title: Autoshaped choice in artificial neural networks: implications for behavioral economics and neuroeconomics.
abstract: An existing neural network model of conditioning was used to simulate autoshaped choice. In this phenomenon, pigeons first receive an autoshaping procedure with two keylight stimuli X and Y separately paired with food in a forward-delay manner, intermittently for X and continuously for Y. Then pigeons receive unreinforced choice test trials of X and Y concurrently present. Most pigeons choose Y. This preference for a more valuable response alternative is a form of economic behavior that makes the phenomenon relevant to behavioral economics. The phenomenon also suggests a role for Pavlovian contingencies in economic behavior. The model used, in contrast to others, predicts autoshaping and automaintenance, so it is uniquely positioned to predict autoshaped choice. The model also contemplates neural substrates of economic behavior in neuroeconomics, such as dopaminergic and hippocampal systems. A feedforward neural network architecture was designed to simulate a neuroanatomical differentiation between two environment-behavior relations X-R1 and Y-R2, [corrected] where R1 and R2 denote two different emitted responses (not unconditionally elicited by the reward). Networks with this architecture received a training protocol that simulated an autoshaped-choice procedure. Most networks simulated the phenomenon. Implications for behavioral economics and neuroeconomics, limitations, and the issue of model appraisal are discussed.
authors: CEIC, University of Guadalajara, Mexico. Electronic address: jburgos@cucba.udg.mx.
authors: José E
authors: JE
authors: Burgos
authors: CEIC, University of Guadalajara, Mexico.
authors: Óscar
authors: Ó
authors: García-Leal
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D002755:Choice Behavior; D010856:Columbidae; D003198:Computer Simulation; D060909:Economics, Behavioral; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology
keywords: Autoshaped choice; Behavioral economics; Neural networks; Neuroeconomics; Pavlovian contingencies