@article{2e2c6f04511a4fb1bcdefc72ed367f4f,
title = "Interval timing under a behavioral microscope: Dissociating motivational and timing processes in fixed-interval performance",
abstract = "The distribution of latencies and interresponse times (IRTs) of rats was compared between two fixed-interval (FI) schedules of food reinforcement (FI 30 s and FI 90 s), and between two levels of food deprivation. Computational modeling revealed that latencies and IRTs were well described by mixture probability distributions embodying two-state Markov chains. Analysis of these models revealed that only a subset of latencies is sensitive to the periodicity of reinforcement, and prefeeding only reduces the size of this subset. The distribution of IRTs suggests that behavior in FI schedules is organized in bouts that lengthen and ramp up in frequency with proximity to reinforcement. Prefeeding slowed down the lengthening of bouts and increased the time between bouts. When concatenated, latency and IRT models adequately reproduced sigmoidal FI response functions. These findings suggest that behavior in FI schedules fluctuates in and out of schedule control; an account of such fluctuation suggests that timing and motivation are dissociable components of FI performance. These mixture-distribution models also provide novel insights on the motivational, associative, and timing processes expressed in FI performance. These processes may be obscured, however, when performance in timing tasks is analyzed in terms of mean response rates.",
keywords = "Bouts, Computational modeling, Fixed-interval, Interval timing, Motivation, Pre-feeding, Rats, Response duration",
author = "Daniels, {Carter W.} and Federico Sanabria",
note = "Funding Information: This research was supported by the National Institutes of Health (MH094562), a seed grant from the College of Liberal Arts and Sciences, Arizona State University, and a Grant-in-Aid of Research awarded to the first author from the National Academy of Sciences, administered by Sigma Xi, The Scientific Research Society. The results of this study comprise portions of the first author{\textquoteright}s master{\textquoteright}s thesis. We thank Raul Garcia, Paula Overby, Christine Herrera, Jesse St. Amand, Sanjana Khana, Natasha Sinchuk, and Jake Gilmour for helping with data collection. Briana Martinez, Andrew Nye, and Cavan Winikates also provided important support to data collection. Elizabeth Watterson, Gabriel Mazur, and Ryan Brackney provided helpful discussions. Portions of these data were presented at the 2014 meeting of the Society for the Quantitative Analysis of Behavior, Chicago, Illinois; the 2014 meeting of the International Society of Comparative Psychology, Bogot{\'a}, Colombia; the 2015 meeting of the Association for Behavioral Analysis International, San Antonio, Texas; and the 2015 Fall meeting of the Comparative Cognition Society, Chicago, Illinois. We thank Peter Killeen for his helpful discussions, advice, and feedback on earlier drafts of this document. Feedback from an anonymous reviewer and a reviewer identified as Armando Machado was invaluable. Finally, the first author would like to dedicate this article to his grandfather, Robert Lillion Carter, who always provided helpful guidance and support. Publisher Copyright: {\textcopyright} 2016, Psychonomic Society, Inc.",
year = "2017",
month = mar,
day = "1",
doi = "10.3758/s13420-016-0234-1",
language = "English (US)",
volume = "45",
pages = "29--48",
journal = "Learning and Behavior",
issn = "1543-4494",
publisher = "Springer New York",
number = "1",
}