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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. One implication is that people should be more accurate in predicting future events when trained on idealized rather than on the actual distributions of items. In other words, we predict the best way to convey information to people is to present it in a distorted, idealized form. Idealization of training distributions is predicted to reduce the harmful noise induced by immutable bottlenecks in people's memory retrieval processes. In contrast, machine learning systems that selectively weight (i.e., retrieve) all training examples at test should not benefit from idealization. These conjectures are strongly supported by several studies and supporting analyses. Unlike machine systems, people's test performance on a target distribution is higher when they are trained on an idealized version of the distribution rather than on the actual target distribution. Optimal machine classifiers modified to selectively and stochastically sample from memory match the pattern of human performance. These results suggest firm limits on human rationality and have broad implications for how to train humans tasked with important classification decisions, such as radiologists, baggage screeners, intelligence analysts, and gamblers. categorization | cognitive modeling | uncertainty | diffusion modeling W hen making decisions, people often retrieve a limited set of items from memory (1-4). These retrieved items provide evidence for competing options. For example, a dark cloud may elicit memories of heavy rains, leading one to pack an umbrella instead of sunglasses. Likewise, when viewing an X-ray, a radiologist may retrieve memories of similar X-rays from other patients. Whether or not these other patients have a tumor may provide evidence for or against the presence of a tumor in the current patient. This process of sequential retrieval from memory, evidence accumulation, and final decision is formalized by diffusion models of choice and response time (Fig. 1). Diffusion models have successfully accounted for human learning and decision performance in a number of domains (1,5,6). These models accumulate noisy evidence retrieved from memory until a decision boundary is crossed. Wider boundaries imply higher accuracy and longer response times. For a given boundary, diffusion models optimally integrate evidence.However, according to the diffusion model, it is impossible to make optimal decisions within finite time (i.e., when the boundaries are finite). For probabilistic problems, such as determining whether a tumor is canc...

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. Participants were then trained to distinguish between two categories of imaginary animals, using categorization tests early in the training and at the end (160 trials). A recognition test followed, in order to evaluate explicit exemplar memory. Similar discrimination performance was found in control tasks for both groups. For the categorization task, autistic participants did not use any identifiable strategy early in the training, but used strategies similar to those of the nonautistic participants by the end, with the same level of accuracy. Memory for the exemplars was poor in both groups. Our findings confirm that categorization may be successfully performed by autistics, but may necessitate longer exposure to material, as the top-down use of rules may be only secondary to a guessing strategy in autistics.

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 while emotionally negative feedback would amplify age deficits—but that this pattern would reverse when the task involved a high cognitive load. Experiment 1 used emotional face feedback and revealed an interaction between age, valence of the feedback and task load. When the task placed minimal load on cognitive control resources, happy face feedback attenuated age-related deficits in initial rule learning and angry face feedback led to age-related deficits in initial rule learning and set shifting. However, when the task placed a high load on cognitive control resources, we found that angry face feedback attenuated age-related deficits in initial rule learning and set shifting, whereas happy face feedback led to age-related deficits in initial rule learning and set shifting. Experiment 2 used less emotional point feedback and revealed age-related deficits in initial rule learning and set shifting under low and high cognitive load for point gain and point loss conditions. The present research demonstrates that emotional feedback can attenuate age-related learning deficits – but only positive feedback for tasks with a low cognitive load and negative feedback for tasks with high cognitive load.

S. W. Allen and L. R. Brooks (1991) have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. Regehr and L. R. Brooks (1993) argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts.