[20]
findingBUILT IN, NOT PREDICTED: the model's small negative errors follow from the clipping imposed to match dopamine cells' floor, as the authors state
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content_tags.findings[21]
findingre-inspection of published primate datasets found no support for gradual migration there either
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content_tags.findings[22]
quantity24 of 45, from 11 rats — cells that passed pharmacological confirmation as dopamine neurons out of those meeting the electrophysiological criteria
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content_tags.quantities[0]
quantity5.7 ± 3.5 Hz — average baseline firing rate of the confirmed dopamine cells
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.quantities[1]
quantity8 of 11 — cells tested in which conditioned responses to the cue developed during recording
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content_tags.quantities[2]
quantity74 ± 33 ms; 53 ± 9 ms — latencies of the newly conditioned excitatory and inhibitory responses to the cue
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.quantities[3]
quantity-0.12 ± 0.17 — average modulation index at the time of an omitted second cue, a nonsignificant departure from baseline
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content_tags.quantities[4]
quantity-0.21 ± 0.20; p < 0.01; n = 11 — average modulation index when an expected reward was omitted, a significant reduction from baseline
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content_tags.quantities[5]
quantity∼0.6 — threshold value of the eligibility-trace decay parameter below which stepwise migration of the prediction error reappears
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content_tags.quantities[6]
quantity-0.05 — ceiling imposed on the model's negative prediction error, to match the limited range over which dopamine cells can be inhibited
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content_tags.quantities[7]
quantity0.9 and 0.005 — the lambda and alpha that reproduced the recorded dopamine pattern
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content_tags.quantities[8]
quantityabout 0.6 — the lambda threshold below which stepwise migration reappears for every alpha
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content_tags.quantities[9]
quantity-0.05 — the imposed floor on negative prediction error, matching dopamine cells' limited inhibitory range
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content_tags.quantities[10]
quantity24 of 45 — electrophysiologically plausible cells that passed the pharmacological identification
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content_tags.quantities[11]
quantity5.7 Hz, 1.5 ms — mean firing rate and spike duration of the accepted dopamine cells
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content_tags.quantities[12]
quantity-0.21 vs -0.12 — modulation index for omitted reward (significant) against omitted cue (not)
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content_tags.quantities[13]
quantity74 ms and 53 ms — latency of the new conditioned excitations and inhibitions
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content_tags.quantities[14]
variableoddball sequences (independent)
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content_tags.variables[1]
variabledecay parameter (independent)
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content_tags.variables[2]
variablefiring rate (dependent)
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content_tags.variables[4]
variablediscount factor (control)
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content_tags.variables[5]
variableintertrial interval (control)
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content_tags.variables[6]
variablecontrol paradigms (control)
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content_tags.variables[7]
variablelearning rate
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content_tags.variables[9]
variableeligibility trace
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content_tags.variables[10]
variablelatency
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content_tags.variables[11]
variableomission
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content_tags.variables[12]
definitioneligibility trace
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content_tags.definitions[0]
definitiontemporal difference
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content_tags.definitions[1]
definitionlearning rate parameter
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content_tags.definitions[3]
definitioncomplete serial compound stimulus
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content_tags.definitions[6]
definitiontemporal difference
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content_tags.definitions[7]
definitionprediction error
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content_tags.definitions[8]
definitioneligibility trace
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content_tags.definitions[9]
definitionserial compound
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content_tags.definitions[10]
definitionmodulation index
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content_tags.definitions[11]
definitionTD(0)
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content_tags.definitions[12]
definitionTD(1) model
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content_tags.definitions[13]
definitionTD(0) model
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content_tags.definitions[14]
key-termsDA cells, prediction error, temporal difference, eligibility trace, learning rate, TD(0), serial compound, omission, conditioned, apomorphine, ventral tegmental area, modulation index
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.
t-test p as written: paired t test
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content_tags.statistics[0]
standard deviation SD as written: (mean ± SD)
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content_tags.statistics[1]
standard error of the mean SEM as written: Error bars represent SEM.
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content_tags.statistics[2]
standard deviation threshold criterion SDs as written: ≥2 SDs of the mean baseline firing rate
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content_tags.statistics[3]
histogram as written: averaged peri-event time histograms
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content_tags.statistics[4]
paired t test
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content_tags.statistics[5]
modulation index
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content_tags.statistics[6]
SD
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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
methodextracellular single-unit recording from pharmacologically identified midbrain dopamine cells during first acquisition, paired with a systematic sweep of the TD algorithm's parameter space — a second reading DIFFERING from the recorded value; recorded: electrophysiology
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alternatives.method
phenomenonthe shift of dopamine cell firing from unexpected reward to the cue that predicts it — a second reading DIFFERING from the recorded value; recorded: dopamine reward-prediction-error signalling during classical conditioning
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alternatives.phenomenon

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