Putting the Nonsocial Into Social Neuroscience: A Role for Domain-General Priority Maps During Social Interactions

Ramsey, Richard; Ward, Rob

doi:10.1177/1745691620904972

Cited by 42 publications

(52 citation statements)

References 112 publications

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“…Reliance on domain-general executive control mechanisms in both aesthetic and nonaesthetic contexts provide empirical evidence for the proposal that the underlying cognitive control mechanisms that support aesthetic appraisal are comparable to those that support general-purpose behaviour (Bara et al, 2021). More generally, these findings provide support for broader theoretical models from social and cognitive neuroscience, which emphasise the role played by domain-general executive systems in information processing (Binney & Ramsey, 2020;Barrett, 2012;Duncan, 2010;Ramsey & Ward, 2020;Spunt & Adolphs, 2017). In contrast, we provide no support for accounts of aesthetic information processing that propose roles for partly distinct mechanisms between aesthetic and non-aesthetic contexts (Goldman, 2001;Guyer, 2005).…”

Section: Theoretical Impact: Specialised Vs Generalist Accounts Of Aesthetic Experiencementioning

confidence: 57%

Investigating the Role of Executive Resources across Aesthetic and Non-Aesthetic Judgments

Bara¹,

Binney²,

Ramsey³

2021

Preprint

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Aesthetic judgments dominate much of daily life by guiding how we evaluate objects, people, and experiences in our environment. One key question that remains unanswered is the extent to which more specialised or largely general cognitive resources support aesthetic judgments. To investigate this question in the context of executive resources, we examined the extent to which a central working memory load produces similar or different reaction time interference on aesthetic compared to non-aesthetic judgments. Across three pre-registered experiments that used Bayesian multi-level modelling approaches (N>100 per experiment), we found clear evidence that a central working memory load produces similar reaction time interference on aesthetic judgments relative to non-aesthetic (motion) judgments. We also showed that this similarity in processing across aesthetic versus non-aesthetic judgments holds across variations in the form of art (people vs landscape; Exps. 1-3), medium type (artwork vs photographs; Exp. 2) and load content (art images vs letters; Exps. 1-3). These findings suggest that across a range of experimental contexts, as well as different processing streams in working memory (e.g., visual vs verbal), aesthetic and motion judgments commonly rely on a domain-general executive system, rather than a system that is more specifically tied to aesthetic judgments. In doing so, these findings shine new light on the cognitive architecture that supports aesthetic judgments, as well as how domain-general executive systems operate more generally in cognition.

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Section: Theoretical Impact: Specialised Vs Generalist Accounts Of Aesthetic Experiencementioning

confidence: 57%

Investigating the Role of Executive Resources across Aesthetic and Non-Aesthetic Judgments

Bara¹,

Binney²,

Ramsey³

2021

Preprint

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“…[44]). Third, general cognitive and brain functions, such as those associated with aspects of memory, attention, and semantics, have been undervalued in HRI research to date, even though strong arguments exist for considering these in combination with more specialised, categoryselective processes [59][60][61].…”

Section: Box 2 Cautionary Tales For Cognitive Neuroscience Investigamentioning

confidence: 99%

Mind Meets Machine: Towards a Cognitive Science of Human–Machine Interactions

Cross

Ramsey

2021

Trends in Cognitive Sciences

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As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting humanmachine interactions is becoming increasingly pressing. We introduce a framework for studying the cognitive and brain mechanisms that support humanmachine interactions, leveraging advances made in cognitive neuroscience to link different levels of description with relevant theory and methods. We highlight unique features that make this endeavour particularly challenging (and rewarding) for brain and behavioural scientists. Overall, the framework offers a way to study the cognitive science of human-machine interactions that respects the diversity of social machines, individuals' expectations and experiences, and the structure and function of multiple cognitive and brain systems. From Social Cognition to Social Machines Over a decade ago, Microsoft founder Bill Gates prophesied a robotics revolution that would see staggering leaps in the progress and sophistication of robots, and predicted 'a robot in every home' in the near future [1]. Although the ubiquity of household robots has yet to be realised, we are opening our doors to an increasing number of artificially intelligent machines (see Glossary). Concurrently, a growing number of robotics start-ups are focusing on developing companion robots for the home or assistance robots to serve in complex, human-interactive contexts, including schools, hospitals, and care homes [2]. As progress towards developing machines that take on increasingly sophisticated social roles continues apace, the cognitive and brain mechanisms that underpin social engagement with these machines remain largely unknown. A greater understanding of the psychological and neurobiological foundations of human interactions with artificially intelligent social machines (henceforth referred to as 'social machines') has important implications for the design and programming of socially engaging and collaborative artificial agents. It is equally critical to use this understanding of human-machine interaction to further our knowledge of the flexibility and limits of neurocognitive processes supporting human social behaviour towards both human and artificial agents. Highlights Although machines designed to socially interact with humans are proliferating, our understanding of the mental processes supporting such interactions remains limited.

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“…Overall, our central argument in this section is that, for more substantial progress to be made, we encourage the field to fully embrace research perspectives from separate but related domains. Progress will be facilitated by a greater focus on generalized processes and other general frameworks, which are not necessarily or strictly tied to visual or motor processing per se [99][100][101][102].…”

Section: Open Accessmentioning

confidence: 99%

Watch and Learn: The Cognitive Neuroscience of Learning from Others’ Actions

Ramsey

Kaplan

Cross

2021

Trends in Neurosciences

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The mirror neuron system has dominated understanding of observational learning from a cognitive neuroscience perspective. Our review highlights the value of observational learning frameworks that integrate a more diverse and distributed set of cognitive and brain systems, including those implicated in sensorimotor transformations, as well as in more general processes such as executive control, reward, and social cognition. We argue that understanding how observational learning occurs in the real world will require neuroscientific frameworks that consider how visuomotor processes interface with more general aspects of cognition, as well as how learning context and action complexity shape mechanisms supporting learning from watching others. Learning from Watching Others Is a Fundamental Human SkillWhether learning to dance Gangnam Style, open a bottle of champagne, or tie shoelaces, humans learn a great deal by simply watching others [1,2]. Learning by observation can have many benefits over physical practice without observation. This is especially true in dangerous or novel environments where poor initial performance can be costly, such as learning to drop in on a skateboard ramp without first watching someone else do it successfully. Equally, learning by watching others is beneficial in social situations where active participation is not possible, such as watching your supervisor give critical feedback to a colleague. As such, observational learning has been of considerable interest to experts from a variety of disciplines, including social and developmental psychology, sport and exercise science, comparative biology and robotics, where researchers are examining how we learn from watching others across a range of behaviors, from simple motor movements to complex social interactions [1,[3][4][5]. HighlightsUnderstanding how the human brain translates visual information into skilled motor performance has been assisted and constrained by the discovery of mirror neurons.Emerging evidence highlights how observational motor learning involves a far more diffuse network of brain regions and cognitive processes, which are shaped by the context and complexity of the motor task to be learned.A greater emphasis on combining functional decomposition and functional integration approaches should facilitate paradigms and discoveries that move us closer toward understanding how we learn from watching others in complex, real-world scenarios.

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Putting the Nonsocial Into Social Neuroscience: A Role for Domain-General Priority Maps During Social Interactions

Cited by 42 publications

References 112 publications

Investigating the Role of Executive Resources across Aesthetic and Non-Aesthetic Judgments

Investigating the Role of Executive Resources across Aesthetic and Non-Aesthetic Judgments

Mind Meets Machine: Towards a Cognitive Science of Human–Machine Interactions

Watch and Learn: The Cognitive Neuroscience of Learning from Others’ Actions

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