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Author(s): Grossberg, S. |
Year: 1972
Citation: Mathematical Biosciences, 15, 39-67
Abstract: Neural networks are derived from psychological postulates about punishment and avoidance. The classical notion that drive reduction is reinforcing is replaced by a precise physiological alternative akin to Miller s ""Go"" mechanism and Estes s ""amplifier"" elements. Cell clusters dj and .9/j are introduced which supply negative and positive incentive motivation, respectively, for classical conditioning of sensory-motor acts. The dj cells are persistently turned on by shock (on-cells). The dj cells are transiently turned on by shock termination (off-cells). The rebound from .#j cell activation to dj cell activation replaces drive reduction in the case of shock. Classical conditioning from sensory cells .9 to the pattern of activity playing on arousal cells .#1 = (.9/ J .9/j) can occur. Sufficiently positive net feedback from .9/ I to!/ can release sampling, and subsequenlte arning, by prescribedc ells in !/ of motor output controls. Once sampled, these controls can be reactivated by !/ on recall trials. This concept avoids some difficulties of two-factor theories of punishment and avoidance. Estes stimulus sampling theory of punishment is neurally interpreted. Recent psychophysiological data and concepts are qualitatively analyzed in terms of network analogs. These concepts include aspects of relaxation, or elicitation, theory, which claims that an unconditioned response of relief precedes reinforcement; the concept of ""effective reinforcement,"" which notes that shock offset and fear of situational cues can influence reward in opposite ways, as is illustrated by one-way and two-way avoidance tasks; classical and instrumental properties of a CS+ paired with shock, a CS- paired with no-shock, and feedback stimuli contingent on the avoidance response, including transfer of their effects from classical to instrumental conditioning experiments; autonomically nonchalant asymptotic avoidance performance originally motivated by fear; forced extinction of the conditioned avoidance response (CAR) without fear extinction; response suppression without an avoidance response; relief without an avoidance response; opposite effects of contingent and noncontingent punishment on fear and suppression of consummatory responding; punishment hypothesis of avoidance learning, describing rewarding effects of terminating proprioceptive cues that correspondt o nonavoidancer esponses;r esponse( or no-responseg) eneralization from one shock level to a different level; rewarding effect of response-contingent reduction in frequency of shock.
Topics:
Biological Learning,
Mathematical Foundations of Neural Networks,
Models:
Other,
