Molar and latent models of cognitive slowing: Implications for aging, dementia, depression, development, and intelligence

Fisher, Donald L.; Glaser, Robert

doi:10.3758/bf03214549

Cited by 54 publications

(55 citation statements)

References 56 publications

(86 reference statements)

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“…Like some others (e.g., Bashore, Osman, & HeZey, 1989;Fisher & Glaser, 1996;Fisk, Fisher, & Rogers, 1992;Madden, 1992;Sliwinski, 1997), we endorse specialised-slowing hypotheses about selective eVects of ageing on distinct processing stages. Yet in one key respect, our theoretical position abides by the generalised-slowing hypothesis (cf.…”

Section: Questions and Answers About Cognitive Ageingmentioning

confidence: 81%

Executive-process interactive control: A unified computational theory for answering 20 questions (and more) about cognitive ageing

Meyer

Glass

Seymour

et al. 2001

European Journal of Cognitive Psychology

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Although the eVects of ageing on human information processing and performance have been studied extensively, many fundamental questions about cognitive ageing remain to be answered de nitively. For example, what are the sources of age-related slowing? How much is working-memory capacity reduced in older adults? Is time-sharing ability lost with age? Answering such questions requires a uni ed computational theory that characterises the interactive operations of many component mental processes and integrates diverse data on cognitive ageing. Toward ful lling this requirement, an executive-process interactive control (EPIC) architecture has been extended to model performance of both young and older adults. EPIC models yield accurate accounts of ageing eVects on reaction times and accuracy in basic dual-task and working-memory paradigms. From these accounts, it appears that time-sharing ability and working-memory capacity decrease relatively little until after 70 years of age. Before age 70, at least some apparent performance decrements may be attributable to conservative executive processes and ineYcient task procedures rather than decreased ''hardware'' functionality. By clarifying and deepening such insights, uni ed computational theories like EPIC will help answer many questions about cognitive ageing.

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Section: Questions and Answers About Cognitive Ageingmentioning

confidence: 81%

Executive-process interactive control: A unified computational theory for answering 20 questions (and more) about cognitive ageing

Meyer

Glass

Seymour

et al. 2001

European Journal of Cognitive Psychology

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“…This has been taken as evidence for a decrease in the speed of processing with age. This view has received much support (e.g., Birren, 1965;Cerella, 1985Cerella, , 1990Cerella, Poon, & Williams, 1980;Fisk & Warr, 1996;Salthouse, 1985Salthouse, , 1996Salthouse, Kausler, & Saults, 1988) and much criticism and debate (Allen, Ashcraft, & Weber, 1992;Allen et al, 1993;Cerella, 1994;Fisk & Fisher, 1994;Fisher & Glaser, 1996;Hartley, 1992;Hertzog, 1992;Lima et al, 1991;Madden, 1989;Madden, Pierce, & Allen, 1992;Myerson et al, 1992;Myerson, Wagstaff, & Hale, 1994;Perfect, 1994). Ratcliff, Spieler, and McKoon (2000) argued against this view from a theoretical perspective by showing that in the frameworks of explicit models, there are multiple ways that slowing in mean RT can be produced, such as changes in the rate of accumulation of evidence, decision criteria settings, or both.…”

Section: Brinley Plotsmentioning

confidence: 99%

A Diffusion Model Analysis of the Effects of Aging in the Lexical-Decision Task.

Ratcliff¹,

Thapar²,

Gómez³

et al. 2004

Psychology and Aging

278

329

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The effects of aging on response time (RT) are examined in 2 lexical-decision experiments with young and older subjects (age 60-75). The results show that the older subjects were slower than the young subjects, but more accurate. R. diffusion model provided a good account of RTs, their distributions, and response accuracy. The fits show an 80-100-ms slowing of the nondecision components of RT for older subjects relative to young subjects and more conservative decision criterion settings for older subjects than for young subjects. The rates of accumulation of evidence were not significantly different for older compared with young subjects (less than 2% and 5% higher for older subjects relative to young subjects in the 2 experiments).Across a wide variety of cognitive tasks, research has shown that processing slows with age. For some tasks, especially those like letter discrimination that depend heavily on peripheral processes, this is not surprising (e.g., . However, for other tasks it might be expected that performance would improve with age. One such task is lexical decision, the task of interest in this article. Over a lifetime of 60 to 70 years, the number of encounters with many words must greatly exceed the number of encounters in the first 20 years. Yet despite so many years of practice, lexical-decision response times (RTs) increase with age. For example, Allen, Madden, and Crozier (1991) found average RTs of 800 ms for older adults compared with 500 ms for young adults. Word frequency effects, longer RTs with lower frequency words, are also larger for older adults (see Allen et al., 1991;Allen, Madden, Weber, & Groth, 1993;Allen, Sliwinski, & Bowie, 2002;Lima, Hale, & Myerson, 1991).Recently, Ratcliff, Gomez, and McKoon (2004) have applied the diffusion model for twochoice decisions (Ratcliff, 1978(Ratcliff, , 1981(Ratcliff, , 1985(Ratcliff, , 1988(Ratcliff, , 2002Ratcliff & Rouder, 1998Ratcliff & Smith, 2004;Ratcliff, Van Zandt, & McKoon, 1999) to lexical-decision data. The model allows processing to be separated into several components: the rate at which information about the stimulus string of letters accumulates in the decision system (which reflects the goodness of match between the test string and lexical memory), the criteria that determine the amounts of information that must be accumulated before a decision can be made, nondecision

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“…We assume, in short, that the task network is a directed acyclic network. A more extensive introduction can be found in Fisher and Glasser (1996) or Schweickert, Fisher and Goldstein (1992). An introduction to several other network models of processing is given in Liu (1996).…”

Section: Directed Acyclic Networkmentioning

confidence: 99%

Selective Influence and Response Time Cumulative Distribution Functions in Serial-Parallel Task Networks

Schweickert¹,

Giorgini²,

Dzhafarov³

2000

Journal of Mathematical Psychology

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Molar and latent models of cognitive slowing: Implications for aging, dementia, depression, development, and intelligence

Cited by 54 publications

References 56 publications

Executive-process interactive control: A unified computational theory for answering 20 questions (and more) about cognitive ageing

Executive-process interactive control: A unified computational theory for answering 20 questions (and more) about cognitive ageing

A Diffusion Model Analysis of the Effects of Aging in the Lexical-Decision Task.

Selective Influence and Response Time Cumulative Distribution Functions in Serial-Parallel Task Networks

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