Theory of Mind: A Neural Prediction Problem

By Jorie Koster-Hale, Rebecca Saxe. 2013

439 citations recorded by the source corpus.

Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others’ goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind.

Subfields: Cognitive Neuroscience, Social Psychology, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Social Psychology

In Cognitive Science & Psychology

Citations in the corpus

Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, background): “This is also a well-studied ﬁeld in brain and cognitive science by ﬁlling in the gaps of knowledge by imagining new ideas [85] or using statistical approaches [86].”
Testing theory of mind in large language models and humans (2024, background): “…the ability to reason about mental states—in combination with information about context, past experience and knowledge of social norms—helps individual reduce uncertainty and commit to likely hypotheses, allowing for successful navigation of the social environment as active agents 44,45 .”
How Do Expectations Shape Perception? (2018, background): “Alternatively, the site of integration may depend on the level of detail afforded by the prediction [29,142].”
Deconstructing intent to reconstruct morality (2015, background): “Progress on this question is likely to come through the convergence of low-level theories of neural computation with high-level theories of psychological organization [71].”
From cognitivism to autopoiesis: towards a computational framework for the embodied mind (2016, background): “For example, the contrast of expected versus unexpected social norm violations can hypothetically be used to reveal the putative social-norm prediction error region of the brain (Koster-Hale and Saxe 2013).”
Predictive Processing: A Canonical Cortical Computation (2018, neutral): “Predictive processing as a conceptual framework for understanding brain has a long tradition in the fields of computational and cognitive neuroscience, and has been elegantly summarized elsewhere (Clark, 2013; Koster-Hale and Saxe, 2013).”
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, background): “This is also a well-studied ﬁeld in brain and cognitive science by ﬁlling in the gaps of knowledge by imagining new ideas[85] or using a statistical approaches [86].”
Visualising mental representations: A primer on noise-based reverse correlation in social psychology (2017, background): “…Lower levels implement, for example, the continuation of regular spatial patterns behind occlusions or in the blind spot of our retina (Komatsu, 2006; Shimojo, 2014), whereas higher levels implement how we mentally represent others and their expected behaviour (Koster-Hale & Saxe, 2013).”