Intact Multisensory Integration of Low-Level Visual and Auditory Information in ASD
Bibliographic record
Abstract
Research suggests that deficient multisensory integration (MSI) may partially underlie sensory-seeking or sensory-aversion behaviors in Autism Spectrum Disorders (ASD) (Iarocci & McDonald, 2006). Most of the evidence supporting this hypothesis comes from studies that use socio-communicative stimuli (i.e., speech, faces) or complex cognitive tasks. This study’s goal was to investigate MSI abilities in ASD using low-level stimuli that are void of social content to disentangle multisensory integration from the confounding role of a possible social deficit. To do so, 20 adolescents / adults with and without ASD completed 2 low-level MSI tasks. For the flash-beep illusion task (Shams et al. 2000), participants responded whether they saw 1 or 2 flashes (F) while simultaneously hearing 0, 1, or 2 beeps (B). They were exposed to four non-illusion trials (i.e., 2F2B, 2F0B, 1F1B, 1F0B) and two illusion trials (i.e., fission/fusion illusions), whereby a discordant number of flashes and beeps were presented. Illusion susceptibility (i.e., accuracy) was measured. For the target detection task, participants were asked to respond as quickly and accurately as possible (using a button press) to either visual (flash), auditory (beep) or audiovisual stimuli (flash and beep presented together) (Williams et al., 2010). RTs were measured for all conditions. For the flash-beep illusion task, the ASD group was equally susceptible to the fission illusion, and significantly more susceptible to the fusion illusion. This indicates no evidence of impaired MSI in ASD, and may even speak to a more automatic and less selective MSI process. For the target detection task, no between-group differences in RT were found across conditions; both groups demonstrated multisensory facilitation on audiovisual trials. Results suggest that MSI for simple, non-social information is an intact ability in ASD. Since the same participants completed both tasks, we are assessing whether MSI abilities are consistent across tasks for each participant. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".