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Humans use multiscale predictive representations in the brain to guide navigation over long distances. These representations show hierarchical organization, with longer predictive horizons in the anterior prefrontal cortex and shorter ones in the posterior hippocampus.

Keywords:
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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience

Background:

  • Human navigation relies on learned relational structures for exploration and goal achievement.
  • Existing research on relational knowledge and planning is limited to small-scale, controlled environments.
  • Understanding how stored knowledge supports navigation in realistic, continuous settings remains an open question.

Purpose of the Study:

  • To investigate how multiscale predictive representations guide naturalistic human navigation.
  • To determine the hierarchical organization of these predictive scales in the brain.
  • To explore the roles of the prefrontal cortex and hippocampus in large-scale spatial navigation.

Main Methods:

  • Model-based representational similarity analysis of neuroimaging data.
  • Participants navigated long, realistic paths in a virtual reality environment.
  • Analysis focused on pattern similarity to successor points within varying predictive horizons.

Main Results:

  • A gradient of predictive horizons was observed across prefrontal and hippocampal regions.
  • Anterior prefrontal cortex (PFC) exhibited the largest predictive horizons.
  • Posterior hippocampus demonstrated the smallest predictive horizons, with intermediate regions like anterior hippocampus and orbitofrontal cortex falling in between.

Conclusions:

  • Multiscale predictive representations are hierarchically organized in the brain, supporting goal-directed navigation.
  • Findings provide empirical support for computational models of navigation and hierarchical planning.
  • This research advances understanding of cognitive maps in everyday, long-distance navigation.