Dataset Information

Measuring Individual Differences in Decision Biases: Methodological Considerations.

ABSTRACT: Individual differences in people's susceptibility to heuristics and biases (HB) are often measured by multiple-bias questionnaires consisting of one or a few items for each bias. This research approach relies on the assumptions that (1) different versions of a decision bias task measure are interchangeable as they measure the same cognitive failure; and (2) that some combination of these tasks measures the same underlying construct. Based on these assumptions, in Study 1 we developed two versions of a new decision bias survey for which we modified 13 HB tasks to increase their comparability, construct validity, and the participants' motivation. The analysis of the responses (N = 1279) showed weak internal consistency within the surveys and a great level of discrepancy between the extracted patterns of the underlying factors. To explore these inconsistencies, in Study 2 we used three original examples of HB tasks for each of seven biases. We created three decision bias surveys by allocating one version of each HB task to each survey. The participants' responses (N = 527) showed a similar pattern as in Study 1, questioning the assumption that the different examples of the HB tasks are interchangeable and that they measure the same underlying construct. These results emphasize the need to understand the domain-specificity of cognitive biases as well as the effect of the wording of the cover story and the response mode on bias susceptibility before employing them in multiple-bias questionnaires.

SUBMITTER: Aczel B

PROVIDER: S-EPMC4652008 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Measuring Individual Differences in Decision Biases: Methodological Considerations.

Aczel Balazs B Bago Bence B Szollosi Aba A Foldes Andrei A Lukacs Bence B

Frontiers in psychology 20151119

Individual differences in people's susceptibility to heuristics and biases (HB) are often measured by multiple-bias questionnaires consisting of one or a few items for each bias. This research approach relies on the assumptions that (1) different versions of a decision bias task measure are interchangeable as they measure the same cognitive failure; and (2) that some combination of these tasks measures the same underlying construct. Based on these assumptions, in Study 1 we developed two version ...[more]

PMID: 26635677

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Project description:Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.

Dataset Information

Measuring Individual Differences in Decision Biases: Methodological Considerations.

Publications

Measuring Individual Differences in Decision Biases: Methodological Considerations.

Similar Datasets

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