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How to say no in recognition tests of visual working memory: Testing unidimensional and two-dimensional models with continuous or discrete memory states


Lin, Hsuan-Yu; Oberauer, Klaus (2019). How to say no in recognition tests of visual working memory: Testing unidimensional and two-dimensional models with continuous or discrete memory states. Journal of Experimental Psychology: Learning, Memory, and Cognition:ePub ahead of print.

Abstract

We constructed 4 working memory recognition models to predict behavior in the local recognition task (also called change detection), in which both content (e.g., color) and context (e.g., location) information are necessary to make correct recognition decisions. The theoretical assumptions incorporated in the models come from crossing 2 contrasts: One is the contrast between discrete-state models with continuous-strength models. The other contrast pertains to the dimensionality of information involved in the recognition process: either unidimensional (as in single-process recognition models) or two-dimensional (as in dual-process models). We compared the models to data from three local-recognition experiments using sequentially presented visual materials. All three experiments revealed intrusion costs (i.e., higher false alarms to probes matching a list element in the wrong context than to new probes) and U-shaped serial-position curves for all probe types. The two-dimensional continuous-strength model predicted these results best both qualitatively and quantitatively. The unidimensional and two-dimensional discrete-state models were able to predict the qualitative pattern of serial-position curves but failed to predict a sufficient amount of intrusion cost. The unidimensional continuous-strength model failed even to predict the qualitative pattern of the serial position effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Abstract

We constructed 4 working memory recognition models to predict behavior in the local recognition task (also called change detection), in which both content (e.g., color) and context (e.g., location) information are necessary to make correct recognition decisions. The theoretical assumptions incorporated in the models come from crossing 2 contrasts: One is the contrast between discrete-state models with continuous-strength models. The other contrast pertains to the dimensionality of information involved in the recognition process: either unidimensional (as in single-process recognition models) or two-dimensional (as in dual-process models). We compared the models to data from three local-recognition experiments using sequentially presented visual materials. All three experiments revealed intrusion costs (i.e., higher false alarms to probes matching a list element in the wrong context than to new probes) and U-shaped serial-position curves for all probe types. The two-dimensional continuous-strength model predicted these results best both qualitatively and quantitatively. The unidimensional and two-dimensional discrete-state models were able to predict the qualitative pattern of serial-position curves but failed to predict a sufficient amount of intrusion cost. The unidimensional continuous-strength model failed even to predict the qualitative pattern of the serial position effects. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Psychology
Dewey Decimal Classification:150 Psychology
Language:English
Date:18 March 2019
Deposited On:25 Mar 2019 12:52
Last Modified:25 Mar 2019 12:52
Publisher:American Psychological Association
ISSN:0278-7393
Additional Information:The data and the analysis is available on Open Science Framework at: https://osf.io/xfdsb/.
OA Status:Closed
Publisher DOI:https://doi.org/10.1037/xlm0000700
Related URLs:https://osf.io/xfdsb/
PubMed ID:30883171
Project Information:
  • : FunderSNSF
  • : Grant ID100014_135002
  • : Project TitleThe Capacity Limit of Working Memory: Continuous or Discrete?

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