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Related Experiment Video

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A Machine Learning Approach for Detecting Vicarious Trial and Error Behaviors.

Jesse T Miles1, Kevan S Kidder2, Ziheng Wang2

  • 1Graduate Program in Neuroscience, University of Washington, Seattle, WA, United States.

Frontiers in Neuroscience
|July 26, 2021
PubMed
Summary

Vicarious trial and error behaviors (VTEs) are periods of indecision during decision-making. Machine learning successfully identified VTEs using trajectory data, suggesting a broader neural network beyond the hippocampus.

Keywords:
VTEdecision-makinggammahippocampusmachine learningneural oscillationsthetavicarious trial and error

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Area of Science:

  • Neuroscience
  • Behavioral Science
  • Machine Learning

Background:

  • Vicarious trial and error behaviors (VTEs) are proposed as behavioral markers of deliberation and indecision during decision-making.
  • Understanding the neural basis of VTEs requires distinguishing them from non-VTEs.

Purpose of the Study:

  • To identify VTEs from non-VTEs using trajectory-based features and machine learning in rats performing a spatial delayed alternation task.
  • To evaluate the utility of hippocampal field potential oscillations in classifying VTEs.
  • To determine if combining trajectory and oscillation features improves VTE classification.

Main Methods:

  • Rats performed a spatial delayed alternation task on an elevated plus maze.
  • Trajectory-based features and machine learning classifiers were used to identify VTEs.
  • Hippocampal field potential oscillations were analyzed for VTE classification.

Main Results:

  • Trajectory-based features effectively distinguished VTEs from non-VTEs with high accuracy.
  • Hippocampal oscillations showed above-chance performance in classifying VTEs but identified fewer VTE trials.
  • Combining trajectory and oscillation features did not improve classification performance over trajectory features alone.

Conclusions:

  • Trajectory-based features provide a robust method for classifying VTEs in binary decision tasks.
  • While hippocampal activity is involved, VTEs likely rely on a neural network extending beyond the hippocampus.
  • Further research is needed to fully elucidate the neural underpinnings of VTEs.