Retinal-specific category learning

Rosedahl, Luke A.; Eckstein, Miguel P.; Ashby, F. Gregory

doi:10.1038/s41562-018-0370-z

Cited by 26 publications

(22 citation statements)

References 51 publications

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“…It is unlikely that 10 trials with no accuracy feedback made categorization learning in the untrained visual hemifield during the location transfer test. These results indicate that category learning transferred to the other visual hemifield, suggesting that category learning in the current setting is not location specific, as previously found [9,[12][13][14][15][16] (but see [17]). The results also suggest that category learning lasted until after the VPL post-test, assuring that VPL occurred while the categorization remained effective.…”

Section: Methodssupporting

confidence: 82%

“…The unit for one category is activated when an orientation belonging to the category is presented. Results of experiment 2 and other studies indicate that category learning is not location specific and therefore occurs in a relatively higher-level stage [9,[12][13][14][15][16] (but see [17]). Although an orientation is being trained for a detection task, the unit of the orientation at a location-specific feature stage is activated and provides input to the category stage.…”

Section: Discussionmentioning

confidence: 69%

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Category-Induced Transfer of Visual Perceptual Learning

et al. 2019

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Section: Methodssupporting

confidence: 82%

Section: Discussionmentioning

confidence: 69%

Category-Induced Transfer of Visual Perceptual Learning

et al. 2019

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“…Overall, the SDM has the potential to be a valuable tool in both experimental design and human performance enhancement. A future research goal should be to generalize the SDM to account for many other factors that are known to affect human category learning, including fatigue (Maddox et al, 2009), stress (Ell, Cosley, & McCoy, 2011), and the retinal location of the stimulus during training versus testing (Rosedahl, Eckstein, & Ashby, 2018). The SDM can then be used to improve human–computer partnerships for important categorization tasks such as radiologists scanning X-rays for tumors, TSA agents examining bag scans for banned items, and more.…”

Section: Discussionmentioning

confidence: 99%

A difficulty predictor for perceptual category learning

Rosedahl

Ashby

2019

Journal of Vision

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Predicting human performance in perceptual categorization tasks in which category membership is determined by similarity has been historically difficult. This article proposes a novel biologically motivated difficulty measure that can be generalized across stimulus types and category structures. The new measure is compared to 12 previously proposed measures on four extensive data sets that each included multiple conditions that varied in difficulty. The studies were highly diverse and included experiments with both continuous- and binary-valued stimulus dimensions, a variety of different stimulus types, and both linearly and nonlinearly separable categories. Across these four applications, the new measure was the most successful at predicting the observed rank ordering of conditions by difficulty, and it was also the most accurate at predicting the numerical values of the mean error rates in each condition.

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“…Given the importance of categorization, it is natural that much research has focused on what makes a categorization task difficult. Since the classic study of Shepard et al (1961), the field has examined a number of factors that might affect difficulty, including the nature of the category structures (Alfonso-Reese et al, 2002); the motivational state of the learner (Ell et al, 2011); and various methods of training and testing (e.g., Rosedahl et al, 2018; Spiering & Ashby, 2008a). Here we examine the role of two factors that could potentially impact performance: whether the contrasting categories are linearly or nonlinearly separable, and the effect of increasing variability on stimulus dimensions that are irrelevant to the categorization decision.…”

Section: Linear Separabilitymentioning

confidence: 99%

Linear separability, irrelevant variability, and categorization difficulty.

Rosedahl¹,

Ashby²

2022

Journal of Experimental Psychology: Learning, Memory, and Cogni

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In rule-based (RB) category-learning tasks, the optimal strategy is a simple explicit rule, whereas in information-integration (II) tasks, the optimal strategy is impossible to describe verbally. This study investigates the effects of two different category properties on learning difficulty in category learning tasks-namely, linear separability and variability on stimulus dimensions that are irrelevant to the categorization decision. Previous research had reported that linearly separable II categories are easier to learn than nonlinearly separable categories, but Experiment 1, which compared performance on linearly and nonlinearly separable categories that were equated as closely as possible on all other factors that might affect difficulty, found that linear separability had no effect on learning. Experiments 1 and 2 together also established a novel dissociation between RB and II category learning: increasing variability on irrelevant stimulus dimensions impaired II learning but not RB learning. These results are all predicted by the best available measures of difficulty in RB and II tasks.

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Retinal-specific category learning

Cited by 26 publications

References 51 publications

Category-Induced Transfer of Visual Perceptual Learning

Category-Induced Transfer of Visual Perceptual Learning

A difficulty predictor for perceptual category learning

Linear separability, irrelevant variability, and categorization difficulty.

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