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Aphasiology

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Word retrieval in connected speech in Alzheimer’sdisease: a review with meta-analyses

Gitit Kavé & Mira Goral

To cite this article: Gitit Kavé & Mira Goral (2018) Word retrieval in connected speechin Alzheimer’s disease: a review with meta-analyses, Aphasiology, 32:1, 4-26, DOI:10.1080/02687038.2017.1338663

To link to this article: https://doi.org/10.1080/02687038.2017.1338663

Published online: 13 Jun 2017.

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Word retrieval in connected speech in Alzheimer’s disease: areview with meta-analysesGitit Kavéa,b and Mira Goralc

aDepartment of Education and Psychology, The Open University, Ra’anana, Israel; bCenter for Memory andAttention Disorders, Neurology Division, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; cSpeech-Language-Hearing Sciences, Lehman College, West Bronx, NY, USA

ABSTRACTBackground: Language assessment in Alzheimer’s disease (AD)demonstrates deficits in single-word production, but rarelyincludes connected speech.Aims: If Alzheimer’s disease leads to deficits in lexical retrieval, weshould find reduced word output in connected speech, limitedlexical diversity, an increase in retrieval errors, and an associationbetween scores on tests of single-word production andmeasures ofword retrieval in context. The purpose of this review was to inves-tigate these hypotheses across studies of connected speech in AD.Main Contribution: A search of Medline and PsychNET identified51 articles (published in 1985–2016) that reported data from 1,114individuals with AD and 1,280 cognitively healthy individuals. Ameta-analysis showed a significant but small reduction in wordoutput in AD. No consistent decrease in the proportion of uniquewords was documented, but there was indication that individualswith AD producemore frequent words than do controls. An analysisof errors documented a moderate effect size, showing that indivi-duals with AD commit more retrieval errors of all types. A relativelystrong association was found between picture-naming scores andmeasures of word retrieval in connected speech.Conclusions: The review shows that individuals with AD experi-ence significant lexical retrieval difficulties in connected speech.

ARTICLE HISTORYReceived 4 January 2017Accepted 24 May 2017

KEYWORDSNarrative; discourse;spontaneous speech; wordfinding; lexical retrieval

Introduction

Alzheimer’s disease (AD) is associated with compromised cognitive functions, includingimpairments of memory, executive function, and language. Research on the productionof single words shows that persons with AD retrieve fewer correct responses than docognitively intact participants, both on picture-naming tests (e.g., Balthazar, Cendes, &Damasceno, 2008; Cuetos, Gonzalez-Nosti, & Martínez, 2005; Masterson et al., 2007) andon verbal fluency tasks (e.g., Arroyo-Anlló, Lorber, Rigaleau, & Gil, 2012; Haugrud,Crossley, & Vrbancic, 2011; Henry, Crawford, & Phillips, 2004). These word retrievaldeficits are believed to indicate that AD leads to impairments in lexical representationor in access to conceptual knowledge (Adlam, Patterson, Bozeat, & Hodges, 2010;

CONTACT Gitit Kavé gkave@012.net.il

APHASIOLOGY, 2018VOL. 32, NO. 1, 4–26https://doi.org/10.1080/02687038.2017.1338663

© 2017 Informa UK Limited, trading as Taylor & Francis Group

Garrard, Lambon Ralph, Patterson, Pratt, & Hodges, 2005; Laisney et al., 2011). It is likelythat such deficits would appear not only on structured tests but also in connectedspeech. We investigate this possibility through a comprehensive review of the literature.

Nicholas, Obler, Albert, and Helm-Estabrooks (1985) described connected speech ofindividuals with AD as empty. Yet, empty speech could reflect lexical-semantic impair-ments as well as difficulties in message construction, in pragmatics, or in higher-ordercognitive functions. That is, individuals with AD who produce connected speech mayexperience temporary lack of access to lexical entries or more permanent inability toreach these entries due to degradation of storage. Alternatively, they might experiencedifficulties in planning the overall message (Blanken, Dittmann, Haas, & Wallesch, 1987),producing empty speech due to impairment in formulating meaningful informationrather than due to a search for specific words. Furthermore, deterioration of pragmaticsmight result in empty speech, as persons with AD shift conversational topics inappro-priately (Dijkstra, Bourgeois, Allen, & Burgio, 2004), do not pay full attention to thelistener’s needs (Carlomagno, Santoro, Menditti, Pandolfi, & Marini, 2005), and do notaccommodate their speech to the task at hand (Kemper, Anagnopoulos, Lyons, &Heberlein, 1994). Empty speech could also reflect limitations in working memory (e.g.,Almor, Kempler, MacDonald, Andersen, & Tyler, 1999) or deficits in other higher-ordercognitive functions, such as monitoring or inhibition (Glosser & Deser, 1991). Note,though, that Feyereisen, Berrewaerts, and Hupet (2007) found no association betweenscores on tests of inhibition and connected speech variables. Thus, although perfor-mance of persons with AD on tests of single-word production suggests that equivalentdifficulties would also occur in connected speech, different factors might affect retrievalin and out of context. Consequently, retrieval difficulties in speech should be evaluatedon their own.

One of the problems in deriving general conclusions about word retrieval in con-nected speech is that studies have used a multitude of elicitation methods to samplespeech, and each method might place different constraints on production (Boschi et al.,2017; Olness, Ulatowska, & Wertz, 2002). Another problem is that studies vary in themeasures that they use to indicate success or failure of word retrieval in context. Unlikethe clearly defined correct or incorrect responses on tests of single-word production, inconnected speech many words might be appropriate, and determining whether anindividual retrieved the right word or failed to do so is often not a straightforwarddecision (Kavé & Goral, 2016a). Despite these problems and the heterogeneity in theliterature, we believe that certain generalisations about word retrieval in connectedspeech in AD are possible.

In a recent review of the literature on normal ageing and aphasia, we tested fourhypotheses regarding intact and impaired word retrieval in connected speech (Kavé &Goral, 2016a). We hypothesised that word retrieval difficulties would lead to (1) reducedproductivity in connected speech, (2) more limited lexical variety in connected speech,(3) an increase in retrieval errors and a decrease in fluency, and (4) an associationbetween retrieval difficulties on tests of single-word production and measures of wordretrieval in context. The use of word count measures (Hypothesis 1) rests on findingsfrom people with non-fluent aphasia who produce significantly fewer words than doneurologically healthy participants (e.g., Helm-Estabrooks, Albert, & Nicholas, 2014;Kertesz, 1982; Marini, Caltagirone, Pasqualetti, & Carlomagno, 2007). Similarly, according

APHASIOLOGY 5

to de Lira, Minett, Bertolucci, and Ortiz (2014), persons with AD use fewer words than docontrol participants. The use of measures of lexical diversity (Hypothesis 2) relies on theassumption that individuals who have trouble finding words are likely to repeat thewords that they retrieve and to use only highly accessible words. Indeed, most indivi-duals with aphasia use a restricted set of words (e.g., Armstrong, 2001; Behrns, Wengelin,Broberg, & Hartelius, 2009; Fergadiotis & Wright, 2011). In the same vein, Bucks, Singh,Cuerden, and Wilcock (2000) report restricted lexical diversity in AD. Our third hypoth-esis is based on findings that people with aphasia demonstrate an increase in wordsubstitution errors, as well as an increase in hesitations and false starts (e.g., Pashek &Tompkins, 2002). Similar manifestation of retrieval failure may be expected in peoplewith AD (e.g., Ripich & Terrell, 1988). According to our fourth hypothesis, if retrievaldifficulties reflect lexical deficits, these difficulties should occur across tasks (e.g., Kavé &Goral, 2016b). Our previous review shows that research on aphasia generally confirmsthese four hypotheses. In contrast, cognitively intact older adults do not provide fewerwords than do younger adults, they use greater lexical diversity, and there are no strongcorrelations between their performance on tests of single-word production and mea-sures of retrieval in connected speech. Furthermore, healthy older adults experiencerather minor word retrieval difficulties in context that do not emerge for all elicitationmodes or for all variables (Kavé & Goral, 2016a).

Lexical retrieval difficulties characterise many individuals with AD, and thus we testedwhether our four hypotheses apply to connected speech production in AD. Becausestudies of language in people with AD often include relatively few participants, combin-ing results across studies could significantly contribute to our understanding of thephenomena at hand, and a meta-analysis could further quantify the key findings in thefield (Gates & March, 2016). Therefore, the main objectives of the present review are toexamine whether individuals with AD exhibit word retrieval difficulties in connectedspeech, and to determine whether different measures of word retrieval in connectedspeech lead to similar conclusions. The review is organised according to the fourhypotheses derived from our previous research.

