Tactile language systems transmit language through temporally sequenced vibrotactile patterns on the skin. However, the Inter-Stimulus Interval (ISI) separating successive patterns is usually chosen a priori and applied uniformly, without grounding in individual perceptual performance or learning dynamics. This work introduces a user-centered approach for deriving ISI values from longitudinal behavioral data, using each participant's voice onset time (VOT) as a functional measure for decoding readiness. Over 28 consecutive days of practice in tactile letter recognition, the participants' VOTs systematically decreased and stabilized in accordance with the power law of practice. These stabilized VOT values were then used to derive performance-based ISIs. In a following word recognition task engaging untrained 2-3 letter words, ISIs derived from stabilized performance yielded higher recognition accuracy (67.4%) than a fixed baseline ISI of 75 ms (28.3%). Instead of identifying an optimal ISI value, these results illustrate the viability of a reproducible, performance-driven methodology for ISI allocation, supporting the development of wearable haptic displays that adapt timing parameters to users' learned decoding behavior.
Nyasulu et al. (Thu,) studied this question.