Rats and mice rapidly update timed behaviors

Abstract

Keeping track of time intervals is a crucial aspect of behavior and cognition. Many theoretical models of how the brain times behavior make predictions for steady-state performance of well-learned intervals, but the rate of learning intervals in these models varies greatly, ranging from one-shot learning to learning over thousands of trials. Here, we explored how quickly rats and mice adapt to changes in interval durations using a serial fixed-interval task. In the first experiment, animals experienced randomly selected fixed-intervals of 12, 24, 36, 48, or 60 s, for blocks ranging from 13 to 21 trials. Consistent with previous work, animals abruptly increased lever pressing as reward availability approached, and these 'start times' scaled with the interval duration for both species. We then quantified the rate of updating to new trial durations and found that rodents consistently updated their start times within 2-3 trials following a change in interval duration, before stabilizing their behavior by the third or fourth trial. To account for repeated exposures to fixed-interval durations, a second set of animals was tested with new fixed-intervals after being trained on the serial fixed-interval task described above. Next, a third group was trained on fixed-interval durations that were generated de novo in each day. In each of these contexts, rodents rapidly increased or decreased their start times to mirror new FI durations following exposure to 1-2 trials of new intervals following block transitions. This work adds to growing evidence for rapid duration learning across species, highlighting the need for timing models to be capable of rapid updating in dynamic temporal scenarios.

Publisher

Springer Heidelberg

Publication Date

1-24-2025

Publication Title

Animal Cognition

Department

Neuroscience

Document Type

Article

DOI

https://doi.org/10.1007/s10071-025-01930-9

Keywords

Interval timing, Learning, Dynamic timing

Language

English

Format

text

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