Conceptual Knowledge Influences Decision Making Differently in Individuals with High or Low Cognitive Flexibility: An ERP Study

Abstract

ObjectiveStudies using the Iowa Gambling Task (IGT) have distinguished between good and bad decision makers and have provided an explanation for deficits in decision making. Previous studies have demonstrated a link between Wisconsin Card Sorting Test (WCST) performance and IGT performance, but the results were not consistent and failed to explain why WCST performance can predict IGT performance. The present study aimed to demonstrate that WCST performance can predict IGT performance and to identify the cognitive component of the WCST that affects IGT performance using event-related potentials (ERPs).MethodsIn this study, 39 healthy subjects (5 subjects were excluded) were divided into a high group and a low group based on their global score on the WCST. A single-choice version of the IGT was used to eliminate the impact of retrieval strategies on the choice evaluation process and interference due to uncorrelated decks. Differences in the underlying neural mechanisms and explicit knowledge between the two groups during the three stages of the decision-making process were described.ResultsBased on the information processing perspective, we divided the decision-making process into three stages: choice evaluation, response selection, and feedback processing. The behavioral results showed that the highly cognitively flexible participants performed better on the IGT and acquired more knowledge of the task. The ERP results showed that during the choice evaluation stage, the P300 recorded from central and parietal regions when a bad deck appeared was larger in the high group participants than in the low group participants. During the response selection stage, the effect of choice type was significant only in the frontal region in the high group, with a larger effect for passing. During the feedback evaluation stage, a larger FRN was evoked for a loss than for a win in the high group, whereas the FRN effect was absent in the low group.ConclusionCompared with the participants with low cognitive flexibility, the participants with high cognitive flexibility performed better on the IGT, acquired more knowledge of the task, and displayed more obvious somatic markers. The low group participants showed reduced working memory abilities during the choice evaluation stage. The appropriate somatic markers reflected by the DPN is formed only when conceptual knowledge is gained in the response selection stage. The absence of an FRN effect in the subjects who performed poorly on the WCST suggests a significant deficit in feedback learning and reward prediction.

High Cognitive Flexibility Learners Perform Better in Probabilistic Rule Learning

“…This finding may indicate that the highflexibility group used the response criterion correctly more frequently and that this group may have a greater mastery and confidence surrounding rule learning. Using the Iowa Gambling task, Dong et al (2016) showed that people with high flexibility showed explicit knowledge of task rules whereas the low group did not, which is consistent with the higher response accuracy of the high flexibility group in this study. Their research also showed that the lower P300 amplitude of the low flexibility group in the stimulus selection evaluation stage might be due to the lower cognitive and abstract generalization abilities or working memory abilities of the low flexibility group.…”

“…Successful hypothesis testing requires flexible switching among multiple possible hypotheses. High cognitive flexibility learners show better abstract induction, working memory, and feedback learning abilities during the Iowa Gambling task (Dong et al, 2016), which has some common neural basis of rule learning (Hartstra et al, 2010). We speculate that high-flexibility individuals may display more accurate and faster rule acquisition during rule learning as a result of their cognitive advantages.…”

“…For children, cognitive flexibility is a significant predictor of academic performance (Stad et al, 2018). High cognitive flexibility learners, including children (Lehto and Elorinne, 2003) and adults (Dong et al, 2016), usually show better performance on learning task, such as the Iowa Gambling task, which involves decision-making under uncertainly and has partly common neural mechanisms with rule learning (Hartstra et al, 2010).…”

“…It is impossible for learners to achieve complete error-free performance, and they eventually accept certain inevitable mistakes (Craig et al, 2011). This study aims to explore, for the first time, whether healthy adults with high cognitive flexibility show an advantage during a probabilistic rule learning task, just as in other rule-based learning tasks (i.e., Iowa Gambling Tasks; Dong et al, 2016). The WCST is perhaps the most widely used tool to measure cognitive flexibility in neuropsychology at present.…”

Feedback processing in adolescents with prenatal cocaine exposure: an electrophysiological investigation

“…The P300 during decision-making and outcome feedback may be associated with evaluation of probability and estimation of future chances based upon working memory and past experiences (Padron, Fernandez-Rey, Acuna, & Pardo-Vazquez, 2016; Wang et al, 2015). Indeed, smaller P300 amplitudes have been associated with increased cognitive rigidity and poorer performance in tasks of executive function (Dong, Du, & Qi, 2016). As the P300 may represent further processing of the ramifications of choices, a smaller P300 suggests less efficient processing in this domain.…”