Animal timing: a synthetic approach

Inspired by Spence's seminal work on transposition, we propose a synthetic approach to understanding the temporal control of operant behavior. The approach takes as primitives the temporal generalization gradients obtained in prototypical concurrent and retrospective timing tasks and then combines them to synthetize more complex temporal performances. The approach is instantiated by the learning-to-time (LeT) model. The article is divided into three parts. In the first part, we review the basic findings concerning the generalization gradients observed in fixed-interval schedules, the peak procedure, and the temporal generalization procedure and then describe how LeT explains them. In the second part, we use LeT to derive by gradient combination the typical performances observed in mixed fixed-interval schedules, the free-operant psychophysical procedure, the temporal bisection task, and the double temporal bisection task. We also show how the model plays the role of a useful null hypothesis to examine whether temporal control in the bisection task is relative or absolute. In the third part, we identify a set of issues that must be solved to advance our understanding of temporal control, including the shape of the generalization gradients outside the range of trained stimulus durations, the nature of temporal memories, the influence of context on temporal learning, whether temporal control can be inhibitory, and whether temporal control is also relational. These issues attest to the heuristic value of a Spencean approach to temporal control.The authors MPC, AM, and MV were supported by grants SFRH/BD/73875/2010, PTDC/MHC-PCN/3540/2012, and IF/01624/2013/CP1158/CT0012, respectively, from the Portuguese Foundation for Science and Technology. This study was conducted at the Psychology Research Centre, University of Minho, and partially supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and when applicable co-financed by FEDER under the PT2020 Partnership Agreement (UID/PSI/01662/2013).