Abstract
Probabilistic learning is a research program that aims to understand how animals and humans learn and adapt their behavior in situations where the pairing between cues and outcomes is not always completely reliable. This chapter provides an overview of the challenges of probabilistic learning for models of the brain and behavior. We discuss the historical background of probabilistic learning, its theoretical foundations, and its applications in various fields such as psychology, neuroscience, and artificial intelligence. We also review some key findings from experimental studies on probabilistic learning, including the role of feedback, attention, memory, and decision-making processes. Finally, we highlight some of the current debates and future directions in this field.
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Notes
- 1.
In the Weather Prediction Task, subjects are presented with combinations of playing cards with different patterns (i.e., geometric figures) combinations. The subjects must learn to use them to predict the weather (i.e., rain or sun). During training, subjects are presented with combinations of cards (i.e., one to four cards), while each specific combination is probabilistically associated with the two outcomes.
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Marchant, N., Canessa, E., Chaigneau, S.E. (2023). Challenges from Probabilistic Learning for Models of Brain and Behavior. In: Veloz, T., Khrennikov, A., Toni, B., Castillo, R.D. (eds) Trends and Challenges in Cognitive Modeling. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. https://doi.org/10.1007/978-3-031-41862-4_6
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