AB096. Neurophysiological measures of stigma stereotypes
Bibliographic record
Abstract
Background: The perceptions surrounding assistive technology have been shown to be increasingly stigmatizing in older adult populations. This stigmatization can lead individuals to the abandonment of the assistive device. Until now, the methods of identifying or predicting the stigma surrounding assistive technology has mostly been qualitative in nature. Here we present a novel quantitate and qualitative research study that uses neuro-cognitive (psychophysics and EEG) and eye tracking technology, in addition to a new questionnaire to investigate the stigma associated with assistive devices. Therefore, this approach plays a major role in understanding and predicting the neural and physiological correlates associated to stigma. Methods: Thirty-four older adults (>50 years) took part in the study. To determine the psychophysiological predictors of stigma surrounding assistive technologies, we monitored brain activity using EEG, heart rate and eye movements using an eye-tracker while participants viewed a series of images containing either an older or younger individual in different social scenarios (e.g., talking to doctor, at coffee shop). In each scenario, the individual uses either no assistive device, a low stigmatizing device (e.g., iPad), or a high stigmatizing device (e.g., electronic magnifier). Results: Here we present preliminary analysis of the eye movement data. Analysis shows that in comparison to images that contained a low stigmatizing device, in images that contain high stigmatizing devices, the latency to fixate the device is shorter, first fixation duration is longer, and the total number of fixations on the device are higher. The environment that the devices is used in has no effect on eye movement metrics. Conclusions: Although the sample size is small, and based on a healthy older-adult population, these initial observations would indicate that latency to fixate and first fixation duration are predictors of stigma associated with assistive devices. Future research should expand this prediction to those actively using assistive devices, and how the measures predict abandonment over time.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".