Learning Mode and Exemplar Sequencing in Unsupervised Category Learning

Overview

Journal J Exp Psychol Learn Mem Cogn

Publisher American Psychological Association

Specialty Psychology

Date 2009 Apr 22

PMID 19379046

Citations 9

Authors

Dagmar Zeithamova

W Todd Maddox

Affiliations

Soon will be listed here.

Abstract

Exemplar sequencing effects in incidental and intentional unsupervised category learning were investigated to illuminate how people form categories without an external teacher. Stimuli were perfectly separable into 2 categories based on 1 of 2 dimensions of variation. Sequencing of the first 20 training stimuli was manipulated. In the blocked condition, 10 Category A stimuli were followed by 10 Category B stimuli. In the intermixed condition, these 20 stimuli were ordered randomly. Experiment 1 revealed an interaction between learning mode and sequence, with better intentional learning for intermixed sequences but better incidental learning for blocked sequences. Experiment 2 showed that manipulating trial-to-trial variability along each dimension can impact intentional learning. Training sequences that emphasized variation along the category-relevant dimension resulted in better performance than sequences that emphasized variation along the category-irrelevant dimension. The results suggest that unsupervised category learning is influenced by the mode of learning and the order and nature of encountered exemplars.

Citing Articles

Memory, perceptual, and motor costs affect the strength of categorical encoding during motor learning of object properties.

Cesanek E, Flanagan J, Wolpert D Sci Rep. 2023; 13(1):8619.

PMID: 37244891 PMC: 10224949. DOI: 10.1038/s41598-023-33515-2.

Investigating interactions between types of order in categorization.

Mezzadri G, Reynaud-Bouret P, Laloe T, Mathy F Sci Rep. 2022; 12(1):21625.

PMID: 36517553 PMC: 9751307. DOI: 10.1038/s41598-022-25776-0.

Ready to Learn: Incidental Exposure Fosters Category Learning.

Unger L, Sloutsky V Psychol Sci. 2022; 33(6):999-1019.

PMID: 35617541 PMC: 9343890. DOI: 10.1177/09567976211061470.

A Computational Model of Context-Dependent Encodings During Category Learning.

Carvalho P, Goldstone R Cogn Sci. 2022; 46(4):e13128.

PMID: 35411959 PMC: 9285726. DOI: 10.1111/cogs.13128.

Linear separability, irrelevant variability, and categorization difficulty.

Rosedahl L, Ashby F J Exp Psychol Learn Mem Cogn. 2021; 48(2):159-172.

PMID: 33871263 PMC: 8523591. DOI: 10.1037/xlm0001000.

References

Stewart N, Brown G, Chater N . Sequence effects in categorization of simple perceptual stimuli. J Exp Psychol Learn Mem Cogn. 2002; 28(1):3-11. DOI: 10.1037//0278-7393.28.1.3. View

Ashby F, Queller S, Berretty P . On the dominance of unidimensional rules in unsupervised categorization. Percept Psychophys. 1999; 61(6):1178-99. DOI: 10.3758/bf03207622. View

Yamauchi T, Love B, Markman A . Learning nonlinearly separable categories by inference and classification. J Exp Psychol Learn Mem Cogn. 2002; 28(3):585-93. DOI: 10.1037//0278-7393.28.3.585. View

Jones M, Love B, Maddox W . Recency effects as a window to generalization: separating decisional and perceptual sequential effects in category learning. J Exp Psychol Learn Mem Cogn. 2006; 32(2):316-32. DOI: 10.1037/0278-7393.32.3.316. View

Medin D, Bettger J . Presentation order and recognition of categorically related examples. Psychon Bull Rev. 2013; 1(2):250-4. DOI: 10.3758/BF03200776. View

Clapper J, Bower G . Adaptive categorization in unsupervised learning. J Exp Psychol Learn Mem Cogn. 2002; 28(5):908-923. DOI: 10.1037//0278-7393.28.5.908. View

Ashby F, Townsend J . Varieties of perceptual independence. Psychol Rev. 1986; 93(2):154-79. View

Goldstone R . Isolated and interrelated concepts. Mem Cognit. 1996; 24(5):608-28. DOI: 10.3758/bf03201087. View

Clapper J . Prior knowledge and correlational structure in unsupervised learning. Can J Exp Psychol. 2007; 61(2):109-127. DOI: 10.1037/cjep20070012. View

10.

Pelli D . The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat Vis. 1997; 10(4):437-42. View

11.

Colreavy E, Lewandowsky S . Strategy development and learning differences in supervised and unsupervised categorization. Mem Cognit. 2008; 36(4):762-75. DOI: 10.3758/mc.36.4.762. View

12.

Fried L, Holyoak K . Induction of category distributions: a framework for classification learning. J Exp Psychol Learn Mem Cogn. 1984; 10(2):234-57. DOI: 10.1037//0278-7393.10.2.234. View

13.

Stewart N, Chater N . The effect of category variability in perceptual categorization. J Exp Psychol Learn Mem Cogn. 2002; 28(5):893-907. DOI: 10.1037//0278-7393.28.5.893. View

14.

Billman D, Knutson J . Unsupervised concept learning and value systematicity: a complex whole aids learning the parts. J Exp Psychol Learn Mem Cogn. 1996; 22(2):458-475. DOI: 10.1037/0278-7393.22.2.458. View

15.

Kaplan A, Murphy G . The acquisition of category structure in unsupervised learning. Mem Cognit. 1999; 27(4):699-712. DOI: 10.3758/bf03211563. View

16.

Clapper J . When more is less: negative exposure effects in unsupervised learning. Mem Cognit. 2006; 34(4):890-902. DOI: 10.3758/bf03193435. View

17.

Zeithamova D, Maddox W . The role of visuospatial and verbal working memory in perceptual category learning. Mem Cognit. 2007; 35(6):1380-98. DOI: 10.3758/bf03193609. View

18.

Nosofsky R . Attention, similarity, and the identification-categorization relationship. J Exp Psychol Gen. 1986; 115(1):39-61. DOI: 10.1037//0096-3445.115.1.39. View

19.

Medin D, Wattenmaker W, Hampson S . Family resemblance, conceptual cohesiveness, and category construction. Cogn Psychol. 1987; 19(2):242-79. DOI: 10.1016/0010-0285(87)90012-0. View

20.

Nosofsky R . Overall similarity and the identification of separable-dimension stimuli: a choice model analysis. Percept Psychophys. 1985; 38(5):415-32. DOI: 10.3758/bf03207172. View