A neural network model of foraging decisions made under predation risk

• Published in: Cognitive, affective, & behavioral neuroscience Vol. 5; no. 4; pp. 434 - 451
• Main Authors: COLEMAN, Scott L, BROWN, Vincent R, LEVINE, Daniel S, MELLGREN, Roger L
• Format: Journal Article
• Language: English
• Published: Austin, TX Psychonomic Society 01-12-2005; Springer Nature B.V
• Subjects: Animal ethology; Animals; Appetitive Behavior; Avoidance Learning; Awareness; Biological and medical sciences; Decision Making; Fear; Foraging behavior; Fundamental and applied biological sciences. Psychology; Hunger; Mammalia; Motivation; Neural networks; Neural Networks (Computer); Predation; Predatory Behavior; Probability Learning; Psychology. Psychoanalysis. Psychiatry; Vertebrata; Affect affectivity; Motivation; Animal; Cognition; Emotion emotionality; Simulation model; Neural network; Connectionism; Foraging behavior; Predation risk
• ISSN: 1530-7026; 1531-135X
• DOI: 10.3758/CABN.5.4.434

This article develops the cognitive-emotional forager (CEF) model, a novel application of a neural network to dynamical processes in foraging behavior. The CEF is based on a neural network known as the gated dipole, introduced by Grossberg, which is capable of representing short-term affective reactions in a manner similar to Solomon and Corbit's (1974) opponent process theory. The model incorporates a trade-off between approach toward food and avoidance of predation under varying levels of motivation induced by hunger. The results of simulations in a simple patch selection paradigm, using a lifetime fitness criterion for comparison, indicate that the CEF model is capable of nearly optimal foraging and outperforms a run-of-luck rule-of-thumb model. Models such as the one presented here can illuminate the underlying cognitive and motivational components of animal decision making.