Recognizing Spoken Words: The Neighborhood Activation Model

Abstract

Objective-A fundamental problem in the study of human spoken word recognition concerns the structural relations among the sound patterns of words in memory and the effects these relations have on spoken word recognition. In the present investigation, computational and experimental methods were employed to address a number of fundamental issues related to the representation and structural organization of spoken words in the mental lexicon and to lay the groundwork for a model of spoken word recognition.Design-Using a computerized lexicon consisting of transcriptions of 20,000 words, similarity neighborhoods for each of the transcriptions were computed. Among the variables of interest in the computation of the similarity neighborhoods were: 1) the number of words occurring in a neighborhood, 2) the degree of phonetic similarity among the words, and 3) the frequencies of occurrence of the words in the language. The effects of these variables on auditory word recognition were examined in a series of behavioral experiments employing three experimental paradigms: perceptual identification of words in noise, auditory lexical decision, and auditory word naming.Results-The results of each of these experiments demonstrated that the number and nature of words in a similarity neighborhood affect the speed and accuracy of word recognition. A neighborhood probability rule was developed that adequately predicted identification performance. This rule, based on Luce's (1959) choice rule, combines stimulus word intelligibility, neighborhood confusability, and frequency into a single expression. Based on this rule, a model of auditory word recognition, the neighborhood activation model, was proposed. This model describes the effects of similarity neighborhood structure on the process of discriminating among the acoustic-phonetic representations of words in memory. The results of these experiments have important implications for current conceptions of auditory word recognition in normal and hearing impaired populations of children and adults.Since the publication of Oldfield's (1966) seminal article, "Things, Words and the Brain," a great deal of attention has been devoted to the structural organization of words in the mental lexicon. Most of this research, however, has focused on the structure of higher level aspects of lexical representations, namely the semantic and conceptual organization of lexical items in memory (e.g., Miller & Johnson-Laird, 1976;Smith, 1978). As a consequence, little attention has been directed to the structural organization of the representations of sensory Copyright © 1998 and perceptual information used to gain access to these higher level sources of information. The goal of the present investigation was to explore in detail this structure and its implications for perception of spoken words by normal and hearing-impaired listeners.In the present set of studies, structure will be defined specifically in terms of similarity relations among the sound patterns of words. Similarity will serve as th...

Time Course of Frequency Effects in Spoken-Word Recognition: Evidence from Eye Movements

“…We compared three different implementations of frequency: frequency operating on resting-activation levels, frequency operating on connection weights, and frequency applied in a postactivation decision rule, as suggested by Luce and colleagues in the NAM (Goldinger et al, 1989;Luce, 1986;Luce & Pisoni, 1998). In each of these simulations, frequency is viewed as a central component of lexical access; that is, frequency plays a role as soon as lexical candidates become active (this is described in more detail below).…”

“…(5) would be a frequency-weighted variant of the R. D. Luce (1959) choice rule if TRACE response strengths were used as SWP estimates. While Luce (1986) and Luce and Pisoni (1998) used segmental confusion matrices to estimate SWPs, they also suggested that a TRACE-like interactive activation system could form the front-end to the NAM, such that SWPs would correspond to activations. A key difference in such an implementation compared to the standard TRACE model is the locus of frequency effects.…”

“…The number of competitors, their frequency of occurrence in the language, as well as the frequency of occurrence of the target word itself all affect recognition (e.g., Luce & Pisoni, 1998;Marslen-Wilson, 1987. The present study focuses on the time course of frequency effects for words and their close competitors.…”

“…In distributed learning models, the representations of high-frequency words would be activated more rapidly because highfrequency mappings are better learned, resulting in stronger connection weights (Gaskell & Marslen-Wilson, 1997;Plaut, McClelland, Seidenberg, & Patterson, 1996). In contrast, the Neighborhood Activation Model (henceforth, NAM; Luce, 1986;Luce & Pisoni, 1998) places the locus of frequency effects in a decision stage that follows initial lexical activation. In this model, the input stimulus activates a set of acoustic-phonetic patterns that shares some degree of similarity with the input; these acoustic-phonetic patterns in turn activate word-decision units that are tuned to them.…”

Syntactic predictability in the recognition of carefully and casually produced speech.

“…There is ample evidence in the literature suggesting that phonological overlap among words increases lexical competition (e.g. Allopenna, Magnuson, & Tanenhaus, 1998;Brouwer et al, 2012a;Luce & Pisoni, 1998;McQueen & Viebahn, 2007). We would therefore expect that a casual speaking style should increase lexical competition and slow down word recognition.…”