The connectome‐based prediction of trust propensity in older adults: A resting‐state functional magnetic resonance imaging study

Chen, Yiqi; He, Hao; Wang, Lin; Yang, Jiaying; Tan, Shan; Wan, Tao; Guan, Qing

doi:10.1002/hbm.26385

Cited by 6 publications

(1 citation statement)

References 101 publications

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“…Meanwhile, economic games, such as trust game, dictator game, public goods game, and social dilemmas, provide a direct and potentially more accurate means to assess trust by observing individual actions in well-defined contexts ( Thielmann et al, 2020 ). This approach is beneficial for deducing levels of trust from real decisions and minimizing the impact of social desirability bias ( Chen et al, 2023 ). When integrated, behavioral observations from economic games and self-reported beliefs yield a more comprehensive perspective on trust by combining the strengths of observed actions and declared beliefs.…”

Section: Interpersonal Trustmentioning

confidence: 99%

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

Li,

Wu,

Huang

et al. 2024

Front. Psychol.

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The rapid advancement of artificial intelligence (AI) has impacted society in many aspects. Alongside this progress, concerns such as privacy violation, discriminatory bias, and safety risks have also surfaced, highlighting the need for the development of ethical, responsible, and socially beneficial AI. In response, the concept of trustworthy AI has gained prominence, and several guidelines for developing trustworthy AI have been proposed. Against this background, we demonstrate the significance of psychological research in identifying factors that contribute to the formation of trust in AI. Specifically, we review research findings on interpersonal, human-automation, and human-AI trust from the perspective of a three-dimension framework (i.e., the trustor, the trustee, and their interactive context). The framework synthesizes common factors related to trust formation and maintenance across different trust types. These factors point out the foundational requirements for building trustworthy AI and provide pivotal guidance for its development that also involves communication, education, and training for users. We conclude by discussing how the insights in trust research can help enhance AI’s trustworthiness and foster its adoption and application.

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Section: Interpersonal Trustmentioning

confidence: 99%

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

Li,

Wu,

Huang

et al. 2024

Front. Psychol.

View full text Add to dashboard Cite

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The connectome‐based prediction of trust propensity in older adults: A resting‐state functional magnetic resonance imaging study

Chen

Wang

et al. 2023

Human Brain Mapping

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A recent neuropsychoeconomic model of trust propensity argues that an individual uses economic (executive functions) and social (social cognition) rationality strategies to transform the risk of treachery (affect) into positive expectations of reciprocity, promoting trust in another person. Previous studies have shown that the trust of older adults is associated with affect and social cognition. However, little is known about the intrinsic functional connectivity correlated with trust propensity or whether trust propensity is associated with executive functions in older adults. In this study, we examined the association between trust propensity (measured by a one‐shot trust game [TG]), social preference (measured by a one‐shot dictator game), and executive functions (measured by a battery of neuropsychological tests). We also performed connectome‐based predictive modeling (CPM) and computational lesion analysis to identify the key large‐scale resting‐state functional connectivity (RSFC) underlying the prediction of trust propensity. Our behavioral results showed a lower trust propensity in older adults in our study than in younger adults in a previous meta‐analysis. Furthermore, trust propensity was associated with social preference, but there was no significant relationship between trust propensity and executive functions. The neuroimaging results showed that the cingulo‐opercular network (CON) and the default mode network (DMN), rather than the frontoparietal network (FPN), significantly contributed to the prediction of trust propensity in older adults. Our findings suggest that older adults rely less on economic rationality (executive functions, associated with FPN) in trust games. Rather, they are likely to depend more on social rationality (social cognition, associated with social preference and DMN) to resolve the risk of treachery (affect, associated with CON) in trust dilemmas. This study contributes to a better understanding of the neural underpinnings of older adults' trust propensity.

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Prediction of individual performance and verbal intelligence scores from resting‐state fMRI in children and adolescents

He,

Kou

2024

Intl J of Devlp Neuroscience

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The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome‐based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting‐state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting‐state features from whole‐brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10‐fold cross‐validation framework, our findings revealed that prediction models based on whole‐brain functional connectivity effectively predicted performance IQ scores( ) but not verbal IQ scores( ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto‐parietal task control network (FTPN) for performance IQ scores ( ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

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The connectome‐based prediction of trust propensity in older adults: A resting‐state functional magnetic resonance imaging study

Cited by 6 publications

References 101 publications

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust

The connectome‐based prediction of trust propensity in older adults: A resting‐state functional magnetic resonance imaging study

Prediction of individual performance and verbal intelligence scores from resting‐state fMRI in children and adolescents

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