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

Klados, Manousos A.; Pandria, Niki; Micheloyannis, Sifis; Margulies, Daniel S.; Bamidis, Panagiotis D.

doi:10.1016/j.ijpsycho.2017.05.003

Cited by 18 publications

(29 citation statements)

References 75 publications

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“…Math anxious individuals show a faster processing speed and a higher error rate, lack attentiveness, show lower participation in class and fleeting learning behaviour (Ashcraft & Faust, 1994, Ashcraft & Moore, 2009. Today, such avoidance behaviours can be explained by neuroscientific findings, which indicate that the experience of MA is associated with brain areas that are involved in pain processing and less so in executive functioning (Hartwright et al, 2018;Klados, Pandria, Micheloyannis, Margulies, & Bamidis, 2017;Lyons & Beilock, 2012;Suárez-Pellicioni, Núñez-Peña, & Colomé, 2013;Young et al, 2012).…”

Section: The Math Anxiety-performance Linkmentioning

confidence: 99%

Relation of state- and trait-math anxiety to intelligence, math achievement and learning motivation

2019

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This study investigates math anxiety (MA) by comparing trait-components of MA with real-time assessments of situational anxiety responses (state-components) in children. The research to date on MA in children is somewhat disparate in regard to methodology, and firm conclusions regarding the relation of MA to intelligence, math achievement and learning motivation are not readily drawn. Typically, the measures used in the MA research have differed by operationalizing either trait-MA and/or state- (or statelike)-MA, but have failed to compare the implications of their respective assumptions and the significance of their findings. Trait-MA and state-MA, self-ratings of math skills, attitudes towards mathematics, math achievement, the social anxiety, test anxiety, learning motivation and intelligence of 1,179 students (48.1% girls) from grades 4 and 5, were assessed. The findings yield evidence of a pronounced state-trait discrepancy. A negative correlation between state-MA and math achievement was observed for all intelligence levels, even when controlling for test- and social-anxiety traits, while there was no negative relation between trait-MA and achievement. State-MA was associated with lower intelligence, lower self-ratings, more negative attitudes, higher performance avoidance and work avoidance goals. In contrast, trait-MA was slightly related to higher mastery approach goals. The failure to adequately differentiate between state- and trait-based research into MA appears to be one reason for key inconsistencies between research findings and warrants further investigations.

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Section: The Math Anxiety-performance Linkmentioning

confidence: 99%

Relation of state- and trait-math anxiety to intelligence, math achievement and learning motivation

2019

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“…estímulos que provocam sentimentos negativos, sendo considerada um indicador de que esteja ocorrendo uma mobilização de recursos atencionais (CARRETIÉ et al, 2004 (2007). Klados et al (2017)…”

Section: Métodounclassified

“…As evidências destacam ainda que os efeitos com AM estão associados a menor ativação cortical durante estágios iniciais do processamento de estímulos numéricos no contexto de tarefas cognitivas, independente da complexidade da tarefa (KLADOS, 2017). Anteriormente a isso, a AM prejudica a desativação da RMP (Rede de Modo Padrão) durante tarefas matemáticas, indicando uma redução na eficiência de processamento.…”

Section: Estudantes Universitáriosunclassified

Bases Neurais da Ansiedade Matemática: implicações para o processo de ensino-aprendizagem

Moura-Silva

Neto²,

Gonçalves

2020

Bolema

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Resumo A Ansiedade Matemática é um fenômeno global e altamente prevalente, possuindo marcadores fisiológicos, cognitivos e comportamentais. No entanto, pouco se conhece sobre seus mecanismos neurais subjacentes. Fornecemos uma Revisâo Sistemática de estudos que investigaram os correlatos neurais da Ansiedade Matemática (AM) na última década e discutimos suas implicações para o processo de ensino aprendizagem. Foram selecionadas pesquisas que avaliaram parâmetros fisiológicos da funçâo cerebral de indivíduos com AM através de bancos de dados eletrônicos, atentando-se a critérios de inclusâo e exclusâo delineados. A qualidade da literatura foi analisada a partir dos 11 itens da escala de qualidade PEDro e conduzida pelo fluxograma de seleçâo de estudos PRISMA, resultando na inclusâo de 14 estudos neurocientíficos. Em geral, a literatura vem sugerir que as redes neurais de medo e de dor sâo estimuladas antes e durante tarefas numéricas em indivíduos com alta AM. Além disso, há uma capacidade reduzida de Memória de Trabalho e déficit de atençâo/inibiçâo em indivíduos com alta AM. Eles também sâo mais propensos a cometer erros em tarefas matemáticas, tem representações menos precisas de magnitude numérica, abordam os problemas matemáticos de maneira diferente de seus pares menos ansiosos e tendem a elevar mais recursos de controle cognitivo para concluir objetivos com estímulos aversivos relacionados ao raciocínio matemático, podendo impactar a eficiência de processamento e gerar déficits de desempenho. Resultados suportam, ainda, que os efeitos da AM estâo associados à uma menor ativaçâo cortical já durante os estágios iniciais do processamento de estímulos numéricos, independente da complexidade da tarefa. Implicações para guiar a prática do professor que ensina Matemática sâo discutidas à luz das evidências.

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“…For this purpose, the AMIGOS dataset [28] was used herein. Features derived by functional connectivity networks were adopted for classification purposes, because (i) our prior studies [29][30][31][32][33][34][35] suggest that there is an alternation in functional connectivity networks during affective processing, while (ii) to the best of our knowledge, there is no other study that has assessed the value of functional connectivity features in the automatic recognition of personality. ReliefF [36] methodology was adopted in order to rank the produced features, while the first ten common features with the highest ranking were fed to an SVM classifier in order to predict each trait separately.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing

et al. 2020

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Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.

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

Cited by 18 publications

References 75 publications

Relation of state- and trait-math anxiety to intelligence, math achievement and learning motivation

Relation of state- and trait-math anxiety to intelligence, math achievement and learning motivation

Bases Neurais da Ansiedade Matemática: implicações para o processo de ensino-aprendizagem

Automatic Recognition of Personality Profiles Using EEG Functional Connectivity during Emotional Processing

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