* East Carolina University / ** Portland State University
Discourse is defined as any language “beyond the boundaries of isolated sen-tences” (Ultowska & Olness, 2004, p. 300), and it allows people to do things together; tease each other, build things, share feelings, and make plans for the future. Yet discourse requires more than simply generating a continuous stream of linguistic elements. Discourse production requires both within-sentence (i.e.
microlinguistic) elements and processes that are traditionally associated with the field of linguistics (e.g. phonemes & syntax) as well as between- sentence elements and processes to produce a coherent message. In this chapter, we focus on how this delicate balance between micro- and macrolinguistic pro-cesses change and are maintained within discourse as people age. To do this, we initially review the interactive-construction model of discourse. Next, we review and macrolinguistic processes within aging research. For micro-linguistic, we focus on lexical diversity, which can be defined as the range of vocabulary used by a person within a discourse sample. For the macrolinguis-tic process, we focus on coherence, which can be defined as how discourse is connected and organized beyond the grammar of a sentence. For both lexical diversity and coherence, we review common analysis techniques, what occurs to these processes as we age, and the cognitive and linguistic systems that under-pin these aspects of discourse. Finally, we conclude the chapter by highlighting areas of future research within lexical diversity and coherence research that are important to understanding discourse as people age.
Aging effects on discourse production
Imagine meeting a friend at a café. She arrives late and does not greet you or even ask you how you are doing. Instead, she jumps directly into the middle of a story:
(1) I had to stop to ask this man. You know, I hate how the roads are laid out in this city. It is just terrible. I saw this man just standing on the side of the road.
I think he was planting a tree. I’m not sure. He might have been a city worker
doi 10.1075/z.200.04kin
© 2016 John Benjamins Publishing Company
or business owner. I stopped. I pulled over. I was driving. By the way, my car is making a funny noise, but I’ll tell you more about that later. I asked this man for directions. He told me to turn right on Bright Street, so here I am.
Throughout her story, the speaker continually jumps around, sharing with the audience the story’s climax before stating what the problem was. Clearly, such a narrative would fail to convey the intended message without further clarification.
To make matters worse, the speaker interjects what appear to be unrelated top-ics (e.g. her car problems). In fact, half of her story is off-topic. These off-topic comments cause the audience more difficulty in following and interpreting her intended message.
The story conveyed is an example of one type of discourse. Discourse pro-duction requires more than simply generating a continuous stream of linguistic elements. It requires different stages of processing, and it requires the within- sentence (i.e. microlinguistic) units and processes that are traditionally associated with the field of linguistics. These include the sounds and meanings that make-up words and how the words are arranged within a sentence. Difficulty in com-prehending the above story is not due to grammatical errors; each utterance is constructed following syntactic rules. Discourse also includes between-sentence (i.e. macrolinguistic) processes that govern how utterances are arranged into a coherent message. To successfully navigate discourse, both the microlinguistic and macrolinguistic elements must be arranged into a structure that conveys the intended message to the audience and is appropriate for the situation. It is this collection of elements “beyond the boundaries of isolated sentences” (Ulatowska
& Olness, 2004, p. 8), which differentiates successful discourse from randomly generated utterances; and, it is at the level beyond the utterance, the macrolin-guistic level, as to why a breakdown in communication may occur between the speaker of the above story and the listener.
For this chapter, several discourse models and prototypical forms of discourse (i.e. narrative discourse, evencasts, and recounts) that are commonly used during social interactions are overviewed. Next, the chapter focuses on how cognitive changes during the aging process affect discourse production in older adults.
These include micro- and macrolinguistic processes, specifically lexical diversity and coherence, respectively. Finally, future directions for research are suggested.
Interactive-construction model of discourse
Researchers use discourse models to formalize and test their understanding of how discourse is processed and understood by individuals. Several models of dis-course have been developed that focus on different aspects of the processes that
underlie language production and comprehension. Of particular interest is the interactive-construction model created by Kintsch and van Dijk (1978).
Kintsch and van Dijk (1978) completed the seminal work in integrative dis-course processing, and their model serves as the foundation for much of the re-search in this area. They describe discourse structure as requiring four levels of representation. Unique to this model is that all discourse genres are considered.
The first level (surface) includes traditional linguistic units such as phonemes and morphemes as well as word combinations that lead to the sentences. The second level (semantic) represents the concepts expressed and the links between them.
