[HTML][HTML] Mixtures of strategies underlie rodent behavior during reversal learning
NM Le, M Yildirim, Y Wang, H Sugihara… - PLOS Computational …, 2023 - journals.plos.org
In reversal learning tasks, the behavior of humans and animals is often assumed to be
uniform within single experimental sessions to facilitate data analysis and model fitting …
uniform within single experimental sessions to facilitate data analysis and model fitting …
[HTML][HTML] Uncertainty in learning and decision-making: Introduction to the special issue
I Levy, D Schiller - Cognitive, Affective, & Behavioral Neuroscience, 2023 - Springer
Uncertainty is a fundamental aspect of the environment. This special issue presents
interdisciplinary research on decision-making and learning under uncertainty. Thirty-one …
interdisciplinary research on decision-making and learning under uncertainty. Thirty-one …
Persistent Decision-Making in Mice, Monkeys, and Humans
Humans have the capacity to persist in behavioural policies, even in challenging
environments that lack immediate reward. Our persistence is the scaffold on which many …
environments that lack immediate reward. Our persistence is the scaffold on which many …
Neuronal representation of a working memory-based decision strategy in the motor and prefrontal cortico-basal ganglia loops
T Yoshizawa, M Ito, K Doya - eneuro, 2023 - eneuro.org
While animal and human decision strategies are typically explained by model-free and
model-based reinforcement learning (RL), their choice sequences often follow simple …
model-based reinforcement learning (RL), their choice sequences often follow simple …
[HTML][HTML] Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations
Foraging theory has been a remarkably successful approach to understanding the behavior
of animals in many contexts. In patch-based foraging contexts, the marginal value theorem …
of animals in many contexts. In patch-based foraging contexts, the marginal value theorem …
A meta reinforcement learning account of behavioral adaptation to volatility in recurrent neural networks
Natural environments often exhibit various degrees of volatility, ranging from slowly
changing to rapidly changing contingencies. How learners adapt to changing environments …
changing to rapidly changing contingencies. How learners adapt to changing environments …
Contributions of statistical learning to learning from reward feedback
Natural environments are abundant with patterns and regularities. These regularities can be
captured through statistical learning, which strongly influences perception, memory, and …
captured through statistical learning, which strongly influences perception, memory, and …
[PDF][PDF] COMPUTATIONAL MODELING OF THE ABILITY TO READ CHANGES OF COOPERATIVE VS COMPETITIVE INTENTIONS: DEVELOPMENTAL CHANGES IN …
TTA Nong, C Qu, Y Li, JH Woo, JB Van der Henst… - 2023 - psyarxiv.com
The ability to decipher the intentions of other agents and to mentalize how one's own choice
might influence others' behavior is essential for social interaction, especially when these …
might influence others' behavior is essential for social interaction, especially when these …
Computational mechanisms underlying the emergence of theory of mind in children
The ability to decipher the intentions of other agents and to mentalize how one's own choice
might influence others' behavior is essential for social interaction, especially when these …
might influence others' behavior is essential for social interaction, especially when these …
Computational modeling applied to strategic social decision making and Theory of Mind across different species of primates
T Nong - 2023 - theses.hal.science
One of the main difficulties to understand others stems from the inaccessibility to their minds:
their goals, beliefs, desires are often inferred from their behavior. The set of cognitive …
their goals, beliefs, desires are often inferred from their behavior. The set of cognitive …