Attentional distraction and physiological arousal in misophonia
Bibliographic record
Abstract
Attention (Task 1) We will conduct a study looking into the role of attention in misophonia. Specifically, the study described follows on from existing literature showing differences in attention in misophonia (Frank et al., 2020, Simner et al., 2022, Ghorbani et al., 2023), as well as differences in physiological responses in misophonia (Edelstein et al., 2013, Kumar et al., 2017, Schroder et al., 2019, Siepsiak et al., 2023). In the current study we pull these threads together to test whether there are differences in behavioural or physiological responses to an attentional distraction task between people with misophonia, and controls without misophonia. Specifically, our attention task has sounds (including misophonic trigger sounds) only as a background element (I.e., sounds are not task relevant); sounds do not need to be attended in order to complete the task making it more naturalistic. The task can be considered in terms of two parallel components: a task-relevant visual display (from which we obtain behavioural responses) and task-irrelevant auditory presentation (from which we derive the psychophysiological responses). The experiment implemented in Psychopy (Pierce et al., 2019) will be available on github. The visual component of the task (following Forster et al., 2011) involves presentation of a 3x3 grid containing a mixture of letters and numbers where participants are asked to categorise each character on the screen as a letter or a number by key press (preceded by a fixation cross of 1s). There are minimally N=120 arrays generating at least 1080 *behavioural* responses (accuracy, RT). Overall duration is determined by the auditory presentation component which involves the presentation of 120 x 10s intervals, half of which are silent (N=60) and half of which contain a sound (20 each of misophonic, unpleasant and neutral sounds), and gives a fixed number of 120 *psychophysiological* responses recorded during the task. The onset of the sound is unpredictable but synchronised to the 2nd, 3rd, 4th or 5th (/9) response made to the visual display. Sounds were obtained from Hansen, Leber & Saygin (2021) and pre-categorised via their prior ratings from misophonics and controls. Interoception (Task 2) Finally, we are additionally interested in interoception and alexithymia. Interoception has been implicated in the neuroscience of misophonia (Kumar et al., 2017) previously, but to date only self-reported interoceptive abilities have been measured (Andermane et al., 2023). Here we look at an objective measure of interoception using a heart beat tracking task (following Garfinkel et al., 2016) as well as the trait alexithymia, measured using the Toronto Alexithymia Scale-20 (TAS-20), associated with difficulties identifying and describing feelings, and impaired interoceptive abilities (Shah et al., 2016).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".