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A Minimal Turing Test: Reciprocal Sensorimotor Contingencies for Interaction Detection.

Pamela Barone1,2, Manuel G Bedia3,4, Antoni Gomila1,2

  • 1Department of Psychology, University of the Balearic Islands, Palma, Spain.

Frontiers in Human Neuroscience
|April 9, 2020
PubMed
Summary
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This study introduces a minimal Turing test focusing on reciprocal sensorimotor contingencies. Findings suggest humans detect machine interaction by recognizing these short-time-window contingencies, crucial for AI development.

Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Robotics

Background:

  • The classical Turing test assesses machine intelligence via text-based conversation.
  • Basic human interaction relies on embodied, context-dependent sensorimotor contingencies.
  • Detecting interaction involves integrating proprioceptive, interoceptive, and sensorimotor patterns.

Purpose of the Study:

  • To investigate the minimal information required for interaction detection.
  • To design a "minimal" Turing test based on reciprocal sensorimotor contingencies.
  • To determine if humans use interaction detection to distinguish between human and machine agents.

Main Methods:

  • Utilized a novel perceptual crossing paradigm.
  • Presented participants with human and computerized agent movements in repeated encounters.
Keywords:
Turing testinteractionperceptual crossingreciprocitysensorimotor contingencies

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  • Employed auditory or audiovisual feedback across different experimental blocks.

    • Main Results:

    • Participants demonstrated reliance on interaction detection to differentiate agents.
    • Evidence suggests the use of reciprocal sensorimotor contingencies within short time windows.
    • Both explicit responses and implicit dynamics indicated contingency detection.

    Conclusions:

    • Reciprocal sensorimotor contingencies are key to experiencing interaction.
    • Machines must generate these contingencies to pass a minimal Turing test.
    • This research refines our understanding of embodied AI interaction detection.