Keep it simple: Identification of basic versus complex emotions in spoken language in individuals with autism spectrum disorder without intellectual disability: A meta‐analysis study
Bibliographic record
Abstract
Daily functioning involves identifying emotions in spoken language, a fundamental aspect of social interactions. To date, there is inconsistent evidence in the literature on whether individuals with autism spectrum disorder without intellectual disability (ASD-without-ID) experience difficulties in identification of spoken emotions. We conducted a meta-analysis (literature search following the PRISMA guidelines), with 26 data sets (taken from 23 peer-reviewed journal articles) comparing individuals with ASD-without-ID (N = 614) and typically-developed (TD) controls (N = 640), from nine countries and in seven languages (published until February 2020). In our analyses there was no sufficient evidence to suggest that individuals with HF-ASD differ from matched controls in the identification of simple prosodic emotions (e.g., sadness, happiness). However, individuals with ASD-without-ID were found to perform significantly worse than controls in identification of complex prosodic emotions (e.g., envy and boredom). The level of the semantic content of the stimuli presented (e.g., sentences vs. strings of digits) was not found to have an impact on the results. In conclusion, the difference in findings between simple and complex emotions calls for a new-look on emotion processing in ASD-without-ID. Intervention programs may rely on the intact abilities of individuals with ASD-without-ID to process simple emotions and target improved performance with complex emotions. LAY SUMMARY: Individuals with autism spectrum disorder without intellectual disability (ASD-without-ID) do not differ from matched controls in the identification of simple prosodic emotions (e.g., sadness, happiness). However, they were found to perform significantly worse than controls in the identification of complex prosodic emotions (e.g., envy, boredom). This was found in a meta-analysis of 26 data sets with 1254 participants from nine countries and in seven languages. Intervention programs may rely on the intact abilities of individuals with ASD-without-ID to process simple emotions.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".