Abstract
This study uses event-related brain potentials (ERPs) to investigate the electrophysiological correlates of numeric conflict monitoring in math-anxious individuals, by analyzing whether math anxiety is related to abnormal processing in early conflict detection (as shown by the N450 component) and/or in a later, response-related stage of processing (as shown by the conflict sustained potential; Conflict-SP). Conflict adaptation effects were also studied by analyzing the effect of the previous trial’s congruence in current interference. To this end, 17 low math-anxious (LMA) and 17 high math-anxious (HMA) individuals were presented with a numerical Stroop task. Groups were extreme in math anxiety but did not differ in trait or state anxiety or in simple math ability. The interference effect of the current trial (incongruent-congruent) and the interference effect preceded by congruence and by incongruity were analyzed both for behavioral measures and for ERPs. A greater interference effect was found for response times in the HMA group than in the LMA one. Regarding ERPs, the LMA group showed a greater N450 component for the interference effect preceded by congruence than when preceded by incongruity, while the HMA group showed greater Conflict-SP amplitude for the interference effect preceded by congruence than when preceded by incongruity. Our study showed that the electrophysiological correlates of numeric interference in HMA individuals comprise the absence of a conflict adaptation effect in the first stage of conflict processing (N450) and an abnormal subsequent up-regulation of cognitive control in order to overcome the conflict (Conflict-SP). More concretely, our study shows that math anxiety is related to a reactive and compensatory recruitment of control resources that is implemented only when previously exposed to a stimuli presenting conflicting information.
Highlights
The anxiety towards mathematics has been defined as a ‘‘feeling of tension and apprehension surrounding the manipulation of numbers and the solving of mathematical problems in academic, private and social settings’’ [1]
This study aimed to investigate numeric conflict monitoring and conflict adaptation in high math-anxious individuals with the help of the event-related brain potentials (ERPs) technique, in order to investigate further whether math anxiety is related to difficulties in early and/or later stages of conflict processing, and to better understand math anxiety-related differences in the execution of attentional control when conflict is encountered in processing
As far as we know, this is the first time that numeric conflict monitoring and adaptation are studied with ERPs in math anxious individuals
Summary
The anxiety towards mathematics has been defined as a ‘‘feeling of tension and apprehension surrounding the manipulation of numbers and the solving of mathematical problems in academic, private and social settings’’ [1]. This type of anxiety has been attracting considerable research interest in recent years given that its negative impact on students’ mathematical development is becoming increasingly clear In this respect, math anxiety is one of the main causes of math avoidance, the tendency of these students to avoid courses and career paths that are related to numbers, a response that stops their mathematical learning at an earlier stage as compared to their low math-anxious counterparts [2]. Math anxiety is one of the main causes of math avoidance, the tendency of these students to avoid courses and career paths that are related to numbers, a response that stops their mathematical learning at an earlier stage as compared to their low math-anxious counterparts [2] This fact has its negative consequences on their professional development, employment opportunities, and even salary prospects. It has been demonstrated that high math-anxious individuals show: less precise representations of numerical magnitudes [3]; difficulties in counting objects in a visual enumeration task [4]; difficulties in solving complex arithmetic problems [5]; difficulties in processing large-split solutions in simple arithmetic verification [6]; greater cognitive effort and resource investment in preparation for a task goal [7]; abnormal error monitoring for errors committed in a numerical task [8], etc
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