2001 ◽  
Vol 130 (1) ◽  
pp. 77-96 ◽  
Author(s):  
F. Gregory Ashby ◽  
Elliott M. Waldron ◽  
W. William Lee ◽  
Amelia Berkman
Keyword(s):  

2016 ◽  
Author(s):  
Drew Altschul ◽  
Greg Jensen ◽  
Herbert S Terrace

Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sections of paintings with distinctive styles. Humans learned to order both types of stimuli. All subjects classified stimuli at levels substantially greater than that predicted by chance or by feature-driven learning alone, even when stimuli changed one every trial. However, humans more closely resembled monkeys when classifying the more abstract painting stimuli than the photographic stimuli. This points to a common classification strategy in both species, once that humans can rely on in the absence of linguistic labels for categories.


2002 ◽  
Vol 26 (3) ◽  
pp. 303-343 ◽  
Author(s):  
Emmanuel M. Pothos ◽  
Nick Chater
Keyword(s):  

2019 ◽  
Author(s):  
Michael David Lee ◽  
Danielle Navarro

The ALCOVE model of category learning, despite its considerable success in accounting for human performance across a wide range of empirical tasks, is limited by its reliance on spatial stimulus representations. Some stimulus domains are better suited to featural representation, characterizing stimuli in terms of the presence or absence of discrete features, rather than as points in a multidimensional space. We report on empirical data measuring human categorization performance across a featural stimulus domain and show that ALCOVE is unable to capture fundamental qualitative aspects of this performance. In response, a featural version of the ALCOVE model is developed, replacing the spatial stimulus representations that are usually generated by multidimensional scaling with featural representations generated by additive clustering. We demonstrate that this featural version of ALCOVE is able to capture human performance where the spatial model failed, explaining the difference in terms of the contrasting representational assumptions made by the two approaches. Finally, we discuss ways in which the ALCOVE categorization model might be extended further to use “hybrid” representational structures combining spatial and featural components.


1991 ◽  
Vol 98 (3) ◽  
pp. 409-429 ◽  
Author(s):  
John R. Anderson

Sign in / Sign up

Export Citation Format

Share Document