[HTML][HTML] Fidelity of the representation of value in decision-making

PM Bays, BA Dowding - PLoS computational biology, 2017 - journals.plos.org
The ability to make optimal decisions depends on evaluating the expected rewards
associated with different potential actions. This process is critically dependent on the fidelity …

Incrementing non-matching-but not matching-to-sample is rapidly learned in an automated version of the odor span task

TJ Wagner, K Bruce, M Galizio - Animal Cognition, 2022 - Springer
The odor span task (OST) is frequently used to assess memory capacity in rodents. Odor
stimuli are presented in a large arena and choices of session-novel odors produce food …

[HTML][HTML] Computational mechanisms underlying motivation to earn symbolic reinforcers

DC Burk, C Taswell, H Tang, BB Averbeck - bioRxiv, 2023 - ncbi.nlm.nih.gov
Reinforcement learning (RL) is a theoretical framework that describes how agents learn to
select options that maximize rewards and minimize punishments over time. We often make …

[HTML][HTML] Parallel representation of value-based and finite state-based strategies in the ventral and dorsal striatum

M Ito, K Doya - PLoS computational biology, 2015 - journals.plos.org
Previous theoretical studies of animal and human behavioral learning have focused on the
dichotomy of the value-based strategy using action value functions to predict rewards and …

Rapid learning of odor–value association in the olfactory striatum

DJ Millman, VN Murthy - Journal of Neuroscience, 2020 - Soc Neuroscience
Rodents can successfully learn multiple novel stimulus–response associations after only a
few repetitions when the contingencies predict reward. The circuits modified during such …

Rats can make relative perceptual judgments about sequential stimuli

C Perry, G Felsen - Animal cognition, 2012 - Springer
In their natural environment, animals often make decisions based on abstract relationships
among multiple stimulus representations. Humans and other primates can determine not …

Computational mechanisms underlying motivation to earn symbolic reinforcers

DC Burk, C Taswell, H Tang… - Journal of …, 2024 - Soc Neuroscience
Reinforcement learning is a theoretical framework that describes how agents learn to select
options that maximize rewards and minimize punishments over time. We often make …

[HTML][HTML] Combined model-free and model-sensitive reinforcement learning in non-human primates

B Miranda, WMN Malalasekera… - PLoS computational …, 2020 - journals.plos.org
Contemporary reinforcement learning (RL) theory suggests that potential choices can be
evaluated by strategies that may or may not be sensitive to the computational structure of …

Rapid Sensorimotor Reinforcement in the Olfactory Striatum

DJ Millman, VN Murthy - bioRxiv, 2019 - biorxiv.org
Rodents can successfully learn multiple, novel stimulus-response associations after only a
few repetitions when the contingencies predict reward. The circuits modified during such …

[HTML][HTML] A range-normalization model of context-dependent choice: a new model and evidence

A Soltani, B De Martino, C Camerer - PLoS computational biology, 2012 - journals.plos.org
Most utility theories of choice assume that the introduction of an irrelevant option (called the
decoy) to a choice set does not change the preference between existing options. On the …