Literature DB >> 30024260

Bias to (and away from) the extreme: Comparing two models of categorical perception effects.

Ryan M Best1, Robert L Goldstone1.   

Abstract

Categorical perception (CP) effects manifest as faster or more accurate discrimination between objects that come from different categories compared with objects that come from the same category, controlling for the physical differences between the objects. The most popular explanations of CP effects have relied on perceptual warping causing stimuli near a category boundary to appear more similar to stimuli within their own category and/or less similar to stimuli from other categories. Hanley and Roberson (2011), on the basis of a pattern not previously noticed in CP experiments, proposed an explanation of CP effects that relies not on perceptual warping, but instead on inconsistent usage of category labels. Experiments 1 and 2 in this article show a pattern opposite the one Hanley and Roberson pointed out. Experiment 3, using the same stimuli but with different choice statistics (i.e., different probabilities of each face being the target), obtains the same pattern as the one Hanley and Roberson showed. Simulations show that both category label and perceptual models are able to reproduce the patterns of results from both experiments, provided they include information about the choice statistics. This suggests 2 conclusions. First, the results described by Hanley and Roberson should not be taken as evidence in favor of a category label model. Second, given that participants did not receive feedback on their choices, there must be some mechanism by which participants monitor their own choices and adapt to the choice statistics present in the experiment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Entities:  

Year:  2018        PMID: 30024260     DOI: 10.1037/xlm0000609

Source DB:  PubMed          Journal:  J Exp Psychol Learn Mem Cogn        ISSN: 0278-7393            Impact factor:   3.051


  3 in total

1.  Lexical Information Guides Retuning of Neural Patterns in Perceptual Learning for Speech.

Authors:  Sahil Luthra; João M Correia; Dave F Kleinschmidt; Laura Mesite; Emily B Myers
Journal:  J Cogn Neurosci       Date:  2020-07-14       Impact factor: 3.225

2.  Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit.

Authors:  Gavin M Bidelman; Claire Pearson; Ashleigh Harrison
Journal:  J Cogn Neurosci       Date:  2021-01-19       Impact factor: 3.420

3.  Effects of Noise on the Behavioral and Neural Categorization of Speech.

Authors:  Gavin M Bidelman; Lauren C Bush; Alex M Boudreaux
Journal:  Front Neurosci       Date:  2020-02-27       Impact factor: 4.677

  3 in total

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