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Record W4400770748 · doi:10.1523/eneuro.0270-24.2024

From Learning to Choosing: How Decision-Making Evolves with Experience in Rats

2024· article· en· W4400770748 on OpenAlexafffund
Kendra M. Loedige, Mohammed U. Al-youzbaki

Bibliographic record

VenueeNeuro · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsTwo-alternative forced choiceDeliberationAnimal cognitionPsychologyArtificial intelligenceCognitive psychologyStimulus (psychology)Computer scienceCognitionNeuroscience

Abstract

fetched live from OpenAlex

Decision-making is a fundamental process that guides actions by selecting between various options based on their known or presumed outcomes, often using sensory inputs (Carandini and Churchland, 2013).The neural mechanisms by which the brain integrates complex information to make decisions are typically studied by measuring neural recordings, response times, and choice accuracy using well-trained animals (Carandini and Churchland, 2013).These experiments often employ two-alternative forced-choice (2AFC) designs, where animals are trained to choose between two stimuli presented simultaneously, assuming that the animal learns the value of each stimulus and decides based on an internal comparative evaluation.However, the traditional 2AFC design may be limited, as it does not consider that stimuli are not encountered simultaneously in nature and that decision-making strategies evolve during learning (White et al., 2024).Kacelnik et al. ( 2011) highlight these concerns, arguing that animals' choices on a 2AFC task could be predicted by latencies observed when animals are provided with a single offer.Furthermore, they suggest that the act of deliberation, observed when animals slow their response times to make a choice, could be an artifact produced as animals learn the 2AFC.The recent study by White et al. (2024) in eNeuro aimed to address these limitations by investigating decision-making dynamics during the initial learning phase.By isolating learning values of individual stimuli from the decision-making process, the researchers sought to understand how male rats can make choices for the first time on a 2AFC and how those choices change with experience.The behavioral experiment comprised two main stages: value learning and choice learning (Fig. 1).In the value learning stage, rats were introduced to a single stimulus of either high or low luminance, where nose pokes at the port below the stimulus led to the delivery of a high (16% sucrose) or low (4% sucrose) reward, respectively.The choice learning stage consisted of single-offer trials for two-thirds of the total trials, where rats were presented with either the high or low-luminance cue.The remaining one-third of trials were dual-offer trials, where animals were simultaneously offered both high-and low-luminance stimuli, randomized by side.Rats' response latencies and choice percentages were measured.During the initial stages of choice learning, rats consistently preferred the highluminance stimuli.Median response latencies for dual-offer trials were greater compared with single offers and were greater for single-offer trials with low-value stimuli compared with those with high-value stimuli.These latency differences were most pronounced in the first session but persisted over the remaining sessions.These differences indicate that even after dissociating the value learning stage from the decision-making process, rats showed evidence of deliberation, which remained present with experience.To examine changes in the response time distribution that occur with experience, the authors utilized an ExGauss fitting.In this model, response times are fitted with Gaussian and exponential components, considering the peak and tail of the response time distribution, respectively.This enables the quantification of sensorimotor processing (Gaussian component) and variability (exponential component) within the data.The fitting showed that when choosing the high-value reward, there was increased variability in

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.345
Teacher spread0.269 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations1
Published2024
Admission routes2
Has abstractyes

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