TISK 1.0: An easy-to-use Python implementation of the time-invariant string kernel model of spoken word recognition

Abstract

This article describes a new Python distribution of TISK, the time-invariant string kernel model of spoken word recognition (Hannagan et al. in Frontiers in Psychology, 4, 563, 2013). TISK is an interactive-activation model similar to the TRACE model (McClelland & Elman in Cognitive Psychology, 18, 1–86, 1986), but TISK replaces most of TRACE’s reduplicated, time-specific nodes with theoretically motivated time-invariant, open-diphone nodes. We discuss the utility of computational models as theory development tools, the relative merits of TISK as compared to other models, and the ways in which researchers might use this implementation to guide their own research and theory development. We describe a TISK model that includes features that facilitate in-line graphing of simulation results, integration with standard Python data formats, and graph and data export. The distribution can be downloaded from https://github.com/maglab-uconn/TISK1.0.