Psychopharmacology of latent inhibition: a neural network approach.

A neural network model of classical conditioning is applied to the description of some aspects of the psychopharmacology of latent inhibition (LI). According to the model, LI is manifested because preexposure of the conditioned stimulus (CS) reduces Novelty, defined as proportional to the sum of the mismatches between predicted and observed events, thereby reducing attention to the CS and retarding conditioning. In the framework of the model, it is assumed that indirect dopaminergic (DA) agonists (e.g. amphetamine and nicotine) increase, and DA receptor antagonists (e.g. haloperidol and alpha-flupenthixol) decrease, the effect of Novelty on attention. Computer simulations demonstrate that, under these assumptions, the model correctly describes: (1) the impairment of LI by amphetamine when a strong unconditioned stimulus (US) is used, (2) the impairment of LI by amphetamine when a nonsalient CS is used, (3) the impairment of LI by amphetamine administration when a short CS is used, (4) the facilitation of LI by alpha-flupenthixol when a weak US is used, (5) the facilitation of LI by haloperidol when a nonsalient CS is used, (6) the facilitation of LI by haloperidol with a strong US, and (7) the facilitation of LI by haloperidol with extended conditioning.