LANGUAGE PROCESSING Progress Report No . 23 ( 1999 ) Indiana University Neighborhood Density , the Tip-ofthe-Tongue Phenomenon , and Aging

A tip-of-the-tongue (TOT) elicitation task was used with younger adults in Experiment 1 and with older adults in Experiment 2 to examine the influence of word frequency, neighborhood density, and neighborhood frequency on the retrieval of phonological word forms from the lexicon. The results of Experiment 1 replicated the results of Harley and Bown (1998): More TOT states were elicited for words with low frequency and sparse neighborhood density (i.e., words with few similar sounding words). However, the results from Experiment 2 showed that in addition to word frequency and neighborhood density, neighborhood frequency, the mean frequency of phonological neighbors, also influenced lexical retrieval for speech production in older adults. Specifically, neighborhood-frequency interacted with word frequency and with neighborhood density. More TOT states were elicited for words with low neighborhood frequency and low word frequency. In addition, more TOT states were elicited for words with low neighborhood-frequency and sparse neighborhoods. These results demonstrate that the number of similar sounding words affects lexical retrieval in production as well as perception. Furthermore, the influence of these lexical characteristics on the process of retrieving word-forms during speech production changes with age. The results are discussed within the context of the Node Structure Theory (MacKay, 1987), a model of cognitive processing. Introduction Speech production is a rapid and efficient process. However, there are instances in which the fluent retrieval of a lexical item fails to occur. One example of failed retrieval occurs in tip-of-the-tongue (TOT) states. Tip-of-the-tongue states occur when only partial information associated with a word can be retrieved: information regarding the meaning or syntactic class of the word may be accessible, but not the complete phonological form of the word. The ability to access partial information often results in one having a “feeling of knowing” the word, despite being unable to retrieve all the information associated with the word. Factors that Affect Lexicalization and TOTs The process of retrieving a word form from lexical memory during speech production is called lexicalization. Several factors affect the speed and accuracy of lexicalization. The factors that affect the speed and accuracy of normal, unimpaired, lexicalization also affect instances of incomplete lexicalization, or TOT states. One factor that affects the speed and accuracy of normal lexicalization, as well as TOT states, is word frequency. Using a picture-naming task, Oldfield and Wingfield (1965) demonstrated that pictures of high-frequency words were named more quickly than were pictures of low-frequency words. Stemberger (1985; Stemberger & MacWhinney, 1986; see also Dell, 1990) found that phonological speech errors, such as spoonerisms (switching the initial phoneme of two nearby words, such as “darn bore” instead of “barn door”), perseverations (carrying-over a phoneme from a word produced earlier, such as “Ladies and lentlemen.” instead of “Ladies and gentlemen.”), and anticipations (producing a phoneme 3 This is about the only point that researchers agree on. Debates continue about the processes involved in speech production. For example, Garrett (1976) and Levelt (1989) take a modular approach to processing, whereas Dell (1986) and Harley (1984; 1993) argue for an interactive approach. There are also debates about where certain types of information are represented in the speech production system. For example, Roelofs, Meyer, and Levelt (1998) argue that syntactic information is accessed at the lemma level, whereas Caramazza and Miozzo (1998) claim that syntactic information is accessed at the lexeme level. NEIGHBORHOOD DENSITY AND TOTS 133 from an upcoming word, such as “pig pig” instead of “big pig”), occurred more often for low-frequency words than for high-frequency words. Finally, more TOT states occur and are experimentally induced in low frequency words than in high frequency words (Brown & McNeill, 1966; Burke, MacKay, Worthley, & Wade, 1991; Harley & Bown, 1998; cf. Yaniv & Meyer, 1987). Taken together, these findings suggest that the well-documented perceptual disadvantage of low-frequency words (i.e., poorer identification and slower response times) is paralleled by a similar disadvantage in production, as evidenced by greater difficulty with lexicalization. Another factor that affects the speed and accuracy of both perception and lexicalization is neighborhood density. Neighborhood density refers to the number of words that are phonologically similar to a given target word (Luce & Pisoni, 1998). A rough measure of phonological similarity can be obtained by determining the number of new words that are created by the addition, deletion, or substitution of a phoneme in a target word. For example, the word “cat” has as