Human Neuroimaging Reveals Differences in Activation and Connectivity between Real and Pantomimed Tool Use
Bibliographic record
Abstract
Because the sophistication of tool use is vastly enhanced in humans compared with other species, a rich understanding of its neural substrates requires neuroscientific experiments in humans. Although functional magnetic resonance imaging (fMRI) has enabled many studies of tool-related neural processing, surprisingly few studies have examined real tool use. Rather, because of the many constraints of fMRI, past research has typically used proxies such as pantomiming despite neuropsychological dissociations between pantomimed and real tool use. We compared univariate activation levels, multivariate activation patterns, and functional connectivity when participants used real tools (a plastic knife or fork) to act on a target object (scoring or poking a piece of putty) or pantomimed the same actions with similar movements and timing. During the Execute phase, we found higher activation for real versus pantomimed tool use in sensorimotor regions and the anterior supramarginal gyrus, and higher activation for pantomimed than real tool use in classic tool-selective areas. Although no regions showed significant differences in activation magnitude during the Plan phase, activation patterns differed between real versus pantomimed tool use and motor cortex showed differential functional connectivity. These results reflect important differences between real tool use, a closed-loop process constrained by real consequences, and pantomimed tool use, a symbolic gesture that requires conceptual knowledge of tools but with limited consequences. These results highlight the feasibility and added value of employing natural tool use tasks in functional imaging, inform neuropsychological dissociations, and advance our theoretical understanding of the neural substrates of natural tool use. SIGNIFICANCE STATEMENT The study of tool use offers unique insights into how the human brain synthesizes perceptual, cognitive, and sensorimotor functions to accomplish a goal. We suggest that the reliance on proxies, such as pantomiming, for real tool use has (1) overestimated the contribution of cognitive networks, because of the indirect, symbolic nature of pantomiming; and (2) underestimated the contribution of sensorimotor networks necessary for predicting and monitoring the consequences of real interactions between hand, tool, and the target object. These results enhance our theoretical understanding of the full range of human tool functions and inform our understanding of neuropsychological dissociations between real and pantomimed tool use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".