Using Video Modeling to Teach Facial Expression Recognition in Individuals Diagnosed with Autism Spectrum Disorder in a Variety of Contexts
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a developmental disability that appears within the first three years of life. ASD involves impairments in social communication and interaction which includes limitations in verbal and non verbal communication such as understanding and using body language, gestures, and facial expressions (American Psychiatric Association, 2013). Facial expressions are vital in communicating social and emotional cues and understanding facial expressions is important to understanding simple social situations and acting appropriately (Akmanpglu, 2015). In this study, we will attempt to increase individuals with ASD’s ability to recognize 3 basic human emotions, happy, sad, and mad, and their corresponding facial expression in different social contexts. This will be done using a teaching method known as video modeling. This study will be a multiple baseline across targets design with 5 participants. It will take place over 9 weeks with 30 minute sessions each week. We hypothesize that the participants will be able to determine how a person is feeling just by reading their facial expressions instead of the contextual cues of situations more often than before the video modelling sessions took place. Learning to label these emotions in others could possibly lead to having more appropriate responses and interactions with others displaying these emotions in the future. Faculty Mentors: Russ Powell and Miranda Macauley Department: Psychology (Honours)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".