Frontiers in Neuroscience (Jan 2025)

Integrated information theory reveals the potential role of the posterior parietal cortex in sustaining conditioning responses in classical conditioning tasks

  • Tien Cuong Phi,
  • Shin Ishii,
  • Shin Ishii,
  • Shin Ishii,
  • Masashi Kondo,
  • Masanori Matsuzaki,
  • Masanori Matsuzaki,
  • Masanori Matsuzaki,
  • Masanori Matsuzaki,
  • Ken Nakae

DOI
https://doi.org/10.3389/fnins.2025.1512724
Journal volume & issue
Vol. 19

Abstract

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Classical conditioning is a fundamental associative learning process in which repeated pairings of a conditioned stimulus (CS) with an unconditioned stimulus (US) lead to the CS eliciting a conditioned response (CR). Previous research has identified key neural regions involved in processing reward-predicting cues and mediating licking behavior. However, the mechanisms that sustain high conditioned response rates across repeated sessions remain elusive, particularly regarding how the reward expectation is represented on a session-by-session basis. While early learning phases in classical conditioning have been extensively studied, the neural mechanisms that support consistent performance over time remain unclear. In this study, we sought to understand how cortical regions, particularly the posterior parietal cortex (PPC), contribute to maintaining high CR rates across sessions. Using the core complex framework derived from Integrated Information Theory (IIT), we explored the dynamics of neural networks during sessions of high CR performance. Our findings suggest that while traditional functional connectivity (FC) methods struggled to capture the complexity of sustained behavioral engagement, the core complex framework revealed key regions, notably the PPC, that were significantly correlated with enhanced CR sessions. This work suggests the potential role of the PPC in supporting reward expectations and maintaining consistent behavioral responses. By applying the core complex framework to investigate neural substrates of sustained behavior, we provide novel insights into the interaction of cortical networks during classical conditioning, offering promising directions for future research in associative learning and behavior.

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