An adaptive drift-diffusion model of interval timing dynamics

作者：Luzardo Andre; Ludvig Elliot A; Rivest Francois^*

来源：Behavioural Processes, 2013, 95: 90-99.

DOI：10.1016/j.beproc.2013.02.003

• Interval timing
• Drift-diffusion processes
• Cyclic schedules
• Learning
• Computational models
• Pigeons

Animals readily learn the timing between salient events. They can even adapt their timed responding to rapidly changing intervals, sometimes as quickly as a single trial. Recently, drift-diffusion models widely used to model response times in decision making have been extended with new learning rules that allow them to accommodate steady-state interval timing, including scalar timing and timescale invariance. These time-adaptive drift-diffusion models (TDDMs) work by accumulating evidence of elapsing time through their drift rate, thereby encoding the to-be-timed interval. One outstanding challenge for these models lies in the dynamics of interval timing when the to-be-timed intervals are non-stationary. On these schedules, animals often fail to exhibit strict timescale invariance, as expected by the TDDMs and most other timing models. Here, we introduce a simple extension to these TDDMs, where the response threshold is a linear function of the observed event rate. This new model compares favorably against the basic TDDMs and the multiple-time-scale (MTS) habituation model when evaluated against three published datasets on timing dynamics in pigeons. Our results suggest that the threshold for triggering responding in interval timing changes as a function of recent intervals. This article is part of a Special Issue entitled: SQAB 2012.

• 出版日期2013-5