Method

We searched Medline and PsychNET for peer-reviewed records, using the term Alzheimer’stogether with each of the following terms: connected speech, connected language, picturedescription, spontaneous speech, speech production, narrative, discourse, conversation,and storytelling. We placed no restrictions as to language or publication dates andcompleted the search in October 2016. The database search yielded 1,253 results, and 12additional studies were identified through reference lists of the papers that came up in thesearch. Figure 1 shows the distribution of identified records according to search terms inboth databases together, including duplicates. Initial screening involved titles andabstracts, leading to the selection of 137 records that were further assessed in their full-text format. All identified records were written in English. To be included in the review,studies had to report lexical measures of connected speech in a group of individuals withAD, and these measures had to be compared to measures that were derived from thespeech of healthy individuals. Screening excluded (a) studies that examined production ofsingle words or sentences but not of connected speech; (b) studies of connected speech

6 G. KAVÉ AND M. GORAL

that did not report any measures of lexical retrieval; (c) studies that investigated writtenlanguage; (d) studies that focused on the effects of intervention; (e) studies that reportedneither group means nor individual data from which such means could be calculated; and(f) reviews, conference abstracts, or single case studies. Initial screening was completed bythe first author, with secondary screenings performed by the two authors. Disagreementson article selection were documented and resolved through discussion. Following the full-text assessment, 51 records were retained for review. Figure 2 presents a flow chart of thesearch procedure.

Results

The 51 studies that entered the review were published between 1985 and 2016, andincluded 1,114 participants with AD and 1,280 healthy controls. We first report demo-graphic findings for all 51 studies, and then present studies that examined variables thatpertained to each of our four hypotheses. Some studies reported data on only onevariable, and thus contributed to testing only one hypothesis. Other studies examinedvariables that pertained to more than one hypothesis so that data from one paper couldhave contributed to testing more than one hypothesis.

Demographic findings

Table 1 presents a summary of the number of participants in each article under reviewas well as their age, education, and cognitive status. Samples varied in size (AD:range = 6–63, median = 17, mean = 21.84, SD = 14.91; Control: range = 5–141,

Figure 1. The number of records identified through Medline and PsychNet, including duplicates,according to search term (all terms were used together with “Alzheimer’s”).

APHASIOLOGY 7

median = 19, mean = 25.10, SD = 20.94). Eleven studies included more than one ADgroup (e.g., at different levels of the disease or at different ages). We decided to collapsedata from these subgroups because the other 40 studies included similar participants ofvarying stages and different ages who were not divided into subgroups. Thus, the fewreported subgroups would not be sufficient for determining the effects of dementiaseverity. Several studies included participants with mild cognitive impairment (MCI),other dementias, or aphasia. Performance of these participants was not reviewed. Wedid not combine data from MCI and AD groups. Some studies included individuals whoalso participated in other studies, with or without full disclosure of this issue. Thesestudies were retained in the review.

Age at testing of persons with AD varied between 45 and 97, with a mean of 73.34(weighted by sample size) and a median of 73.10. This age range indicates that studiesincluded early-onset dementia. Moreover, the mean age suggests that participants withAD were younger than the average person with AD (see Hebert, Weuve, Scherr, & Evans,2013). One study reported the median rather than the mean age, and 21 (41%) studiesreported the mean age but not the age range. The majority of studies (n = 38, 75%)stated that the AD and the control groups were age matched, although in over a third ofthese studies no statistical comparison was presented. In seven (14%) studies agematching was not reported, and in six (12%) studies age was not matched across groups.

The number of years of formal schooling acquired by persons with AD rangedbetween 0 (Lai, Pai, & Lin, 2009) and 22 (Tomoeda, Bayles, Trosset, Azuma, & McGeagh,1996), with a mean of 10.79 (weighted by sample size) and a median of 11.86. One study

Figure 2. A flow chart of the literature search.

8 G. KAVÉ AND M. GORAL

Table 1. Demographic characteristics of reviewed studies.

Study N AD N ControlMeanage

Meaneducation

Matchage

Matcheducation

MeanMMSE

1 Ahmed, De Jager, Haigh, and Garrard(2013)

18 18 74.00 13.10 Yes Yes 21.70

2 Almor et al. (1999) 11 9 80.70 15.00 Yes Yes 21.003 Altmann et al. (2001) 10 15 80.40 15.00 Yes Yes 20.454 Bates, Harris, Marchman, Wulfeck, and

Kritchevsky (1995)16 25 73.30 14.10 Yes Yes 20.37

5 Bayles, Tomoeda, Kaszniak, Stern, andEagans (1985)

28 30 72.25 12.70 – – –

6 Blanken et al. (1987) 10 5 – – Yes – –7 Brandão, Castelló, van Dijk, de Mattos

Pimenta Parente, and Peña-Casanova(2009)

18 16 79.83 5.50 Yes Yes 18.95

8 Bridges and Van Lancker Sidtis (2013) 11 5 71.09 10.90 No No 16.829 Bschor, Kühl, and Reischies (2001) 41 40 76.03 9.10 No No 17.0410 Bucks et al. (2000) 8 16 67.40 10.30 Yes No 15.0011 Carlomagno et al. (2005) 21 18 64.71 8.61 Yes Yes 18.8212 Chapman et al. (1995) 12 24 67.50 – Yes Yes 22.4013 Chenery and Murdoch (1994) 7 7 74.29 – Yes – –14 Croisile et al. (1996) 11 24 70.70 11.70 Yes Yes 18.9015 de Lira, Ortiz, Campanha, Bertolucci, and

Minett (2011)60 61 77.20 6.80 No Yes 19.70

16 de Lira et al. (2014) 26 20 71.43 5.90 Yes Yes 20.3217 Dijkstra et al. (2004) 30 30 80.20 13.90 Yes Yes 14.2018 Drummond et al. (2015) 14 41 73.40 12.00 Yes No 22.7019 Ehrlich, Obler, and Clark (1997) 16 16 74.30 11.50 Yes Yes 19.4020 Feyereisen et al. (2007) 13 13 74.77 11.38 Yes Yes 23.4621 Forbes-McKay et al. (2013) 31 30 76.03 11.71 Yes Yes 22.2922 Gayraud et al. (2011) 20 20 76.60 – Yes Yes 22.6223 Giles, Patterson, and Hodges (1996) 48 18 67.50 11.08 Yes Yes 18.8124 Glosser and Deser (1991) 9 17 64.33 – No No 15.4025 Hier et al. (1985) 26 15 73.60 – – – –26 Hoffmann et al. (2010) 30 15 70.60 12.17 Yes Yes 15.2327 Illes (1989) 10 10 67.85 – Yes Yes –28 Kavé and Goral (2016b) 20 20 76.10 11.00 Yes Yes 22.1529 Kavé and Levy (2003) 14 48 76.64 11.21 Yes Yes 21.7930 Kemper et al. (1994) 16 16 67.20 13.10 Yes Yes 18.7031 Kemper, Lyons, & Anagnopoulos (1995) 16 16 67.80 13.10 Yes Yes 18.7032 Lai (2014) 20 20 72.80 9.70 Yes Yes –33 Lai et al. (2009) 30 32 72.00 8.00 Yes Yes –34 Laine, Laakso, Vuorinen, and Rinne (1998) 11 19 67.40 – Yes Yes 18.7035 March, Wales, and Pattison (2006) 26 26 78.80 8.00 Yes Yes 20.5036 March, Pattison, and Wales (2009) 26 26 78.80 8.00 Yes Yes 20.5037 McNamara, Obler, Au, Durso, and Albert

(1992)15 141 65.10 13.00 – – –

38 Mendez and Ashla-Mendez (1991) 18 18 72.60 10.50 Yes Yes 18.9039 Murray (2010) 17 14 75.94 13.06 Yes Yes –40 Nicholas et al. (1985) 19 30 66.50 12.60 Yes Yes –41 Ripich, Vertes, Whitehouse, Fulton, and

Ekelman (1991)11 11 74.72 12.27 Yes Yes 16.10

42 Ripich, Carpenter, and Ziol (1997) 60 50 72.89 13.48 – – 17.9343 Ripich, Carpenter, and Ziol (2000) 60 47 72.89 14.11 Yes Yes 17.9344 Ripich and Terrell (1988) 6 6 77.30 – – – –45 Sajjadi, Patterson, Tomek, and Nestor

(2012)20 30 68.00 12.50 Yes – 22.50

46 Singh, Bucks, and Cuerden (2001) 8 8 67.80 16.00 Yes No 15.1047 Smith et al. (1989) 15 15 82.50 9.41 – – –48 Tomoeda et al. (1996) 63 52 76.52 13.56 Partial Partial 14.1149 Van Lancker Sidtis et al. (2015) 12 18 66.70 16.70 No Yes 21.8050 Zimmerer et al. (2016) 48 38 71.50 11.60 No No 18.7051 Zraick et al. (2011) 8 21 78.70 14.00 Yes Yes 24.50

(Continued )

APHASIOLOGY 9

reported the level of education on a scale rather than by the number of years, and 10studies did not present any information about the average level of education. The rangeof years of formal schooling was reported in only 22 (43%) studies. Some studiesspecified that they excluded individuals who had either a high (e.g., 12 years or above)or a low (e.g., below eighth grade) level of education. Two-thirds of studies (n = 34, 67%)stated that the AD and control groups were matched in education, but in 14 (41%) ofthese studies no statistical comparison was presented. In 10 (20%) studies educationmatching was not reported, and in seven (14%) studies education was not matchedacross groups.

