Age effects on category learning, categorical perception, and generalization

Learned Categorical Perception (CP) occurs when the members of different categories come to look more dissimilar (“between-category separation”) and/or members of the same category come to look more similar (“within-category compression”) after a new category has been learned. To measure learned CP and its physiological correlates we compared dissimilarity judgments and Event Related Potentials (ERPs) before and after learning to sort multi-featured visual textures into two categories by trial and error with corrective feedback. With the same number of training trials and feedback, about half the participants succeeded in learning the categories (“learners”: criterion 80% accuracy) and the rest did not (“non-learners”). At both lower and higher levels of difficulty, successful learners showed significant between-category separation in pairwise dissimilarity judgments after learning compared to before; their late parietal ERP positivity (LPC, usually interpreted as decisional) also increased and their occipital negativity (N1) (usually interpreted as perceptual) decreased. LPC increased with response accuracy and N1 amplitude decreased with between-category separation for the Learners. Non-learners showed no significant changes in dissimilarity judgments, LPC or N1, within or between categories. This is behavioral and physiological evidence that category learning can alter perception. We sketch a neural net model for this effect.

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Auditory Perceptual Category Formation Does Not Require Perceptual Warping

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00806 ◽

2015 ◽

Vol 27 (8) ◽

pp. 1659-1673

Author(s):

Valeria C. Caruso ◽

Evan Balaban

Keyword(s):

Category Learning ◽

Categorical Perception ◽

Physical Distance ◽

Perceptual Space ◽

Learning Mechanisms ◽

Perceptual Dimension ◽

Pitch Range ◽

Perceptual Distance ◽

Perceptual Category ◽

Category Formation

Categorical perception occurs when a perceiver's stimulus classifications affect their ability to make fine perceptual discriminations and is the most intensively studied form of category learning. On the basis of categorical perception studies, it has been proposed that category learning proceeds by the deformation of an initially homogeneous perceptual space (“perceptual warping”), so that stimuli within the same category are perceived as more similar to each other (more difficult to tell apart) than stimuli that are the same physical distance apart but that belong to different categories. Here, we present a significant counterexample in which robust category learning occurs without these differential perceptual space deformations. Two artificial categories were defined along the dimension of pitch for a perceptually unfamiliar, multidimensional class of sounds. A group of participants (selected on the basis of their listening abilities) were trained to sort sounds into these two arbitrary categories. Category formation, verified empirically, was accompanied by a heightened sensitivity along the entire pitch range, as indicated by changes in an EEG index of implicit perceptual distance (mismatch negativity), with no significant resemblance to the local perceptual deformations predicted by categorical perception. This demonstrates that robust categories can be initially formed within a continuous perceptual dimension without perceptual warping. We suggest that perceptual category formation is a flexible, multistage process sequentially combining different types of learning mechanisms rather than a single process with a universal set of behavioral and neural correlates.

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Erratum: How and Why Does Category Learning Cause Categorical Perception?

International Journal of Comparative Psychology ◽

10.46867/ijcp.2018.31.00.01 ◽

2018 ◽

Vol 31 ◽

Author(s):

Fernanda Pérez-Gay ◽

Christian Thériault ◽

Madeline Gregory ◽

Hisham Sabri ◽

Dan Rivas ◽

...

Keyword(s):

Category Learning ◽

Categorical Perception

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Perceived similarity ratings predict generalization success after traditional category learning and a new paired-associate learning task

10.31234/osf.io/x8c9n ◽

2019 ◽

Cited By ~ 1

Author(s):

Stefania Rene Ashby ◽

Caitlin Bowman ◽

Dagmar Zeithamova

Keyword(s):

Category Learning ◽

Categorical Perception ◽

Paired Associate ◽

Generalization Test ◽

Learning Task ◽

Paired Associate Learning ◽

Perceived Similarity ◽

Associate Learning ◽

Subsequent Test ◽

Similarity Ratings

The current study investigated category learning across two experiments using face-blend stimuli that formed face families controlled for within- and between-category similarity. Experiment 1 was a traditional feedback-based category learning task, with three family names serving as category labels. In Experiment 2, the shared family name was encountered in the context of a face—full name paired-associate learning task, with a unique first name for each face. A subsequent test that required participants to categorize new faces from each family showed successful generalization in both experiments. Furthermore, perceived similarity ratings for pairs of faces were collected before and after learning, prior to generalization test. In Experiment 1, similarity ratings increased for faces within a family and decreased for faces that were physically similar but belonged to different families. In Experiment 2, overall similarity ratings decreased after learning, driven primarily by decreases for physically similar faces from different families. The post-learning category bias in similarity ratings was predictive of subsequent generalization success in both experiments. The results indicate that individuals formed generalizable category knowledge prior to an explicit demand to generalize, and did so both when attention was directed towards category-relevant similarities (Experiment 1) and when attention was directed towards individuating faces within a family (Experiment 2). The results tie together research on category learning and categorical perception and extend them beyond a traditional category learning task. NOTE: This paper has not yet been published in a peer-reviewed journal.