A proposition is the smallest semantic unit and is typically a predicate with one or more arguments (i.e., verb or preposition plus noun). The third level (situ-ational) is the relations among concepts that represent the situations and/or events depicted. The fourth level (structural) is the organization of the concept units, represented sequentially and/or temporally. At this level the structure of the discourse is identified, for example the script for a procedure. The basis of integrative discourse models, like that of Kintsch and van Dijk’s, is that several cognitive operations are involved at each level of representation. However, cogni-tive processes are descripcogni-tively implicated and not specifically identified. Van Dijk (1997) described the integration of language and cognition when describing three main dimensions of discourse: language use (referred to as verbal structure by Stemmer, 1999); cognition (referred to as communication of beliefs by Stemmer, 1999); and action and interaction in social situations (i.e., pragmatics, conversa-tion). Though these dimensions are assumed to be interrelated and influence one another (Kintsch, 1998; Stemmer, 1999; van Dijk, 1997), the specific operations and processes remain elusive.
The core of Kintsch and van Dijk’s (1978) model is realized within two stages.
First, the utterances are produced with propositional links between them. These propositional links are called micro-propositions. Secondly, the micro-proposi-tions held within short-term memory for enough time and often with repeated exposure begin a generalization processes that produces a gist proposition within long-term memory. The gist propositions are not related to the links between ut-terances but the overall gist of the discourse. Kintsch and van Dijk (1978) called these propositions macro-propositions.
Micro-propositions are illustrated in example (11a) and (11b):
(11) a. Erica drove the car.
b. The car was going fast.
Utterances (11a) and (11b) may be represented as having the micro-propositions in (12a) and (12b):
(12) a. Drove (agent: ERICA, object: CAR).
b. Going (Theme: Car, Manner: Fast)
In the Interactive-Construction (IC) model, these micro-propositions are con-structed directly from utterances on the basis of lexical information stored in memory. As they enter short-term or working memory, micro-propositions are hierarchically and temporally organized. Since short-term memory is limited, only a few micro-propositions are held within memory as the next cycle begins processing. If at any time, the next utterance into working memory does not share any relations with the micro-proposition stored within memory, the listener must search long-term memory (i.e. their world knowledge) to make an inference about the relations between the text and the sentence (Kintsch & van Dijk, 1978).
For utterances (11a) and (11b), the concept CAR is a link between them.
Of course, to understand a narrative, a listener requires more than a hier-archical list of relationships between the semantic elements of a story. These macro- propositions create a global, hierarchical structure of the main themes needed to understand a story. These macro-propositions are created in parallel with the micro-propositions and represent the world knowledge and strategies that the listener uses to decide what to keep and what to discard (Kintsch, 1994).
Macro- propositions are formed by a series of semantic mapping rules. These rules reduce and organize the information of the story to a manageable level for the listener. Kintsch and van Dijk (1978) describes three semantic mapping rules for macro-propositions:
1. Deletion: propositions that have no connection, direct or indirect, to a previ-ous proposition will be deleted
2. Generalization: propositions can be replaced by more general propositions 3. Construction: propositions can be replaced by conventionalized facts.
While the IC model describes the general cognitive process and provides general rules for how discourse is processed and comprehended, the model only describes these cognitive systems in relation to the four levels of representation. The mod-el does not specify how these cognitive systems function to produce discourse comprehension or production. For example, in Kinstch and van Dijk (1978), they limit the working memory to four micro-propositions per cycle.
To illustrate why example (1) is difficult to comprehend in relation to IC mod-el, consider the number of non-linking utterances. Two utterances from example (1) are reproduced here as (15a) and (15b).
(15) a. I had to stop to ask this man.
b. You know, I hate how the roads are laid out in this city.
In (15a), there is the micro-proposition: stop(agent: I) and ask(agent I, object:
man). Yet (15b) does not contain any information related to (15a), except the ego- centric use of I. Therefore, to try and understand the story, the listener must search long-term memory for a connection between utterances (15a) and (15b).
The global concepts needed to understand the gist of the story are not directly stated; instead, the listener has to make inferences. For the above example, the listener can assume that the speaker is on the road, hence her immediate dislike of it, and has to stop and ask a man for direction because of the terrible road sys-tem. This inferencing is cognitively more difficult than generalizing directly from lexical items present in the discourse sample.