Participants with AD scored between 0 (Hoffmann et al., 2010) and 29 (Chapman,Ulatowska, King, Johnson, & McIntire, 1995) on the Mini-Mental Status Exam (MMSE,Folstein, Folstein, & McHugh, 1975), with a mean of 19.07 (weighted by sample size) anda median of 18.95. Twelve studies did not report the mean MMSE scores, and 26 studiesdid not report MMSE score range. Studies that did not report MMSE scores mostlypresented a different measure of dementia severity (e.g., Clinical Dementia Rating;Hughes, Berg, Danziger, Coben, & Martin, 1982; Dementia Rating Scale; Mattis, 1988).Overall, the findings demonstrate large variance in cognitive level, both within andacross studies.

Most studies (n = 35, 69%) were conducted in English, but studies written in Englishthat examined 10 other languages were also included in the review: French andPortuguese—three studies in each language; Chinese, German, and Hebrew—two stu-dies in each language; Catalan, Finish, Hungarian, Italian, and Spanish—one study ineach language. No studies compared bilinguals to monolinguals and none comparedperformance between speakers of one language and speakers of another language.

Because elicitation mode could affect speakers’ performance and dictate the outcomemeasures that researchers report (Boschi et al., 2017), we also present a summary of thenumber of studies according to elicitation method (see Figure 3). We used the followingclassification: (a) picture description: participants were asked to describe a single picture,most often the Cookie Theft picture from the Boston Diagnostic Aphasia Examination(Goodglass & Kaplan, 1983); (b) a biographical interview: participants were asked to referto personal information; (c) picture sequence or film description: participants were askedto narrate a story from several scenes; (d) mixed methods: a study that used severalmethods; and (e) other: miscellaneous methods, such as the description of an everydayobject or a daily routine, or referential communication in which one participant is asked

Table 1. (Continued).

Study N AD N ControlMeanage

Meaneducation

Matchage

Matcheducation

MeanMMSE

Total 1,114 1,280Range 6–63 5–141 45–97 0–22 Y = 38 Y = 34 0–29Median 17 19 73.10 11.86 N = 6 N = 7 18.95Mean 21.84 25.10 73.34 10.79 NR = 7 NR = 10 19.07

The number of persons with AD was collapsed across subgroups; means of age, education, and MMSE scores arepresented for individuals with AD, without control participants; A dash indicates that the information was notreported (NR); Yes = matching reported, with or without actual statistics. Partial = controls were matched to only oneAD subgroup.

10 G. KAVÉ AND M. GORAL

to identify pictures for another participant, so that the listener could carry out a giventask. As shown in Figure 3, the most common elicitation modes involved picturedescriptions (37%) and biographical interviews (27%).

Findings according to hypothesis

To allow comparison across studies, we first extracted means and standard deviationsfor each group, then calculated an effect size for that variable, and then entered thecalculated effect into a meta-analysis. We used r as an effect size measure (as recom-mended by Field & Gillett, 2010), and computed it through Wilson’s (2001) onlinecalculator. Following Borenstein, Hedges, Higgins, and Rothstein (2009), we conductedthe meta-analysis of these effect sizes with a random effect model.

Hypothesis 1Our first hypothesis predicted that individuals with AD would say fewer words thanwould healthy individuals. Table 2 presents the mean word number for persons with ADand for controls in all 23 studies that reported this measure, with three of themreporting data on two tasks. Three studies provided only the mean but not the standarddeviation of the word number and were therefore excluded from the analysis. To makesure that results did not vary by elicitation mode, we compared the effect sizes in studiesthat used the Cookie Theft picture (Goodglass & Kaplan, 1983) to elicit speech and instudies that used other elicitation methods. This comparison revealed no significantdifference, Z = 0.023, ns. A combined meta-analysis showed that the mean effect sizeacross all samples that reported word number was ρ = −0.170, 95% CI [‒0.316, ‒0.024], Z= ‒2.285, p < .05. That is, participants with AD said significantly fewer words than didhealthy participants, confirming our first hypothesis, but the overall effect was relatively

Figure 3. Reviewed articles by elicitation method.

APHASIOLOGY 11

small. Figure 4 plots the distribution of individual effects. Notice that studies differed inthe type of prompting that they offered as well as in the words that they counted (e.g.,unrelated content and repetitions were included by some studies but excluded byothers). Not all studies referred to these aspects and therefore it was impossible toassess their influence on the effect size.

Hypothesis 2To test our second hypothesis according to which lexical diversity would be limited inAD, we first looked at production of unique words, then at the type-token ratio (TTR),and finally at word frequency and formulaic language. Three studies examined thenumber of unique words, defined as the number of words that appeared only once ineach speech sample (Dijkstra et al., 2004; Hier, Hagenlocker, & Shindler, 1985; Smith,Chenery, & Murdoch, 1989). Smith et al. (1989) reported no significant group differencein unique words but did not provide the actual group means or the statistical test. Hieret al. (1985) presented the mean number of unique words by group. Since theirstatistical analysis included a group of individuals with vascular dementia, we re-ana-lysed their data to compare the AD mean directly to the control mean. We found thatindividuals with AD produced significantly fewer unique words than did healthy

Table 2. Means and standard deviations of word number, by group.Study Task AD mean AD SD Control mean Control SD Effect size (r)

Ahmed et al. (2013) Picturea 77.4 35.1 87.5 52.7 −0.11Almor et al. (1999) Interview 466.5 331 432.2 100 0.07Bayles et al. (1985) Other 214 101 284 100 −0.33Bschor et al. (2001) Picture 86.3 40.2 88.2 56.9 −0.02Carlomagno et al. (2005) Referential 433.33 115.6 191.8 68.4 0.78

Picture 70.1 45.97 68 65.8 0.02Croisile et al. (1996) Picture 66.82 29 88.67 46.67 −0.27de Lira et al. (2014) Picture 39.3 17.1 99 50.6 −0.59Drummond et al. (2015) Sequence 128 61.2 97.7 43.2 0.28Ehrlich et al. (1997) Mixed 98.33 44.1 123.75 39.7 −0.29Feyereisen et al. (2007)b Referential 225 111 420 172 −0.56

Picture 116.2 69.82 116.2 52.3 0Hier et al. (1985) Picture 71.1 34.5 114.2 9 −0.59Kavé and Goral (2016b) Picture 68.9 52.87 56.1 14.81 0.16Kemper et al. (1995) Other 38 21 220 142 −0.67

Other 87 28 234 138 −0.59McNamara et al. (1992)c Picture 102.7 78.2 88.5 64.2 0.09Mendez and Ashla-Mendez (1991)d Picture 80.5 25.1 113.2 26.1 −0.54Murray (2010) Picture 161.53 83.98 270.14 100.77 −0.51Nicholas et al. (1985) Picture 103.4 71.2 85.3 50.1 0.15Ripich et al. (1991) Other 507.45 213.97 617 128.56 −0.30Ripich and Terrell (1988) Interview 620.5 348.8 251 127.1 0.58Singh et al. (2001) Interview 1020.8 214.8 1168.9 83.9 −0.41Tomoeda et al. (1996) Picture 139.71 87.08 165.44 83.04 −0.15Zimmerer et al. (2016) Picture 90.6 39.6 95.2 39.8 −0.06Zraick et al. (2011)e Picture 117.88 14.62 131.86 23.87 −0.28Weighted mean all tasks N = 26 154.54 72.06 166.62 128.25 −0.170Weighted mean Cookie Theft N = 13 82.56 42.24 93.07 83.84 −0.169

Effect sizes were computed with Wilson’s (2001) online calculator.aPicture = The Cookie Theft picture (Goodglass & Kaplan, 1983), except in Murray (2010) and Tomoeda et al. (1996).bMeans represent the first trial.cControl data refer to the group that matched in age to the participants with AD.dMeans represent the number of words per minute.eElicitation was limited to 1 minute.

