Non-invasive brain stimulation in information systems research: A proof-of-concept study
Bibliographic record
Abstract
One of the founding experiments in the field of Neuro-Information-Systems (NeuroIS), which aims at exploring the neural correlates of the technology acceptance model, suggests that perceived ease of use (PEoU) is associated with activity in the dorsolateral prefrontal cortex (DLPFC) while perceived usefulness is associated with activity in the insula, caudate nucleus and anterior cingulate cortex. To further assess the link between DLPFC and PEoU, transcranial direct current stimulation (tDCS) was applied over bilateral DLPFC (F3 and F4) immediately before an online shopping task. Forty-two participants were divided in three stimulation groups: left anodal/right cathodal, left cathodal/right anodal and sham. No change in PEoU was observed post stimulation but participants in the left anodal/right cathodal stimulation group took longer to make a purchase compared to sham stimulation and had different visual fixation patterns over the buy buttons. This is, to our knowledge, the first use of non-invasive brain stimulation in the field of NeuroIS. Although the involvement of DLPFC in PEoU could not be confirmed, the present study suggests that non-invasive brain stimulation may be a useful research tool in NeuroIS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".