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Learning to maximize reward rate: a model based on semi-Markov decision processes.

Arash Khodadadi1, Pegah Fakhari1, Jerome R Busemeyer1

  • 1Department of Psychological and Brain Sciences, Indiana University Bloomington, IN, USA.

Frontiers in Neuroscience
|June 7, 2014
PubMed
Summary

Animals learn to optimize decision-making under time constraints by adjusting decision thresholds for different conditions. This research proposes a novel theoretical framework using semi-Markov decision processes to maximize rewards effectively.

Keywords:
average reward rate maximizationdecision thresholddiffusion processreinforcement learningsemi-Markov decision processsequential sampling modelsspeed-accuracy trade-off

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Area of Science:

  • Cognitive Neuroscience
  • Behavioral Economics
  • Computational Neuroscience

Background:

  • Animals face a trade-off between decision quality and time in time-limited scenarios.
  • Sequential sampling models explore how organisms make decisions over time.
  • Optimizing decision thresholds is crucial for maximizing outcomes within temporal constraints.

Purpose of the Study:

  • To provide a theoretical framework for understanding how animals learn optimal decision thresholds.
  • To maximize total expected reward within a limited time interval.
  • To investigate adaptive decision-making strategies in variable environments.

Main Methods:

  • Modeled the problem using semi-Markov decision processes (SMDP).
  • Each experimental condition was treated as a state in the SMDP.
  • Employed a biologically plausible learning algorithm to find optimal decision thresholds.

Main Results:

  • Maximizing reward requires setting condition-specific decision thresholds.
  • The proposed model learns to adjust thresholds over time.
  • Initial high thresholds lead to sub-optimal performance, which improves with experience.

Conclusions:

  • Optimal decision-making in time-limited situations involves adaptive threshold setting.
  • The SMDP framework provides a robust method for modeling this learning process.
  • The model demonstrates that experience refines decision strategies for better reward maximization.