A Brain-Inspired Model of Theory of Mind

An ALE meta-analysis of the neural evidence of facial emotion processing in autism

OBJECTIVE

Facial emotion recognition is central to successful social interaction. People with autism spectrum disorder (ASD) have difficulties in this area. However, neuroimaging evidence on facial emotion processing in ASD has been diverse. This study aims to identify common and consistent brain activity patterns during facial emotion processing in autism.

METHODS

Following PRISMA guidelines, 22 fMRI studies (539 ASD, 502 typically developing participants (TD) were included.

RESULTS

Both groups showed significant activation in the right fusiform gyrus (FG) and left fusiform face area (FFA). In addition, TD participants showed increased left amygdala activity. Compared to TD, ASD individuals had increased activation in the right cerebellum lobule VI and left secondary visual cortex. Age-based subgroup analysis showed that ASD children showed increased activity in bilateral FG, and ASD adults and TD children in the right FG. Finally, adults from both groups had increased activity in the right FG in the within-group and conjunction analyses.

CONCLUSIONS

These results suggest that ASD and TD engage core face processing areas similarly while TD may use core and an extended social brain network. Findings of this study underscore the role of fusiform face area in facial emotion processing along with more insights into the neural processing of facial emotions.

Adverse childhood experiences differently affect Theory of Mind brain networks in schizophrenia and healthy controls

Patients with schizophrenia (SZ) show impairments in both affective and cognitive dimensions of theory of mind (ToM). SZ are also particularly vulnerable to detrimental effect of adverse childhood experiences (ACE), influencing the overall course of the disorder and fostering poor social functioning. ACE associate with long-lasting detrimental effects on brain structure, function, and connectivity in regions involved in ToM. Here, we investigated whether ToM networks are differentially affected by ACEs in healthy controls (HC) and SZ, and if these effects can predict the disorder clinical outcome. 26 HC and 33 SZ performed a ToM task during an fMRI session. Whole-brain functional response and connectivity (FC) were extracted, investigating the interaction between ACEs and diagnosis. FC values significantly affected by ACEs were entered in a cross-validated LASSO regression predicting Positive and Negative Syndrome Scale (PANSS), Interpersonal Reactivity Index (IRI), and task performance. ACEs and diagnosis showed a widespread interaction at both affective and cognitive tasks, including connectivity between vmPFC, ACC, precentral and postcentral gyri, insula, PCC, precuneus, parahippocampal gyrus, temporal pole, thalamus, and cerebellum, and functional response in the ACC, thalamus, parahippocampal gyrus and putamen. FC predicted the PANSS score, the fantasy dimension of IRI, and the AToM response latency. Our results highlight the crucial role of early stress in differentially shaping ToM related brain networks in HC and SZ. These effects can also partially explain the clinical and behavioral outcomes of the disorder, extending our knowledge of the effects of ACEs.

Neural activations during cognitive and affective theory of mind processing in healthy adults with a family history of alcohol use disorder

BACKGROUND

Social cognition impairments are a common feature of alcohol use disorders (AUD). However, it remains unclear whether these impairments are solely the consequence of chronic alcohol consumption or whether they could be a marker of vulnerability.

METHODS

The present study implemented a family history approach to address this question for a key process of social cognition: theory of mind (ToM). Thirty healthy adults with a family history of AUD (FH+) and 30 healthy adults with a negative family history of AUD (FH-), matched for age, sex, and education level, underwent an fMRI cartoon-vignette paradigm assessing cognitive and affective ToM. Participants also completed questionnaires evaluating anxiety, depressive symptoms, childhood trauma, and alexithymia.

RESULTS

Results indicated that FH+ individuals differed from FH- individuals on affective but not cognitive ToM processing, at both the behavioral and neural levels. At the behavioral level, the FH+ group had lower response accuracy for affective ToM compared with the FH- group. At the neural level, the FH+ group had higher brain activations in the left insula and inferior frontal cortex during affective ToM processing. These activations remained significant when controlling for depressive symptoms, anxiety, and childhood trauma.

