scholarly journals Category variability, exemplar similarity, and perceptual classification

2001 ◽  
Vol 29 (8) ◽  
pp. 1165-1175 ◽  
Author(s):  
Andrew L. Cohen ◽  
Robert M. Nosofsky ◽  
Safa R. Zaki
Keyword(s):  
Perceptual Classification ◽  
Category Variability

scholarly journals Perceptual classification of chromatic modulation

2005 ◽  
Vol 22 (1) ◽  
pp. 117-117
Author(s):  
ROMAIN BOUET ◽  
KENNETH KNOBLAUCH
Keyword(s):  
Perceptual Classification ◽  
Visual Neuroscience ◽  
Production Error ◽  
Claude Bernard

(article appeared in Visual Neuroscience (2004), 21, 283–289Due to a production error, the second author affiliation is incorrectly cited. It should have read as follows:INSERM U371, Cerveau et Vision, IFR 19, Institut Fédératif des Neurosciences, Université Claude Bernard Lyon I, Bron France

scholarly journals A test of the regulatory fit hypothesis in perceptual classification learning

2006 ◽  
Vol 34 (7) ◽  
pp. 1377-1397 ◽  
Author(s):  
W. Todd Maddox ◽  
Grant C. Baldwin ◽  
Arthur B. Markman
Keyword(s):  
Regulatory Fit ◽  
Classification Learning ◽  
Perceptual Classification

The Implicit/Explicit Strategy

2019 ◽  
pp. 89-129
Author(s):  
Eli Alshanetsky
Keyword(s):  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Perceptual Classification ◽  
Color Recognition ◽  
Implicit And Explicit

On the proposed solution to the puzzle, we recognize the correct formulations of our thoughts by relying on our implicit knowledge of what we are thinking. After discussing an analogous puzzle in the case of basic perceptual classification and constructing a model of implicit knowledge for the simpler case of color recognition, the chapter extends the model to the trickier case of thought. On this model, our implicit knowledge of an item consists in its stored signature—the invariant aspect of experience that its instances share. On the proposed solution, the process that mediates between implicit and explicit knowledge is not itself wholly sub-personal. Instead, it is best understood as straddling the personal/sub-personal divide. A deeper source of the puzzle that emerges from this chapter’s discussion of our involvement in articulation lies in the conflation between two types of freedom (or control) that we may have over a response.

Perceptual Classification Images from Vernier Acuity Masked by Noise

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 2-2 ◽  
Author(s):  
A J Ahumada
Keyword(s):  
Fourier Transforms ◽  
Internal Noise ◽  
Discrimination Performance ◽  
Ideal Observer ◽  
Classification Rule ◽  
Horizontal Line ◽  
Vernier Acuity ◽  
Perceptual Classification ◽  
Classification Images ◽  
The Ideal

Letting external noise rather than internal noise limit discrimination performance allows information to be extracted about the observer's stimulus classification rule. A perceptual classification image is the correlation over trials between the noise amplitude at a spatial location and the observer's responses. If, for example, the observer followed the rule of the ideal observer, the response correlation image would be an estimate of the ideal observer filter, the difference between the two unmasked images being discriminated. Perceptual classification images were estimated for a Vernier discrimination task. The display screen had 48 pixels deg−1 horizontally and vertically. The no-offset image had a dark horizontal line of 4 pixels, a 1 pixel space, and 4 more dark pixels. Classification images were based on 1600 discrimination trials with the line contrast adjusted to keep the error rate near 25%. In the offset image, the second line was one pixel higher. Unlike the ideal observer filter (a horizontal dipole), the observer perceptual classification images are strongly oriented. Fourier transforms of the classification images had a peak amplitude near 1 cycle deg−1 and an orientation near 25 deg. The spatial spread is much more than image blur predicts, and probably indicates the spatial position uncertainty in the task.

scholarly journals Further attempts to clarify the importance of category variability for categorisation

2012 ◽  
Vol 24 (2) ◽  
pp. 203-220 ◽  
Author(s):  
Amotz Perlman ◽  
Ulrike Hahn ◽  
Darren J. Edwards ◽  
Emmanuel M. Pothos
Keyword(s):  
Category Variability
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