Quantitative or qualitative development in decision making?

A key question in the developmental sciences is whether developmental differences are quantitative or qualitative. For example, does age increase the speed in processing a task (quantitative differences) or does age affect the way a task is processed (qualitative differences)? Until now, findings in the domain of decision making have been based on the assumption that developmental differences are either quantitative or qualitative. In the current study, we took a different approach in which we tested whether development is best described as being quantitative or qualitative. We administered a judgment version and a choice version of a decision-making task to a developmental sample (njudgment = 109 and nchoice = 137; Mage = 12.5 years, age range = 9–18). The task, the so-called Gambling Machine Task, required decisions between two options characterized by constant gains and probabilistic losses; these characteristics were known beforehand and thus did not need to be learned from experience. Data were analyzed by comparing the fit of quantitative and qualitative latent variable models, so-called multiple indicator multiple cause (MIMIC) models. Results indicated that individual differences in both judgment and choice tasks were quantitative and pertained to individual differences in “consideration of gains,” that is, to what extent decisions were guided by gains. These differences were affected by age in the judgment version, but not in the choice version, of the task. We discuss implications for theories of decision making and discuss potential limitations and extensions. We also argue that the MIMIC approach is useful in other domains, for example, to test quantitative versus qualitative development of categorization, reasoning, math, and memory

Math-Failure Associations, Attentional Biases, and Avoidance Bias: The Relationship with Math Anxiety and Behaviour in Adolescents

Background: Math anxiety in adolescence negatively affects learning math and careers. The current study investigated whether three cognitive biases, i.e. math-failure associations, attentional biases (engagement and disengagement), and avoidance bias for math, were related to math anxiety and math behaviour (math grade and math avoidance behaviour). Methods: In total, 500 secondary school students performed three cognitive bias tasks, questionnaires and a math performance task, and reported their grades. Results: Math-failure associations showed the most consistent associations with the outcome measures. They were associated with higher math anxiety above and beyond sex and education level. Those math-failure associations were also associated with lower grades and more avoidance behaviour, however, not above and beyond math anxiety. Engagement bias and avoidance tendency bias were associated with math avoidance behaviour, though the avoidance bias finding should be interpreted with care given the low reliability of the measure. Disengagement biases were not associated with any math anxiety nor behaviour outcome measure. Conclusions: Whereas a more reliable instrument for avoidance bias is necessary for conclusions on the relations with math performance and behaviour, the current results do suggest that math-failure associations, and not attentional bias, may play a role in the maintenance of math anxiety.</p