Subgoal- and Goal-Related Prediction Errors in Medial Prefrontal Cortex
Bibliographic record
Abstract
Abstract A longstanding view of the organization of human and animal behavior holds that behavior is hierarchically organized, meaning that it can be understood as directed towards achieving superordinate goals through subordinate goals, or subgoals. For example, the superordinate goal of making coffee can be broken down as accomplishing a series of subgoals, namely boiling water, grinding coffee, pouring cream, etc. Learning and behavioral adaptation depend on prediction-error signals, which have been observed in ventral striatum (VS) and medial prefrontal cortex (mPFC). In past work, we have shown that prediction error signals (PEs) can be linked not only to superordinate goals, but also to subgoals. Here we present two functional magnetic resonance imagining experiments that replicate and extend these findings. In the first experiment, we replicated the finding that mPFC signals subgoal-related PEs, independently of goal PEs. Together with our past work, this experiment reveals that BOLD responses to PEs in mPFC are unsigned. In the second experiment, we showed that when a task involves both goal and subgoal PEs, mPFC shows only goal-related PEs, suggesting that context or attention can strongly impact hierarchical PE coding. Furthermore, we observed a dissociation between the coding of PEs in mPFC and VS. These experiments suggest that the mPFC selectively attends to information at different levels of hierarchy depending on the task context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".