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A machine learning-based model for the quantification of mental conflict

We often encounter mental conflict in our lives. Such mental conflict has long been regarded as subjective. However, a machine learning method can be used to quantify the temporal dynamics of conflict between reward and curiosity from behavioral time-series.

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Fig. 1: Decoding reward–curiosity conflict from rat behavioral data.


  1. Price, A. W. Mental Conflict (Issues in Ancient Philosophy) (Routledge, 1994). This book is one of the most important classic works about mental conflict.

  2. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction 2nd edn (Bradford Books, 2018). This book is an introduction to RL by the developers of RL.

  3. Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010). This is a review article about FEP by the founder of FEP.

    Article  Google Scholar 

  4. Ito, M. & Doya, K. Validation of decision-making models and analysis of decision variables in the rat basal ganglia. J. Neurosci. 29, 9861–9874 (2009). This study used the slot-machine task in rats to validate Q-learning; we used their behavioral data to demonstrate that our method was able to decode the curiosity of animals.

    Article  Google Scholar 

  5. Samejima, K., Ueda, Y., Doya, K. & Kimura, M. Representation of action-specific reward values in the striatum. Science 310, 1337–1340 (2005). This paper showed how action values are represented in the striatum, and also estimated temporal dynamics of the randomness of action selection based on Q-learning.

    Article  Google Scholar 

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This is a summary of: Konaka, Y. et al. Decoding reward–curiosity conflict in decision-making from irrational behaviors. Nat. Comput. Sci. (2023).

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A machine learning-based model for the quantification of mental conflict. Nat Comput Sci 3, 370–371 (2023).

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