Theory of Mind: A Neural Prediction Problem

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

Theory of Mind: A Neural Prediction Problem

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.National Science Foundation (U.S.) (Award 0645960)National Science Foundation (U.S.) (Award 095518)National Institutes of Health (U.S.) (Grant 1R01 MH096914-01A1

Quantification and ACD: Evidence from Real-Time Sentence Processing

Data files and documentation available at http://hdl.handle.net/1721.1/76676Quantifiers, unlike proper names or definite descriptions, cannot be given the semantics of referring expressions. This fact has triggered a long standing debate in formal semantics and syntax as to the combinatorial means by which quantifiers are integrated into a sentence. The present paper contributes to this debate through an investigation of quantifier comprehension during real-time sentence processing. We present evidence showing that two potentially independent processes—the integration of a quantifier in object position and the resolution of antecedent-contained deletion (ACD)—are linked. Our data show, more specifically, that the resolution of a downstream ACD site is facilitated during real-time sentence processing if the upstream DP hosting the ACD site is quantificational but not if it is definite. We discuss these findings in the context of a QUANTIFIER RAISING based approach and a type-shifting-based approach to quantifier integration. We argue that facilitation of ACD resolution by an upstream quantifier is only expected by theories, such as the QUANTIFIER RAISING approach, which employ the same mechanism for both processes. We then compare the QUANTIFIER RAISING-based account with a non-grammatical experience-based approach to our data, which attempts to explain the findings in terms of corpus frequencies. Although we cannot rule out such an alternative at this stage, we offer reasons to believe that an account that exploits QUANTIFIER RAISING has an explanatory advantage

Thinking about seeing: Perceptual sources of knowledge are encoded in the theory of mind brain regions of sighted and blind adults

Blind people's inferences about how other people see provide a window into fundamental questions about the human capacity to think about one another's thoughts. By working with blind individuals, we can ask both what kinds of representations people form about others' minds, and how much these representations depend on the observer having had similar mental states themselves. Thinking about others' mental states depends on a specific group of brain regions, including the right temporo-parietal junction (RTPJ). We investigated the representations of others' mental states in these brain regions, using multivoxel pattern analyses (MVPA). We found that, first, in the RTPJ of sighted adults, the pattern of neural response distinguished the source of the mental state (did the protagonist see or hear something?) but not the valence (did the protagonist feel good or bad?). Second, these neural representations were preserved in congenitally blind adults. These results suggest that the temporo-parietal junction contains explicit, abstract representations of features of others' mental states, including the perceptual source. The persistence of these representations in congenitally blind adults, who have no first-person experience with sight, provides evidence that these representations emerge even in the absence of relevant first-person perceptual experiences.National Science Foundation (U.S.) (Award 0645960)National Science Foundation (U.S.) (Award 095518)National Institutes of Health (U.S.) (Grant 1R01 MH096914-01A1

Localizing Pain Matrix and Theory of Mind networks with both verbal and non-verbal stimuli

Functional localizer tasks allow researchers to identify brain regions in each individual's brain, using a combination of anatomical and functional constraints. In this study, we compare three social cognitive localizer tasks, designed to efficiently identify regions in the "Pain Matrix," recruited in response to a person's physical pain, and the "Theory of Mind network," recruited in response to a person's mental states (i.e. beliefs and emotions). Participants performed three tasks: first, the verbal false-belief stories task; second, a verbal task including stories describing physical pain versus emotional suffering; and third, passively viewing a non-verbal animated movie, which included segments depicting physical pain and beliefs and emotions. All three localizers were efficient in identifying replicable, stable networks in individual subjects. The consistency across tasks makes all three tasks viable localizers. Nevertheless, there were small reliable differences in the location of the regions and the pattern of activity within regions, hinting at more specific representations. The new localizers go beyond those currently available: first, they simultaneously identify two functional networks with no additional scan time, and second, the non-verbal task extends the populations in whom functional localizers can be applied. These localizers will be made publicly available.National Institutes of Health (U.S.) (Grant 1R01 MH096914-01A1

Mentalizing regions represent distributed, continuous, and abstract dimensions of others' beliefs

