Evaluating the Bayesian causal inference model of intentional binding through computational modeling.

ABSTRACT: Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation t

SUBMITTER: Tanaka T 

PROVIDER: S-EPMC10844324 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature