Individual differences in sensitivity to food stimuli: Relationship between cognitive biases and trait eating behavior

Appetite ◽  
2006 ◽  
Vol 47 (2) ◽  
pp. 265 ◽  
Author(s):  
J. Harrold ◽  
M. Field ◽  
C. Hall ◽  
M. Healy ◽  
N. Williams ◽  
...  
Keyword(s):  
Individual Differences ◽  
Eating Behavior ◽  
Cognitive Biases

scholarly journals Measuring individual differences in cognitive biases: The Cognitive Bias Inventory

2021 ◽  
Author(s):  
Vincent Berthet ◽  
Vincent de Gardelle
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Bias ◽  
Cognitive Biases ◽  
Temporal Stability ◽  
Real Life ◽  
Future Research ◽  
Personality Factors ◽  
Wrong Answer ◽  
Outcome Bias

This article described the behavioral measurement of six classic cognitive biases (framing, availability, anchoring, overconfidence, hindsight/outcome bias, confirmation bias). Each measure showed a satisfactory level of reliability with regard both to internal consistency (mean Cronbach’s alpha = .77) and temporal stability (mean test-retest correlation = .71). Multivariate analysis supported the hypothesis that each cognitive bias captures specific decision-making processes as the six biases: (a) were virtually uncorrelated (mean correlation = .08), thus indicating no general decision-making competence factor, (b) were moderately correlated with other relevant constructs (the A-DMC components, cognitive ability, decision-making styles, and personality factors), (c) were more related to performance on a narrow domain of decision-making (the ability to overcome an intuitive wrong answer as measured by the CRT) than to the general success in real-life decision-making as measured by the Decision Outcomes Inventory (DOI). We introduce this set of behavioral tasks as the Cognitive Bias Inventory (CBI), a psychometric tool allowing for the reliable assessment of individual differences in six common, independent cognitive shortcuts. The CBI appears as a useful tool for future research on decision-making competence and how it relates to decision errors.

scholarly journals The Measurement of Individual Differences in Cognitive Biases: A Review and Improvement

2021 ◽  
Vol 12 ◽  
Author(s):  
Vincent Berthet
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Biases ◽  
Empirical Work ◽  
Confirmation Bias ◽  
Generic Measures

Individual differences have been neglected in decision-making research on heuristics and cognitive biases. Addressing that issue requires having reliable measures. The author first reviewed the research on the measurement of individual differences in cognitive biases. While reliable measures of a dozen biases are currently available, our review revealed that some measures require improvement and measures of other key biases are still lacking (e.g., confirmation bias). We then conducted empirical work showing that adjustments produced a significant improvement of some measures and that confirmation bias can be reliably measured. Overall, our review and findings highlight that the measurement of individual differences in cognitive biases is still in its infancy. In particular, we suggest that contextualized (in addition to generic) measures need to be improved or developed.

scholarly journals Inter-Individual Differences in Food Addiction and Other Forms of Addictive-Like Eating Behavior

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 325
Author(s):  
Paul Brunault ◽  
Nicolas Ballon
Keyword(s):  
Individual Differences ◽  
Eating Behavior ◽  
Food Addiction ◽  
Compulsive Eating

The “addictive-like eating behavior” phenotype encompasses different terms or concepts, including “food addiction” (FA), “eating addiction” or “compulsive eating behavior” [...]

scholarly journals Actigraphy-Derived Sleep Is Associated with Eating Behavior Characteristics

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 852
Author(s):  
Rocío Barragán ◽  
Faris M. Zuraikat ◽  
Victoria Tam ◽  
Samantha Scaccia ◽  
Justin Cochran ◽  
...  
Keyword(s):  
Individual Differences ◽  
Eating Behavior ◽  
Dietary Restraint ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Fragmentation ◽  
Poor Sleep ◽  
Sleep Onset Latency ◽  
Fragmentation Index ◽  
Behavior Characteristics

Poor sleep is a determinant of obesity, with overconsumption of energy contributing to this relationship. Eating behavior characteristics are predictive of energy intake and weight change and may underlie observed associations of sleep with weight status and obesity risk factors. However, relationships between sleep and dimensions of eating behavior, as well as possible individual differences in these relations, are not well characterized. Therefore, the aim of this study was to evaluate whether sleep behaviors, including duration, timing, quality, and regularity relate to dietary restraint, disinhibition, and tendency towards hunger and to explore whether these associations differ by sex. This cross-sectional study included 179 adults aged 20–73 years (68.7% women, 64.8% with BMI ≥ 25 kg/m2). Sleep was evaluated by accelerometry over 2 weeks. Eating behavior dimensions were measured with the Three-Factor Eating Questionnaire. Prolonged wake after sleep onset (WASO) (0.029 ± 0.011, p = 0.007), greater sleep fragmentation index (0.074 ± 0.036, p = 0.041), and lower sleep efficiency (−0.133 ± 0.051, p = 0.010) were associated with higher dietary restraint. However, higher restraint attenuated associations of higher WASO and sleep fragmentation with higher BMI (p-interactions < 0.10). In terms of individual differences, sex influenced associations of sleep quality measures with tendency towards hunger (p-interactions < 0.10). Stratified analyses showed that, in men only, higher sleep fragmentation index, longer sleep onset latency, and lower sleep efficiency were associated with greater tendency towards hunger (β = 0.115 ± 0.037, p = 0.003, β = 0.169 ± 0.072, p = 0.023, β = −0.150 ± 0.055, p = 0.009, respectively). Results of this analysis suggest that the association of poor sleep on food intake could be exacerbated in those with eating behavior traits that predispose to overeating, and this sleep-eating behavior relation may be sex-dependent. Strategies to counter overconsumption in the context of poor quality sleep should be evaluated in light of eating behavior traits.