CONCLUSIONS

These findings highlight difficulties during affective ToM processing among first-degree relatives of AUD patients, supporting the idea that some of the impairments exhibited by these patients may already be present before the onset of AUD and may be considered a marker of vulnerability.

Atypical social cognition processing in bulimia nervosa: an fMRI study of patients thinking of others' mental states

BACKGROUND

Feeding and eating disorders are severe mental disorders that gravely affect patients' lives. In particular, patients with anorexia nervosa (AN) or bulimia nervosa (BN) appear to have poor social cognition. Many studies have shown the relationship between poor social cognition and brain responses in AN. However, few studies have examined the relationship between social cognition and BN. Therefore, we examined which brain regions impact the ability for social cognition in patients with BN.

METHODS

We used task-based functional magnetic resonance imaging (fMRI) to examine brain responses during a social cognition task and the Reading Mind in the Eyes Test (RMET). During the fMRI, 22 women with BN and 22 healthy women (HW) took the RMET. Participants also completed the eating disorder clinical measures Bulimic Investigatory Test, Edinburgh (BITE) and Eating Disorders Examination Questionnaire (EDE-Q), the Patient Health Questionnaire (PHQ-9) measure of depression; and the Generalized Anxiety Disorder (GAD-7) measure of anxiety.

RESULTS

No difference was observed in the RMET scores between women with BN and HW. Both groups showed activation in brain regions specific to social cognition. During the task, no differences were shown between the groups in the BOLD signal (p < 0.05, familywise error corrected for multiple comparisons). However, there was a tendency of more robust activation in the right angular gyrus, ventral diencephalon, thalamus proper, temporal pole, and middle temporal gyrus in BN (p < 0.001, uncorrected for multiple comparisons). Moreover, HW showed a positive correlation between RMET scores and the activation of two regions: medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC); however, no significant correlation was observed in women with BN.

CONCLUSIONS

While activation in the mPFC and ACC positively correlated to the RMET scores in HW, no correlation was observed in BN patients. Therefore, women with BN might display modulated neural processing when thinking of others' mental states. Further examination is needed to investigate neural processing in BN patients to better understand their social cognition abilities.

TRIAL REGISTRATION

UMIN, UMIN000010220. Registered 13 March 2013, https://rctportal.niph.go.jp/s/detail/um?trial_id=UMIN000010220.

On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks

Theory of Mind is referred to the ability of inferring other's mental states, and it plays a crucial role in social cognition and learning. Biological evidences indicate that complex circuits are involved in this ability, including the mirror neuron system. The mirror neuron system influences imitation abilities and action understanding, leading to learn through observing others. To simulate this imitative learning behavior, a Theory-of-Mind-based Imitative Reinforcement Learning (ToM-based ImRL) framework is proposed. Employing the bio-inspired spiking neural networks and the mechanisms of the mirror neuron system, ToM-based ImRL is a bio-inspired computational model which enables an agent to effectively learn how to act in an interactive environment through observing an expert, inferring its goals, and imitating its behaviors. The aim of this paper is to review some computational attempts in modeling ToM and to explain the proposed ToM-based ImRL framework which is tested in the environment of River Raid game from Atari 2600 series.

Meet the authors: Yuxuan Zhao, Enmeng Lu, and Yi Zeng

Yuxuan Zhao, associate professor, Enmeng Lu, research engineer, and Yi Zeng, professor and lab director, have proposed a brain-inspired bodily self-perception model based on biological findings on monkeys and humans. This model can reproduce various rubber hand illusion (RHI) experiments, which helps reveal the RHI's computational and biological mechanisms. They talk about their view of data science and research plans for brain-inspired robot self-modeling and ethical robots.

