AI Article Synopsis
- The frontal lobes are crucial for voluntary actions and pursuing goals, but their exact roles are still not fully understood.
- Researchers propose a new model called the Hierarchical Error Representation (HER), which uses predictive coding to explain how complex behaviors can be learned and performed.
- This model integrates various research findings and offers a fresh perspective on the functions of the prefrontal cortex, linking neural activity to broader behavioral outcomes.
Article Abstract
The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications for understanding the frontal cortex across multiple levels of description, from the level of single neurons to behavior.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832795 | PMC |
| http://dx.doi.org/10.1038/s41598-018-21407-9 | DOI Listing |
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