Dynamics of Sensorimotor Interactions in Embodied Cognition
Bibliographic record
Abstract
We interact with our environment through perception and action. Perception is based on sensory components while actions are based on motor components. It is commonly accepted that these sensorimotor components constitute the foundation of knowledge (i.e., percepts and concepts), action and emotion. However, whether or not these components remain part of knowledge, action and emotion is still being debated (see Glenberg, Witt, & Metcalfe, 2013). According to the classical symbolic/abstracted approach of cognition, cognitive processes operate on symbols that are abstracted from these components. Reversely, embodied cognition theory states that knowledge, action and emotion remain grounded in these sensorimotor components (see Wilson, 2002). This embodiment revolution assumes that the interactions between present and absent —but simulated in memory— sensory-motor components determine the emergence of knowledge, action and emotion (Barsalou, 2008). It also implies that perception, memory (in particular conceptual knowledge), action and emotion interact together in a closer way that previously thought (e.g. Riou, Lesourd, Brunel & Versace, 2011; Corveleyn, Lopez-Moliner & Coello, 2012; Vermeulen et al., 2013). Despite the accumulation of empirical evidence showing that perception, memory, action and emotion interact together, less is known about the dynamics of these interactions. It remains to precise the temporal dynamic (when these interactions occur), the neural underlying networks, and the factors that modulate these interactions. The present research topic focuses on the dynamic relationship between present and absent sensorimotor components across perception, memory, action and emotion in a grounded cognition perspective. This research topic aims 1) to demonstrate the validity of the embodied cognition theories 2) to highlight the dynamics of emergence of conceptual knowledge, action and emotion 3) to provide a comprehensive state-of-the-art theoretical explanation and/or models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".