Changes in the distribution of response rates across the CS-US interval: Evidence that responding switches between two distinct states.

Abstract

Two experiments used the peak procedure to examine timing of conditioned responses in a magazine approach paradigm with rats. A conditioned stimulus (CS) was reinforced with food on 50% of trials. Food was delivered at a fixed time, either 20 s, 30 s or 40 s into the CS presentation. Response rates were recorded during non-reinforced CS presentations that extended well beyond the scheduled time of food delivery. The mean response rate (averaged over many trials) increased during the CS, peaking at the expected time of reinforcement, and decreased again. Detailed analyses of the frequency distribution of response rates showed that responding was described by two distinct distributions, consistent with the rat being in a low response state on some trials and in a high response state on other trials. Modeling of these frequency distributions showed that the systematic rise and fall in response rate across a trial was primarily explained by a change in the proportion of time that the rat spent in the low versus high response state. However, the change in responding was also explained in part by a continuous shift in the high response state, such that responding in that state increased and then decreased gradually across the trial. These results support accounts that describe response timing as an abrupt change from low to high responding during the CS, but also provide evidence for a continuous change in conditioning strength across the duration of the CS. The implications of these findings for timing and associative theories of conditioning are discussed.

Interval timing under a behavioral microscope: Dissociating motivational and timing processes in fixed-interval performance

“…The proposed model is consistent with previous attempts to characterize schedule-controlled behavior in terms of twostate Markov models (Brackney et al, 2011;Gibbon, 1995;Harris, 2015;Myerson & Miezin 1980;Shull et al, 2001). For example, Harris (2015) compared two models of aggregate fixed-time (FT) performance, a two-state and a continuous change model.…”

“…For example, Harris (2015) compared two models of aggregate fixed-time (FT) performance, a two-state and a continuous change model. Within both models was a nested mixture model in which, according to some probability, animals would be in an engaged state (emitting a response) and, with complementary probability, in a disengaged state (not emitting a response).…”

“…Performance in FI schedules is thus likely an expression of cycling into and out of multiple behavioral states until reinforcement is obtained. Although previous studies have attempted to implement models embodying this notion Harris, 2015;Kirkpatrick, 2002), their implementation in FI schedules remains poorly characterized and has yet to be fully appreciated. Validation and implementation of fluctuation models in the analysis of FI performance would provide clarity on whether or not timing and motivation are related processes (Balcı, 2014;Balcı et al, 2010;Belke & Christie-Fougere, 2006;Galtress & Kirkpatrick, 2010;Galtress, Marshall, & Kirkpatrick, 2012;Kirkpatrick, 2014;Ludvig, Balcı, & Spetch, 2011;Ludvig, Conover, & Shizgal, 2007;Plowright, Church, Behnke, & Silverman, 2000;Sanabria, Thrailkill, & Killeen, 2009).…”

“…Indeed, Pavlov observed that, in the presence of a temporally extended conditioned stimulus, well-trained animals initially pause before responding continually until the expected time of reinforcement (Pavlov, 1927;Rescorla, 1967;Vogel, Brandon, & Wagner, 2003). The resemblance between FI performance and performance in Pavlovian analogues has stimulated much theory development (Harris, 2015;Kalafut, Freestone, MacInnis, & Church, 2014;Molet & Miller, 2014).…”

Temporal averaging across multiple response options: insight into the mechanisms underlying integration

“…The times at which rats initiate and terminate the high state of responding, referred to as “start” and “stop” times, respectively, are known to be temporally controlled. Therefore, for each trial, we iteratively fit three flat lines (first low state, high state, and second low state), with the transition at each time bin, until the absolute residuals between these lines and the data were minimized (for a recent alternative method for this analysis see Harris 2015). Start and stop times could not be reliably identified during trials where rats made less than three responses (7 percent of trials in total).…”