12 G. KAVÉ AND M. GORAL

individuals. However, because word number differed across groups, we believe that thecorrect measure would be the proportion of unique words out of the total number ofwords. Calculation of this measure based on the means provided in the paper showed avery similar proportion of unique words within the AD and the control groups (justunder 60%). Dijkstra et al. (2004) reported a significant group difference in the numberof unique words, favouring the healthy group. Yet, they did not report the proportion ofunique words or the total number of words, so it is unclear whether participants with ADsaid fewer unique words simply because they said fewer words overall.

Six studies presented TTR data, which is the number of unique word types divided byall word tokens (see summary in Table 3). Although TTR is a relatively straightforwardmeasure, it has been used differently in these studies, with a focus on particular parts ofspeech versus all words or on one task versus another. As Kemper et al. (1994) andKemper, Lyons, and Anagnopoulos (1995) studied the same participants, there were toofew studies with independent TTR measures for a meta-analysis. Table 3 shows thatgroup differences did not always reach significance and were in the opposite directionfrom expectation on some tasks. Bucks et al. (2000) added other measures that areknown to be less affected by sample length (e.g., Brunet’s Index, Honore’s Statistics),reporting significant group differences on the former measure but not the latter. Hence,variables of word uniqueness and TTR provide no consistent indication that persons withAD produce a lower proportion of unique words than do healthy participants.

Lexical diversity was further assessed by analysing the frequency of wordsselected by speakers and the use of formulaic language. Gayraud, Lee, and Barkat-Defradas (2011) as well as Kavé and Goral (2016b) found that the words selected byindividuals with AD were more frequent in the language than the words selected bycontrol participants. Similarly, Bridges and Van Lancker Sidtis (2013) reported thatpersons with AD provided fewer low-frequency words, although they presented no

Figure 4. Forest plot of effect sizes for group differences in word number.

APHASIOLOGY 13

details (means, statistics) to support this conclusion. Zimmerer et al. (2016) found nogroup difference in the frequency of single words. Yet, when looking at two wordsthat appear together in the language, they documented more occurrences of for-mulaic expressions in AD speech relative to the speech of healthy participants.Bridges and Van Lancker Sidtis (2013) as well as Van Lancker Sidtis, Choi, Alken,and Sidtis (2015) counted the number of words that appeared within formulaicexpressions and reported that a greater share of speech was formulaic in the ADgroup than in the control group. Each of these studies used a different methodology,and there were too few studies to perform a meta-analysis. Nevertheless, the findingssuggest that persons with AD use a more frequent set of words and rely on familiarword combinations or formulaic language more often than do healthy participants.

Hypothesis 3Our third hypothesis predicted that individuals with AD would commit more lexicalretrieval errors than would healthy individuals. We extracted data from 39 studies thateither tallied errors explicitly or reported data that pertained to retrieval failure whileinvestigating other issues (e.g., examining pronoun use as part of discourse analysis).There was great variability in the definition of errors across studies (e.g., lexical substitu-tions, indefinite terms, repetitions, revisions), as well as in the exact measures that wereused (e.g., total numbers, number per clause, proportions). To minimise this variability,we classified the errors into six categories and conducted a separate meta-analysis foreach category. If a study reported that group differences were not significant but did notprovide the actual relevant numbers, we entered an effect of zero to the analysis.Table 4 summarises the results according to error category.

Fifteen studies reported data on word substitution errors, including semanticallyrelated but incorrect words or semantically unrelated words that were inappropriate inthe context in which they appeared. Table 4 shows that in most studies individuals withAD produced more substitution errors than did control participants. The overall effect

Table 3. Summary of results from studies that examined type-token ratio (TTR).

Study TTRMeanAD SD AD

Meancontrol

SDcontrol Comment

Blanken et al. (1987) Nouns 0.64 – 0.86 – The authors report that groupdifferences were significant,with no statistics

Verbs 0.61 – 0.78 –

Bucks et al. (2000) All words 0.26 0.04 0.32 0.02 Group differences weresignificant

Kavé and Goral(2016b)

All words 0.82 0.12 0.87 0.05 There were no significant groupdifferences for either measureNouns 0.71 0.16 0.76 0.10

Kemper et al. (1994) All words: Picture 0.63 0.14 0.77 0.09 Analyses of both tasks togetherfound no significant groupdifferences

All words: barrier 0.60 0.09 0.55 0.10

Kemper et al. (1995) All words: solo 0.51 0.12 0.58 0.12 Analyses of both tasks togetherfound no significant groupdifferences

All words: joint 0.48 0.09 0.52 0.10

Ripich et al. (1997) All words: time 1 0.69 0.08 0.61 0.08 Group differences weresignificant but analysestreated TTR together withother measures

All words: time 2 0.64 0.17 0.66 0.30

A dash indicates that the information was not reported.

14 G. KAVÉ AND M. GORAL

Table 4. Summary of studies that tested retrieval failure, by error category.Error category Studies that supported hypothesis Studies that did not support hypothesis

Word substitution Altmann et al. (2001) Croisile et al. (1996)Bates et al. (1995) de Lira et al. (2011)Bridges and Van Lancker Sidtis (2013) Glosser and Deser (1991)Carlomagno et al. (2005) Illes (1989)Chenery and Murdoch (1994) Lai et al. (2009)Forbes-McKay et al. (2013) Sajjadi et al. (2012)Nicholas et al. (1985)Sajjadi et al. (2012)Ripich and Terrell (1988)Zimmerer et al. (2016)

Empty speech Dijkstra et al. (2004) Lai (2014)Ehrlich et al. (1997) Nicholas et al. (1985)a

Feyereisen et al. (2007) Smith et al. (1989)Glosser and Deser (1991)Hier et al. (1985)Kemper et al. (1995)Lai (2014)

Pronouns Almor et al. (1999) Chapman et al. (1995)Altmann et al. (2001) Chenery and Murdoch (1994)Bucks et al. (2000) Glosser and Deser (1991)Chenery and Murdoch (1994) Hier et al. (1985)Dijkstra et al. (2004) March et al. (2006)b

Ehrlich et al. (1997) Ripich et al. (2000)c

Kavé and Goral (2016b) Lai et al. (2009)Kavé and Levy (2003)Kemper et al. (1995)Lai (2014)Nicholas et al. (1985)Ripich and Terrell (1988)

Dysfluency Forbes-McKay et al. (2013) Bayles et al. (1985)Gayraud et al. (2011) Bridges and Van Lancker Sidtis (2013)Hoffmann et al. (2010) Gayraud et al. (2011)Illes (1989) Singh et al. (2001)Sajjadi et al. (2012)Singh et al. (2001)

Repetitions and false starts Bayles et al. (1985) Chenery and Murdoch (1994)Brandão et al. (2009) Croisile et al. (1996)Croisile et al. (1996) Lai (2014)de Lira et al. (2011) Lai et al. (2009)Dijkstra et al. (2004) Ripich and Terrell (1988)Drummond et al. (2015)McNamara et al. (1992)Nicholas et al. (1985)Tomoeda et al. (1996)

Composite error variable Chapman et al. (1995) Blanken et al. (1987)Croisile et al. (1996) Lai (2014)de Lira et al. (2011) Ripich et al. (1997)Kavé and Levy (2003)Lai et al. (2009)McNamara et al. (1992)Murray (2010)

Studies that appear in both columns of a single category reported data for more than one measure or for more thanone task.

aNicholas et al. (1985) reported that individuals with AD tended to use emptier language, but provided no directstatistical comparison; re-analysis of the means showed a non-significant difference.

bMarch et al. (2006) documented both spatial and personal pronouns; findings concern personal pronouns only.cRipich et al. (2000) reported that individuals with AD made more reference errors than did control participants, with nostatistical comparison of the difference; re-analysis of the means showed a non-significant difference.

APHASIOLOGY 15

was significant, with an effect size of ρ = 0.272, 95% CI [0.196, 0.349], Z = 6.972, p < .001.Nine studies looked at empty speech and indefinite terms (e.g., thing). The meta-analysisfound an effect size of ρ = 0.362, 95% CI [0.267, 0.457], Z = 7.469, p < 0.001. Eighteenstudies reported data on pronoun use, referring to pronoun use in general (e.g., totalnumbers, the ratio of pronouns to nouns) or to reference errors (e.g., pronouns withoutclear antecedents, wrong pronouns). Individuals with AD used more pronouns and mademore errors in pronoun use than did healthy older adults, with a significant effect size ofρ = 0.292, 95% CI [0.149, 0.435], Z = 4.004, p < .001. Eight studies looked at variousmeasures of dysfluency, including the number and duration of empty and filled pauses,with an overall effect size of ρ = 0.617, 95% CI [0.517, 0.718], Z = 12.028, p < .001.Thirteen studies examined word repetitions, revisions, and false starts, with an effect sizeof ρ = 0.308, 95% CI [0.198, 0.418], Z = 5.477, p < .001. Ten studies used a compositevariable to aggregate several types of errors, with an effect size of ρ = 0.333, 95% CI[0.180, 0.486], Z = 4.253, p < .001.

