Intuitive experimentation in the physical world

Summary

• How do people actively experiment to discover latent properties within the physical world?
• Performed experiments where participants interacted with objects in order to identify the masses or attractive/repulsive forces that governed their movement
• The interactions selectively produced evidence that revealed the physical property being investigated
  • Active learners produced more accurate inferences than passive and yoked observers
• Links: [ website ]

Background

• From a causal perspective, actions can be seen as interventions which help reveal how to world works
  • Unclear how these actions are chosen, and how complex and informative they are
• Active learning studies in humans usually focus on differentiating between a relatively small number of discrete hypotheses
  • Evidence for its utility has also been mixed
• This study explores active learning in a continuously dynamic environment
  • Learner must decide where, how, and when to intervene
• Prior work using a similar simulated environment demonstrated that humans are worse at detecting attraction versus repulsion when passively observing
  • Divergence corresponds to asymmetry in evidence, since objects that repel each other rarely spend enough time close together

Methods

• Aim to establish how effective participants’ actions are at reducing uncertainty about the properties they’re asked about
• 2D microworld environment - contains a number of colored pucks with varying masses and local (magnet-like) pair-wise forces
  • Active: allow participants to grab objects by clicking on them and dragging
  • Passive: participants only observe the world
  • Yoked: passively observe actions of active participant
• Ideal Observer (IO): predicts how the scene would unfold given various assumptions about the properties and comparing to whats observed
  • Predictive Divergence (PD): take divergence between simulations varying the property and what is observed, averaging over all possible settings for other properties

Results

• Just looking at accuracy, active participants were significantly better than passive and yoked participants, which performed similarly
  • Active participants’ performance was moderately predictive of their yoked counterparts’ (r=0.49)
• In general, participants were worse at inferring masses compared to forces
  • Active learners benefited the most for inferring repulsion
  • Yoked participants had marginal improvement in inferring forces
• While active participants generated the same amount of evidence overall as passive participants, they generated more evidence about force
• PD for force and mass of target was higher for active participants compared to passive, also for intervening periods of active participants compared to observing periods
• Giving the same learning goal to yoked participants raises their performance to the level of their active counterparts

Conclusion

• Provides additional evidence for the importance of active learning and volitional control of interventions
• How these sophisticated strategies are learned remains an open question