Intersensory facilitation effects explained using an additive activation model of initiation
Bibliographic record
Abstract
In a simple reaction time (RT) task, subjects must respond to a single stimulus with a response that is known in advance. However, RTs can differ depending on the stimulus modality whereby auditory RTs are typically faster than visual. Furthermore, combining modalities results in RTs that are faster than either stimulus alone produces. The mechanisms underlying this effect, known as intersensory facilitation, are currently unclear. Recent studies have hypothesized that activation related to initiation can sum under certain circumstances (e.g. startle). Of interest in the current study was whether a model of additive initiation-related activation could explain the intersensory facilitation effect. Twelve participants performed a RT task requiring a targeted wrist extension following either a visual go-signal, an auditory signal, or a combination of both with a varying stimulus onset asynchrony (SOA) (e.g. auditory stimulus presented either 0, 25, 50, 75, 100, or 125 ms after visual go-signal). Electromyography (EMG) from the wrist prime movers, as well as displacement was recorded from all participants. As expected, RTs were shorter when both stimulus modalities were presented concurrently. Importantly, as the SOA increased, the observed results closely fit RTs predicted by a model involving additive initiation slopes. Specifically, stimulus presentation times resulting in the largest amount of activation overlap resulted in the greatest relative RT speeding. These results indicate that the neural signals arising from the differing stimuli may converge at a common structure in the brain responsible for response initiation.Acknowledgments: Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".