Abstract Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. We hypothesized that neural populations estimate these states by processing sensory history through recurrent interactions which reflect the internal model of the world. To test this, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state - monkey’s displacement from the goal - was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that task demands shape the neural interactions in PPC, leading them to embody a world model that consolidates information and tracks task-relevant hidden states.
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Development of a Real-Time Trust/Distrust Metric Using Interactive Hybrid Cognitive Task Analysis
While there is increased interest in how trust spreads in Human Autonomy Teams (HATs), most trust measurements are subjective and do not examine real-time changes in trust. To develop a trust metric that consists of objective variables influenced by trust/distrust manipulations, we conducted an Interactive hybrid Cognitive Task Analysis (IhCTA) for a Remotely Piloted Aerial System (RPAS) HAT. The IhCTA adapted parts of the hybrid Cognitive Task Analysis (hCTA) framework. In this paper, we present the four steps of the IhCTA approach, including 1) generating a scenario task overview, 2) generating teammate-specific event flow diagrams, 3) identifying interactions and interdependencies impacted by trust/distrust manipulations, and 4) processing RPAS variables based on the IhCTA to create a metric. We demonstrate the application of the metric through a case study that examines how the influence of specific interactions on team state changes before and after the spread of distrust.
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- Award ID(s):
- 2019805
- PAR ID:
- 10499795
- Publisher / Repository:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 67
- Issue:
- 1
- ISSN:
- 1071-1813
- Page Range / eLocation ID:
- 2128 to 2136
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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