Sensitivity to Context in Human Interactions

Waddup, Oliver J.; Błasiak, Paweł; Yearsley, James M.; Wojciechowski, Bartosz; Pothos, Emmanuel M.

doi:10.3390/math9212784

Cited by 5 publications

(4 citation statements)

References 51 publications

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“…Additionally, people who are naturally intuitive in their personalities tend to be better at processing social information (Mega & Volz, 2017;Garrison & Melendy, 2022). The study also pointed out the importance of being sensitive to social rules in handling relationships and different situations (Waddup et al, 2021;Ibañez & Manes, 2012). Lastly, it suggested that changing how people see others' feelings and thoughts could improve their social skills (Lieberman, 2006).…”

Section: Social Intelligence Of the University Studentsmentioning

confidence: 89%

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Interrelating the Emotional Styles and Social Intelligence of University Students

Gonzaga,

Abrenilla,

Dumanhog

et al. 2024

Am. J. Hum. Psychol.

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Emotional Styles and Social Intelligence are significant factors in maintaining a healthy well-being and good interpersonal relationships, which contribute to one’s mental health. Due to the changing circumstances, a decline in students’ mental health was evident. This study delves into the correlation between Emotional Styles and the Social Intelligence of university students, providing insights into how emotional well-being influences social relationships. A total of 375 university students participated, providing a diverse sample for a comprehensive investigation into these variables. The analyzed data revealed that the level of Emotional Styles is moderately evident among university students, emphasizing social intuition as the highest. On the other hand, there is a slightly high level of Social Intelligence, particularly the social information processing dimension. Pearson r analysis showed a positive correlation between the variables (p= <0.001; r=0.472). Furthermore, linear regression analysis highlights social intuition (p=0.041; t=2.049) and sensitivity to context (p=0.003; t=2.953) as significant predictors of Social Intelligence, emphasizing the importance of understanding nonverbal cues and contextual sensitivity in interpersonal interactions. With these results, the research recommends exploring additional factors, such as empathy and self-regulation, on the dimensions of Emotional Styles that could significantly influence Social Intelligence. Moreover, this research might consider increasing the sample size to include more diverse representations from the LGBTQ+ community in their demographic profile.

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Section: Social Intelligence Of the University Studentsmentioning

confidence: 89%

“…The study's findings are reinforced by several research studies that illuminate the relationship between emotional well-being and social dynamics. et al, 2015;Van Bavel et al, 2016;Yamashita et al, 2016;Waddup et al, 2021).…”

Section: Social Intelligence Of the University Studentsmentioning

confidence: 99%

Interrelating the Emotional Styles and Social Intelligence of University Students

Gonzaga,

Abrenilla,

Dumanhog

et al. 2024

Am. J. Hum. Psychol.

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“…It is possible that such properties could be employed for cognitive findings which have previously benefited from quantum models using entanglement, such as conceptual combination [ 55 , 56 ] or bipartite games, such as Prisoner’s Dilemma, with communication [ 57 ]. A particularly interesting direction for application is whether quantum circuits can offer new perspectives on more efficient memory architectures, taking advantage of superpositions and entanglement to multiply query memory representations.…”

Section: Related and Further Workmentioning

confidence: 99%

Quantum Circuit Components for Cognitive Decision-Making

Widdows

Rani

Pothos

2023

Entropy

Self Cite

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This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer ‘yes’ or ‘no’, so the population that answers ‘yes’ to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

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“…It is possible that such properties could be employed for cognitive findings which have previously benefited from quantum models using entanglement, such as conceptual combination (Aerts and Gabora, 2005;Bruza et al, 2015) or bipartite games, like Prisoner's Dilemma, with communication (Waddup et al, 2021). A particularly interesting direction for application is whether quantum circuits can offer new perspectives on more efficient memory architectures, taking advantage of superpositions and entanglement to multiply query memory representations.…”

Section: Related and Further Workmentioning

confidence: 99%

Quantum Circuit Components for Cognitive Decision-Making

Widdows¹,

Rani²,

Pothos³

2023

Preprint

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Since the 1990's, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can however be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020's, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modelling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.

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Sensitivity to Context in Human Interactions

Cited by 5 publications

References 51 publications

Interrelating the Emotional Styles and Social Intelligence of University Students

Interrelating the Emotional Styles and Social Intelligence of University Students

Quantum Circuit Components for Cognitive Decision-Making

Quantum Circuit Components for Cognitive Decision-Making

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