Visual category learning: Navigating the intersection of rules and similarity

The spacing effect in remote information-integration category learning

The present experiments examined whether the temporal distribution of procedural category learning experiences would impact learning outcomes. Participants completed the remote category learning experiments on a smartphone in one of two learning conditions: massed or distributed. Consistent with expectations, distributed learners in both experiments reached higher accuracy levels than massed learners. In Experiment 1 the effect disappeared after accounting for reaction time differences, suggesting that it was driven by attentional mechanisms. In Experiment 2, the spacing advantage was only present for previously studied items during a post-learning test, suggesting a role of consolidation. In both experiments, it seems likely that temporal spacing helped participants discover the optimal information-integration categorization strategy. These results suggest that adult category learning is facilitated by temporal spacing. Future work may further explore the effects of temporal and contextual distinctiveness of learning experiences on category learning outcomes.

The Underappreciated Benefits of Interleaving for Category Learning

The present study examined the effects of study schedule (interleaving vs. blocking) and feature descriptions on category learning and metacognitive predictions of learning. Across three experiments, participants studied exemplars from different rock categories and later had to classify novel exemplars. Rule-based and information-based categorization was also manipulated by selecting rock sub-categories for which the optimal strategy was the one that aligned with the extraction of a simple rule, or the one that required integration of information that may be difficult to describe verbally. We observed consistent benefits of interleaving over blocking on rock classification, which generalized to both rule-based (Experiment 1) and information-integration learning (Experiments 1-3). However, providing feature descriptions enhanced classification accuracy only when the stated features were diagnostic of category membership, indicating that their benefits were limited to rule-based learning (Experiment 1) and did not generalize to information-integration learning (Experiments 1-3). Furthermore, our examination of participants' metacognitive predictions demonstrated that participants were not aware of the benefits of interleaving on category learning. Additionally, providing feature descriptions led to higher predictions of categorization even when no significant benefits on actual performance were exhibited.

Conditions under which college students cease learning

Introduction

Effective learning involves the acquisition of information toward a goal and cessation upon reaching that goal. Whereas the process of learning acquisition is well understood, comparatively little is known about how or when learning ceases under naturalistic, open-ended learning conditions in which the criterion for performance is not specified. Ideally, learning should cease once there is no progress toward the goal, although this has never been directly tested in human learners. The present set of experiments explored the conditions under which college students stopped attempting to learn a series of inductive perceptual discrimination problems.

Methods

Each problem varied by whether it was solvable and had a criterion for success. The first problem was solvable and involved a pre-determined criterion. The second problem was solvable, but with no criterion for ending the problem so that learners eventually achieved a highly accurate level of performance (overlearning). The third problem was unsolvable as the correct answer varied randomly across features. Measures included the number of trials attempted and the outcome of each problem.

Results and Discussion

Results revealed that college students rarely ceased learning in the overlearning or unsolvable problems even though there was no possibility for further progress. Learning cessation increased only by manipulating time demands for completion or reducing the opportunity for reinforcement. These results suggest that human learners show laudable, but inefficient and unproductive, attempts to master problems they should cease.

Evaluating the effectiveness of different perceptual training methods in a difficult visual discrimination task with ultrasound images

Recent work has shown that perceptual training can be used to improve the performance of novices in real-world visual classification tasks with medical images, but it is unclear which perceptual training methods are the most effective, especially for difficult medical image discrimination tasks. We investigated several different perceptual training methods with medically naïve participants in a difficult radiology task: identifying the degree of hepatic steatosis (fatty infiltration of the liver) in liver ultrasound images. In Experiment 1a (N = 90), participants completed four sessions of standard perceptual training, and participants in Experiment 1b (N = 71) completed four sessions of comparison training. There was a significant post-training improvement for both types of training, although performance was better when the trained task aligned with the task participants were tested on. In both experiments, performance initially improves rapidly, with learning becoming more gradual after the first training session. In Experiment 2 (N = 200), we explored the hypothesis that performance could be improved by combining perceptual training with explicit annotated feedback presented in a stepwise fashion. Although participants improved in all training conditions, performance was similar regardless of whether participants were given annotations, or underwent training in a stepwise fashion, both, or neither. Overall, we found that perceptual training can rapidly improve performance on a difficult radiology task, albeit not to a comparable level as expert performance, and that similar levels of performance were achieved across the perceptual training paradigms we compared.

Transfer of category learning to impoverished contexts

Learning often happens in ideal conditions, but then must be applied in less-than-ideal conditions - such as when a learner studies clearly illustrated examples of rocks in a book but then must identify them in a muddy field. Here we examine whether the benefits of interleaving (vs. blocking) study schedules, as well as the use of feature descriptions, supports the transfer of category learning in new, impoverished contexts. Specifically, keeping the study conditions constant, we evaluated learners' ability to classify new exemplars in the same neutral context versus in impoverished contexts in which certain stimulus features are occluded. Over two experiments, we demonstrate that performance in new, impoverished contexts during test is greater for participants who received an interleaved (vs. blocked) study schedule, both for novel and for studied exemplars. Additionally, we show that this benefit extends to both a short (3-min) or long (48-h) test delay. The presence of feature descriptions during learning had no impact on transfer. Together, these results extend the growing literature investigating how changes in context during category learning or test impacts performance and provide support for the use of interleaving to promote the far transfer of category knowledge to impoverished contexts.