Biomedical subjects

F Gregory Ashby

Publications and source records attributed to F Gregory Ashby.

5 recordsLinked to original sources

PubMed2003

Delayed feedback effects on rule-based and information-integration category learning.

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches.

Discrimination Learning↗

PubMed2003

Category learning deficits in Parkinson's disease.

Sixteen patients with Parkinson's disease (PD), 15 older controls (OCs), and 109 younger controls (YCs) were compared in 2 category-learning tasks. Participants attempted to assign colored geometric figures to 1 of 2 categories. In rule-based tasks, category membership was defined by an explicit rule that was easy to verbalize, whereas in information-integration tasks, there was no salient verbal rule and accuracy was maximized only if information from 3 stimulus components was integrated at some predecisional stage. The YCs performed the best on both tasks. The PD patients were highly impaired compared with the OCs, in the rule-based categorization task but were not different from the OCs in the information-integration task. These results support the hypothesis that learning in these 2 tasks is mediated by functionally separate systems.

Aged↗

PubMed2002

What makes a categorization task difficult?

To understand why some categorization tasks are more difficult than others, we consider five factors that may affect human performance--namely, covariance complexity, optimal accuracy level with and without internal noise, orientation of the optimal categorization rule, and class separability. We argue that covariance complexity, an information-theoretic measure of complexity, is an excellent predictor of task difficulty. We present an experiment that consists of five conditions using a simulated medical decision-making task In the task human observers view hundreds of hypothetical patient profiles and classify each profile into Disease Category A or B. Each profile is a continuous-valued, three-dimensional stimulus consisting of three vertical bars, where each bar height represents the result of a medical test. Across the five conditions, covariance complexity was systematically manipulated. Results indicate that variation in performance is largely a function of covariance complexity and partly a function of internal noise. The remaining three factors do not explain performance results. We present a challenge to categorization theorists to design models that account for human performance as predicted by covariance complexity.

Adult↗

PubMed2002

Multiple attention systems in perceptual categorization.

Five observers categorized inverted L-shaped stimuli according to the length of the horizontal line segment. A centrally located spatial cue preceded the stimulus on each trial. On 80% of the trials, the (relevant) horizontal line segment fell within the cued location, and on 20% of the trials the (irrelevant) vertical line segment fell within the cued location. The empirical results provide support for the hypothesis that perceptual attention can focus on the stimulus attribute inside the spatially cued location at the same time that decisional attention is focused on the (relevant) horizontal attribute--that is, the results suggest that perceptual and decisional attention can function independently during categorization. Decision bound models and extended generalized context models that assume separate perceptual and decisional attention systems were fitted to the data. Versions of the models that assume that the spatial cue affected perceptual attention were superior to versions that assume no effect on perceptual attention. These theoretical analyses support the functional independence hypothesis and suggest that formal theories of categorization should model the effects of perceptual and decisional attention separately.

Attention↗

PubMed2002

Observational versus feedback training in rule-based and information-integration category learning.

The effects of two different kinds of categorization training were investigated. In observational training, observers are presented with a category label and then shown an exemplar from that category. In feedback training, they are shown an exemplar, asked to assign it to a category, and then given feedback about the accuracy of their response. These two types of training were compared as observers learned two types of category structures--those in which optimal accuracy could be achieved via some explicit rule-based strategy, and those in which optimal accuracy required integrating information from separate perceptual dimensions at some predecisional stage. There was an overall advantage for feedback training over observational training, but most importantly, type of training interacted strongly with type of category structure. With rule-based structures, the effects of training type were small, but with information-integration structures, accuracy was substantially higher with feedback training, and people were less likely to use suboptimal rule-based strategies. The implications of these results for current theories of category learning are discussed.

Concept Formation↗