PubMed · 7288067
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E B Forrest. 1981. Feedback and the visual process.. https://pubmed.ncbi.nlm.nih.gov/7288067/
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M. Oaksford, N. Chater, and J. Larkin (2000) proffered a Bayesian model in which conditional inferences are a direct function of conditional probabilities. In the current article, the authors first considered this model regarding the processing of negatives in conditional reasoning. Its predictions were evaluated against a large-scale meta-analysis (W. J. Schroyens, W. Schaeken, & G. d'Ydewalle, 2001b). This evaluation shows that the model is flawed: The relative size of the negative effects does not match predictions. Next, the authors evaluated the model in relation to inferences about affirmative conditionals, again considering the results of a meta-analysis (W. J. Schroyens, W. Schaeken, & G. d'Ydewalle, 2001a). The conditional probability model is countered by the data reported in literature; a mental models based model produces a better fit. The authors conclude that a purely probabilistic model is deficient and incomplete and cannot do without algorithmic processing assumptions if it is to advance toward a descriptively adequate psychological theory.
A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction of motion of an object held in their right hand, which subjects were required to restrain. The results show that predictive learning was local to the direction of forces experienced during learning and that the magnitude of predictive responses was greatly reduced for novel directions of action of the left hand. The pattern of generalization shows that the representation of predictive learning is spatially local and can be approximated as having a spatially narrow Gaussian basis function.
Drug addiction can be understood as a pathological subversion of normal brain learning and memory processes strengthened by the motivational impact of drug-associated stimuli, leading to the establishment of compulsive drug-seeking habits. Such habits evolve through a cascade of complex associative processes with Pavlovian and instrumental components that may depend on the integration and coordination of output from several somewhat independent neural systems of learning and memory, each contributing to behavioral performance. Data are reviewed that help to define the influences of conditioned Pavlovian stimuli on goal-directed behavior via sign-tracking, motivational arousal, and conditioned reinforcement. Such influences are mediated via defined corticolimbic-striatal systems converging on the ventral striatum and driving habit-based learning that may depend on the dorsal striatum. These systems include separate and overlapping influences from the amygdala, hippocampus, and cingulate and medial prefrontal cortex on drug-seeking as well as drug-taking behavior, including the propensity to relapse.