The analyses of the six error categories showed similar effect sizes for all error types,except for dysfluency, which had a larger effect size. A careful examination of the effectsize of dysfluency errors showed that the findings reported by Forbes-McKay, Shanks,and Venneri (2013), who studied a relatively large group of participants, might haveskewed the results. While dysfluency was counted numerically in all other studies,Forbes-McKay et al. (2013) used ratings (from severely impaired to normal) whichmight have been affected by measures of content rather than by hesitation alone.Removal of this study from the analysis substantially decreased the effect size tounder 0.4. Finally, a meta-analysis that included all error measures from the six errorcategories together revealed an effect size of ρ = 0.316, 95% CI [0.285, 0.348], Z = 19.761,p < .001. Thus, the hypothesis that persons with AD would err more was confirmed.

Hypothesis 4According to our last hypothesis, performance on structured tests of single-word pro-duction should correlate with measures of word retrieval in connected speech. Thirteenstudies examined this association, with great diversity in the variables under study (seeTable 5). Five studies analysed correlations across all participants together rather thanfor the AD group alone, so it might be incorrect to conclude from these studies that thedifficulties that characterise AD appear both in isolation and in context. Of the 25significant correlations presented in Table 5, 16 (64%) were found for picture-namingscores, seven (28%) were found for fluency tests, and two (8%) were found for compo-site scores. Of the 17 non-significant correlations presented in the table, half pertainedto picture-naming scores and half to fluency scores.

In order to assess the effect size of the association, we selected the correlations thatwere computed within the AD samples alone. We limited the analysis to the associationbetween picture-naming scores and speech measures because performance on verbalfluency tasks is highly affected by executive functioning. We converted all negativecorrelations (i.e., between naming success and errors in speech) into positive correla-tions, and found an effect size of ρ = 0.521, 95% CI [0.381, 0.661], Z = 7.279, p < .001.Hence, in line with our fourth hypothesis, there was an overall strong associationbetween naming scores and connected speech variables. Figure 5 presents a funnelplot of the distribution of effects relative to the confidence interval.

16 G. KAVÉ AND M. GORAL

Table5.

Summaryof

correlations

betweenstructured

tasksof

wordprod

uctio

nandretrievalvariables

inconn

ectedspeech.

Stud

yStructured

task

Speech

variable

r

Sign

ificant

find

ings

Ahmed

etal.(2013)

CAMCO

Gexpression

scoreb

#of

allinformationun

its0.81

Altm

annet

al.(2001)

Semantic

paraph

asiason

object

naming

#of

errorsperhu

ndredwords

0.53

c

Brandãoet

al.(2009)a,d

CRon

object

naming

%of

incompleteprop

osition

s−0.58

Bschor

etal.(2001)a

Phon

emicfluency

Wordnu

mber

0.32

CRon

object

naming

#of

allinformationun

its0.39

Semantic

fluency

#of

allinformationun

its0.42

Phon

emicfluency

#of

allinformationun

its0.47

Gileset

al.(1996)

CRon

object

naming

#of

allinformationun

its0.35

Semantic

fluency

#of

allinformationun

its0.38

Kavé

andGoral

(2016b)

CRon

object

naming

%contentwords

ofallw

ords

0.58

CRon

object

naming

%no

unsof

allw

ords

0.66

CRon

object

naming

%pron

ouns

ofallw

ords

−0.48

CRon

object

naming

Wordfrequency

−0.67

CRon

object

naming

Wordleng

th0.46

Semantic

fluency

%no

unsof

allw

ords

0.45

March

etal.(2009)a,e

CRon

object

naming

#of

noun

sdividedby

#of

clauses

0.46

CRon

object

naming

#of

spatiald

eixisdividedby

#of

clauses

−0.28

Semantic

fluency

#of

noun

sdividedby

#of

clauses

0.35

McN

amaraet

al.(1992)

CRon

object

naming

#of

lemmarepairs

0.52

MendezandAshla-Mendez(1991)

aCR

onob

ject

naming

Words

perminute

0.37

Phon

emicfluency

Words

perminute

0.57

Murray(2010)

ABCD

lingu

istic

expression

Performance

deviations

f−0.53

Nicho

laset

al.(1985)

CRon

object

naming

#of

indefinite

term

s−0.55

CRon

object

naming

#of

inform

ationun

its0.63

CRon

actio

nnaming

#of

inform

ationun

its0.56

Non

-significant

find

ings

Almor

etal.(1999)

CRon

object

naming

%pron

ouns

ofpron

ominalexpression

s0.005

Brandãoet

al.(2009)a

CRon

object

naming

%of

repeated

prop

osition

s0.02

Bschor

etal.(2001)a

CRon

object

naming

Wordnu

mber

0.04

Semantic

fluency

Wordnu

mber

0.14

Kavé

andGoral

(2016b)

CRon

object

naming

Wordnu

mber

−0.16

CRon

object

naming

Type-token

ratio

ofallw

ords

0.34

CRon

object

naming

Type-token

ratio

ofno

uns

0.13

CRon

object

naming

Nou

nfrequency

−0.15

Semantic

fluency

Wordnu

mber

0.16

Semantic

fluency

%contentwords

ofallw

ords

0.15

(Con

tinued)

APHASIOLOGY 17

Table5.

(Con

tinued).

Stud

yStructured

task

Speech

variable

r

Semantic

fluency

%pron

ouns

ofallw

ords

−0.20

Semantic

fluency

Type-token

ratio

ofallw

ords

−0.06

Semantic

fluency

Type-token

ratio

ofno

uns

−0.04

Semantic

fluency

Wordfrequency

−0.37

Semantic

fluency

Nou

nfrequency

−0.14

Semantic

fluency

Wordleng

th0.24

Laineet

al.(1998)a

CRon

object

naming

#of

references

with

outantecedents

−0.43

Therwas

takenfrom

theoriginalstud

iesrather

than

compu

tedas

aneff

ectsize

measure.C

R:Co

rrectrespon

ses.

a Correlatio

nswerecalculated

forparticipantswith

ADtogether

with

otherp

articipants(eith

ercontrolp

articipantsor

participantswith

otherdementias,or

both),un

likeotherstudies

inwhich

they

werecalculated

fortheAD

grou

palon

e.bCA

MCO

Gexpression

scoreinclud

espicturenaming,

verbalfluency,andmatchingto

descrip

tion.

c Altm

annet

al.(2001)repo

rttheaccoun

tedvariancewith

inaregression

analysis,and

thetablepresents

itssquare

root.

dBrandãoet

al.(2009)analysed

correlations

forinform

ativeandno

n-inform

ativetasks,andthetablepresentscorrelations

fortheinform

ativetask

alon

e.e M

arch

etal.(2009)analysed

correlations

forseveralcon

nected

speech

tasks,andthetablepresentsthecorrelations

onlyforthedescrip

tionof

theCo

okie

Theftpicture.

f Perform

ance

deviations

includ

edfillers,repetition

s,irrelevantwords,revisions,and

non-specificwords.

18 G. KAVÉ AND M. GORAL

Discussion

We tested four hypotheses concerning word retrieval in connected speech in AD byreviewing 51 studies. Despite the diversity in elicitation modes as well as in themeasures under study, the aggregation of results lends support to three of thesehypotheses. Specifically, the analyses showed that persons with AD retrieve fewerwords and make more errors in connected speech compared to healthy people, andthat their performance on tests of single-word production is associated with theirperformance in context. We first discuss the evidence concerning our four hypothesesand then address more general methodological concerns.