Brain-inspired bodily self-perception model for robot rubber hand illusion

The core of bodily self-consciousness involves perceiving ownership of one's body. A central question is how body illusions like the rubber hand illusion (RHI) occur. Existing theoretical models still lack satisfying computational explanations from connectionist perspectives, especially for how the brain encodes body perception and generates illusions from neuronal interactions. Moreover, the integration of disability experiments is also neglected. Here, we integrate biological findings of bodily self-consciousness to propose a brain-inspired bodily self-perception model by which perceptions of bodily self are autonomously constructed without any supervision signals. We successfully validated the model with six RHI experiments and a disability experiment on an iCub humanoid robot and simulated environments. The results show that our model can not only well-replicate the behavioral and neural data of monkeys in biological experiments but also reasonably explain the causes and results of RHI at the neuronal level, thus contributing to the revelation of mechanisms underlying RHI.

A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition

During dynamic social interaction, inferring and predicting others' behaviors through theory of mind (ToM) is crucial for obtaining benefits in cooperative and competitive tasks. Current multi-agent reinforcement learning (MARL) methods primarily rely on agent observations to select behaviors, but they lack inspiration from ToM, which limits performance. In this article, we propose a multi-agent ToM decision-making (MAToM-DM) model, which consists of a MAToM spiking neural network (MAToM-SNN) module and a decision-making module. We design two brain-inspired ToM modules (Self-MAToM and Other-MAToM) to predict others' behaviors based on self-experience and observations of others, respectively. Each agent can adjust its behavior according to the predicted actions of others. The effectiveness of the proposed model has been demonstrated through experiments conducted in cooperative and competitive tasks. The results indicate that integrating the ToM mechanism can enhance cooperation and competition efficiency and lead to higher rewards compared with traditional MARL models.

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units

The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain–computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

A brain-inspired intention prediction model and its applications to humanoid robot

With the development of artificial intelligence and robotic technology in recent years, robots are gradually integrated into human daily life. Most of the human-robot interaction technologies currently applied to home service robots are programmed by the manufacturer first, and then instruct the user to trigger the implementation through voice commands or gesture commands. Although these methods are simple and effective, they lack some flexibility, especially when the programming program is contrary to user habits, which will lead to a significant decline in user experience satisfaction. To make that robots can better serve human beings, adaptable, simple, and flexible human-robot interaction technology is essential. Based on the neural mechanism of reinforcement learning, we propose a brain-inspired intention prediction model to enable the robot to perform actions according to the user's intention. With the spike-timing-dependent plasticity (STDP) mechanisms and the simple feedback of right or wrong, the humanoid robot NAO could successfully predict the user's intentions in Human Intention Prediction Experiment and Trajectory Tracking Experiment. Compared with the traditional Q-learning method, the training times required by the proposed model are reduced by (N² − N)/4, where N is the number of intentions.

A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents

Artificial Intelligence (AI) systems are increasingly applied to complex tasks that involve interaction with multiple agents. Such interaction-based systems can lead to safety risks. Due to limited perception and prior knowledge, agents acting in the real world may unconsciously hold false beliefs and strategies about their environment, leading to safety risks in their future decisions. For humans, we can usually rely on the high-level theory of mind (ToM) capability to perceive the mental states of others, identify risk-inducing errors, and offer our timely help to keep others away from dangerous situations. Inspired by the biological information processing mechanism of ToM, we propose a brain-inspired theory of mind spiking neural network (ToM-SNN) model to enable agents to perceive such risk-inducing errors inside others' mental states and make decisions to help others when necessary. The ToM-SNN model incorporates the multiple brain areas coordination mechanisms and biologically realistic spiking neural networks (SNNs) trained with Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP). To verify the effectiveness of the ToM-SNN model, we conducted various experiments in the gridworld environments with random agents' starting positions and random blocking walls. Experimental results demonstrate that the agent with the ToM-SNN model selects rescue behavior to help others avoid safety risks based on self-experience and prior knowledge. To the best of our knowledge, this study provides a new perspective to explore how agents help others avoid potential risks based on bio-inspired ToM mechanisms and may contribute more inspiration toward better research on safety risks.

