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Record W2601034848 · doi:10.1093/schbul/sbx021.274

196. Schizophrenia Patients Show Enhanced Responses to Loss Avoidance in Frontostriatal Circuits

2017· article· en· W2601034848 on OpenAlexaff
James A. Waltz, Ziye Xu, Elliot C. Brown, Rebecca Ruiz, James M. Gold

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyStimulus (psychology)Valence (chemistry)Reinforcement learningAudiologySalience (neuroscience)Mean squared prediction errorDevelopmental psychologyCognitive psychologyMedicineArtificial intelligenceComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Background: In previous work (Gold et al., 2012), we found that, in the context of a reinforcement learning (RL) task, patients with schizophrenia (SZ) were more likely to subjectively value gains and instances of loss-avoidance similarly, although instances of loss-avoidance were objectively neutral. This finding was most true of avolitional SZ patients and was attributed to maladaptive representations of expected value (EV), in the presence of intact signaling of reward prediction errors (RPEs). In the current study, using fMRI, we investigated the neural basis of this phenomenon, hypothesizing that SZ patients would exhibit aberrant neural signals related to value-signaling in frontostriatal loops, rather than attenuated RPE signals. Methods: We administered a variant of an RL paradigm (Pessiglione et al. 2006) to 27 chronic SZ patients and 27 controls. Participants learned three probabilistic discriminations (choice of the better stimulus led to better outcome 70% of the time, choice of the worse stimulus led to worse outcome 70% of the time). In a “Gain-Miss” pair, outcomes were either a gain of 25 cents neutral. In a “Loss-Avoid” pair, outcomes were either neutral or a loss of 25 cents. In a “Correct-Incorrect” pair, subjects received only “Correct” and “Incorrect” as pictorial feedback. We examined neural responses to outcomes in nodes of reward and salience networks identified a priori. We also examined contrasts in these areas between outcomes with the same RPE valence but different experienced values (GAIN – LOSS-AVOID, e.g.) and between outcomes with the same experienced value but different RPE valence (LOSS-AVOID – MISS, e.g.). Results: We found that SZ patients showed ENHANCED [LOSS-AVOID – MISS] (RPE-valence) contrasts, relative to controls, in right ventral striatum [VS; t(52) = 2.005, P = .050]. We also observed that SZ patients showed REDUCED [GAIN – LOSS-AVOID] (outcome-valence) contrasts, relative to controls, in dorsal anterior cingulate cortex, dorsomedial prefrontal cortex, and bilateral anterior insula (all t’s > 2.5, all Ps < .02). The between-group differences in these contrasts were largely attributable to ENHANCED responses to loss-avoidance in SZ patients, rather than reduced responses to gains or misses. Finally, we observed significant correlations between neural signals evoked by loss-avoidance in right VS and both Avolition (r = .427) and Anhedonia/Asociality (r = .456) subscores from the SANS. Conclusion: These results provide further evidence for intact RPE signaling in chronic, medicated SZ patients, suggesting that RPE-signaling may even be super-normal, in the case of loss-avoidance. The observation that instances of loss-avoidance evoke stronger frontostriatal responses in individuals with more severe negative symptoms suggests a mechanism by which motivational deficits may emerge from excessive avoidance-learning in SZ.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.266
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
Has abstractyes

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