Timing and Foraging: Gibbon's Scalar Expectancy Theory and Optimal Patch Exploitation

Abstract

We present a study that links optimal foraging theory (OFT) to behavioral timing. OFT's distinguishing feature is the use of models that compute the most advantageous behavior for a particular foraging problem and compare the optimal solution to empirical data with little reference to psychological processes. The study of behavioral timing, in contrast, emphasizes performance in relation to time, most often without strategic or functional considerations. In three experiments, reinforcer-maximizing behavior and timing performance are identified and related to each other. In all three experiments starlings work in a setting that simulates food patches separated by a flying distance between the two perches. The patches contain a variable and unpredictable number of reinforcers and deplete suddenly without signal. Before depletion, patches deliver food at fixed intervals (FI). Our main dependent variables are the times of occurrence of three behaviors: the “peak” in pecking rate (Peak), the time of the last peck before “giving in” (GIT), and the time for “moving on” to a new patch (MOT). We manipulate travel requirement (Experiment 1), level of deprivation and FI (Experiment 2), and size of reinforcers (Experiment 3). For OFT, Peak should equal the FI in all conditions while GIT and MOT should just exceed it. Behavioral timing and Scalar Expectancy Theory (SET) in particular predict a Peak at around the FI and a longer (unspecified) GIT, and make no prediction for MOT. We found that Peak was close to the FI and GIT was approximately 1.5 times longer, neither being affected by travel, hunger, or reinforcer size manipulations. MOT varied between 1.5 and just over 3 times the FI, was responsive to both travel time and the FI, and did not change when the reinforcer rate was manipulated. These results support the practice of producing models that explicitly separate information available to the subject from strategic use of this information.