ERPs Differentially Reflect Automatic and Deliberate Processing of the Functional Manipulability of Objects
Bibliographic record
Abstract
It is known that the functional properties of an object can interact with perceptual, cognitive, and motor processes. Previously we have found that a between-subjects manipulation of judgment instructions resulted in different manipulability-related memory biases in an incidental memory test. To better understand this effect we recorded electroencephalography (EEG) while participants made judgments about images of objects that were either high or low in functional manipulability (e.g., hammer vs. ladder). Using a between-subjects design, participants judged whether they had seen the object recently (Personal Experience), or could manipulate the object using their hand (Functionality). We focused on the P300 and slow-wave event-related potentials (ERPs) as reflections of attentional allocation. In both groups, we observed higher P300 and slow wave amplitudes for high-manipulability objects at electrodes Pz and C3. As P300 is thought to reflect bottom-up attentional processes, this may suggest that the processing of high-manipulability objects recruited more attentional resources. Additionally, the P300 effect was greater in the Functionality group. A more complex pattern was observed at electrode C3 during slow wave: processing the high-manipulability objects in the Functionality instruction evoked a more positive slow wave than in the other three conditions, likely related to motor simulation processes. These data provide neural evidence that effects of manipulability on stimulus processing are further mediated by automatic vs. deliberate motor-related processing.
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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 | 0.004 |
| 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 | 0.000 |
| 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".