Attentional bias towards smartphone stimuli is associated with decreased interoceptive awareness and increased physiological reactivity
Bibliographic record
Abstract
Excessive smartphone use has been linked to negative psychological outcomes and may also be associated with cognitive impairments and disruptions in mind-body interaction, though the underlying mechanisms remain unclear. Here, we investigated attentional bias towards marginal smartphone stimuli and its relationship with interoceptive awareness and physiological cue reactivity in healthy young adults. Fifty-eight participants completed a letter detection task with varying perceptual loads, during which task-irrelevant smartphone-related or scrambled images were presented in the background. Cardiac responses were recorded to assess physiological reactivity. Participants also completed two questionnaires for interoceptive awareness and self-report smartphone addiction. Using a designed and automated clustering based on behavioural responses, participants were classified into two groups: one group exhibited distraction from smartphone background only under low perceptual load, while the other showed consistent attentional bias regardless of load. Notably, the latter group reported significantly lower interoceptive awareness and higher smartphone addiction scores. Additionally, they exhibited heart rate acceleration in response to smartphone stimuli, indicating heightened arousal, whereas the former group showed heart rate deceleration. These findings demonstrate that consistent attentional bias towards smartphone stimuli is associated with reduced interoceptive awareness, specifically a decreased tendency to notice and trust internal bodily sensations, and increased physiological reactivity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".