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Scholarly Works (3 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2024)

Whether it be your ability to consistently recognize the face of your friend across varying contexts, or the ability of a dermatologist to differentiate cancerous skin lesions from benign ones, many tasks and domains are reliant upon the ability to classify items into one of many possible categories. This can be challenging, especially when category members present in diverse ways or closely resemble members of other categories. Research has shown that category acquisition can be facilitated by opportunities to directly compare items together. In particular, comparisons may support rapid improvements in the discovery and pick up of information, resulting in deeper processing of category structure and enhanced category representations. The goal of this dissertation was to elucidate the most effective methods for constructing comparisons to accelerate the mechanisms of perceptual learning that underlie successful categorization. In particular, emphasis was given to exploring the value of paired comparison trials, which involved the concurrent presentation of items from two different categories for discrimination. In Experiments 1 and 2, learning trial structure and task were manipulated to test paired comparison learning against more common classification-based approaches. Results revealed that paired comparison learning was an effective way to learn the classification of a large set of categories, particularly when the domain was novel to the learner. Experiment 3 broke down paired comparisons into its separate learning components to evaluate how learning is advanced. Results revealed asymmetric learning gains in favor categories framed as the target of the trial relative to categories framed as the distractor. Experiment 4 measured the perceptual changes induced by paired comparison learning, finding evidence for within-category compression and between-category expansion. Finally, Experiment 5 evaluated adaptive learning methods to enhance comparison efficacy by testing an adaptive comparison procedure, previously used in face learning, in the domain of skin lesion classification. A partial replication of results was observed, and suggestions are given as to how the procedure may be improved. Altogether, this work has important implications for our understanding of the role of perceptual learning in high-level tasks generally, as well as for how it interacts with comparison opportunities specifically.

    Cover page: Learning Through Comparisons: An Evaluation of Simultaneous Comparison Trials in Perceptual Category Learning
    • Article
    • Peer Reviewed
    Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 46 (2024)
    Recent research indicates that paired comparisons can accelerate perceptual learning of challenging dermatological lesion categories. Here we investigated whether the role of object categories as targets or distractors differentially influences learning outcomes. The frequency with which a given category occupied the target position was manipulated across three learning conditions: Always-Never, where half of 10 categories were always shown as target and the other half never shown as target; Often-Rarely, where half of categories appeared 75% as targets and 25% as distractors, with reversed presentation frequency for the other half; and Equal Split learning, in which all categories appeared as targets or distractors equally often. After learning, transfer results indicated that all conditions yielded equivalent overall learning, but categories prioritized more often as targets exhibited greater learning gains. These findings implicate differential processing of images in comparisons, even when no information regarding target vs. distractor was given prior to feedback.
      Cover page: Target vs. Distractor: Does the Role of a Category In Comparisons Influence Learning? Evidence from Skin Cancer ClassificationCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
      Recent work suggests that learning perceptual classifications can be enhanced by combining single item classifications with adaptive comparisons triggered by each learner’s confusions. Here, we asked whether learning might work equally well using all comparison trials. In a face identification paradigm, we tested single item classifications, paired comparisons, and dual instance classifications that resembled comparisons but required two identification responses. In initial results, the comparisons condition showed evidence of greater efficiency (learning gain divided by trials or time invested). We suspected that this effect may have been driven by easier attainment of mastery criteria in the comparisons condition, and a negatively accelerated learning curve. To test this idea, we fit learning curves and found data consistent with the same underlying learning rate in all conditions. These results suggest that paired comparison trials may be as effective in driving learning of multiple perceptual classifications as more demanding single item classifications.
        Cover page: Comparisons in Adaptive Perceptual Category LearningCreative Commons 'BY' version 4.0 license
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