The aging decision maker: Cognitive aging and the adaptive selection of decision strategies.

Mata, Rui; Schooler, Lael J.; Rieskamp, Jörg

doi:10.1037/0882-7974.22.4.796

Cited by 309 publications

(296 citation statements)

References 68 publications

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“…An additional sensitivity tests by including indicators for early, mid, and late trials in all regression models found no evidence of an association between learning and the outcomes. The lack of a learning effect may be attributable to the random order in which the choice tasks were presented and the discreteness of each task.This study provides the first empirical indications of why older adults are not choosing the lowest cost plan available under Part D. In partial support of earlier work (Mata et al, 2007), older adults' information search was less effective. Specifically, they were more likely to use an attribute-based rather than an alternative-based search approach.…”

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confidence: 51%

“…Johnson (1990Johnson ( , 1993 has provided evidence that when asked to select an apartment or a car older adults examined less information, reevaluated information more frequently, reviewed the information for longer time periods, and used more simplified search strategies. Others (Mata, von Helversen, & Rieskamp, 2010;Mata, Schooler, & Rieskamp, 2007) have investigated the effect of aging on the ability to select adaptive decision strategies in relation to different environmental structures. Their results show that older adults tend to use less information and take longer to process it, and they rely more often on simpler decision strategies because they lack the cognitive resources to use the more demanding ones.…”

Section: Age Decision Making and Strategy Selectionmentioning

confidence: 99%

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Choosing the right medicare prescription drug plan: The effect of age, strategy selection, and choice set size.

et al. 2011

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Objective-The Medicare Modernization Act of 2003 (better known as Medicare Part D) represents the most important change to Medicare since its inception in the mid-1960s. The large number of drug plans being offered has raised concern over the complex design of the program. The purposes of this article are to examine the effect of age and choice set size (three vs. nine drug plans) on decision processes, strategy selection, and decision quality within the Medicare Part D program.Methods-One hundred and fifty individuals completed a MouselabWeb study, a computer based program that allowed us to trace the information acquisition process, designed to simulate the official Medicare website.Results-The data reveal that participants identified the lowest cost plan only 46% of the time. As predicted, an increase in choice set size (3 vs. 9) was associated with 0.25 times the odds of correctly selecting the lowest cost plan, representing an average loss of $48.71. Older participants, likewise, tended to make poorer decisions. Conclusion-The study provides some indication that decision strategy mediates the association between age and choice quality, and provides further insight regarding how to better design a choice environment that will improve the performance of older consumers. KeywordsAttribute/alternative processing; choice; prescription drugs; Medicare; older adultsThe Medicare Modernization Act of 2003 (better known as Medicare Part D) represents the most important change to Medicare since its inception in the mid-1960s. The program offers millions of older adults the ability to purchase subsidized prescription drug coverage. One feature that has attracted much attention (Hanoch & Rice, 2006) is the large number of drug plans being offered (a median number of 48 among the states; Kaiser Family Foundation, * Send correspondence to: Yaniv Hanoch, University of Plymouth, School of Psychology, Drake Circus, Plymouth PL4 8AA, UK, Tel. 44 (0) 1752-662779, yaniv.hanoch@plymouth.ac.uk. HHS Public AccessAuthor manuscript Health Psychol. Author manuscript; available in PMC 2017 September 12. ). This large choice set, however, is only one factor rendering the Medicare Part D program one of the most complex health decision environments ever designed (Frank, 2004). As drug plans can also differ in their key features, such as which drugs are included in the formulary, premiums, and cost-sharing requirements, beneficiaries find it difficult to choose among the dozens of drug plans available.The purposes of this article are to examine the effect of age and choice set size (three vs. nine drug plans) on decision processes, strategy selection, and decision quality by using a process-tracing method, Mouselab (Bettman, Johnson, & Payne 1990;Payne, Bettman, & Johnson, 1993). Mouselab allows us to examine the information being sought, the time spent on each piece of information -both the total amount of information and the order in which information is acquired -as well as how age and the decision environment (3 vs. 9 plans) a...

