Abstract
Temporal Binding (TB) is standardly regarded as an implicit measure of the sense of agency (Haggard, 2017). Though the TB effect is robust, an underlying mechanism has not been agreed upon (Hoerl et al., 2020). Here we propose a memory process as an explanation for the observed error in two publicly available datasets. We first replotted the data and found that on average, across both experiments, participants overestimate the length of the shortest timing interval and underestimate the longest interval, a classic regression to the mean pattern. Summary statistics extracted from the data from each experiment were then used as parameters in a simple Bayesian model of memory. Model simulations reproduced the behavioral data for almost all timing intervals and experimental trial-types across two experiments. Adjusting one of the parameters in the model (prior mean for actions) resulted in an improved qualitative fit. We suggest that other more likely sources of error, apart from experienced agency, may account for this result.
Original language | American English |
---|---|
Pages | 3549-3556 |
Number of pages | 8 |
State | Published - 2022 |
Event | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada Duration: Jul 27 2022 → Jul 30 2022 |
Conference
Conference | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 |
---|---|
Country/Territory | Canada |
City | Toronto |
Period | 7/27/22 → 7/30/22 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience
Keywords
- Bayesian models of cognition
- memory
- sense of agency
- temporal binding