Individual differences in epistemically suspect beliefs: The role of susceptibility to cognitive biases

2020 ◽  
Author(s):  
Jakub Šrol
Keyword(s):  
Individual Differences ◽  
Probabilistic Reasoning ◽  
Cognitive Biases ◽  
Political Liberalism ◽  
Real Life ◽  
Religious Faith ◽  
Analytic Thinking ◽  
Evaluation Of Evidence ◽  
Reasoning Biases

The endorsement of epistemically suspect (i.e. paranormal, conspiracy, and pseudoscientific) beliefs is widespread and has negative real-life consequences. Therefore, it is important to understand individual differences in epistemically suspect beliefs and their associations with systematic reasoning errors – cognitive biases. In Study 1 (N = 263), I constructed a novel questionnaire of epistemically suspect beliefs and examined its psychometric properties and relationships with probabilistic reasoning biases. In Study 2 (N = 397), I examined probabilistic reasoning biases and biased evaluation of evidence as predictors of the endorsement of epistemically suspect beliefs, while accounting for analytic thinking and worldview variables. Although probabilistic reasoning biases, analytic thinking, religious faith, and political liberalism consistently predicted epistemically suspect beliefs, the effect of biased evaluation of evidence was partialled out by analytic thinking. Further research will be needed to examine the interplay between analytic thinking and the tendency toward information evaluation biased by one’s existing beliefs.

scholarly journals Individual differences in logical intuitions on reasoning problems presented under two-response paradigm

2019 ◽  
Author(s):  
Roman Burič ◽  
Jakub Šrol
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Cognitive Biases ◽  
Detection Efficiency ◽  
Initial Response ◽  
Conflict Detection ◽  
Process Models ◽  
Dual Process ◽  
Cognitive Reflection ◽  
The Individual

Studies on individual differences in susceptibility to cognitive biases have identified several cognitive dispositions which were thought to predict reasoning by contributing to the efficiency of analytic thought. Recently formulated hybrid models, however, suggest that substantial differences between reasoners may arise early already in the intuitive stages of the reasoning process. To address this possibility, we examined standard individual difference measures, mindware instantiation, and conflict detection efficiency as predictors of the accuracy on conflict reasoning problems presented under a two-response paradigm. This was intended to tease apart the predictors of intuitive responding from those factors which only contribute to reasoning when participants have enough time for analytic engagement. We found that participants correctly solved almost half of conflict reasoning problems already at the initial response stage and that the individual differences in initial reasoning performance were predicted by their cognitive reflection, mindware instantiation, and detection efficiency. The findings advance the specification of hybrid dual-process models and provide corroborating evidence that a part of the link between bias susceptibility and cognitive dispositions is due to differences in intuitive processing.

scholarly journals Predicting individual differences in conflict detection and bias susceptibility during reasoning

2019 ◽  
Author(s):  
Jakub Šrol ◽  
Wim De Neys
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Large Scale ◽  
Cognitive Biases ◽  
Detection Efficiency ◽  
Conflict Detection ◽  
Process Models ◽  
Thinking Dispositions ◽  
Cognitive Reflection

One of the key components of the susceptibility to cognitive biases is the ability to monitor for conflict that may arise between intuitively cued “heuristic” answers and logical principles. While there is evidence that people differ in their ability to detect such conflicts, it is not clear which individual factors are driving these differences. In the present large-scale study (N = 399) we explored the role of cognitive ability, thinking dispositions, numeracy, cognitive reflection, and mindware instantiation (i.e. knowledge of logical principles) as potential predictors of individual differences in conflict detection ability and overall accuracy on a battery of reasoning problems. Results showed that mindware instantiation was the single best predictor of both conflict detection efficiency and reasoning accuracy. Cognitive reflection, thinking dispositions, numeracy, and cognitive ability played a significant but smaller role. The full regression model accounted for 40% of the variance in overall reasoning accuracy, but only 7% of the variance in conflict detection efficiency. We discuss the implications of these findings for popular process models of bias susceptibility.

Cognition and Expert-Level Proficiency in Intelligence Analysis

2019 ◽  
pp. 976-1000
Author(s):  
David T. Moore ◽  
Robert R. Hoffman
Keyword(s):  
Decision Making ◽  
Critical Thinking ◽  
Individual Differences ◽  
Performance Measures ◽  
Cognitive Biases ◽  
Intelligence Community ◽  
Intelligence Analysis ◽  
Robust Performance ◽  
Procedural Skills ◽  
Proficiency Level

Proficiency scaling in the domain of intelligence analysis converges on an answer to the question of what counts as expertise in this domain. Proficiency scales in the domain are based on what are called essential competencies. There are many distinct analytical roles, entailing a specialization of expertise. This chapter discusses macrocognitive models of analyst reasoning and knowledge as a function of proficiency level, including recognition-primed decision making and intuition. This chapter also considers individual differences and conceptualize the different styles to be relatively stable and distinctive approaches to critical thinking. Proficiency scaling entails the issue of whether intelligence analysts are prone to cognitive biases. Analysts must cope with the problem of indeterminate causation, that is, the understanding of events for which there is no single cause, and causal forces include human agency and motivations. Directives in the intelligence community call for robust performance measures, but measuring analyst procedural skills is non-trivial. Finally, the implications for training are discussed.

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