Limits in decision making arise from limits in memory retrieval.

Some decisions, such as predicting the winner of a baseball game, are challenging in part because outcomes are probabilistic. When making such decisions, one view is that humans stochastically and selectively retrieve a small set of relevant memories that provides evidence for competing options. We show that optimal performance at test is impossible when retrieving information in this fashion, no matter how extensive training is, because limited retrieval introduces noise into the decision process that cannot be overcome.

Attenuating age-related learning deficits: emotional valenced feedback interacts with task complexity.

Previous research reveals that older adults sometimes show enhanced processing of emotionally positive stimuli relative to negative stimuli, but that this positivity bias reverses to become a negativity bias when cognitive control resources are less available. In this study, we test the hypothesis that emotionally positive feedback will attenuate well-established age-related deficits in rule learning whereas emotionally negative feedback will amplify age deficits-but that this pattern will reverse when the task involves a high cognitive load. Experiment 1 used emotional face feedback and revealed an interaction among age, valence of the feedback, and task load.

Category induction in autism: slower, perhaps different, but certainly possible.

Available studies on categorization in autism indicate possibly intact category formation, performed through atypical processes. Category learning was investigated in 16 high-functioning autistic and 16 IQ-matched nonautistic participants, using a category structure that could generate a conflict between the application of a rule and exemplar memory. Same-different and matching-to-sample tasks allowed us to verify discrimination abilities for the stimuli to be used in category learning.

A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties.

In this paper, we present a new recurrent bidirectional model that encompasses correlational, competitive and topological model properties. The simultaneous use of many classes of network behaviors allows for the unsupervised learning/categorization of perceptual patterns (through input compression) and the concurrent encoding of proximities in a multidimensional space. All of these operations are achieved within a common learning operation, and using a single set of defining properties.