From the small screen to the big world: mobile apps for teaching real-world face recognition to children with autism
Bibliographic record
Abstract
AN Sung, A Bai, JG Bowen, B Xu, LM Bartlett, JC Sanchez, MD Chin, LJ Poirier, MR Blinkhorn, AC Campbell, JW Tanaka Centre for Autism, Research, Technology and Education (CARTE), Department of Psychology, University of Victoria, Victoria, BC, Canada Abstract: In their everyday situations, individuals with autism spectrum disorder (ASD) encounter problems perceiving and understanding the facial expressions of others. If people with ASD have difficulties interpreting facial emotions, it is not surprising that they would struggle in their daily social interactions. An important question is whether facial emotion skills can be learned through systematic instruction and training. The accessibility, portability, and engagement of mobile devices (ie, smartphones, tablets) afford exciting new opportunities for creating innovative apps in emotional face training. In this article, we review the current crop of facial emotion apps for autism. We evaluate the apps according to the following criteria: face-processing skills, social attributes, and usability. We discuss the key ingredients of face-processing apps that will help a person on the autism spectrum make the transition from the small screen of the mobile device to the big world of real life. Keywords: mobile apps, emotion, facial expression, development, social skills, gamification
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".