A meta-analysis of the difference between persons with AD and healthy older adultsin the total number of words produced in connected speech suggests that the diseaseleads to a statistically significant decrease in word output, in accordance with our firsthypothesis. Studies that recorded the time taken to produce speech mostly support thisfinding, showing that participants with AD provide less output per minute (e.g.,Carlomagno et al., 2005; Hoffmann et al., 2010; Mendez & Ashla-Mendez, 1991; Smithet al., 1989), possibly because they pause more often (Gayraud et al., 2011). We note thata decrease in speech quantity does not by itself indicate that speakers have wordretrieval deficits, but could also reflect other difficulties, such as the inability to planthe overall message. Moreover, our meta-analysis revealed that the decrease in wordoutput was small. A larger decrease in total word output might characterise only severestages of AD, with less noticeable changes in quantity at disease onset (e.g., Bayles &Tomoeda, 2007). We thus suspect that the size of the effect might reflect the hetero-geneity in cognitive level within and across the AD samples. Future research should

Figure 5. Funnel plot presenting effect sizes of the correlation between naming scores andconnected speech variables in individuals with AD.Note: Plot created using Van Rhee, Suurmond, and Hak’s (2015) Meta-Essentials tools. The verticalline is the effect size estimate and the diagonal lines are the 95% confidence interval. Each dot is aseparate correlation.

APHASIOLOGY 19

explore the connection between dementia severity and total output in both cross-sectional studies and in longitudinal designs. In addition, to assess whether wordquantity indicates that participants are searching for words, the connection betweenword number and other measures of retrieval should be further examined.

Review of the findings concerning the production of unique words and the calculation ofTTR did not confirm our second hypothesis, according to which individuals with AD wouldproduce less lexically diverse discourse. We note that relatively few studies examinedmeasures that pertained to lexical diversity in AD, andwe could not conduct ameta-analysison these measures. Furthermore, some studies looked at the number of unique wordsrather than at their proportion relative to the total number of words. Studies that examinedthe proportion of unique words mostly used the TTR (but see Bucks et al., 2000), althoughthis measuremight be skewed by sample length. Therefore, future studies could investigatelexical diversity in connected speech in AD in more detail, employing measures thataccommodate small and varying amounts of speech output. One possible direction forfurther research is to investigate the lexical characteristics of the words that persons with ADretrieve. The review identified some studies in which individuals with AD produced morefrequent words as well as more formulaic expressions, but these aspects of lexical diversitywere tested in too few studies to make strong generalisations at this point. We believe thatexamination of other lexical aspects would shed light onword retrieval in connected speechin AD. For example, research could look at whether persons with AD use more concretelanguage than do healthy speakers, especially in interviews rather than in picture descrip-tion as the later call for concrete language.

Consistent with our third hypothesis, a series of meta-analyses revealed that indivi-duals with AD exhibit more retrieval errors than do healthy controls. This was trueregardless of the error category and of the specific measures under study. As Altmann,Kempler, and Andersen (2001) have observed, no particular error type dominated. Ourfindings are consistent with those reported for people with aphasia, who commitmultiple retrieval errors when producing connected speech (e.g., Andreetta,Cantagallo, & Marini, 2012; Kavé & Goral, 2016a; Pashek & Tompkins, 2002). Thus, errorsmost likely indicate reliable difficulties in retrieval and should be routinely evaluated inAD. Yet, speech analysis will not be incorporated into clinical settings unless it is fast andsimple (Forbes-McKay & Venneri, 2005), and error analysis often requires labour-inten-sive manual coding. We believe that the future of connected speech analysis lies incomputational tools, as used for example by Fraser, Meltzer, and Rudzicz (2015).Automated tools can more easily identify pronouns than any other type of speecherrors. Our review suggests that pronoun use is a good marker of difficulties in ADand hence it might have clinical utility, possibly through the use of automated tools.Note, though, that in order for such a measure to be useful in standard clinical evalua-tions, it will have to be judged against large-scale normative data. Norms of pronoun usein healthy older individuals are not currently available and would need to beestablished.

Our forth hypothesis assumed that there would be significant associations betweenmeasures of retrieval in and out of context in the AD population, and our findingssupported this assumption. Such significant associations have been reported before forpeople with aphasia, but not for healthy older adults (Kavé & Goral, 2016a). Theassociation suggests that AD leads to robust difficulties that reflect underlying

20 G. KAVÉ AND M. GORAL

impairments rather than task-specific demands. In addition, the documented associationsupports the common practice of using naming tests to predict word retrieval inspontaneous speech. Nevertheless, three major caveats should be kept in mind regard-ing the analysis of Hypothesis 4. First, several studies reported more than one correlationfor the same participants, and we did not control for this overlap. Second, some studiesfocused on information units in connected speech without direct reference to wordretrieval. We note that retrieval of information units does not equal retrieval of specificlexical items. In fact, it may not attest to successful word retrieval at all, since the sameinformation could be conveyed with different words, some being less precise thanothers. Third, non-significant correlations were sometimes mentioned without the actualnumbers, thus not entering the analysis. For example, Nicholas et al. (1985) reported that13 of their 14 variables of empty speech (e.g., empty phrases, deictic terms, or pronounswithout antecedents) did not correlate with object or action-naming scores, but they didnot provide the numeric value of these correlations. Although in other analyses weentered these null results into the analysis, we chose not to do so in this analysisbecause there were too many such variables from one study. Thus, taken together,the correlation results provide support to the idea that performance on picture-namingtasks predicts measures of word retrieval in context, but conclusions must be drawnwith caution. Notice also that the correlations were far from perfect, highlighting the factthat lexical retrieval in context is affected not only by the mechanisms that affect single-word retrieval (e.g., semantics, phonology). Other factors such as syntax, the location ofa word within a sentence, speed of processing, working memory, communication goals,or advanced planning (Griffin & Spieler, 2006) have been studied to some extent in AD,but their exact impact on word retrieval is yet to be defined.

More generally, our findings point to retrieval difficulty in connected speech producedby people with AD. This conclusion contrasts with the conclusion of our previous reviewof the literature on word retrieval in connected speech in healthy ageing (Kavé & Goral,2016a). While healthy older adults appear to be using context to compensate for theirdifficulties in single-word retrieval, persons with AD might be less successful in doing so.Persons with AD speak relatively fluently, unlike people with non-fluent aphasia, but theystill initiate less spontaneous speech than do healthy adults. Impaired cognitive abilitiesmost likely hinder the use of self-monitoring in AD, and deteriorating semantics probablylimits the use of correct word alternatives when retrieval fails, as done by healthy speakers.In addition, individuals with AD might be less able to accommodate their speech to thelistener’s needs (Carlomagno et al., 2005; Kemper et al., 1994), thus possibly paying lessattention to the effectiveness of their word selection.

Extraction of demographic data from the 51 reviewed articles revealed several meth-odological shortcomings. First, studies that compare persons with AD and healthy parti-cipants do not always match groups in terms of age and education, and often report onlypartial data. Second, participants with AD were on average relatively young, and theirmean cognitive level was rather high, thus not necessarily representing the typical personwith AD. We note further that because studies include small patient samples, the effects ofindividual differences on language production have received very little attention in the ADpopulation. However, recent research on healthy cognitive ageing has increased its focuson such differences (e.g., Cahana-Amitay et al., 2015; Park & Festini, 2017; Salthouse, 2017).It is thus important to examine how demographic characteristics (e.g., age, education,

APHASIOLOGY 21

bilingualism, dementia severity, or other medical problems), as well as performance onother cognitive tests, contribute to language use in AD.

This review is a step towards understanding word retrieval difficulties in connectedspeech in AD, demonstrating that individuals with AD speak less, err more, and theirerrors are related to more fundamental difficulties in lexical selection, as documented onstandard picture-naming tests. Future studies could benefit from following the trajec-tories of these difficulties along the disease progression in larger samples of participantswith AD. Moreover, further research is required into the effect of demographic andcognitive characteristics on word retrieval in connected speech in AD.

Acknowledgement

The authors wish to thank Adi Amit for her invaluable help with the meta-analyses.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

References marked with an asterisk were included in the review.

