Aging and confidence judgments in item recognition.

Voskuilen, Chelsea; Ratcliff, Roger; McKoon, Gail

doi:10.1037/xlm0000425

Cited by 8 publications

(7 citation statements)

References 114 publications

(239 reference statements)

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“…However, there has to be variability from trial to trial in the stimulus representation (Ratcliff, 1978). To have such variability and a population code, a population code distribution could be assumed with a mean that varied from trial to trial so the combined SD was the across-trial SD in drift rate (as in the RTCON2 model for confidence and multichoice decision making; Ratcliff & Starns, 2009, their Figure 2; Ratcliff & Starns, 2013; Voskuilen & Ratcliff, 2016; Voskuilen, Ratcliff, & McKoon, 2018).…”

Section: Population Code Models and The Two-choice Diffusion Modelmentioning

confidence: 99%

Decision making on spatially continuous scales.

Ratcliff

2018

Psychological Review

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118

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A new diffusion model of decision making in continuous space is presented and tested. The model is a sequential sampling model in which both spatially continuously distributed evidence and noise are accumulated up to a decision criterion (a 1D line or a 2D plane). There are two major advances represented in this research. The first is to use spatially continuously distributed Gaussian noise in the decision process (Gaussian process or Gaussian random field noise) which allows the model to represent truly spatially continuous processes. The second is a series of experiments that collect data from a variety of tasks and response modes to provide the basis for testing the model. The model accounts for the distributions of responses over position and response time distributions for the choices. The model applies to tasks in which the stimulus and the response coincide (moving eyes or fingers to brightened areas in a field of pixels) and ones in which they do not (color, motion, and direction identification). The model also applies to tasks in which the response is made with eye movements, finger movements, or mouse movements. This modeling offers a wide potential scope of applications including application to any device or scale in which responses are made on a 1D continuous scale or in a 2D spatial field.

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Section: Population Code Models and The Two-choice Diffusion Modelmentioning

confidence: 99%

Decision making on spatially continuous scales.

Ratcliff

2018

Psychological Review

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118

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“…The answers to these questions hint at different interpretations of what underlies the correlation between age differences in response times and age differences in cognitive abilities. There is a growing number of studies on age differences in diffusion model parameters (Ball and Aschenbrenner 2018;Janczyk et al 2018;Madden et al 2010;McGovern et al 2018;Ratcliff 2012, 2013;Ratcliff 2008;Ratcliff et al 2001Ratcliff et al , 2004Ratcliff et al , 2006Ratcliff et al , 2010Spaniol et al 2006Spaniol et al , 2011Thapar et al 2003;Voskuilen et al 2018). Dully et al (2018) gave an overview of the state of the literature in their systematic review.…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing number of studies on age differences in diffusion model parameters ( Ball and Aschenbrenner 2018 ; Janczyk et al 2018 ; Madden et al 2010 ; McGovern et al 2018 ; McKoon and Ratcliff 2012 , 2013 ; Ratcliff 2008 ; Ratcliff et al 2001 , 2004 , 2006 , 2010 ; Spaniol et al 2006 , 2011 ; Thapar et al 2003 ; Voskuilen et al 2018 ). Dully et al ( 2018 ) gave an overview of the state of the literature in their systematic review.…”

Section: Introductionmentioning

confidence: 99%

Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains?

et al. 2020

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In comparison to young adults, middle-aged and old people show lower scores in intelligence tests and slower response times in elementary cognitive tasks. Whether these well-documented findings can both be attributed to a general cognitive slow-down across the life-span has become subject to debate in the last years. The drift diffusion model can disentangle three main process components of binary decisions, namely the speed of information processing, the conservatism of the decision criterion and the non-decision time (i.e., time needed for processes such as encoding and motor response execution). All three components provide possible explanations for the association between response times and age. We present data from a broad study using 18 different response time tasks from three different content domains (figural, numeric, verbal). Our sample included people between 18 to 62 years of age, thus allowing us to study age differences across young-adulthood and mid-adulthood. Older adults generally showed longer non-decision times and more conservative decision criteria. For speed of information processing, we found a more complex pattern that differed between tasks. We estimated mediation models to investigate whether age differences in diffusion model parameters account for the negative relation between age and intelligence, across different intelligence process domains (processing capacity, memory, psychometric speed) and different intelligence content domains (figural, numeric, verbal). In most cases, age differences in intelligence were accounted for by age differences in non-decision time. Content domain-general, but not content domain-specific aspects of non-decision time were related to age. We discuss the implications of these findings on how cognitive decline and age differences in mental speed might be related.

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“…They tend to be overconfident about the quality of their memory performance. On the other hand, there are almost as many studies that have found only minor or even no age effects on the accuracy of metamemory 28 , 37 – 39 . Metacognition in other functional domains, e.g., problem solving, linguistics, perception, even seems to elude any age effects 28 , 40 , 41 .…”

Section: Introductionmentioning

confidence: 99%

Age-related differences in visual confidence are driven by individual differences in cognitive control capacities

Klever

Mamassian

Billino

2022

Sci Rep

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Visual perception is not only shaped by sensitivity but also by confidence, i.e., the ability to estimate the accuracy of a visual decision. Younger observers have been reported to have access to a reliable measure of their own uncertainty when making visual decisions. This metacognitive ability might be challenged during ageing due to increasing sensory noise and decreasing cognitive control resources. We investigated age effects on visual confidence using a visual contrast discrimination task and a confidence forced-choice paradigm. Younger adults (19–38 years) showed significantly lower discrimination thresholds than older adults (60–78 years). To focus on confidence sensitivity above and beyond differences in discrimination performance, we estimated confidence efficiency that reflects the ability to distinguish good from bad perceptual decisions. Confidence efficiency was estimated by comparing thresholds obtained from all trials and trials that were judged with relatively higher confidence, respectively. In both age groups, high confidence judgments were associated with better visual performance, but confidence efficiency was reduced in older adults. However, we observed substantial variability across all participants. Controlling for age group, confidence effciency was closely linked to individual differences in cognitive control capacities. Our findings provide evidence for age-related differences in confidence efficiency that present a specific challenge to perceptual performance in old age. We propose that these differences are driven by cognitive control capacities, supporting their crucial role for metacognitive efficiency.

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Aging and confidence judgments in item recognition.

Cited by 8 publications

References 114 publications

Decision making on spatially continuous scales.

Decision making on spatially continuous scales.

Do Non-Decision Times Mediate the Association between Age and Intelligence across Different Content and Process Domains?

Age-related differences in visual confidence are driven by individual differences in cognitive control capacities

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