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Neural computations underlying inverse reinforcement learning in the human brain.

Sven Collette^1,2, Wolfgang M Pauli^1,2, Peter Bossaerts^3,4

¹Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, United States.

|October 31, 2017

Summary

Humans use inverse reinforcement learning (IRL) to infer reward structures by observing others, not just imitate actions. Brain imaging shows this abstract process involves understanding the observed agent's values, not the observer's own.

Keywords:

fMRI human neuroscience reinforcement learning reverse inference social neuroscience

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Neuroeconomics

Background:

Inverse reinforcement learning (IRL) is a computational framework for inferring reward functions from observed behavior.
Understanding whether humans employ IRL to infer others' goals and reward structures is crucial for social cognition research.

Purpose of the Study:

To investigate if the human brain implements inverse reinforcement learning (IRL) during social learning.
To determine if IRL is an abstract cognitive process distinct from simple imitation.

Main Methods:

Functional magnetic resonance imaging (fMRI) was used to monitor brain activity in participants observing agents making choices in a probabilistic slot machine task.
Computational modeling and formal model comparison were employed to distinguish between IRL and imitation strategies.

Main Results:

Participants' behavior was best explained by an inverse reinforcement learning (IRL) model, not a simple imitation strategy.
fMRI data revealed that the anterior dorsomedial prefrontal cortex (dmPFC) encoded action-values relative to the observed agent's perspective.

Conclusions:

The human brain utilizes inverse reinforcement learning (IRL) to infer underlying reward structures from observed actions.
The observed neural activity in dmPFC suggests IRL is an abstract cognitive process, dissociable from the observer's own preferences and values.