Adlam, A. L. R., Patterson, K., Bozeat, S., & Hodges, J. R. (2010). The Cambridge semantic memorytest battery: Detection of semantic deficits in semantic dementia and Alzheimer’s disease.Neurocase, 16, 193–207. doi: 10.1080/13554790903405693

*Ahmed, S., de Jager, C. A., Haigh, A. M., & Garrard, P. (2013). Semantic processing in connectedspeech at a uniformly early stage of autopsy-confirmed Alzheimer’s disease. Neuropsychology,27, 79–85. doi: 10.1037/a0031288

*Almor, A., Kempler, D., MacDonald, M. C., Andersen, E. S., & Tyler, L. K. (1999). Why do Alzheimerpatients have difficulty with pronouns? Working memory, semantics, and reference in compre-hension and production in Alzheimer’s disease. Brain and Language, 67, 202–227. doi: 10.1006/brln.1999.2055

*Altmann, L. J., Kempler, D., & Andersen, E. S. (2001). Speech errors in Alzheimer’s disease:Reevaluating morphosyntactic preservation. Journal of Speech, Language, and HearingResearch, 44, 1069–1082. doi: 10.1044/1092-4388(2001/085)

Andreetta, S., Cantagallo, A., & Marini, A. (2012). Narrative discourse in anomic aphasia.Neuropsychologia, 50, 1787–1793. doi: 10.1016/j.neuropsychologia.2012.04.003

Armstrong, E. M. (2001). Connecting lexical patterns of verb usage with discourse meanings inaphasia. Aphasiology, 15, 1029–1046. doi: 10.1080/02687040143000375

Arroyo-Anlló, E. M., Lorber, M., Rigaleau, F., & Gil, R. (2012). Verbal fluency in Alzheimer’s diseaseand Aphasia. Dementia, 11, 5–18. doi: 10.1177/1471301211416609

Balthazar, M. L. F., Cendes, F., & Damasceno, B. P. (2008). Semantic error patterns on the Bostonnaming test in normal aging, amnestic mild cognitive impairment, and mild Alzheimer’sdisease: Is there semantic disruption? Neuropsychology, 22, 703–709. doi: 10.1037/a0012919

*Bates, E., Harris, C., Marchman, V., Wulfeck, B., & Kritchevsky, M. (1995). Production of complexsyntax in normal ageing and Alzheimer’s disease. Language and Cognitive Processes, 10, 487–539. doi: 10.1080/01690969508407113

Bayles, K., & Tomoeda, C. (2007). Cognitive-communication disorders of dementia. Abingdon, UK:Plural Publishing.

22 G. KAVÉ AND M. GORAL

*Bayles, K. A., Tomoeda, C. K., Kaszniak, A. W., Stern, L. Z., & Eagans, K. K. (1985). Verbalperseveration of dementia patients. Brain and Language, 25, 102–116. doi: 10.1016/0093-934X(85)90123-3

Behrns, I., Wengelin, A., Broberg, M., & Hartelius, L. (2009). A comparison between written andspoken narratives in aphasia. Clinical Linguistics and Phonetics, 23, 507–528. doi: 10.1080/02699200902916129

*Blanken, G., Dittmann, J., Haas, J. C., & Wallesch, C. W. (1987). Spontaneous speech in seniledementia and aphasia: Implications for a neurolinguistic model of language production.Cognition, 27, 247–274. doi: 10.1016/S0010-0277(87)80011-2

Borenstein, M., Hedges, L. V., Higgins, J., & Rothstein, H. R. (2009). Introduction to meta-analysis.Chichester, UK: John Whiley & Sons. doi:10.1002/9780470743386

Boschi, V., Catricalà, E., Consonni, M., Chesi, C., Moro, A., & Cappa, S. F. (2017). Connected speech inneurodegenerative language disorders: A review. Frontiers in Psychology, 8, 269. doi: 10.3389/fpsyg.2017.00269

*Brandão, L., Castelló, F. G., van Dijk, T. A., de Mattos Pimenta Parente, M. A., & Peña-Casanova, J.(2009). Cognition and discourse production in Alzheimer’s disease: Using informative prompts.Psychology & Neuroscience, 2, 147–155. doi: 10.3922/j.psns.2009.2.006

*Bridges, K. A., & Van Lancker Sidtis, D. (2013). Formulaic language in Alzheimer’s disease.Aphasiology, 27, 799–810. doi: 10.1080/02687038.2012.757760

*Bschor, T., Kühl, K. P., & Reischies, F. M. (2001). Spontaneous speech of patients with dementia ofthe Alzheimer type and mild cognitive impairment. International Psychogeriatrics, 13, 289–298.doi: 10.1017/S1041610201007682

*Bucks, R. S., Singh, S., Cuerden, J. M., & Wilcock, G. K. (2000). Analysis of spontaneous, conversa-tional speech in dementia of Alzheimer type: Evaluation of an objective technique for analysinglexical performance. Aphasiology, 14, 71–91. doi: 10.1080/026870300401603

Cahana-Amitay, D., Spiro, A., Cohen, J. A., Oveis, A. C., Ojo, E. A., Sayers, J. T., . . . Albert, M. L. (2015).Effects of metabolic syndrome on language functions in Aging. Journal of the InternationalNeuropsychological Society, 21, 116–125. doi: 10.1017/S1355617715000028

*Carlomagno, S., Santoro, A., Menditti, A., Pandolfi, M., & Marini, A. (2005). Referential communica-tion in Alzheimer’s type dementia. Cortex, 41, 520–534. doi: 10.1016/S0010-9452(08)70192-8

*Chapman, S. B., Ulatowska, H. K., King, K., Johnson, J. K., & McIntire, D. D. (1995). Discourse in earlyAlzheimer’s disease versus normal advanced aging. American Journal of Speech-LanguagePathology, 4, 124–129. doi: 10.1044/1058-0360.0404.124

*Chenery, H. J., & Murdoch, B. E. (1994). The production of narrative discourse in response toanimations in persons with dementia of the Alzheimer’s type: Preliminary findings. Aphasiology,8, 159–171. doi: 10.1080/02687039408248648

*Croisile, B., Ska, B., Brabant, M. J., Duchene, A., Lepage, Y., Aimard, G., & Trillet, M. (1996).Comparative study of oral and written picture description in patients with Alzheimer’s disease.Brain and Language, 53, 1–19. doi: 10.1006/brln.1996.0033

Cuetos, F., Gonzalez-Nosti, M., & Martínez, C. (2005). The picture-naming task in the analysis of cognitivedeterioration in Alzheimer’s disease. Aphasiology, 19, 545–557. doi: 10.1080/02687030544000010

*de Lira, J. O., Minett, T. S. C., Bertolucci, P. H. F., & Ortiz, K. Z. (2014). Analysis of word number andcontent in discourse of patients with mild to moderate Alzheimer’s disease. Dementia andNeuropsychologia, 8, 260–265. doi: 10.1590/S1980-57642014DN83000010

*de Lira, J. O., Ortiz, K. Z., Campanha, A. C., Bertolucci, P. H. F., & Minett, T. S. C. (2011).Microlinguistic aspects of the oral narrative in patients with Alzheimer’s disease. InternationalPsychogeriatrics, 23, 404–412. doi: 10.1017/S1041610210001092

*Dijkstra, K., Bourgeois, M. S., Allen, R. S., & Burgio, L. D. (2004). Conversational coherence:Discourse analysis of older adults with and without dementia. Journal of Neurolinguistics, 17,263–283. doi: 10.1016/S0911-6044(03)00048-4

*Drummond, C., Coutinho, G., Fonseca, R. P., Assunção, N., Teldeschi, A., de Oliveira-Souza, R., . . .Mattos, P. (2015). Deficits in narrative discourse elicited by visual stimuli are already present inpatients with mild cognitive impairment. Frontiers in Aging Neuroscience, 7, 96. doi: 10.3389/fnagi.2015.00096

APHASIOLOGY 23

*Ehrlich, J. S., Obler, L. K., & Clark, L. (1997). Ideational and semantic contributions to narrativeproduction in adults with dementia of the Alzheimer’s type. Journal of Communication Disorders,30, . doi: 10.1016/0021-9924(95)00053-4

Fergadiotis, G., & Wright, H. H. (2011). Lexical diversity for adults with and without aphasia acrossdiscourse elicitation tasks. Aphasiology, 25, 1414–1430. doi: 10.1080/02687038.2011.603898

*Feyereisen, P., Berrewaerts, J., & Hupet, M. (2007). Pragmatic skills in the early stages ofAlzheimer’s disease: An analysis by means of a referential communication task. InternationalJournal of Language and Communication Disorders, 42, 1–17. doi: 10.1080/13682820600624216

Field, A. P., & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical andStatistical Psychology, 63, 665–694. doi: 10.1348/000711010X502733

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state: A practical method forgrading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. doi:10.1016/0022-3956(75)90026-6

*Forbes-McKay, K., Shanks, M. F., & Venneri, A. (2013). Profiling spontaneous speech decline inAlzheimer’s disease: A longitudinal study. Acta Neuropsychiatrica, 25, 320–327. doi: 10.1017/neu.2013.16

Forbes-McKay, K. E., & Venneri, A. (2005). Detecting subtle spontaneous language decline in earlyAlzheimer’s disease with a picture description task. Neurological Sciences, 26, 243–254. doi:10.1007/s10072-005-0467-9

Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015). Linguistic features identify Alzheimer’s disease innarrative speech. Journal of Alzheimer’s Disease, 49, 407–422. doi: 10.3233/JAD-150520

Garrard, P., Lambon Ralph, M. A., Patterson, K., Pratt, K. H., & Hodges, J. R. (2005). Semantic featureknowledge and picture naming in dementia of Alzheimer’s type: A new approach. Brain andLanguage, 93, 79–94. doi: 10.1016/j.bandl.2004.08.003

Gates, N. J., & March, E. G. (2016). A neuropsychologist’s guide to undertaking a systematic reviewfor publication: Making the most of PRISMA guidelines. Neuropsychology Review, 26, 109–120.doi: 10.1007/s11065-016-9318-0

*Gayraud, F., Lee, H. R., & Barkat-Defradas, M. (2011). Syntactic and lexical context of pauses andhesitations in the discourse of Alzheimer patients and healthy elderly subjects. ClinicalLinguistics and Phonetics, 25, 198–209. doi: 10.3109/02699206.2010.521612

*Giles, E., Patterson, K., & Hodges, J. R. (1996). Performance on the Boston cookie theft picturedescription task in patients with early dementia of the Alzheimer’s type: Missing information.Aphasiology, 10, 395–408. Doi: 10.1080/02687039608248419

*Glosser, G., & Deser, T. (1991). Patterns of discourse production among neurological patientswith fluent language disorders. Brain and Language, 40, 67–88. doi: 10.1016/0093-934X(91)90117-J

Goodglass, H., & Kaplan, E. (1983). The assessment of aphasia and related disorders. Philadelphia, PA:Lea & Febiger.

