The crossmodal congruency effect, a tool incorporation metric, suffers from a learning effect with repeated exposures
Bibliographic record
Abstract
The incorporation of a tool into a person9s body representation is well established. Quantitative measures play an important role in assessing tool incorporation levels for tool use paradigms. The crossmodal congruency effect (CCE) is used to quantify tool incorporation without being susceptible to experimenter biases. The crossmodal congruency task is a visual-tactile interference task that is used to calculate the CCE score as a difference in response time for incongruent and congruent trials. Here we show that this metric is susceptible to a learning effect that causes attenuation of the CCE score due to repeated task exposure sessions. This study investigated the conditions under which CCE scores attenuated due to task overexposure and tested if a modified version of the crossmodal congruency task could reduce the learning effect. Our work also sought to examine if the attenuated CCE scores returned to baseline values after a period of time. Thirty subjects were tested up to a maximum of ten times and four of these subjects were retested after a four month delay period. We show that CCE score reduced as early as the second exposure with a 14.5% drop between first and second exposures (p=0.027). Importantly, we found evidence that a modified version of the crossmodal congruency task, in which each exposure was reduced from eight to four test blocks, reduced the drop between first and second exposure from an average of 14.5% to 6.5% without significantly increasing variability of the measurement. Additionally, we found that three out of four subjects that were retested after a four month period returned to near-baseline CCE scores. This study highlights the importance of limiting exposure to the crossmodal congruency task, and proposes a modified approach to improve the use of this psychophysical assessment in the future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".