The shopping brain: Math anxiety modulates brain responses to buying decisions

Jones, William J.; Childers, Terry L.; Jiang, Yang

doi:10.1016/j.biopsycho.2011.10.011

Cited by 53 publications

(62 citation statements)

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“…Jones, Childers, and Jiang (2012) administered the AMAS twice; the first time as a paper-and-pencil and the second time as an online survey. The reported test-retest reliability was very high (r = .91), providing initial evidence both for testretest reliability and AMAS suitability for online measurement.…”

Section: Applications Of Computerized / Online Surveys For Math Anxiementioning

confidence: 99%

Norms and validation of the online and paper-and-pencil versions of the Abbreviated Math Anxiety Scale (AMAS) for Polish adolescents and adults

et al. 2018

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The Abbreviated Math Anxiety Scale (AMAS) is one of the most popular instruments measuring math anxiety (MA). It has been validated across several linguistic and cultural contexts. In this study, we investigated the extent of administration method invariance of the AMAS by comparing results (average scores, reliabilities, factorial structure) obtained online with those from paper-and-pencil. We administered the online version of the AMAS to Polish students. Results indicate that psychometric properties of the AMAS do not differ between online and paper-and-pencil administration. Additionally, average scores of the AMAS did not differ considerably between administration forms, contrary to previous results showing that computerized measurement of MA leads to higher scores. Therefore, our results provide evidence for the usefulness of the AMAS as a reliable and valid MA measurement tool for online research and online screening purposes across cultures and also large similarity between administration forms outside an American-English linguistic and cultural context. Finally, we provide percentile and standard norms for the AMAS for adolescents and adults (in the latter case for both online and paper-and-pencil administration) as well as critical differences for the comparison of both subscales in an individual participant for practical diagnostic purposes.Keywords: math anxiety, Abbreviated Math Anxiety Scale, online survey, validation, norms Journal of Numerical Cognition, 2017, Vol. 3(3), 667-693, doi:10.5964/jnc.v3i3.121 Received: 2017-03-02. Accepted: 2017-08-17. Published (VoR): 2018-01-30.*Corresponding author at: Eberhard Karls Universität Tübingen, Arbeitsbereich Diagnostik und Kognitive Neuropsychologie, Fachbereich Psychologie, Schleichstraße 4 72076 Tübingen, Germany. E-mail: krzysztof.cipora@gmail.com This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Math Underachievement -Its Causes and ConsequencesPoor numeracy may even be more harmful for an individual than poor literacy (Butterworth, Varma, & Laurillard, 2011). Considerable large-scale economic consequences of math underachievement have also been reported (OECD, 2010). Therefore, the cognitive and emotional causes of impaired numeracy are of major interest both theoretically and practically. Regarding emotional aspects, math anxiety (MA) is probably the most prominent and important variable that negatively correlates with math achievement. It has been extensively investigated since the 1960's (see Dowker, Sarkar, & Looi, 2016 for review). A sizeable negative relationship between MA and math performance is evident in previous PISA (Programme for International Student Assessment) studies Journal of Numerical Cognition jnc.psychopen.eu | 2363-8761 across virtually all countries involved in th...

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Section: Applications Of Computerized / Online Surveys For Math Anxiementioning

confidence: 99%

Norms and validation of the online and paper-and-pencil versions of the Abbreviated Math Anxiety Scale (AMAS) for Polish adolescents and adults

et al. 2018

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“…Regarding the impact of MA in the brain's electrophysiology, Suárez-Pellicioni, Núñez-Peña, & Colomé (2013) revealed the relationship between the arithmetic split effect (Ashcraft & Battaglia, 1978) and the P600/P3b component which was more enhanced and delayed during MA state. In addition, P3 seems also to be affected by MA (Jones, Childers, & Jiang, 2012) and more specifically by the buying decisions of individuals suffering from MA. One of our recent electroencephalographic studies (Klados, Simos, Micheloyannis, Margulies, & Bamidis, 2015) revealed that MA demonstrated so reduced ERPs at frontocentral and centroparietal locations (between 380-420 ms) during tasks related to working memory and mathematical processing.…”

Section: Introductionmentioning

confidence: 99%

Math anxiety: Brain cortical network changes in anticipation of doing mathematics

Klados

Pandria

Micheloyannis

et al. 2017

International Journal of Psychophysiology

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Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance.

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“…The late positive component is a positive potential that arises approximately 400-800 ms after the onset of a stimulus associated with explicit recognition memory [18].…”

Section: Lpcmentioning

confidence: 99%

Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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The shopping brain: Math anxiety modulates brain responses to buying decisions

Cited by 53 publications

References 78 publications

Norms and validation of the online and paper-and-pencil versions of the Abbreviated Math Anxiety Scale (AMAS) for Polish adolescents and adults

Norms and validation of the online and paper-and-pencil versions of the Abbreviated Math Anxiety Scale (AMAS) for Polish adolescents and adults

Math anxiety: Brain cortical network changes in anticipation of doing mathematics

Deep Learning for EEG-Based Preference Classification in Neuromarketing

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