Foreperiod and range effects on time interval categorization

Abstract

One factor influencing the perceived duration of a brief interval is the length of the period preceding it, namely the foreperiod (FP). When multiple FPs are varied randomly within a testing session, longer FPs result in longer perceived duration. The purpose of this study was to identify what characteristics modulate this effect. In a task where participants were asked to categorize the duration of target intervals with respect to a 100-ms standard, the FPs were distributed over a 150-, 300-, or 900-ms range with the midpoint (1000 ms) of these distributions being kept constant. The results indicate that the effect of the length of variable FPs on perceived duration was much stronger in the 900-ms range condition. More specifically, this effect is due to the differences between the shortest FPs. The results also reveal that, overall, there are more short responses in the 300-ms condition than in the other range conditions. Moreover, the data reveal that the narrower the distribution, the better the discrimination. One interpretation of the main result (range effect) is that a wider distribution leads to an increased prior uncertainty towards the foreperiod length.

Emotion and Implicit Timing

“…The within-session emotional context might thus have affected RT on neutral trials, thereby limiting the emotion-related differences in RT within the same session. Recently, Gamache et al [ 42 ] showed the importance of discrimination context in time judgment. For instance, when multiple foreperiods (i.e., period of time preceding an interval to be timed) were randomly varied within trial blocks, longer interval durations resulted in longer rather than shorter perceived durations.…”

Prospective and Retrospective Timing Processes: Theories, Methods, and Findings

“…The use of the constant method and of a psychometric function raises the question of the model adopted for drawing the function through the data points. In psychology, it is typical to assume that a phenomenon is distributed normally, and, thus, a Gaussian model is adopted (cumulative normal distribution; see for instance Laflamme, Zakay, Gamache, & Grondin, 2015). Among the other models, Macmillan and Creelman (1991) proposed the logistic and the Weibull functions, and the reader will also find the use of a pseudo-logistic function in the time perception literature (Grondin 2001a;Killeen, Fetterman, & Bizo, 1997).…”

Chronométrage et perception temporelle.

“…Soit une situation où l’on utilise différents temps d’attente (intervalles interessais : IIE), avant la présentation d’un intervalle à estimer. Lorsque l’IIE varie de 0,3 s à 0,6 s d’essai en essai et de façon aléatoire, l’intervalle à estimer a plus de chances de paraître long si l’IIE est long plutôt que court (Grondin & Rammsayer, 2003; voir aussi Gamache, Grondin, & Zakay, 2011, ou Laflamme, Zakay, Gamache & Grondin, 2015).…”