Gesture & Aphasia: Iconic gestures convey part of the message
Bibliographic record
Abstract
Introduction: Gesture, particularly iconic gestures, can convey information absent in speech. Iconic gestures share a direct relation to the concept depicted, and thus could be beneficial during communication for people with aphasia (PWA). The present study aimed to investigate how PWA use iconic gestures and to what degree these convey information absent in speech. Methods: We analyzed videos of semi-structured interviews with 42 PWA and 9 controls from AphasiaBank (MacWhinney et al., 2011). We coded the gestures produced by these individuals. First, in addition to the gesture types identified by Sekine et al. (2013) we specified five iconic representation techniques (based on Müller, 1998): handling, enact, object, shape (van Nispen et al., 2016) and path (see Cocks et al., 2013). Second, based on Colletta et al. (2009),we determined whether a gesture conveyed information absent in speech and essential for understanding PWA’s message. Results: Iconic gestures accounted for approximately 20% of all gestures produced by both PWA (M=21%, SD=13%), and controls (M=22%, SD=17%). Within the category of iconic gestures, PWA often used path gestures (M=33%, SD=32%). Controls used relatively more handling (M=28%, SD=35%) and shape gestures (M=38%, SD=29%). PWA’s gestures (M=13%, SD=14%) were more often essential than controls’ (M=2%, SD=2%). Beside emblems (gestures with a conventional meaning) and concrete deictics (pointing at something), iconic gestures were most often essential for understanding the information conveyed by PWA. Within the category of iconic gesture, handling (M=34%, SD=29%), and enact (M=42%, SD=39%) were the most informative. Path gestures were least often essential (M=10%, SD=22%). Discussion & Conclusion: Gestures, and particularly iconic gestures, produced by PWA convey part of their communicative message and it seems important that interlocutors pay attention to these gestures. Also, clinicians should incorporate this in PWA’s communication advice. Handling and enact gestures frequently convey information absent in the speech of PWA. Only a few of the path gestures, although used relatively often by PWA, conveyed information absent in speech. It is important to note that we observed considerable individual differences. More research is needed to determine whether the communication of PWA could be improved by stimulating the use of informative gesture techniques. References Cocks, N., Dipper, L., Pritchard, M., & Morgan, G. (2013). The impact of impaired semantic knowledge on spontaneous iconic gesture production. Aphasiology, 27(9), 1050-1069. Colletta, J.-M., Kunene, R., Venouil, A., Kaufmann, V., & Simon, J.-P. (2009). Multi-track Annotation of Child Language and Gestures. In M. Kipp, J.-C. Martin, P. Paggio & D. Heylen (Eds.), Multimodal Corpora (Vol. 5509, pp. 54-72): Springer Berlin Heidelberg. MacWhinney, B., Fromm, D., Forbes, M., & Holland, A. (2011). AphasiaBank: Methods for studying discourse Aphasiology, 25, 1286-1307. Müller, C. (1998). Iconicity and Gesture. In S. Santi, I. Guatiella, C. Cave & G. Konopczyncki (Eds.), Oralité et Gestualité: Communication multimodale, interaction (pp. 321-328). Montreal, Paris: L'Harmattan. Sekine, K., Rose, M., Foster, A. M., Attard, M. C., & Lanyon, L. E. (2013). Gesture production patterns in aphasic discourse: In-depth description and preliminary predictions. Aphasiology, 27(9), 1031-1049. van Nispen, K., van de Sandt-Koenderman, W. M. E., Mol, L., & Krahmer, E. (2016). Pantomime production by people with aphasia: What are influencing factors? Journal of Speech Language and Hearing Research, Accepted for publication.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".