Perceptual learning in maze discriminations

In Experiment 1, rats were trained on a discrimination between rubber- and sandpaper-covered arms of a maze after one group had been pre-exposed to these intra-maze cues. Pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made easier by adding further discriminative stimuli, when it now significantly retarded learning. In Experiment 2, rats were trained on an extra-maze spatial discrimination, again after one group, but not another, had been pre-exposed to the extra-maze landmarks. Here too, pre-exposure facilitated subsequent discrimination learning, unless the discrimination was made substantially easier by arranging that the two arms between which rats had to choose were always separated by 135°. The results of both experiments can be explained by supposing that perceptual learning depends on the presence of features common to S+ and S-.     

A Taxonomy of Part-Whole Relations

A taxonomy of part-whole or meronymic relations is developed to explain the ordinary English-speaker's use of the term “part of” and its cognates. The resulting classification yields six types of meronymic relations: 1. component-integral object (pedal-bike), 2. member-collection (ship-fleet), 3. portion-mass (slice-pie), 4. stuff-object (steel-car), 5. feature-activity (paying-shopping), and 6. place-area (Everglades-Florida). Meronymic relations ore further distinguished from other inclusion relations, such as spatial inclusion, and class inclusion, and from several other semantic relations: attribution, attachment, and ownership. This taxonomy is then used to explain cases of apparent intransitivity in merological syllogisms, and standard form syllogisms whose premises express different inclusion relations. The data suggest that intransitivities arise due to equivocations between different types of semantic relations. These results are then explained by means of the relation element theory which accounts for the character and behavior of semantic relations in terms of more primitive relational elements. The inferential phenomena observed are then explained by means of a single principle of element matching.     

Perceptual Categorization: Connectionist Modelling and Decision Rules

Although it is currently popular to model human associative learning using connectionist networks, the mechanism by which their output activations are converted to probabilities of response has received relatively little attention. Several possible models of this decision process are considered here, including a simple ratio rule, a simple difference rule, their exponential versions, and a winner-take-all network. Two categorization experiments that attempt to dissociate these models are reported. Analogues of the experiments were presented to a single-layer, feed-forward, delta-rule network. Only the exponential ratio rule and the winner-take-all architecture, acting on the networks' output activations that corresponded to responses available on test, were capable of fully predicting the mean response results. In addition, unlike the exponential ratio rule, the winner-take-all model has the potential to predict latencies. Further studies will be required to determine whether latencies produced under more stringent conditions conform to the model's predictions.