Harlan D. Harris

jTRACE: A reimplementation and extension of the TRACE model of speech perception and spoken word recognition

This article describes jTRACE, a freely available, cross-platform Java reimplementation of the TRACE model of spoken word recognition. The goal of the reimplementation is to facilitate the use of simulations by researchers who may not have the skills or time necessary to use or extend the original C implementation. In this article, we report a large-scale validation project, in which we have replicated a number of important previous simulations, and then we describe several new features in jTRACE designed to help researchers conduct original TRACE research, as well as to replicate earlier findings. These features include visualization tools, powerful scripting, built-in data graphing, adjustable levels of external and internal noise, and adjustable lexical characteristics, such as frequency of occurrence. Functions for saving and reloading entire simulations facilitate archiving, sharing, and replication and also make jTRACE ideal for educational use, since it comes bundled with several important simulations. jTRACE can be downloaded from magnuson.psy.uconn.edu/jtrace.

Prior Knowledge Enhances the Category Dimensionality Effect

A study of the combined influence of prior knowledge and stimulus dimensionality on category learning was conducted. Subjects learned category structures with the same number of necessary dimensions but with more or fewer additional, redundant dimensions and with either knowledge-related or knowledge-unrelated features. Minimal-learning models predict that all subjects, regardless of condition, either should learn the same number of dimensions or should respond more slowly to each dimension. Despite similar learning rates and response times, subjects learned more features in the high-dimensional than in the low-dimensional condition. Furthermore, prior knowledge interacted with dimensionality, increasing what was learned, especially in the high-dimensional case. A second experiment confirmed that the participants did, in fact, learn more features during the training phase, rather than simply inferring them at test. These effects can be explained by direct associations among features (representing prior knowledge), combined with feedback between features and the category label, as was shown by simulations of the knowledge resonance, or KRES, model of category learning.

Interaction in Spoken Word Recognition Models: Feedback Helps

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Prior Knowledge and Exemplar Frequency

New concepts can be learned by statistical associations, as well as by relevant existing knowledge. We examined the interaction of these two processes by manipulating exemplar frequency and thematic knowledge and considering their interaction through computational modeling. Exemplar frequency affects category learning, with high-frequency items learned more quickly than low-frequency items, and prior knowledge usually speeds category learning. In two experiments in which both of these factors were manipulated, we found that the effects of frequency are greatly reduced when stimulus features are linked by thematic prior knowledge and that frequency effects on single stimulus features can actually be reversed by knowledge. We account for these results with the knowledge resonance model of category learning (Rehder & Murphy, 2003) and conclude that prior knowledge may change representations so that empirical effects, such as those caused by frequency manipulations, are modulated.

jTRACE: A user‐friendly reimplementation and extension of the TRACE model of speech perception and spoken word recognition

jTRACE is a cross‐platform Java reimplementation and extension of the TRACE model of spoken word recognition. TRACE (McClelland and Elman, 1986) is a three‐layer interactive‐activation model of speech perception that takes a pseudoacoustic input representation and accurately simulates many phenomena of human speech processing. The goals of the reimplementation are to provide a user‐friendly version of the model that can run on any current computer platform, extend the model with graphing and scripting tools, and thus provide a tool that is powerful enough for intensive research while easy enough to use in a classroom setting. New features in jTRACE include simulation visualization, results graphing, powerful scripting, internal and external noise parameters, adjustable lexical and phonological characteristics, word recognition metrics, data exporting, and saving/archiving tools. A set of historically relevant simulations is bundled with the program. This feature is ideal for educational applications. Any ...