The Pattern of Responding in the Peak-Interval Procedure with Gaps: An Individual-Trials Analysis
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Abstract
Humans and lower animals time as if using a stopwatch that can be “stopped” or “reset” on command. This view is challenged by data from the peak-interval procedure with gaps: Unexpected retention intervals (gaps) delay the response function in a seemingly continuous fashion, from stop to reset. We evaluated whether these results are an artifact of averaging over trials, or whether subjects use discrete alternatives or a continuum of alternatives in individual-trials: A Probability-of-Reset hypothesis proposes that in individual gap trials subjects stochastically use discrete alternatives (stop/reset), such that when averaged over trials, the response distribution in gap trials falls in between “stop” and “reset”. Alternatively, a Resource Allocation hypothesis proposes that during individual gap trials working memory for the pregap duration decays, such that the response function in individual gap trials is shifted rightward in a continuous fashion. Both hypotheses provided very good fits with the observed individual-trial distributions, although the Resource Allocation hypothesis generated reliably better fits. Results provide support for the usefulness of individual-trial analyses in dissociating theoretical alternatives in interval timing tasks.
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Ok so based on the proposal of gap PI they want to test to see if there is a “continuum of responses in between stop and reset” for the agents in gap trials
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In PI trials, the to-be-timed signal is presented but subject’s responses are not reinforced; typically, in PI trials the average response rate increases after the onset of the to-be-timed signal, reaches a peak about the time when subjects’ responses were (sometimes) reinforced, and declines afterwards (Catania, 1970; Church, 1978) (Figure 1A).
During gap trials, a brief interruption (gap) of the to-be-timed signal prompts a delay in response relative to PI trials by about the duration of the gap, suggesting that on average subjects retain in working memory the pre-gap interval and resume timing after the gap where they left off before the gap, a response mode (alternative) called stop (e.g., Church, 1978; S. Roberts & Church, 1978) (Figure 1A).
However, a quite different response pattern is observed in the reversed version of the PI procedure (Buhusi & Meck, 2000), in which subjects time the absence of a signal (e.g., in the dark) and their timing is interrupted by a signaled (e.g., illuminated) gap (Figure 1B): Subjects delay their response function after the gap for a duration that is approximately the sum of the gap and pre-gap intervals, suggesting that on average they restart the entire timing process after the gap, using a reset response (Buhusi & Meck, 2000).
Fig. 1
The peak-interval (PI) procedure with gaps Subjects time the presence of a signal (Standard, panel A) or the absence of a signal (Reversed, panel B). Subjects are randomly presented with fixed-interval (FI) trials, peak-interval (PI) trials, and gap trials in which the to-be-timed signal is interrupted by a retention-interval (gap). The upper graphs show that in PI trials the average response rate peaks at the to-be-timed interval, and that the presentation of a gap delays the response function. * = reinforcement.
Here we evaluate whether the pattern of response in individual gap trials is consistent with either discrete modes of the internal clock (Gibbon et al., 1984) or a continuum of alternatives (Buhusi, 2003). To reconcile these views, Cabeza de Vaca et al. (1994) proposed a “stochastic” model, denoted here as the Probability-of-Reset hypothesis (PR), which assumes that during individual gap trials subjects use discrete alternatives (stop / reset) with a certain probability, such that the average response functions in gap trials is a mix of the “stop and “reset” responses, thus falling in between stop and reset, and giving the appearance of a continuous range of alternatives. This distinction is lost when analyzing average response functions, and requires special individual-trial analyses.
Here we evaluate whether the pattern of response in individual gap trials is consistent with either discrete modes of the internal clock (Gibbon et al., 1984) or a continuum of alternatives (Buhusi, 2003). To reconcile these views, Cabeza de Vaca et al. (1994) proposed a “stochastic” model, denoted here as the Probability-of-Reset hypothesis (PR), which assumes that during individual gap trials subjects use discrete alternatives (stop / reset) with a certain probability, such that the average response functions in gap trials is a mix of the “stop and “reset” responses, thus falling in between stop and reset, and giving the appearance of a continuous range of alternatives. This distinction is lost when analyzing average response functions, and requires special individual-trial analyses.