More Blobs: A Training Study Examining the Role of Medial-Frontal Cortex in the Development of Perceptual Expertise
Bibliographic record
Abstract
In a recent study, Krigolson and colleagues (2009) demonstrated that a reinforcement learning system within medial-frontal cortex plays a key role in the development of perceptual expertise. Specifically, Krigolson et al. found that when participants learned to discriminate between two families of "blobs" feedback processing elicited an error-related negativity (fERN) – a component of the human event-related brain potential (ERP) evoked by performance feedback. Further, Krigolson et al. observed increases in ERP components associated with object recognition (N250) and response error evaluation (rERN) in participants who demonstrated behavioral learning improvements as gauged by task performance. Here, we utilized the same task at Krigolson et al. but extended training over five days so participants were exposed to 5000 learning trials. In line with the predictions of reinforcement learning theory, the amplitude of the fERN diminished with learning, and somewhat interestingly demonstrated restart costs that align with the observations of traditional learning theory. Further, and novelly, our data also provide unique insight into the N250 (object familiarity) and N170 (object expertise) visual ERP components. Specifically, we propose that the N250 is not a learned effect per se, but instead is a measure of familiarity with a basis in short term memory as we found that N250 amplitude "resets" daily dependent upon object exposure. Further, we find found that the amplitude of the N170 diminished with day-to-day learning, a result counter to studies that have examined its amplitude on a single exposure basis. Meeting abstract presented at VSS 2013
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".