Exploring the interaction between approach-avoidance conflict and memory processing
Bibliographic record
Abstract
The medial temporal lobe (MTL) has been implicated in approach-avoidance (AA) conflict processing, which arises when a stimulus is imbued with both positive and negative valences. Notably, since the MTL has been traditionally viewed as a mnemonic brain region, a pertinent question is how AA conflict and memory processing interact with each other behaviourally. We conducted two behavioural experiments to examine whether increased AA conflict processing has a significant impact on incidental mnemonic encoding and inferential reasoning. In Experiment 1, participants first completed a reward and punishment AA task and were subsequently administered a surprise recognition memory test for stimuli that were presented during high and no AA conflict trials. In Experiment 2, participants completed a reward and punishment task in which they learned the valences of objects presented in pairs (AB, BC pairs). Next, we assessed their ability to integrate information across these pairs (infer A-C relationships) and examined whether inferential reasoning was more challenging across objects with conflicting compared to non-conflicting incentive values. We observed that increased motivational conflict did not significantly impact encoding or inferential reasoning. Potential explanations for these findings are considered, including the possibility that AA conflict and memory processing are not necessarily intertwined behaviourally.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".