Age related changes in discourse production and comprehension are often not related to microlinguistic aspects. As in example (1), healthy older adults still produce grammatical, well-formed utterances, though with an increase in gram-matical errors compared with younger adults (Marini et al., 2005). Like example (1), the difficulty for many older adults is in organizing discourse and inhibiting tangential, unrelated content from entering the discourse sample. The rest of the chapter will discuss how microlinguistic and macrolinguistic abilities manifest in the discourse of older adults.
Microlinguistic analysis
Microlinguistic analyses focus on the within-sentence linguistic elements of lan-guage, which include the phonological, lexical, morphological, and syntactic aspects of discourse (Brownell, 1988). Some microlinguistic measures are doc-umented to change with age. Older adults often experience more word- finding difficulties compared to younger adults (Albert et al., 2009; Connor et al., 2004;
Griffin & Spieler, 2006; see Chapter 7 for more information). Measures of gram-matical processing are also well documented to decline with age. Older adults pro-duce fewer complex utterances compared to younger adults (Kemper, Marquis,
& Thompson, 2001; Kemper & Sumner, 2001; Marini et al., 2005; Shadden, 1997) and produce more errors related to morphological and syntactic processing (Marini et al., 2005). These declines appear to be a result of a decline in working memory (Kemper & Sumner, 2001; see Chapter 2 for more information). Howev-er, many microlinguistic processes are more resistant to age-related changes than macrolinguistic processes (e.g. coherence). Lexical diversity is one of the micro-linguistic measures demonstrated to be resistant to age related decline (Cooper, 1990; Fergadiotis et al., 2011; Kemper & Sumner, 2001). The next section focuses on lexical diversity to provide an example of how language does not change with age and, perhaps, even improves with age. Further examined is the importance of
lexical diversity within discourse and summary of the research on aging and lexical diversity.
Lexical diversity
Our lexicon is an important aspect of communication. It is the building block of discourse, and without access to a diverse vocabulary, the capacity of an individ-ual to communicate effectively may be reduced. Moreover, a larger vocabulary is associated with higher quality stories in both verbal and written forms (Yu, 2009).
Understanding the knowledge and processes that make vocabulary possible is necessary to understand lexical diversity (LD). Chapelle (1994) defined a model of vocabulary that included four domains:
1. The number of words stored by an individual
2. The knowledge of the phonology, semantics, and pragmatics of words 3. The organization of words within memory
4. Cognitive processes associated with word retrieval
Within this model, LD is more closely associated with the number of words stored by an individual (Fergadiotis, 2011). This is because LD can be thought of as an estimate of an individual’s vocabulary size within a specific context. However, the cognitive processes that access and retrieve words are also important for LD.
Retrieval deficits found in participants with aphasia (PWA) illustrate this point.
PWA may have a preserved word storehouse but they have difficulty accessing these words (McNeil & Pratt, 2001). This reduced access lowers measures of LD in PWA. Inherit in this model is the understanding that language depends on both implicit knowledge of language (e.g. size of lexicon) and the capacity of language (e.g. retrieval of lexicon; see Chomsky, 1980). Both knowledge and capacity affect LD. LD is a measure of language knowledge and performance. LD can be defined as the range of vocabulary used by an individual within a discourse sample that reflects the size of an individual’s lexicon and the retrieval processes employed by the individual (Fergadiotis & Wright, 2011).
Measuring lexical diversity
There are several ways to estimate LD: number of different words (NDW); type- token ratio (TTR; Chotlos, 1944); Guiraud index (Guiraud, 1960); Maas’s index (Maas, 1972); D (Malvern & Richards, 1997); and the measure of textual lexical diversity (MTLD; McCarthy, 2005). At their core, these estimates of LD count the
number of unique words produced within a discourse sample, which, in turn, are used to estimate the vocabulary knowledge of an individual (Fergadiotis &
Wright, 2011). However, measuring LD is not straightforward (Fergadiotis, 2011;
Fergadiotis, Wright, & West, 2013; Koizumi & In’nami, 2012), and many of these methods employ sophisticated techniques to correct for problems like sample length. A review of the different types of LD measures follows.
Type-token ratio (TTR) and Number of Different Words (NDW) are the most common estimates of LD (Chotlos, 1944; Templin, 1957). NDW is a count of the number of unique words produced within a discourse sample. The method provides a straightforward quantitative assessment of LD, but it requires sample length to be similar across all participants because on average longer samples have a greater likelihood of having more unique words. To combat this problem, TTR counts the number of unique words (types) and divides it by the total number of words (tokens [Chotlos, 1944]). Ratios closer to 0 reflect less diversity in vocab-ulary, whereas scores closer to 1 reflect greater diversity. Yet, this method suffers from sample length effects as well. According to Heap’s law (1978), as a discourse sample gets longer, the likelihood of encountering a new word decreases com-pared to the number of already used words (i.e. non-unique words) being added to the transcript. Therefore, the number of already used words will increase faster than the number of newly added words. So TTR has the opposite problem from NDW. As the sample gets longer, words will be added, but very few words will be new items. Since TTR is the number of new words divided by total words, longer samples can seem less diverse than shorter samples.
