Interactions between declarative and procedural-learning categorization systems

Abstract

Two experiments tested whether declarative and procedural memory systems operate independently or inhibit each other during perceptual categorization. Both experiments used a hybrid category-learning task in which perfect accuracy could be achieved if a declarative strategy is used on some trials and a procedural strategy is used on others. In the two experiments, only 2 of 53 participants learned a strategy of this type. In Experiment 1, most participants appeared to use simple explicit rules, even though control participants reliably learned the procedural component of the hybrid task. In Experiment 2, participants pre-trained either with the declarative or procedural component and then transferred to the hybrid categories. Despite this extra training, no participants in either group learned to categorize the hybrid stimuli with a strategy of the optimal type. These results are inconsistent with the most prominent single- and multiple-system accounts of category learning. They also cannot be explained by knowledge partitioning, or by the hypothesis that the failure to learn was due to high switch costs. Instead, these results support the hypothesis that declarative and procedural memory systems interact during category learning.

Figure 1

Some stimuli that might be used in an information-integration category learning experiment.

Figure 2

Abstract representation of the categories used in the three conditions of Experiment 1. Each plus denotes the bar width and orientation of an exemplar of Category A and each circle denotes the bar width and orientation of an exemplar of Category B. All stimuli were circular sine-wave gratings of the type shown in Figure 1.

Figure 3

Mean accuracy for each 100-trial block in Experiment 1. Error bars, which were similar in all conditions, are only shown for the Uniform Hybrid condition.

Figure 4

Experiment 1 accuracy where Information-Integration condition participants are separated by the strategy that best described their responses (II = Information Integration, RB = Rule Based).

Figure 5

Stimuli and optimal category boundaries used in the training and transfer conditions of Experiment 2 (RB = Rule-Based Training condition; II = Information-Integration Training condition).

Figure 6

Mean accuracy for each 100-trial block during training and transfer in both conditions of Experiment 2 (II Training = Information-Integration Training condition, RB Training = Rule-Based Training condition).

Figure 7

Decision bounds that provided the best fit to the last 100 trials of the transfer (i.e., hybrid) session of Experiment 2 for those participants whose responses were best fit by a model that assumed a linear information-integration strategy.