The role of the hippocampus in approach-avoidance conflict decision-making: Evidence from rodent and human studies
Bibliographic record
Abstract
The hippocampus (HPC) has been traditionally considered to subserve mnemonic processing and spatial cognition. Over the past decade, however, there has been increasing interest in its contributions to processes beyond these two domains. One question is whether the HPC plays an important role in decision-making under conditions of high approach-avoidance conflict, a scenario that arises when a goal stimulus is simultaneously associated with reward and punishment. This idea has its origins in rodent work conducted in the 1950s and 1960s, and has recently experienced a resurgence of interest in the literature. In this review, we will first provide an overview of classic rodent lesion data that first suggested a role for the HPC in approach-avoidance conflict processing and then proceed to describe a wide range of more recent evidence from studies conducted in rodents and humans. We will demonstrate that there is substantial, converging cross-species evidence to support the idea that the HPC, in particular the ventral (in rodents)/anterior (in humans) portion, contributes to approach-avoidance conflict decision making. Furthermore, we suggest that the seemingly disparate functions of the HPC (e.g. memory, spatial cognition, conflict processing) need not be mutually exclusive.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".