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Poster A19 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
Bidirectional Interactions between Evidence Accumulation and Rule Learning during Sensory Predictions
Lucas Benjamin1,2 (), Benjamin Morillon1, Valentin Wyart2,3; 1Institut des neurosciences des systèmes, Aix-Marseille Université Marseille, France, 2Département d’Etudes Cognitives, École Normale Supérieure Paris, France, 3Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale,
Learning to structure incoming sensory input is crucial to build an appropriate mental model of one’s environment. An abundant literature has shown that, when faced with sequences of stimuli, humans can accumulate sensory evidence, learn statistical dependencies between consecutive or non-consecutive elements, or even find abstract rules in an efficient fashion. However, although each system has been tested using ad-hoc paradigms, we lack theory and data on how these abilities might articulate with each other. While recent work has begun to investigate the relationship between statistical inference and rule learning, here we propose an experimental framework to jointly investigate evidence accumulation and rule learning in the same sensory prediction task. To do so, we presented participants with sequences of 10 Gabor patterns whose orientations could or could not follow a hidden rule in the form of a +45° switch in the middle of the sequence. Participants were always asked to estimate the angle of the last (10th) element in the sequence, while the presentation stopped after 3, 5, 7 or 9 elements, forcing them to make predictions more or less deep into the future. Computational modeling shows that this design correctly separates different inference strategies, making different assumptions about the interactions between evidence accumulation and rule learning. Pilot data suggests that human behavior in this task has the required diversity to investigate this question.
Keywords: Evidence Accumulation Rule Learning Sensory Prediction