Flexible social inference facilitates targeted social learning when rewards are not observable

• URL: http://arxiv.org/abs/2212.00869v2
• Date: Sat, 5 Aug 2023 17:11:58 GMT
• Title: Flexible social inference facilitates targeted social learning when rewards are not observable
• Authors: Robert D. Hawkins, Andrew M. Berdahl, Alex "Sandy" Pentland, Joshua B. Tenenbaum, Noah D. Goodman, P. M. Krafft
• Abstract summary: Groups coordinate more effectively when individuals are able to learn from others' successes. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior.
• Score: 58.762004496858836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments where success is hidden from public view. We suggest that social inference capacities may help bridge this gap, allowing individuals to update their beliefs about others' underlying knowledge and success from observable trajectories of behavior. We compared our social inference model against simpler heuristics in three studies of human behavior in a collective sensing task. In Experiment 1, we found that average performance improves as a function of group size at a rate greater than predicted by non-inferential models. Experiment 2 introduced artificial agents to evaluate how individuals selectively rely on social information. Experiment 3 generalized these findings to a more complex reward landscape. Taken together, our findings provide insight into the relationship between individual social cognition and the flexibility of collective behavior.

Related papers

• Learning mental states estimation through self-observation: a developmental synergy between intentions and beliefs representations in a deep-learning model of Theory of Mind [0.35154948148425685]
  Theory of Mind (ToM) is the ability to attribute beliefs, intentions, or mental states to others. We show a developmental synergy between learning to predict low-level mental states and attributing high-level ones. We propose that our computational approach can inform the understanding of human social cognitive development.
  arXiv  Detail & Related papers  (2024-07-25T13:15:25Z)
• Social Motion Prediction with Cognitive Hierarchies [19.71780279070757]
  We introduce a new benchmark, a novel formulation, and a cognition-inspired framework. We present Wusi, a 3D multi-person motion dataset under the context of team sports. We develop a cognitive hierarchy framework to predict strategic human social interactions.
  arXiv  Detail & Related papers  (2023-11-08T14:51:17Z)
• Self-supervised Hypergraph Representation Learning for Sociological Analysis [52.514283292498405]
  We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. First, we propose an effective hypergraph awareness and a fast line graph construction framework. Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
  arXiv  Detail & Related papers  (2022-12-22T01:20:29Z)
• Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy [0.0]
  Theory of Mind (TOM) is the ability to attribute to other agents' beliefs, intentions, or mental states in general. We show the synergy between learning to predict low-level mental states, such as intentions and goals, and attributing high-level ones, such as beliefs. We propose that our architectural approach can be relevant for the design of future adaptive social robots.
  arXiv  Detail & Related papers  (2022-10-17T21:12:39Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• The world seems different in a social context: a neural network analysis of human experimental data [57.729312306803955]
  We show that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals. An analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions.
  arXiv  Detail & Related papers  (2022-03-03T17:19:12Z)
• Deep reinforcement learning models the emergent dynamics of human cooperation [13.425401489679583]
  Experimental research has been unable to shed light on how social cognitive mechanisms contribute to the where and when of collective action. We leverage multi-agent deep reinforcement learning to model how a social-cognitive mechanism--specifically, the intrinsic motivation to achieve a good reputation--steers group behavior.
  arXiv  Detail & Related papers  (2021-03-08T18:58:40Z)
• Expertise and confidence explain how social influence evolves along intellective tasks [10.525352489242396]
  We study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We report empirical evidence on theories of transactive memory systems, social comparison, and confidences on the origins of social influence. We propose a cognitive dynamical model inspired by these theories to describe the process by which individuals adjust interpersonal influences over time.
  arXiv  Detail & Related papers  (2020-11-13T23:48:25Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)
• Intrinsic Motivation for Encouraging Synergistic Behavior [55.10275467562764]
  We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
  arXiv  Detail & Related papers  (2020-02-12T19:34:51Z)

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.