Adaptively triggered comparisons enhance perceptual category learning: evidence from face learning.

Categorical learning is important and often challenging in both specialized domains, such as medical image interpretation, and commonplace ones, such as face recognition. Research has shown that comparing items from different categories can enhance the learning of perceptual classifications, particularly when those categories appear highly similar. Here, we developed and tested novel adaptively triggered comparisons (ATCs), in which errors produced during interactive learning dynamically prompted the presentation of active comparison trials.

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion.

Comparisons in Adaptive Perceptual Category Learning.

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 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.

Adaptive vs. Fixed Spacing of Learning Items: Evidence from Studies of Learning and Transfer in Chemistry Education.

Spacing presentations of learning items across time improves memory relative to massed schedules of practice - the well-known spacing effect. Spaced practice can be further enhanced by adaptively scheduling the presentation of learning items to deliver customized spacing intervals for individual items and learners. ARTS - Adaptive Response-time-based Sequencing (Mettler, Massey, & Kellman 2016) determines spacing dynamically in relation to each learner's ongoing speed and accuracy in interactive learning trials.

Comparing Adaptive and Random Spacing Schedules during Learning to Mastery Criteria.

Adaptive generation of spacing intervals in learning using response times improves learning relative to both adaptive systems that do not use response times and fixed spacing schemes (Mettler, Massey & Kellman, 2016). Studies have often used limited presentations (e.g.

The Synergy of Passive and Active Learning Modes in Adaptive Perceptual Learning.

Adaptive learning systems that generate spacing intervals based on learner performance enhance learning efficiency and retention (Mettler, Massey & Kellman, 2016). Recent research in factual learning suggests that initial blocks of passive trials, where learners observe correct answers without overtly responding, produce greater learning than passive or active trials alone (Mettler, Massey, Burke, Garrigan & Kellman, 2018). Here we tested whether this passive + active advantage generalizes beyond factual learning to perceptual learning.

A comparison of adaptive and fixed schedules of practice.

Understanding and optimizing spacing during learning is a central topic for research in learning and memory and has substantial implications for real-world learning. Spacing memory retrievals across time improves memory relative to massed practice-the well-known spacing effect. Most spacing research has utilized fixed (predetermined) spacing intervals.

Perceptual learning modules in mathematics: enhancing students' pattern recognition, structure extraction, and fluency.

Learning in educational settings emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other crucial components of learning, especially improvements produced by experience in the extraction of information: perceptual learning (PL). We suggest that such improvements characterize both simple sensory and complex cognitive, even symbolic, tasks through common processes of discovery and selection.