Efficiency during information transfer between autistic and neurotypical people
Bibliographic record
Abstract
Presented at International Society for Autism Research 2019 Conference (Montreal, Canada), and the Flux Society Congress 2019 (New York, USA). Authors: Catherine J Crompton, Danielle Ropar, Claire VM Evans-Williams, Emma Flynn, Sue Fletcher-Watson. Objective To explore transmission of information between individuals, contrasting autistic, neurotypical, and mixed neurotypical/autistic pairs. Methods A ‘diffusion chain’ technique was used to probe information transfer between pairs of people. A researcher told the first participant in each chain a story, which they then passed on to participant two, who passed it on to participant three, and so on. The story was divided a priori into 30 details, meaning accuracy was scored on a scale from 0-30. Each diffusion chain included eight participants who were either all autistic, all neurotypical, or alternating autistic and neurotypical (n=72). Rate of decline in the number of details recalled at each stage in the chain is a measure of effective information transfer. Results Chains of all autistic and all neurotypical people had similar rates of decline, but when alternating between autistic and neurotypical people information degraded more quickly. Multiple regression found that type of chain and position in chain accounted for 84% of the variance in detail-recall (R2=0.84,F(5,66)=77.05, p<0.0001). Being in a mixed chain significantly predicted detail-recall (B=-6.04,p<0.0001) while being in either the autistic or neurotypical chains did not (B=0.13,p=0.93). Chain position significantly predicted score (B=-2.13,p<0.001). Crucially, an interaction between chain type and chain position indicates that the mixed chain followed a significantly steeper decline in number of details remembered (B=0.57, p<0.05). Conclusion Research to date has dwelt on autistic deficits on lab-based social tasks, that in theory underpin difficulties in real-world interactions. If social cognition is impaired in autism, interactions between two autistic people should be especially challenging. However, our findings suggest that both autistic and neurotypical people benefit from having an interaction partner with the same diagnostic status when performing an information transfer task.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".