A. David Ing

Disrupting feedback processing interferes with rule-based but not information-integration category learning.

The effect of a sequentially presented memory scanning task on rule-based and informationintegration category learning was investigated. On each trial in the short feedback-processing time condition, memory scanning immediately followed categorization. On each trial in the long feedbackprocessing time condition, categorization was followed by a 2.5-sec delay and then memory scanning. In the control condition, no memory scanning was required. Rule-based category learning was significantly worse in the short feedback-processing time condition than in the long feedback-processing time condition or control condition, whereas information-integration category learning was equivalent across conditions. In the rule-based condition, a smaller proportion of observers learned the task in the short feedback-processing time condition, and those who learned took longer to reach the performance criterion than did those in the long feedback-processing time or control condition. No differences were observed in 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.

Delayed Feedback Disrupts the Procedural-Learning System but Not the Hypothesis-Testing System in Perceptual Category Learning.

W. T. Maddox, F. G. Ashby, and C. J. Bohil (2003) found that delayed feedback adversely affects information-integration but not rule-based category learning in support of a multiple-systems approach to category learning. However, differences in the number of stimulus dimensions relevant to solving the task and perceptual similarity failed to rule out 2 single-system interpretations. The authors conducted an experiment that remedied these problems and replicated W. T. Maddox et al.s findings. The experiment revealed a strong performance decrement for information-integration but not rule-based category learning under delayed feedback that was due to an increase in the number of observers using hypothesis-testing strategies to solve the information-integration task, and lower accuracy rates for the few observers using information-integration strategies.

Evidence for a procedural-learning-based system in perceptual category learning.

The consistency of the mapping from category to response location was investigated to test the hypothesis that abstract category labels are learned by the hypothesis testing system to solve rule-based tasks, whereas response position is learned by the procedural-learning system to solve informationintegration tasks. Accuracy rates were examined to isolate global performance deficits, and modelbased analyses were performed to identify the types of response strategies used by observers. A-B training (consistent mapping) led to more accurate responding relative to yes-no training (variable mapping) in the information-integration category learning task. Model-based analyses indicated that the yes-no accuracy decline was due to an increase in the use of rule-based strategies to solve the information-integration task. Yes-no training had no effect on the accuracy of responding or distribution of best-fitting models relative to A-B training in the rule-based category learning tasks. These results both provide support for a multiple-systems approach to category learning in which one system is procedural-learning-based and argue against the validity of single-system approaches.

Category Number Impacts Rule-Based but Not Information-Integration Category Learning: Further Evidence for Dissociable Category-Learning Systems

Category number effects on rule-based and information-integration category learning were investigated. Category number affected accuracy and the distribution of best-fitting models in the rule-based task but had no effect on accuracy and little effect on the distribution of best-fining models in the information-integration task. In the 2 category conditions, rule-based learning was better than information-integration learning, whereas in the 4 category conditions, unidimensional and conjunctive rule-based learning was worse than information-integration learning. Rule-based strategies were used in the 2-category/rule-based condition, but about half of the observers used rule-based strategies in the 4-category unidimensional and conjunctive rule-based conditions. Information-integration strategies were used in the 4-category/ information-integration condition and by the end of training were used in the 2-category/information-integration condition.

Cortical and subcortical brain regions involved in rule-based category learning

The brain regions contributing to rule-based category learning were examined using fMRI. Participants categorized single lines that varied in length and orientation into one of two categories. Category membership was based on the length of the line. Results indicated that left frontal and parietal regions were differentially activated in those participants who learned the task as compared to those who did not. Further, the head of the caudate displayed relative decreases in activation on incorrect trials relative to correct trials. The involvement of this latter structure is likely related to (1) processing an error signal, or (2) volitional switching between potential category rules. Results are consistent with theories suggesting that a frontal-striatal circuit is involved in rule-based category learning.

The impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinson's disease

This study examined the impact of irrelevant dimensional variation on rule-based category learning in patients with Parkinsons disease (PD), older controls (OC), and younger controls (YC). Participants were presented with 4-dimensional, binary-valued stimuli and were asked to categorize each into 1 of 2 categories. Category membership was based on the value of a single dimension. Four experimental conditions were administered in which there were zero, 1, 2, or 3 randomly varying irrelevant dimensions. Results indicated that patients with PD were impacted to a greater extent than both the OC and YC participants when the number of randomly varying irrelevant dimensions increased. These results suggest that the degree of working memory and selective attention requirements of a categorization task will impact whether PD patients are impaired in rule-based category learning, and help to clarify recent discrepancies in the literature.

Criterial noise effects on rule-based category learning: the impact of delayed feedback.

Variability in the representation of the decision criterion is assumed in many category-learning models, yet few studies have directly examined its impact. On each trial, criterial noise should result in drift in the criterion and will negatively impact categorization accuracy, particularly in rule-based categorization tasks, where learning depends on the maintenance and manipulation of decision criteria. In three experiments, we tested this hypothesis and examined the impact of working memory on slowing the drift rate. In Experiment 1, we examined the effect of drift by inserting a 5-sec delay between the categorization response and the delivery of corrective feedback, and working memory demand was manipulated by varying the number of decision criteria to be learned. Delayed feedback adversely affected performance, but only when working memory demand was high. In Experiment 2, we built on a classic finding in the absolute identification literature and demonstrated that distributing the criteria across multiple dimensions decreases the impact of drift during the delay. In Experiment 3, we confirmed that the effect of drift during the delay is moderated by working memory. These results provide important insights into the interplay between criterial noise and working memory, as well as providing important constraints for models of rule-based category learning.

Stimulus modality interacts with category structure in perceptual category learning

Two experiments were conducted that examined information integration and rule-based category learning, using stimuli that contained auditory and visual information. The results suggest that it is easier to perceptually integrate information within these sensory modalities than across modalities. Conversely, it is easier to perform a disjunctive rule-based task when information comes from different sensory modalities, rather than from the same modality. Quantitative model-based analyses suggested that the information integration deficit for across-modality stimulus dimensions was due to an increase in the use of hypothesis-testing strategies to solve the task and to an increase in random responding. The modeling also suggested that the across-modality advantage for disjunctive, rule-based category learning was due to a greater reliance on disjunctive hypothesis-testing strategies, as opposed to unidimensional hypothesis-testing strategies and random responding.

Cognitive complexity effects in perceptual classification are dissociable

It has been proposed that a procedural-based classification system mediates the learning of informationintegration categories, whereas a hypothesis-testing system mediates the learning of rule-based categories. Ashby, Ell and Waldron (2003) provided support for this claim by showing that a button switch introduced during classification transfer adversely affected information-integration but not rule-based performance. Nosofsky, Stanton and Zaki (2005) showed that increasing “cognitive complexity” can lead to button switch costs on rule-based performance. They argue that “cognitive complexity,” and not the existence of separable classification systems, accounts for Ashby et al.’s empirical dissociation. The present study shows that experimental manipulations that increase “cognitive complexity” often have dissociable effects on information-integration and rule-based classification that are predicted a priori from the processing characteristics associated with the procedural-based and hypothesis-testing systems. These results suggest that manipulations of “cognitive complexity” can be dissociated, suggesting that “cognitive complexity” in not a unitary construct that affects a single psychological process.