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confidence: 51%

Section: Age Decision Making and Strategy Selectionmentioning

confidence: 99%

Choosing the right medicare prescription drug plan: The effect of age, strategy selection, and choice set size.

et al. 2011

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“…As control variables, we also collected additional measures of cognitive capacity to assess participants' ability to deliberately control attention and manipulate information in working memory (digit span, symbol task, trail making test; see Mata et al 2007;Tombaugh 2004).…”

Section: Control Variablesmentioning

confidence: 99%

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

et al. 2015

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Why do people gamble? A large body of research suggests that cognitive distortions play an important role in pathological gambling. Many of these distortions are specific cases of a more general misperception of randomness, specifically of an illusory perception of patterns in random sequences. In this article, we provide further evidence for the assumption that gamblers are particularly prone to perceiving illusory patterns. In particular, we compared habitual gamblers to a matched sample of community members with regard to how much they exhibit the choice anomaly 'probability matching'. Probability matching describes the tendency to match response proportions to outcome probabilities when predicting binary outcomes. It leads to a lower expected accuracy than the maximizing strategy of predicting the most likely event on each trial. Previous research has shown that an illusory perception of patterns in random sequences fuels probability matching. So does impulsivity, which is also reported to be higher in gamblers. We therefore hypothesized that gamblers will exhibit more probability matching than nongamblers, which was confirmed in a controlled laboratory experiment. Additionally, gamblers scored much lower than community members on the cognitive reflection task, which indicates higher impulsivity. This difference could account for the difference in probability matching between the samples. These results suggest that gamblers are more willing to bet impulsively on perceived illusory patterns.

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“…In individual 2-h sessions, each research participant completed the following tasks: 1) A preexperimental gambling questionnaire to assess participants' basic demographic information and their gambling involvment (e.g., games of play, frequency of engagement, amount of money they gamble with; see Winters, Specker, & Stinchfield, 1996), 2) a computerized sequential slot-machine choice task (Scheibehenne et al, 2011), 3) three short measures of cognitive capacity (Mata, Schooler, & Rieskamp, 2007) to assess participants' ability to deliberately control attention and manipulate information in working memory, 4) a shortened two-domain version of the Domain-Specific Risk-Attitude Scale (Weber, Blais, & Betz, 2002) to assess participants' likelihood of engagement in risk, their perceptions of risk and the expected benefits they attribute to risks in the gambling and investment domain, 5) a paper-and-pencil version of the DSM-based South Oaks Gambling Screen (Stinchfield, 2002) to screen for indications of problem gambling and pathological gambling, and 6) a paper-and-pencil version of the DSM-based two-item Lie/Bet Questionnaire (Johnson & Hamer, 1997) to screen for pathological gambling.…”

Section: Measures and Proceduresmentioning

confidence: 99%

Illusionary pattern detection in habitual gamblers

Wilke

Scheibehenne

Gaissmaier

et al. 2014

Evolution and Human Behavior

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Does problem gambling arise from an illusion that patterns exist where there are none? Our prior research suggested that "hot hand," a tendency to perceive illusory streaks in sequences, may be a human universal, tied to an evolutionary history of foraging for clumpy resources. Like other evolved propensities, this tendency might be expressed more stongly in some people than others, leading them to see luck where others see only chance. If the desire to gamble is enhanced by illusory pattern detection, such individual differences could be predictive of gambling risk. While previous research has suggested a potential link between cognitive strategies and propensity to gamble, no prior study has directly measured gamblers' cognitive strategies using behavioral choice tasks, and linked them to risk taking or gambling propensities. Using a computerized sequential decision-making paradigm that directly measured subjects' predictions of sequences, we found evidence that subjects who have a greater tendency to gamble also have a higher tendency to perceive illusionary patterns, as measured by their preferences for a random slot machine over a negatively autocorrelated one. Casino gamblers played the random slot machine significantly more often even though a training phase and a history of outcomes were provided. Additionally, we found a marginally significant group difference between gamblers and matched community members in their slot-machine choice proportions. Performance on our behavioral choice task correlated with subjects' risk attitudes toward gambling and their frequency of play, as well as the selection of choice strategies in gambling activities.

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The aging decision maker: Cognitive aging and the adaptive selection of decision strategies.

Cited by 309 publications

References 68 publications

Choosing the right medicare prescription drug plan: The effect of age, strategy selection, and choice set size.

Choosing the right medicare prescription drug plan: The effect of age, strategy selection, and choice set size.

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers

Illusionary pattern detection in habitual gamblers

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