American Sign Language Handshapes Reflect Pressures for Communicative Efficiency
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Abstract (arXiv)
Communicative efficiency is a key topic in linguistics and cognitive psychology, with many studies demonstrating how the pressure to communicate with minimal effort guides the form of natural language. However, this phenomenon is rarely explored in signed languages. This paper shows how handshapes in American Sign Language (ASL) reflect these efficiency pressures and provides new evidence of communicative efficiency in the visual-gestural modality. We focus on hand configurations in native ASL signs and signs borrowed from English to compare efficiency pressures from both ASL and English usage. First, we develop new methodologies to quantify the articulatory effort needed to produce handshapes and the perceptual effort required to recognize them. Then, we analyze correlations between communicative effort and usage statistics in ASL or English. Our findings reveal that frequent ASL handshapes are easier to produce and that pressures for communicative efficiency mostly come from ASL usage, rather than from English lexical borrowing.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 38 references that are in the map. First posted on arXiv 6 Jun 2024.
Builds on
2 of 38 references.
References · 2
- 2019MediaPipe: A Framework for Building Perception Pipelinescited by 572
- 2020MediaPipe Hands: On-device Real-time Hand Trackingcited by 157
Built on by
Citing papers per year: 2024: 1; 2025: 4.
5 papers.
- 2024Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMscited by 18
- 2025Post-training for Efficient Communication via Convention Formationcited by 7
- 2025How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discoursecited by 1
- 2025Emotion Recognition in Signerscited by 1
- 2025Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approachcited by 0
- 2025Emotion Recognition in Signers17 Dec 2025
- 2025How Pragmatics Shape Articulation: A Computational Case Study in STEM ASL Discourse27 Oct 2025
- 2025Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach22 Sep 2025
- 2025Post-training for Efficient Communication via Convention Formation8 Aug 2025
- 2024Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs2 Aug 2024