Revisiting pragmatic abilities in autism spectrum disorders
Bibliographic record
Abstract
In a 2007 paper, we argued that speakers with Autism Spectrum Disorders (ASDs) exhibit pragmatic abilities which are surprising given the usual understanding of communication in that group. That is, it is commonly reported that people diagnosed with an ASD have trouble with metaphor, irony, conversational implicature and other non-literal language. This is not a matter of trouble with knowledge and application of rules of grammar. The difficulties lie, rather, in successful communicative interaction. Though we did find pragmatic errors within literal talk, the transcribed conversations we studied showed many, many successes. A second paper reinforced our finding of a general level of success (de Villiers, Myers, and Stainton 2012). It considered differences within the class of pragmatically-inflected yet literal speech acts. The present paper carries our project forward. It overcomes some of the methodological limitations of the second paper, by increasing sample size, and looking at frequency of use rather than just seeming errors. It also includes a control sample. The emerging results are two-fold. On the one hand, there was a slight, statistically significant difference in frequency of use between our participants and the controls in four sub-categories: indexicals, possessives, polysemy and degree on a scale. In all four, the participants diagnosed with ASDs had fewer occurrences overall, relative to controls. On the other hand, there was no significant difference in error rates between ASDs and controls — not in any of the eight categories of pragmatic determinants of literal content that we coded for. The upshot is that, though there were less-preferred forms for participants with ASDs, they do very well indeed with pragmatic determinants of literal content.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".