The human capacity to reason about others' minds includes making causal inferences about intentions, beliefs, values, and goals. Previous fMRI research has suggested that a network of brain regions, including bilateral temporo-parietal junction (TPJ), superior temporal sulcus (STS), and medial prefrontal-cortex (MPFC), are reliably recruited for mental state reasoning. Here, in two fMRI experiments, we investigate the representational content of these regions. Building on existing computational and neural evidence, we hypothesized that social brain regions contain at least two functionally and spatially distinct components: one that represents information related to others' motivations and values, and another that represents information about others' beliefs and knowledge. Using multi-voxel pattern analysis, we find evidence that motivational versus epistemic features are independently represented by theory of mind (ToM) regions: RTPJ contains information about the justification of the belief, bilateral TPJ represents the modality of the source of knowledge, and VMPFC represents the valence of the resulting emotion. These representations are found only in regions implicated in social cognition and predict behavioral responses at the level of single items. We argue that cortical regions implicated in mental state inference contain complementary, but distinct, representations of epistemic and motivational features of others' beliefs, and that, mirroring the processes observed in sensory systems, social stimuli are represented in distinct and distributed formats across the human brain. Keywords: Theory of mind; fMRI; Multi-voxel pattern analysis (MVPA)NSF Graduate Research Fellowships (Grant 0645960)NSF Graduate Research Fellowships (Grant 1122374)NSF CAREER award (Grant 095518)National Institutes of Health (Grant 1R01 MH096914-01A1)Packard Foundation (Grant 2008-333024

Reduced neural selectivity for mental states in deaf children with delayed exposure to sign language

Early linguistic experience directly facilitates social development in childhood. Here, the authors reveal that children with delayed access to language show delayed development of selective responses in cortical regions involved in thinking about others’ thoughts

From heart to mind: Linking interoception, emotion, and theory of mind

Theory of Mind (ToM) is traditionally characterized as the ability to represent mental states. Such a characterization leaves little room for studying individual differences in ToM – individuals either can, or cannot, represent mental states – and this binary classification cannot quantify the subtle individual differences observed in typical and atypical populations. In recognition of this problem, attempts have been made to provide a more detailed characterization of the constituent psychological processes which support the representation of mental states, and the neurocomputational principles underpinning ToM, in order to identify the source of individual differences. A recent model is of interest as it forwards the novel argument that interoception, perception of the internal state of the body, is a fundamental component of ToM. Here we report the first test of the link between interoception and ToM

Disentangling stimulus plausibility and contextual congruency: Electrophysiological evidence for differential cognitive dynamics

Expectancy mechanisms are routinely used by the cognitive system in stimulus processing and in anticipation of appropriate responses. Electrophysiology research has documented negative shifts of brain activity when expectancies are violated within a local stimulus context (e.g., reading an implausible word in a sentence) or more globally between consecutive stimuli (e.g., a narrative of images with an incongruent end). In this EEG study, we examine the interaction between expectancies operating at the level of stimulus plausibility and at more global level of contextual congruency to provide evidence for, or against, a disassociation of the underlying processing mechanisms. We asked participants to verify the congruency of pairs of cross-modal stimuli (a sentence and a scene), which varied in plausibility. ANOVAs on ERP amplitudes in selected windows of interest show that congruency violation has longer-lasting (from 100 to 500 ms) and more widespread effects than plausibility violation (from 200 to 400 ms). We also observed critical interactions between these factors, whereby incongruent and implausible pairs elicited stronger negative shifts than their congruent counterpart, both early on (100-200 ms) and between 400-500 ms. Our results suggest that the integration mechanisms are sensitive to both global and local effects of expectancy in a modality independent manner. Overall, we provide novel insights into the interdependence of expectancy during meaning integration of cross-modal stimuli in a verification task.Fundacao para a Ciencia e Tecnologia [SFRH/BPD/88374/2012, PTDC/PSI-PCO/110734/2009, UID/BIM/04773/2013 CBMR 1334, PEst-OE/EQB/LA0023/2013, UID/PSI/00050/2013]; Leverhulme Trust [ECF-2014-205]; Max Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviourhttp://creativecommons.org/licenses/by/4.0

Muscleless Motor synergies and actions without movements : From Motor neuroscience to cognitive robotics

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to ‘action’ that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a ‘plastic, configurable’ internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both “real and imagined” actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action ‘execution, imagination and understanding’), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly “act, interact, anticipate and understand” in unstructured natural living spaces