Literature DB >> 29752660

Classification errors and response times over multiple distributed sessions as a function of category structure.

Derek E Zeigler1,2, Ronaldo Vigo3,4.   

Abstract

Learning difficulty orderings for categorical stimuli have long provided an empirical foundation for concept learning and categorization research. The conventional approach seeks to determine learning difficulty orderings in terms of mean classification accuracy. However, it is relatively rare that the stability of such orderings is tested over a period of extended learning. Further, research rarely explores dependent variables beyond classification accuracy that may also indicate relative learning difficulty, such as classification response times (RTs). Using a family of category structures defined over three binary dimensions and four positive examples that is well-known for its robust learning difficulty ordering, we report the results of two experiments that test the stability of the ordering (in terms of both errors and RTs) over multiple category learning sessions. The experimental stimuli consisted of instantiations of each of the six category structures in the family. These take the form of categories consisting of four "flasks" that vary along the binary features of size (large or small), shape (circular or triangular), and color (black or white). Experiment 1 shows that when participants are randomly presented instances of all six types, the difficulty ordering remains stable across all three sessions. This stability is present in terms of mean accuracy (errors) as well as mean RTs. In Experiment 2, participants were repeatedly exposed to category instances of a single type. In terms of errors, the ordering is revealed in the first session and disappears in later sessions. The opposite trend is observed for classification RTs: The ordering is not present in the first session but is revealed in later sessions. This suggests that even when individuals reach a relative degree of expertise in terms of reduced errors, the original degree of difficulty continues to influence processing. We interpret these results in the context of the concept learning and perceptual expertise literatures.

Entities:  

Keywords:  Categorization; Concept learning; Expertise; Perceptual fluency

Mesh:

Year:  2018        PMID: 29752660     DOI: 10.3758/s13421-018-0820-x

Source DB:  PubMed          Journal:  Mem Cognit        ISSN: 0090-502X


  36 in total

1.  A threshold theory for simple detection experiments.

Authors:  R D LUCE
Journal:  Psychol Rev       Date:  1963-01       Impact factor: 8.934

2.  ALCOVE: an exemplar-based connectionist model of category learning.

Authors:  J K Kruschke
Journal:  Psychol Rev       Date:  1992-01       Impact factor: 8.934

3.  Short-term memory scanning viewed as exemplar-based categorization.

Authors:  Robert M Nosofsky; Daniel R Little; Christopher Donkin; Mario Fific
Journal:  Psychol Rev       Date:  2011-04       Impact factor: 8.934

4.  An exemplar-based random walk model of speeded classification.

Authors:  R M Nosofsky; T J Palmeri
Journal:  Psychol Rev       Date:  1997-04       Impact factor: 8.934

5.  The development of expertise in dermatology.

Authors:  G R Norman; D Rosenthal; L R Brooks; S W Allen; L J Muzzin
Journal:  Arch Dermatol       Date:  1989-08

6.  The influence of expertise on X-ray image processing.

Authors:  M Myles-Worsley; W A Johnston; M A Simons
Journal:  J Exp Psychol Learn Mem Cogn       Date:  1988-07       Impact factor: 3.051

7.  Rule-plus-exception model of classification learning.

Authors:  R M Nosofsky; T J Palmeri; S C McKinley
Journal:  Psychol Rev       Date:  1994-01       Impact factor: 8.934

8.  Constructing and Deconstructing Concepts.

Authors:  Charles A Doan; Ronaldo Vigo
Journal:  Exp Psychol       Date:  2016-09

9.  The gist of the abnormal: above-chance medical decision making in the blink of an eye.

Authors:  Karla K Evans; Diane Georgian-Smith; Rosemary Tambouret; Robyn L Birdwell; Jeremy M Wolfe
Journal:  Psychon Bull Rev       Date:  2013-12

10.  Exploring the conceptual universe.

Authors:  Charles Kemp
Journal:  Psychol Rev       Date:  2012-08-27       Impact factor: 8.934

View more
  1 in total

1.  Premise typicality as feature inference decision-making in perceptual categories.

Authors:  Emma L Morgan; Mark K Johansen
Journal:  Mem Cognit       Date:  2021-10-08
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.