Читать книгу The Handbook of Speech Perception - Группа авторов - Страница 73

It is assumed in nearly all models of word recognition that there are multiple components or stages of processing in the mapping from sound to words. The first stage of processing involves the transformation of the auditory input from the peripheral auditory system into a spectro‐temporal representation based on the extraction of auditory patterns or properties from the acoustic signal. This representation is in turn converted at the next stage to a more abstract phonetic‐phonological representation corresponding to the phonetic categories of speech. The representation units at this stage of processing are considered to include segments and (as we will claim) features. These units then interfaces with the lexical processing system where the segment and feature representations map onto the lexicon (words). Here, a particular lexical entry is ultimately selected from a potential set of lexical candidates or competitors. Each lexical entry in turn activates its lexical semantic network where the meaning of the lexical entry is ultimately contacted.

What is critically important is the functional architecture of this hierarchical system. Current models consider that the system is characterized by a distributed, network‐like architecture in which representations at each level of processing are realized as patterns of activation with properties of activation, inhibition, and competition (McClelland & Elman, 1986; McClelland & Rumelhart, 1986; Gaskell & Marslen‐Wilson, 1999). Not only do the dynamic properties of the network influence the degree to which a particular representation (e.g. a feature, a segment, a word) is activated or inhibited, but patterns of activation also spread to other representations that share particular structural properties. It is also assumed that the system is interactive with information flow being bidirectional; lower levels of representations may influence higher levels, and higher levels may influence lower levels. Thus, there is spreading activation not only within a level of representation (e.g. within the lexical network), but also between and within different levels of representation (e.g. phonological, lexical, and semantic levels).

There are several consequences of such a functional architecture, as shown in Figure 5.1. First, there is graded activation throughout the speech‐lexical processing system; that is, the extent to which a given representation is activated is a function of the “goodness” of the input. Thus, the activation of a potential candidate is not all‐or‐none but rather is graded or probabilistic. For example, the activation of a phonetic category such as [k] will be influenced by the extent to which the acoustic‐phonetic input matches its representation. It is worth noting that graded activation is more complex than simply the extent to which a particular phonetic attribute matches its representation. Rather, the extent of activation reflects the totality of the acoustic properties giving rise to a particular phonetic category. Thus, the activation of the phonetic feature [voicing] would include the probabilities of voice onset time, burst amplitude and duration, fundamental frequency, to name a few (see Lisker, 1986). Second, because the system is interactive, activation patterns at one level of processing will influence activation at another level of processing. For example, a poor acoustic‐phonetic exemplar of a phonetic category such as [k] will influence the activation of the lexical representation of a word target such as cat.

Figure 5.1 Several properties of the functional architecture of auditory word recognition and lexical processing are shown, including graded activation, lexical competition, and interactivity (cascading activation). The left and right panels show how one level of processing influences activation of another downstream from it in an interactive system. The left panel shows activation of a good phonetic exemplar [kat] on activation of the lexical representation cat and the graded activation of phonological and semantic competitors in its lexical network. Note spoon which is neither phonologically nor semantically related is not activated. The right panel shows the cascading effects of a poor phonetic input [k*at] on the network. There is reduced activation of its lexical representation and even greater reduction of activation of competitors.

Third, because of the network properties of the system and the structure of the representations, there is competition between potential candidates at each stage of processing (e.g. within the sound [segment and feature], lexical, and semantic levels of representation). The degree of competition is a function of the extent to which the candidate(s) share(s) properties with the particular target. This influences the time course and patterns of activation of the target and, ultimately, the performance of the network. Multiple competing representations may also influence activation and processing at other stages of processing (Gaskell & Marslen‐Wilson, 1999; McClelland, 1979). These properties of the functional architecture of the system – graded activation, interactivity, and competition – influence lexical representations. As we will see, each of these properties provides evidence for features as representational units.