Cognitive load but not immersion plays a significant role in embodied cognition as seen through the spontaneous act of leaning
Bibliographic record
Abstract
An important aspect of embodied approaches to cognition is the idea that human cognition does not occur simply in the brain, but is influenced by a complex bi-directional interplay between the brain, body, and external environment. Though embodied cognition is often studied in a controlled laboratory setting, by its very nature it can arise spontaneously in everyday life (e.g., gesturing). A recent paper by Chisholm et al. suggested that leaning while playing a video game may be another instance of a natural spontaneous expression of embodied cognition that can be studied to gain insight into a person's ongoing covert cognition. Consistent with this proposal, Chisholm et al. found that, like gestures, leaning increases when cognitive demand is increased. However, in Chisholm et al., immersion also increased with cognitive demand. We argue that their test to exclude it as a contributing factor-by holding cognitive demand constant while manipulating immersion-was limited. Despite their test, it remains possible and plausible that cognitive demand has an effect on leaning only when immersion increases. To address this issue, the present study systematically varied demand and immersion. We replicate Chisholm et al.'s finding that leaning increases with cognitive load. We also show that the effect of load is not influenced by a robust and reliable change in immersion. Collectively our results provide new and converging evidence that spontaneous overt embodiment of an individual's intention is modulated by cognitive demand, and emphasises the utility of using natural behaviours to understand the embodiment of cognition.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".