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Categorical Perception of Illusory Contour Defined Shapes

Perception ◽

10.1068/v970201 ◽

1997 ◽

Vol 26 (1_suppl) ◽

pp. 212-212

Author(s):

G Kovács ◽

K Köteles ◽

M Hosseinalikhani ◽

A Lõrincz

Keyword(s):

Category Learning ◽

Illusory Contour ◽

Categorical Perception ◽

Corrective Feedback ◽

Opening Angle ◽

Shape Discrimination ◽

Sensory Modalities ◽

Discrimination Capacity ◽

Learning Test ◽

The Individual

Categorical perception (CP) is the phenomenon when people are better able to distinguish between stimuli of different categories than between stimuli of the same category and it appears for innate and learned stimuli of various sensory modalities. We tested if CP occurs also for illusory contour stimuli. First, in an ABX paradigm the shape discrimination capacity of subjects was tested for convex (‘fat’) or concave (‘thin’) Kanizsa-square-like shapes (cf Rubin, 1996 Perception25 Supplement, 3; pre-category-learning test). The proportion of correct responses and response latency were measured. Second, subjects were instructed that every stimulus belongs to either the ‘fat’ or the ‘thin’ category, and they were trained to categorise the individual stimuli using a 2AFC paradigm with corrective feedback until 90% of correct responses. Third, we measured the shape discrimination capacity of subjects, by repeating the first ABX paradigm (post-category-learning test). Comparison of the ratio of correct responses in the post/pre discrimination tests showed that discrimination of illusory shapes of different categories is enhanced but discrimination of members of the same category is made more difficult by category training. In a second experiment, by increasing the physical differences between the extreme ‘fat’ and extreme ‘thin’ stimuli (increased range of the opening-angle of the Kanizsa-square inducer pacmen), we made the categorisation task easier for the subjects and tested if discrimination capacity is altered by the modified difficulty of the categorisation task. We discuss the effect of difficulty on CP and the relation of illusory CP to that of real luminance contour stimuli.

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Categorical perception effects induced by category learning.

Journal of Experimental Psychology Learning Memory and Cognition ◽

10.1037/0278-7393.24.3.732 ◽

1998 ◽

Vol 24 (3) ◽

pp. 732-753 ◽

Cited By ~ 81

Author(s):

Kenneth R. Livingston ◽

Janet K. Andrews ◽

Stevan Harnad

Keyword(s):

Category Learning ◽

Categorical Perception

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How and Why Does Category Learning Cause Categorical Perception?

International Journal of Comparative Psychology ◽

10.46867/ijcp.2017.30.01.01 ◽

2017 ◽

Vol 30 ◽

Author(s):

Fernanda Pérez-Gay ◽

Christian Thériault ◽

Madeline Gregory ◽

Hisham Sabri ◽

Dan Rivas ◽

...

Keyword(s):

Category Learning ◽

Categorical Perception ◽

Human Subjects ◽

Corrective Feedback ◽

Pairwise Distance ◽

Strong Negative Correlation ◽

Similarity Space ◽

Before And After ◽

Similarity Judgments ◽

Simple Neural Network

Learning to categorize requires distinguishing category members from non-members by detecting the features that covary with membership. Human subjects were trained to sort visual textures into two categories by trial and error with corrective feedback. Difficulty levels were increased by decreasing the proportion of covariant features. Pairwise similarity judgments were tested before and after category learning. Three effects were observed: (1) The lower the proportion of covariant features, the more trials it took to learn the category and the fewer the subjects who succeeded in learning it. After training, (2) perceived pairwise distance increased between categories and, to a lesser extent, (3) decreased within categories, at all levels of difficulty, but only for successful learners. This perceived between-category separation and within-category compression is called categorical perception (CP). A very simple neural network model for category learning using uniform binary (0/1) features showed similar CP effects. CP may occur because learning to selectively detect covariant features and ignore non-covariant features reduces the dimensionality of perceived similarity space. In addition to (1) – (3), the nets showed (4) a strong negative correlation between the proportion of covariant features and the size of the CP effect. This correlation was not evident in the human subjects, probably because, unlike the formal binary features of the input to the nets, which were all uniform, the visual features of the human inputs varied in difficulty.

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