Abstract
For theoretical explanations of data, parameter values estimated from a single dependent measure from one procedure are used to predict alternative dependent measures from many procedures. Theoretical explanations were compared to empirical explanations of data in which known functions and principles were used to fit only selected dependent measures. The comparison focused on the ability of theoretical and empirical explanations to generalize across samples of the data, across dependent measures of behavior, and across different procedures. Rat and human data from fixed-interval and peak procedures, in which principles (e.g., scalar timing) are well known, were described and fit by a theory with independent modules for perception, memory, and decision. The theoretical approach consisted of fitting closed-form equations of the theory to response rate gradients calculated from the data, simulating responses using parameter values previously estimated, and comparing theoretical predictions with dependent measures not used to estimate parameters. Although the empirical and theoretical explanations provided similar fits to the response rate gradients that generalized across samples and had the same number of parameters, only the theoretical explanation generalized across procedures and dependent measures.