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

Abstract

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.

Figure 1

Category structures with (A) 1, (B) 2, or (C) 3 unidimensional decision criteria on the spatial frequency dimension. Each open circle denotes the spatial frequency and spatial orientation of a Gabor pattern from Category A. Each filled circle denotes a Gabor pattern from category B. Each open square denotes a Gabor pattern from category C. Each filled square denotes a Gabor pattern from category D.

Figure 2

Accuracy predictions as a function of drift rate (σ, the standard deviation of the criterial noise distribution) for the ideal observer simulations for the three category structures in Figure 1 (1UD, 2UD, and 3UD). A. Predicted accuracy for a 1 s delay (i.e., t = 1). B. Predicted accuracy for a 5 s delay (i.e., t = 5). C. Accuracy cost of increasing the delay (Predicted accuracy for delay = 1 s minus the predicted accuracy for delay = 5s).

Figure 3

Proportion correct for the delayed and immediate feedback conditions of Experiment 1.

Figure 4

Category structure from Experiment 2. Each open circle denotes the spatial frequency and spatial orientation of a Gabor pattern from Category A. Each filled circle denotes a Gabor pattern from category B. Each open square denotes a Gabor pattern from category C. Each filled square denotes a Gabor pattern from category D.

Figure 5

Proportion correct for the delayed and immediate feedback conditions of Experiment 2. The 3UD data from Experiment 1 have been re-plotted for reference.

Figure 6

Proportion correct for the 3CJ and 3UD conditions from Experiment 3.