Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions

• URL: http://arxiv.org/abs/2407.06762v3
• Date: Wed, 28 Aug 2024 11:26:06 GMT
• Title: Explicit Modelling of Theory of Mind for Belief Prediction in Nonverbal Social Interactions
• Authors: Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling,
• Abstract summary: We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. MToMnet encodes contextual cues and integrates them with person-specific cues (human gaze and body language) in a separate MindNet for each person. Our results demonstrate that MToMnet surpasses existing methods by a large margin while at the same time requiring a significantly smaller number of parameters.
• Score: 9.318796743761224
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose MToMnet - a Theory of Mind (ToM) neural network for predicting beliefs and their dynamics during human social interactions from multimodal input. ToM is key for effective nonverbal human communication and collaboration, yet, existing methods for belief modelling have not included explicit ToM modelling or have typically been limited to one or two modalities. MToMnet encodes contextual cues (scene videos and object locations) and integrates them with person-specific cues (human gaze and body language) in a separate MindNet for each person. Inspired by prior research on social cognition and computational ToM, we propose three different MToMnet variants: two involving fusion of latent representations and one involving re-ranking of classification scores. We evaluate our approach on two challenging real-world datasets, one focusing on belief prediction, while the other examining belief dynamics prediction. Our results demonstrate that MToMnet surpasses existing methods by a large margin while at the same time requiring a significantly smaller number of parameters. Taken together, our method opens up a highly promising direction for future work on artificial intelligent systems that can robustly predict human beliefs from their non-verbal behaviour and, as such, more effectively collaborate with humans.

Related papers

• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding [55.38254464415964]
  Theory of mind evaluations currently focuses on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations. We introduce NegotiationToM, a new benchmark designed to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states.
  arXiv  Detail & Related papers  (2024-04-21T11:51:13Z)
• What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
  We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models. We aim for the models to engage with and generate responses that span a wider contextual scene understanding. Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
  arXiv  Detail & Related papers  (2024-03-20T11:27:20Z)
• MMToM-QA: Multimodal Theory of Mind Question Answering [80.87550820953236]
  Theory of Mind (ToM) is an essential ingredient for developing machines with human-level social intelligence. Recent machine learning models, particularly large language models, seem to show some aspects of ToM understanding. Human ToM, on the other hand, is more than video or text understanding. People can flexibly reason about another person's mind based on conceptual representations extracted from any available data.
  arXiv  Detail & Related papers  (2024-01-16T18:59:24Z)
• Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities [63.90227161974381]
  SimToM is a novel prompting framework inspired by Simulation Theory's notion of perspective-taking. Our approach, which requires no additional training and minimal prompt-tuning, shows substantial improvement over existing methods.
  arXiv  Detail & Related papers  (2023-11-16T22:49:27Z)
• Multimodality and Attention Increase Alignment in Natural Language Prediction Between Humans and Computational Models [0.8139163264824348]
  Humans are known to use salient multimodal features, such as visual cues, to facilitate the processing of upcoming words. multimodal computational models can integrate visual and linguistic data using a visual attention mechanism to assign next-word probabilities. We show that predictability estimates from humans aligned more closely with scores generated from multimodal models vs. their unimodal counterparts.
  arXiv  Detail & Related papers  (2023-08-11T09:30:07Z)
• A Review on Machine Theory of Mind [16.967933605635203]
  Theory of Mind (ToM) is the ability to attribute mental states to others, the basis of human cognition. In this paper, we review recent progress in machine ToM on beliefs, desires, and intentions.
  arXiv  Detail & Related papers  (2023-03-21T04:58:47Z)
• Multimodal foundation models are better simulators of the human brain [65.10501322822881]
  We present a newly-designed multimodal foundation model pre-trained on 15 million image-text pairs. We find that both visual and lingual encoders trained multimodally are more brain-like compared with unimodal ones.
  arXiv  Detail & Related papers  (2022-08-17T12:36:26Z)
• BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios [14.23697277904244]
  This paper uses the combined knowledge of Theory of Mind (ToM) and Object-Context Relations to investigate methods for enhancing collaboration between humans and autonomous systems. We propose a novel and challenging multimodal video dataset for assessing the capability of artificial intelligence (AI) systems in predicting human belief states in an object-context scenario.
  arXiv  Detail & Related papers  (2022-06-21T18:29:17Z)
• Deep Interpretable Models of Theory of Mind For Human-Agent Teaming [0.7734726150561086]
  We develop an interpretable modular neural framework for modeling the intentions of other observed entities. We demonstrate the efficacy of our approach with experiments on data from human participants on a search and rescue task in Minecraft.
  arXiv  Detail & Related papers  (2021-04-07T06:18:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.