Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus ERIC kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Modeling Short- and Long-Term Memory Contributions to Recent Event Recognition

Title: Modeling Short- and Long-Term Memory Contributions to Recent Event Recognition
Language: English
Authors: Nosofsky, Robert M. (ORCID 0000-0002-2494-2719); Cao, Rui (ORCID 0000-0003-0538-5336); Harding, Samuel M. (ORCID 0000-0002-7476-405X); Shiffrin, Richard M.
Source: Journal of Experimental Psychology: Learning, Memory, and Cognition. Feb 2021 47(2):316-342.
Availability: American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org
Peer Reviewed: Y
Page Count: 27
Publication Date: 2021
Document Type: Journal Articles; Reports - Research
Descriptors: Long Term Memory; Short Term Memory; Recognition (Psychology); Cognitive Mapping; Models; Pictorial Stimuli; Experiments; Task Analysis; Familiarity; Item Response Theory
DOI: 10.1037/xlm0000812
ISSN: 0278-7393
Abstract: Participants gave recognition judgments for short lists of pictures of everyday objects. Pictures in a given list were an equal mixture of three types that varied according to the way they were used as targets and foils earlier in the same session. Under consistent-mapping (CM), targets and foils never switch roles; under varied-mapping (VM), targets and foils switch roles randomly across trials; whereas all-new (AN) items are novel on each trial of the experiment. Past research has shown that markedly enhanced performance occurs in CM conditions, leading to conclusions that item-response learning takes place in CM, perhaps automatically. However, almost all past research has compared CM, VM, and AN performance in between-blocks designs in which participants may adopt different cognitive strategies and criterion settings across the conditions. The present mixed-list design holds constant the strategy and criterion settings that are used for CM, VM, and AN items, and produced patterns of performance dramatically different than those observed in pure-list control conditions. We develop an extended version of an exemplar-based random-walk model of probe recognition to account for the major qualitative effects in the data. The data and the modeling provide evidence for strong item-response learning for CM foils but weak item-response learning for CM targets. We consider possible explanations for these effects in our General Discussion.
Abstractor: As Provided
Notes: https://osf.io/t9npc/?view_only=35b26a11761b4f7692cb39dec6278066
Entry Date: 2021
Accession Number: EJ1283152
Database: ERIC