title: An adaptive drift-diffusion model of interval timing dynamics.
abstract: 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.
authors: Champalimaud Neuroscience Programme, Portugal.
authors: Andre
authors: A
authors: Luzardo
authors: Elliot A
authors: EA
authors: Ludvig
authors: François
authors: F
authors: Rivest
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D010856:Columbidae; D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D011930:Reaction Time / Q000502:physiology; D012055:Reinforcement Schedule; D013998:Time Perception / Q000502:physiology*