Guiraud and Maas are two methods that researchers employed to solve the problem of sample length (Koizumi & In’nami, 2012). The methods employ mathematical transforms of TTR. Guiraud (1960) took the square root of tokens and divided word types by the square-root of the tokens (Types/ √Tokens). Maas (1972) applied a log transformation, where the log of the types was divides by the log of the tokens (log[types]/log[tokens]). Yet several researchers have found that these mathematical transformations covary with length (Jarvis, 2002; Malvern &
Richards, 1997; Tweedie & Baayen, 1998). These mathematical transformations did little to solve the problems associated with length.
D and MTLD are more sophisticated calculations that require special soft-ware (Koizumi & In’nami, 2012). D, first developed by Malvern and Richards (1997), combines random sampling with curve fitting to estimate LD. It is based on the idea that TTR will decrease as sample length increases. For samples with a more diverse vocabulary, TTR will decrease more slowly than for samples with a less diverse vocabulary. To estimate D for a sample, 35 tokens are sampled ran-domly from the text without replacement. For this sample, TTR is estimated. This process is repeated 100 times. Then, samples of 36 to 50 tokens are taken from the
text. The TTR ratio of these samples are plotted to form an empirical curve. Then, D uses the least squares approach to solve a formula that maximizes the fit to the empirical TTR curve (McKee, Malvern, & Richards, 2000). Finally, the entire process is run three times, and an average of the three runs serves as the final D.
Several researchers have investigated the validity of D scores (Durán, Malvern, Richards, & Chipere, 2004; Fergadiotis & Wright, 2011; Koizumi &
In’nami, 2012). However, there are two main criticisms of D: (1) language sam-ples require at least 50 tokens and (2) lexical items are randomly sampled. For the first issue, Koizumi and In’nami (2012) found that the requirement for 50 plus tokens removed around 27% of their samples from analysis. Moreover, these sam-ples were more likely to have lower LD scores. This biases the mean of D scores upward, which suggest D scores are affected, somewhat, by sample length. This positive bias may be a significant problem in some cases given that it generates missing data not at random (Fergadiotis et al., 2013). In regards to (2), Jarvis (2002) claimed that random sampling ignores word order. The sample is treated as a series of discrete units, which is contradictory with discourse models that claim individuals form intact mental representations based on the structure (i.e.
word order) of the text (Kintsch & van Dijk, 1978).
MTLD, first developed by McCarthy (2005), tackles the problems associated with sample length and random sampling. It analyzes sequential word strings in a sample until the sample reaches a certain TTR (i.e., .72). When the TTR of .72 is reached, a factor count is increased by one. The process starts again until the end of the sample is reached. Then, the tokens are divided by the factor. The same procedure is repeated by starting at the end of the text and moving to the begin-ning. These two numbers are averaged together to determine the MTLD score (Fergadiotis et al., 2013; Koizumi & In’nami, 2012; McCarthy, 2005). Research-ers have consistently found that MTLD is a reliable measure of LD. Fegadiotis et al. (2013) found MTLD to have little association with sample length and had relatively small variances, which demonstrates the score’s validity. Koizumi and In’nami (2012) also found MTLD to be the least affected by sample length. How-ever, the researchers caution using MTLD in samples of less than 100 tokens.
Lexical diversity in older adults
There is evidence that older adults’ vocabulary increases until their mid to late 60s (Albert, Heller, & Milberg, 1988; Schaie & Willis, 1993) and some evidence that lexical access is somewhat preserved (LaBarge, Edwards, & Knesevich, 1986). Yet the inability to recall certain words does increase with age (see Chapter 7). How-ever, most research in the area of LD and older adults deals with single concepts.
In these studies, older adults have been required to produce definitions (Wechsler, 1981) or name pictures (Howard & Patterson, 1992; Kaplan, Goodglass, &
In these studies, older adults have been required to produce definitions (Wechsler, 1981) or name pictures (Howard & Patterson, 1992; Kaplan, Goodglass, &