Interval Timing: Modeling the break-run-break pattern using start/stop threshold-less drift-diffusion model

TL;DR

This paper introduces a threshold-less drift-diffusion model for animal interval timing that accurately reproduces break-run-break response patterns without relying on start/stop thresholds, outperforming existing models in realism.

Contribution

A novel response rate variation model based on event likelihood, eliminating the need for start/stop thresholds in reproducing timing behavior.

Findings

Successfully modeled 14 different PI experiments.

Outperformed TDDM in realism of individual trials.

Reproduced observed start/stop statistics without thresholds.

Abstract

Animal interval timing is often studied through the peak interval (PI) procedure. In this procedure, the animal is rewarded for the first response after a fixed delay from the stimulus onset, but on some trials, the stimulus remains and no reward is given. The common methods and models to analyse the response pattern describe it as break-run-break, a period of low rate response followed by rapid responding, followed by a low rate of response. The study of the pattern has found correlations between start, stop, and duration of the run period that hold across species and experiment. It is commonly assumed that in order to achieve the statistics with a pacemaker accumulator model it is necessary to have start and stop thresholds. In this paper we will develop a new model that varies response rate in relation to the likelihood of event occurrence, as opposed to a threshold, for changing the response rate. The new model reproduced the start and stop statistics that have been observed in 14 different PI experiments from 3 different papers. The developed model is also compared to the Time-adaptive Drift-diffusion Model (TDDM), the latest accumulator model subsuming the scalar expectancy theory (SET), on all 14 data-sets. The results show that it is unnecessary to have explicit start and stop thresholds or an internal equivalent to break-run-break states to reproduce the individual trials statistics and population behaviour and get the same break-run-break analysis results. The new model also produces more realistic individual trials compared to TDDM.