Generalization of Integrator Models to Foraging: A Robot Study Using the DAC9 Model

Experimental research on decision making has been mainly focused on binary perceptual tasks. The generally accepted models describing the decision process in these tasks are the integrator models. These models suggest that perceptual evidence is accumulated over time until a decision is made. Therefore, the final decision is based solely on recent perceptual information. In behaviorally more relevant tasks such as foraging, it is however probable, that the current choice also depends on previous experience. To understand the implications of considering previous experience in an integrator model we investigate it using a cognitive architecture (DAC9) with a robot performing foraging tasks. Compared to an instantaneous decision making model we show that an integrator model improves performance and robustness to task complexity. Further we show that it compresses the information stored in memory. This result suggests a change in the way actions are retrieved from memory leading to self-generated actions.