Lateralisation of self-esteem: An investigation using a dichotically presented auditory adaptation of the Implicit Association Test

McKay, Ryan; Arciuli, Joanne; Atkinson, A. V.; Bennett, Elaine; Pheils, Elisabeth

doi:10.1016/j.cortex.2009.05.004

Cited by 14 publications

(6 citation statements)

References 58 publications

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“…RH) and participants' task was to attribute each of these traits either to themselves or to other people. Faster response times for linking good qualities to themselves and negative traits to others were observed when the words were presented through the right-ear [76]. Similarly, rightward errors in the line bisection test, which indicate a relative LH hyperfunctioning, were associated with a tendency of participants to describe themselves as active, dominant, mighty, powerful and strong [77].…”

Section: Introductionmentioning

confidence: 99%

The Neural Basis of Optimism and Pessimism

Hecht

2013

Exp Neurobiol

102

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Our survival and wellness require a balance between optimism and pessimism. Undue pessimism makes life miserable; however, excessive optimism can lead to dangerously risky behaviors. A review and synthesis of the literature on the neurophysiology subserving these two worldviews suggests that optimism and pessimism are differentially associated with the two cerebral hemispheres. High self-esteem, a cheerful attitude that tends to look at the positive aspects of a given situation, as well as an optimistic belief in a bright future are associated with physiological activity in the left-hemisphere (LH). In contrast, a gloomy viewpoint, an inclination to focus on the negative part and exaggerate its significance, low self-esteem as well as a pessimistic view on what the future holds are interlinked with neurophysiological processes in the right-hemisphere (RH). This hemispheric asymmetry in mediating optimistic and pessimistic outlooks is rooted in several biological and functional differences between the two hemispheres. The RH mediation of a watchful and inhibitive mode weaves a sense of insecurity that generates and supports pessimistic thought patterns. Conversely, the LH mediation of an active mode and the positive feedback it receives through its motor dexterity breed a sense of confidence in one's ability to manage life's challenges, and optimism about the future.

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Section: Introductionmentioning

confidence: 99%

The Neural Basis of Optimism and Pessimism

Hecht

2013

Exp Neurobiol

102

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“…Fossati et al (2004) find that the right hemisphere has significant effects on encoding negative words under self-perception evaluation, involving right premotor cortex, right medial prefrontal cortex (dorsal), and right extra-striate cortex, which indicates that negative-evaluation is associated with right hemisphere. McKay et al (2010) conduct an in-depth research on neural representation process of positive self-evaluation using dichotic listening method, which verifies that left hemisphere plays a dominant role in positive self-statements. These findings show that the right and left hemispheres have different self-evaluation neural representations.…”

Section: Discussionmentioning

confidence: 97%

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

Peng

Saxena³

et al. 2017

Front. Comput. Neurosci.

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Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method.Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor.Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions.Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

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“…Although the original IAT has traditionally been used to measure attitudes (stereotypes/implicit bias) in social psychology (e.g., Gawronski, 2002; Wilson & Scior, 2013), research has shown that adaptations of the IAT paradigm can be used for associations between non‐social categories as well (e.g., flowers/insects and their association with pleasant/unpleasant attributes; Greenwald et al, 1998). Moreover, the IAT works for the auditory domain as well (McKay, Arciuli, Atkinson, Bennett, & Pheils, 2010) and even for the association between auditory and visual domains (Parise & Spence, 2012). This universal applicability encouraged us to use the SIAT as a social variant for investigating associations between vocal and facial stimuli.…”

Section: Methodsmentioning

confidence: 99%

Supramodal neural networks support top‐down processing of social signals

Sonderfeld

Mathiak

Häring

et al. 2020

Human Brain Mapping

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The perception of facial and vocal stimuli is driven by sensory input and cognitive top‐down influences. Important top‐down influences are attentional focus and supramodal social memory representations. The present study investigated the neural networks underlying these top‐down processes and their role in social stimulus classification. In a neuroimaging study with 45 healthy participants, we employed a social adaptation of the Implicit Association Test. Attentional focus was modified via the classification task, which compared two domains of social perception (emotion and gender), using the exactly same stimulus set. Supramodal memory representations were addressed via congruency of the target categories for the classification of auditory and visual social stimuli (voices and faces). Functional magnetic resonance imaging identified attention‐specific and supramodal networks. Emotion classification networks included bilateral anterior insula, pre‐supplementary motor area, and right inferior frontal gyrus. They were pure attention‐driven and independent from stimulus modality or congruency of the target concepts. No neural contribution of supramodal memory representations could be revealed for emotion classification. In contrast, gender classification relied on supramodal memory representations in rostral anterior cingulate and ventromedial prefrontal cortices. In summary, different domains of social perception involve different top‐down processes which take place in clearly distinguishable neural networks.

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Lateralisation of self-esteem: An investigation using a dichotically presented auditory adaptation of the Implicit Association Test

Cited by 14 publications

References 58 publications

The Neural Basis of Optimism and Pessimism

The Neural Basis of Optimism and Pessimism

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

Supramodal neural networks support top‐down processing of social signals

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