Temporal maps in appetitive Pavlovian conditioning

Abstract

Previous research suggests animals may integrate temporal information into mental representations, or temporal maps. We examined the parameters under which animals integrate temporal information in three appetitive conditioning experiments. In Experiment 1 the temporal relationship between 2 auditory cues was established during sensory preconditioning (SPC). Subsequently, rats were given first order conditioning (FOC) with one of the cues. Results showed integration of the order of cues between the SPC and FOC training phases. In subsequent experiments we tested the hypothesis that quantitative temporal information can be integrated across phases. In Experiment 2, SPC of two short auditory cues superimposed on a longer auditory cue was followed by FOC of either one of the short cues, or of the long cue at different times in the cue. Contrary to our predictions we did not find evidence of integration of temporal information across the phases of the experiment and instead responding to the SPC cues in Experiment 2 appeared to be dominated by generalization from the FOC cues. In Experiment 3 shorter auditory cues were superimposed on a longer duration light cue but with asynchronous onset and offset of the superimposed cues. There is some evidence consistent with the hypothesis that quantitative discrimination of whether reward should be expected during the early or later parts of a cue could be integrated across experiences. However, the pattern of responding within cues was not indicative of integration of quantitative temporal information. Generalization of expected times of reward during FOC seems to be the dominant determinant of within-cue response patterns in these experiments. Consequently, while we clearly demonstrated the integration of temporal order in the modulation of this dominant pattern we did not find strong evidence of integration of precise quantitative temporal information.This article is part of a Special Issue entitled: Associative and Temporal Learning.