Using response time modeling to distinguish memory and decision processes in recognition and source tasks.
Receiver operating characteristic (ROC) functions are often used to make inferences about memory processes, such as claiming that memory strength is more variable for studied versus nonstudied items. However, decision processes can produce the ROC patterns that are usually attributed to memory, so i...
| Publicado en: | Memory & Cognition Vol. 42; no. 8; pp. 1357 - 1373 |
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| Formato: | Artículo |
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Springer Nature
Nov2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=99076376&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 99076376 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Nov2014 vid: 42 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 99076376 10.3758/s13421-014-0432-z ppf: 1357 ppct: 16 formats: fmt: @attributes: type: P size: 938KB tig: atl: Using response time modeling to distinguish memory and decision processes in recognition and source tasks. aug: au: Starns, Jeffrey affil: Department of Psychology, University of Massachusetts - Amherst, 441 Tobin Hall Amherst 01003 USA su: Massachusetts Analysis of variance College students Decision making Memory Chi-squared test Confidence intervals Reaction time T-test (Statistics) Receiver operating characteristic curves Descriptive statistics sug: subj: Analysis of variance College students Decision making Memory Massachusetts Chi-squared test Confidence intervals Reaction time T-test (Statistics) Receiver operating characteristic curves Descriptive statistics keyword: Diffusion model Recognition memory Source memory Unequal variance assumption Diffusion model Recognition memory Source memory Unequal variance assumption ab: Receiver operating characteristic (ROC) functions are often used to make inferences about memory processes, such as claiming that memory strength is more variable for studied versus nonstudied items. However, decision processes can produce the ROC patterns that are usually attributed to memory, so independent forms of data are needed to support strong conclusions. The present experiments tested ROC-based claims about the variability of memory evidence by modeling response time (RT) data with the diffusion model. To ensure that the model can correctly discriminate equal- and unequal-variance distributions, Experiment 1 used a numerousity discrimination task that had a direct manipulation of evidence variability. Fits of the model produced correct conclusions about evidence variability in all cases. Experiments 2 and 3 explored the effect of repeated learning trials on evidence variability in recognition and source memory tasks, respectively. Fits of the diffusion model supported the same conclusions about variability as the ROC literature. For recognition, evidence variability was higher for targets than for lures, but it did not differ on the basis of the number of learning trials for target items. For source memory, evidence variability was roughly equal for source 1 and source 2 items, and variability increased for items with additional learning attempts. These results demonstrate that RT modeling can help resolve ambiguities regarding the processes that produce different patterns in ROC data. The results strengthen the evidence that memory strength distributions have unequal variability across item types in recognition and source memory tasks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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