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review

Theory of Mind (ToM)-the ability of the human mind to attribute mental states to others-is a key component of human cognition. In order to understand other people's mental states or viewpoint and to have successful interactions with others within social and occupational environments, this form of social cognition is essential. The same capability of inferring human mental states is a prerequisite for artificial intelligence (AI) to be integrated into society, for example in healthcare and the motoring industry. Autonomous cars will need to be able to infer the mental states of human drivers and pedestrians to predict their behavior. In the literature, there has been an increasing understanding of ToM, specifically with increasing cognitive science studies in children and in individuals with Autism Spectrum Disorder. Similarly, with neuroimaging studies there is now a better understanding of the neural mechanisms that underlie ToM. In addition, new AI algorithms for inferring human mental states have been proposed with more complex applications and better generalisability. In this review, we synthesize the existing understanding of ToM in cognitive and neurosciences and the AI computational models that have been proposed. We focus on preference learning as an area of particular interest and the most recent neurocognitive and computational ToM models. We also discuss the limitations of existing models and hint at potential approaches to allow ToM models to fully express the complexity of the human mind in all its aspects, including values and preferences.

The Role of Hub and Spoke Regions in Theory of Mind in Early Alzheimer's Disease and Frontotemporal Dementia

Theory of mind (ToM, the ability to attribute mental states to others) deficit is a frequent finding in neurodegenerative conditions, mediated by a diffuse brain network confirmed by 18F-FDG-PET and MR imaging, involving frontal, temporal and parietal areas. However, the role of hubs and spokes network regions in ToM performance, and their respective damage, is still unclear. To study this mechanism, we combined ToM testing with brain 18F-FDG-PET imaging in 25 subjects with mild cognitive impairment due to Alzheimer’s disease (MCI−AD), 24 subjects with the behavioral variant of frontotemporal dementia (bvFTD) and 40 controls. Regions included in the ToM network were divided into hubs and spokes based on their structural connectivity and distribution of hypometabolism. The hubs of the ToM network were identified in frontal regions in both bvFTD and MCI−AD patients. A mediation analysis revealed that the impact of spokes damage on ToM performance was mediated by the integrity of hubs (p < 0.001), while the impact of hubs damage on ToM performance was independent from the integrity of spokes (p < 0.001). Our findings support the theory that a key role is played by the hubs in ToM deficits, suggesting that hubs could represent a final common pathway leading from the damage of spoke regions to clinical deficits.

Modeling the Synchronization of Multimodal Perceptions as a Basis for the Emergence of Deterministic Behaviors

Living organisms have either innate or acquired mechanisms for reacting to percepts with an appropriate behavior e.g., by escaping from the source of a perception detected as threat, or conversely by approaching a target perceived as potential food. In the case of artifacts, such capabilities must be built in through either wired connections or software. The problem addressed here is to define a neural basis for such behaviors to be possibly learned by bio-inspired artifacts. Toward this end, a thought experiment involving an autonomous vehicle is first simulated as a random search. The stochastic decision tree that drives this behavior is then transformed into a plastic neuronal circuit. This leads the vehicle to adopt a deterministic behavior by learning and applying a causality rule just as a conscious human driver would do. From there, a principle of using synchronized multimodal perceptions in association with the Hebb principle of wiring together neuronal cells is induced. This overall framework is implemented as a virtual machine i.e., a concept widely used in software engineering. It is argued that such an interface situated at a meso-scale level between abstracted micro-circuits representing synaptic plasticity, on one hand, and that of the emergence of behaviors, on the other, allows for a strict delineation of successive levels of complexity. More specifically, isolating levels allows for simulating yet unknown processes of cognition independently of their underlying neurological grounding.