A bounded accumulation model of temporal generalization outperforms existing models and captures modality differences and learning effects DOI Creative Commons
Nir Ofir, Ayelet N. Landau

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Abstract Multiple systems in the brain track passage of time and can adapt their activity to temporal requirements (Paton & Buonomano, 2018). While neural implementation timing varies widely between substrates behavioral tasks, at algorithmic level many these behaviors be described as bounded accumulation (Balcı Simen, 2024). So far, from range psychophysical model has only been applied bisection, which participants are requested categorize an interval “long” or “short” 2014; Ofir Landau, 2022). In this work, we extend fit performance generalization task, required being same different compared a standard, reference, duration (Wearden, 1992). Previous models task focused on either group highly trained animals (Birngruber et al., Church Gibbon, 1982; Wearden, Whether few hundreds trials single participants, necessary for comparing across experimental manipulations, not tested. A drift-diffusion with two decision boundaries fits data better than previous models. We ran experiments, one vision audition another examining effect learning. found that modified independently: upper boundary was higher audition, lower decreased learning task.

Язык: Английский

A bounded accumulation model of temporal generalization outperforms existing models and captures modality differences and learning effects DOI Creative Commons
Nir Ofir, Ayelet N. Landau

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 17, 2024

Abstract Multiple systems in the brain track passage of time and can adapt their activity to temporal requirements (Paton & Buonomano, 2018). While neural implementation timing varies widely between substrates behavioral tasks, at algorithmic level many these behaviors be described as bounded accumulation (Balcı Simen, 2024). So far, from range psychophysical model has only been applied bisection, which participants are requested categorize an interval “long” or “short” 2014; Ofir Landau, 2022). In this work, we extend fit performance generalization task, required being same different compared a standard, reference, duration (Wearden, 1992). Previous models task focused on either group highly trained animals (Birngruber et al., Church Gibbon, 1982; Wearden, Whether few hundreds trials single participants, necessary for comparing across experimental manipulations, not tested. A drift-diffusion with two decision boundaries fits data better than previous models. We ran experiments, one vision audition another examining effect learning. found that modified independently: upper boundary was higher audition, lower decreased learning task.

Язык: Английский

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