neighbors the words “scat,” “at,” “hat,” “cut,” and “cap,” as well as other words. Words with many similar sounding words are said to have dense neighborhoods, whereas words with few similar sounding words are said to have sparse neighborhoods. Previous research has shown that words with sparse neighborhoods are recognized more quickly and more accurately than words with dense neighborhoods (Luce & Pisoni, 1998). Previous investigations (Goldinger & Summers, 1989; Harley & Bown, 1998; Vitevitch, 1997a; 1997b) have also demonstrated that neighborhood density can affect speech production. For example, Goldinger and Summers (1989) found that neighborhood density influenced the voice onset time (VOT) for spoken words. VOT refers to the point in time at which vocal fold vibration starts, following the release of a closure (Crystal, 1992). Participants repeated word pairs that differed in the voicing of the initial consonants within the pair (e.g., dutch-touch) and that varied in neighborhood density across pairs. An acoustic analysis showed that the differences in VOT between the first word and the second word of the pairs were larger for word pairs with dense neighborhoods than for word pairs with sparse neighborhoods. These differences decreased across sessions for word pairs with sparse neighborhoods, but increased across sessions for word pairs with dense neighborhoods. Furthermore, Goldinger and Summers found that the interword interval, or the time between the offset of the first word and the onset of the second word within each minimal pair, varied with neighborhood density. The interword interval was greater for dense neighborhood word pairs than for sparse neighborhood word pairs. These results demonstrate that neighborhood density influences certain aspects of timing in speech production. The accuracy of lexical retrieval in speech production is also affected by neighborhood density. Vitevitch (1997a) used tongue twisters containing words that had either dense or sparse neighborhoods to elicit phonological speech errors from participants. He found that more errors occurred in tongue twisters that contained words with sparse neighborhoods than with dense neighborhoods. These results suggest that neighborhood density influences speech production in demonstrable ways (see also Vitevitch, 1997b; Wright, 1997; cf. Jescheniak & Levelt; 1994). Specifically, neighborhood density appears to produce “supportive” or facilitative effects among words. That is, words with many similar sounding words (a dense neighborhood) are produced more accurately than words with few similar sounding words (a sparse neighborhood). These findings contrast with the competitive effect of neighborhood density typically observed in spoken word recognition: Words with dense neighborhoods are recognized more slowly and less accurately than words with sparse neighborhoods (Luce & Pisoni, 1998). 4 An alternate method used to measure "similarity" is to use phoneme confusion matrices as in Luce and Pisoni (1998) in the calculation of Neighborhood Probability Rules (NPRs). Both methods have been successfully used to demonstrate effects of neighborhood density on spoken word recognition. VITEVITCH AND SOMMERS 134 Vitevitch (1997a; see also MacKay & Burke, 1990) speculated that the difference in neighborhood density effects found in speech production and spoken word recognition were due to the differences in the flow of information during speech production and spoken word recognition. That is, in spoken word recognition, acoustic-phonetic input activates many similar sounding words in memory (e.g., Luce & Pisoni, 1998). This candidate set must be winnowed down to a single item that will then retrieve semantic and syntactic information related to that word from the lexicon. Thus, more time will be required to winnow down the candidate set if there are many competitors (Luce & Pisoni, 1998). In contrast, speech production begins with a single conceptual representation that proceeds to activate a single lexical item and a single phonological word form (Levelt, 1989). That single phonological word form then activates the many sub-lexical units that it contains, such as syllables, phonemes, features, etc. Thus, a word that has components shared by many other words (a word with a dense neighborhood) will be able to spread activation along pathways between those components that are well traversed. A word that has components shared by few other words (a word with a sparse neighborhood) will have difficulty spreading activation along the pathways between components that are less traveled. Additional evidence of neighborhood density affecting speech production can be found in the work of Harley and Bown (1998). They recently reported that more TOT states were elicited for words from sparse neighborhoods than for words from dense neighborhoods, and also suggested that neighborhood density played a “supportive” role in speech production (Harley & Bown, 1998). Although Harley and Bown (1998) accounted for their results in t