Project B-T5 - Understanding the effects of temporal context on interval timing: a model-based approach
Accurate estimates of temporal intervals are essential to the survival of humans and animals alike. It is crucial for identifying temporal relationships between events, anticipating actions, calculating rate of returns and decision making. Interestingly, estimates of time intervals depend strongly on (i) the duration of the interval and (ii) the temporal context in which it is estimated. That is, the longer the duration, the larger the estimated error will be (this feature is usually referred to as the scalar variability, and resembles Weber’s Law); and when subjects are asked to estimate intervals that are drawn from a specific range their estimations migrate towards the mean of the range, a feature called regression to the mean. In addition, larger regressions are seen for ranges with longer durations, a feature known as the range effect.
Although the contextual features of duration estimation in particular, and magnitude estimation in general, were first described more than a century ago1,2, their cognitive origin and functional roles remain unclear. A recent interpretation is that they represent the solution of an optimal Bayesian estimator. If a system has knowledge (implicit or explicit) that its measurements of the world (e.g., the duration of a time interval) are inherently noisy, the optimal estimate will combine both the current measurement and previous experience. Indeed, such a Bayesian estimator model has been shown to successfully capture the effects of temporal context on interval timing3, where the ‘system’ in this case is the nervous system. Interestingly, the Bayesian estimation approach can also explain contextual features in distance estimation4, albeit under an alternative set of model assumptions.
In this two-year project we will investigate the effects of temporal context on interval timing by combining theoretical analysis with human psychophysical experiments. In particular, the experiments will be designed to explicitly evaluate the constraints of the existing models. In turn, the experimental findings will advance our theoretical understanding of interval timing, direct model adjustments, and lead to new hypotheses about the neural mechanisms of interval timing in particular, and magnitude estimation in general.
1. Hollingworth HL (1913). The central tendency of judgement. Arch Psychol, 4:44.
2. Treisman M (1963). Temporal discrimination and the indifference interval. Implications for a model of the “internal clock”. Psychol Monogr, 77:1.
3. Jazayeri M, Shadlen MN (2010). Temporal context calibrates interval timing. Nat Neurosci, 13:1020.
4. Petzschner FH, Glasauer S (2011). Iterative Bayesian estimation as an explanation for range and regression effects: a study on human path integration. J Neurosci, 31:17220.
