Neural correlates of mentalizing-related computations during strategic interactions in humans

By Alan N. Hampton, Peter Bossaerts, John P. O'Doherty. Proceedings of the National Academy of Sciences of the United States of America, 2008

517 citations recorded by the source corpus.

Competing successfully against an intelligent adversary requires the ability to mentalize an opponent's state of mind to anticipate his/her future behavior. Although much is known about what brain regions are activated during mentalizing, the question of how this function is implemented has received little attention to date. Here we formulated a computational model describing the capacity to mentalize in games. We scanned human subjects with functional MRI while they participated in a simple two-player strategy game and correlated our model against the functional MRI data. Different model components captured activity in distinct parts of the mentalizing network. While medial prefrontal cortex tracked an individual's expectations given the degree of model-predicted influence, posterior superior temporal sulcus was found to correspond to an influence update signal, capturing the difference between expected and actual influence exerted. These results suggest dissociable contributions of different parts of the mentalizing network to the computations underlying higher-order strategizing in humans.

Subfields: Cognitive Neuroscience, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3c1a856016011da6eb21f310b0fc5adec92eb23f

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Citations in the corpus

Why and how to use virtual reality to study human social interaction: The challenges of exploring a new research landscape (2018, background): “An alternative may be to draw on research in neuroeconomics and develop a model of the human-VC behaviour in which the values and rewards that the human and VC assign to different options can be modelled on a trial-by-trial basis (Hampton et al., 2008).”
Emotion and the Prefrontal Cortex: An Integrative Review (2017, methodology): “DMPFC activation correlated with expected reward outcomes based on inferences about the intentions of the opponent, particularly in participants that were more likely to exhibit mental state strategizing (Hampton et al., 2008).”
Mechanisms of social cognition. (2012, background): “Monitoring own actions (Miele et al. 2011) Monitoring others’ actions (Pelphrey et al. 2004, Saxe et al. 2004) Monitoring others’ trustworthiness (Behrens et al. 2008) Monitoring others’ generosity (Cooper et al. 2010) Monitoring influence on others (Hampton et al. 2008) 4.”
Trial-by-trial data analysis using computational models (2011, unknown): “…et al., 2004; Barraclough et al., 2004; Samejima et al., 2005; Daw et al., 2006; Li et al., 2006; Frank et al., 2007; Tom et al., 2007; Kable and Glimcher, 2007; Lohrenz et al., 2007; Schonberg et al., 2007; Wittmann et al., 2008; Hare et al., 2008; Hampton et al., 2008; Plassmann et al., 2008).”
Multiple Systems for Value Learning (2014, result): “…examining whether expected value correlates in this region comply more with model-based (versus model-free) values, as predicted by computational models, have repeatedly shown evidence for modelbased values there (Beierholm et al., 2011; Daw et al., 2011; Hampton et al., 2006, 2008) (Figure 21.4C).”
The neuroscience of social decision-making. (2011, unknown): “A recent study (Hampton et al. 2008) compared three different computational models with respect to their ability to explain subjects' behavior during a competitive game in which employees can " work " or " shirk " and employers can " inspect " or " not inspect.”
Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning (2012, background): “Similarly, mPFC but not the rACC was implicated in higher-order mentalization in a competitive game (10).”
The Social Bayesian Brain: Does Mentalizing Make a Difference When We Learn? (2014, unknown): “tracking rule of her opponent’s actions [22]:”
Generalization of value in reinforcement learning by humans (2012, methodology): “…the generalization model to behavior [calculated as the difference between choice likelihoods for the base model vs. the generalization model (e.g. Hampton et al., 2008; Simon & Daw, 2011)]. fMRI model regressors were convolved with the canonical HRF and entered into a GLM of each subject’s fMRI…”
Insights from the application of computational neuroimaging to social neuroscience. (2013, methodology): “Hampton et al. [16] therefore extended the ﬁctive learning algorithm to incorporate the effect an opponent’s predictions of ones own actions has on their action selection, endowing the agent with a ‘secondorder’ mental state representation [3,36].”
The Neurobiology of Decision: Consensus and Controversy (2009, unknown): “…it is important to note that the same neural mechanisms we have described above are now known to operate during social decisions (Barraclough et al., 2004; de Quervain et al., 2004; Dorris and Glimcher, 2004; Hampton et al., 2008; Harbaugh et al., 2007; King-Casas et al., 2005; Moll et al., 2006).”