Psychosis is linked to dysregulation of the neuromodulator dopamine and antipsychotic drugs (APDs) work by blocking dopamine receptors. Dopamine-modulated disruption of latent inhibition (LI) and conditioned avoidance response (CAR) have served as standard animal models of psychosis and antipsychotic action, respectively. Meanwhile, the 'temporal difference' algorithm (TD) has emerged as the leading computational model of dopamine neuron firing. In this report TD is extended to include action at the level of dopamine receptors in order to explain a number of behavioral phenomena including the dose-dependent disruption of CAR by APDs, the temporal dissociation of the effects of APDs on receptors vs behavior, the facilitation of LI by APDs, and the disruption of LI by amphetamine. The model also predicts an APD-induced change to the latency profile of CARFa novel prediction that is verified experimentally. The model's primary contribution is to link dopamine neuron firing, receptor manipulation, and behavior within a common formal framework that may offer insights into clinical observations. Neuropsychopharmacology Keywords: dopamine; temporal difference learning; psychosis; conditioned avoidance; latent inhibition; computational modeling
INTRODUCTIONPerturbations in dopamine transmission are central to a number of human illnesses including addiction, Parkinson's disease, attention-deficit hyperactivity disorder (ADHD), and schizophrenia. As a result there has been tremendous interest in understanding the psychological and behavioral functions of dopamine, and a number of overlapping hypotheses have been suggested. These hypotheses include roles for dopamine in hedonia (Wise, 1982), reward learning (Robbins and Everitt, 1996), incentive salience (Berridge and Robinson, 1998), sensorimotor function and anergia (Salamone et al, 1994). In parallel, mathematical models have been used to link some of these psychological constructs to the physiology of the dopamine system (Schultz, 1998;Seamans and Yang, 2004). Many of these models have concentrated on 'normal' dopaminergic conditions (McClure et al, 2003;Schultz et al, 1997), and the current aim is to explore how one such model can be extended to address the abnormal conditions encountered in schizophrenia and their treatment.Of all the mathematical models linking dopamine, behavior, and psychology, the Temporal Difference Learning model (TD) has enjoyed particular success (Montague et al, 1996;Redish, 2004;Schultz et al, 1997). TD is a powerful formal reinforcement learning technique that has been used to solve many challenging machine learning problems. For example, the technique has been used to train computers to play backgammon to the highest human standards by associating a simulated reward with winning a game (Tesauro, 1994). At the heart of TD is a predictionerror signal that is zero if the environment behaves as expected, positive in response to unexpected reward, and negative following omission of expected reward. Striking parallels have been observed between...