Griffin, Z. M., & Spieler, D. H. (2006). Observing the what and when of language production fordifferent age groups by monitoring speakers’ eye movements. Brain and Language, 99, 272–288.doi: 10.1016/j.bandl.2005.08.003

Haugrud, N., Crossley, M., & Vrbancic, M. (2011). Clustering and switching strategies during verbalfluency performance differentiate Alzheimer’s disease and healthy aging. Journal of theInternational Neuropsychological Society, 17, 1153–1157. doi: 10.1017/S1355617711001196

Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013). Alzheimer disease in the United States(2010-2050) estimated using the 2010 census. Neurology, 80, 1778–1783. doi: 10.1212/WNL.0b013e31828726f5

Helm-Estabrooks, N., Albert, M. L., & Nicholas, M. (2014). Manual of aphasia and aphasia therapy.Austin, TX: ProEd.

Henry, J. D., Crawford, J. R., & Phillips, L. H. (2004). Verbal fluency performance in dementia of theAlzheimer’s type: A meta-analysis. Neuropsychologia, 42, 1212–1222.

*Hier, D. B., Hagenlocker, K., & Shindler, A. G. (1985). Language disintegration in dementia:Effects of etiology and severity. Brain and Language, 25, 117–133. doi: 10.1016/0093-934X(85)90124-5

24 G. KAVÉ AND M. GORAL

*Hoffmann, I., Nemeth, D., Dye, C. D., Pákáski, M., Irinyi, T., & Kálmán, J. (2010). Temporalparameters of spontaneous speech in Alzheimer’s disease. International Journal of Speech-Language Pathology, 12, 29–34. doi: 10.3109/17549500903137256

Hughes, C. P., Berg, L., Danziger, W. L., Coben, L. A., & Martin, R. (1982). A new clinical scale for thestaging of dementia. The British Journal of Psychiatry, 140, 566–572. doi: 10.1192/bjp.140.6.566

*Illes, J. (1989). Neurolinguistic features of spontaneous language production dissociate threeforms of neurodegenerative disease: Alzheimer’s, Huntington’s, and Parkinson’s. Brain andLanguage, 37, 628–642. doi: 10.1016/0093-934X(89)90116-8

Kavé, G., & Goral, M. (2016a). Do age-related word retrieval difficulties appear (or disappear) inconnected speech? Aging, Neuropsychology, and Cognition. doi: 10.1080/13825585.2016.1226249

*Kavé, G., & Goral, M. (2016b). Word retrieval in picture descriptions produced by individuals withAlzheimer’s disease. Journal of Clinical and Experimental Neuropsychology, 38, 958–966. doi:10.1080/13803395.2016.1179266

*Kavé, G., & Levy, Y. (2003). Morphology in picture descriptions provided by persons withAlzheimer’s disease. Journal of Speech, Language, and Hearing Research, 46, 341–352.doi:10.1044/1092-4388(2003/027)

*Kemper, S., Anagnopoulos, C., Lyons, K., & Heberlein, W. (1994). Speech accommodations todementia. Journal of Gerontology, 49, P223–P229. doi: 10.1093/geronj/49.5.P223

*Kemper, S., Lyons, K., & Anagnopoulos, C. (1995). Joint storytelling by patients with Alzheimer’sdisease and their spouses. Discourse Processes, 20, 205–217. doi: 10.1080/01638539509544938

Kertesz, A. (1982). Western aphasia battery. New York, NY: Grune & Stratton.*Lai, Y. H. (2014). Discourse features of Chinese-speaking seniors with and without Alzheimer’s

disease. Language and Linguistics, 15, 411–434. doi: 10.1177/1606822X14520665*Lai, Y. H., Pai, H. H., & Lin, Y. T. (2009). To be semantically-impaired or to be syntactically-impaired:

Linguistic patterns in Chinese-speaking persons with or without dementia. Journal ofNeurolinguistics, 22, 465–475. doi: 10.1016/j.jneuroling.2009.03.004

*Laine, M., Laakso, M., Vuorinen, E., & Rinne, J. (1998). Coherence and informativeness of discourse intwo dementia types. Journal of Neurolinguistics, 11, 79–87. doi: 10.1016/S0911-6044(98)00006-2

Laisney, M., Giffard, B., Belliard, S., De La Sayette, V., Desgranges, B., & Eustache, F. (2011). When thezebra loses its stripes: Semantic priming in early Alzheimer’s disease and semantic dementia.Cortex, 47, 35–46. doi: 10.1016/j.cortex.2009.11.001

*March, E. G., Pattison, P., & Wales, R. (2009). The role of cognition in context-dependent languageuse: Evidence from Alzheimer’s disease. Journal of Neurolinguistics, 22, 18–36. doi: 10.1016/j.jneuroling.2008.05.002

*March, E. G., Wales, R., & Pattison, P. (2006). The uses of nouns and deixis in discourse productionin Alzheimer’s disease. Journal of Neurolinguistics, 19, 311–340. doi: 10.1016/j.jneuroling.2006.01.001

Marini, A., Caltagirone, C., Pasqualetti, P., & Carlomagno, S. (2007). Patterns of language improve-ment in adults with non-chronic non-fluent aphasia after specific therapies. Aphasiology, 21,164–186. doi: 10.1080/02687030600633799

Masterson, J., Druks, J., Kopelman, M., Clare, L., Garley, C., & Hayes, M. (2007). Selective naming(and comprehension) deficits in Alzheimer’s disease? Cortex, 43, 921–934. doi: 10.1016/S0010-9452(08)70691-9

Mattis, S. (1988). Dementia rating scale (DRS). Odessa, FL: Psychological Assessment Resources.*McNamara, P., Obler, L. K., Au, R., Durso, R., & Albert, M. L. (1992). Speech monitoring skills in

Alzheimer’s disease, Parkinson’s disease, and normal aging. Brain and Language, 42, 38–51. doi:10.1016/0093-934X(92)90055-J

*Mendez, M. F., & Ashla-Mendez, M. (1991). Differences between multi-infarct dementia andAlzheimer’s disease on unstructured neuropsychological tasks. Journal of Clinical andExperimental Neuropsychology, 13, 923–932. doi: 10.1080/01688639108405108

*Murray, L. L. (2010). Distinguishing clinical depression from early Alzheimer’s disease in elderlypeople: Can narrative analysis help? Aphasiology, 24, 928–939. doi: 10.1080/02687030903422460

APHASIOLOGY 25

*Nicholas, M., Obler, L. K., Albert, M. L., & Helm-Estabrooks, N. (1985). Empty speech in Alzheimer’sdisease and fluent aphasia. Journal of Speech, Language, and Hearing Research, 28, 405–410. doi:10.1044/jshr.2803.405

Olness, G. S., Ulatowska, H., & Wertz, R. T. (2002). Discourse elicitation with pictorial stimuli inAfrican Americans and Caucasians with and without aphasia. Aphasiology, 16, 623–633. doi:10.1080/02687030244000095

Park, D. C., & Festini, S. B. (2017). Theories of memory and aging: A look at the past and a glimpseof the future. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 72,82–90. doi: 10.1093/geronb/gbw066

Pashek, G. V., & Tompkins, C. A. (2002). Context and word class influences on lexical retrieval inaphasia. Aphasiology, 16, 261–286. doi: 10.1080/02687040143000573

*Ripich, D. N., Carpenter, B. D., & Ziol, E. W. (1997). Procedural discourse of men and women withAlzheimer’s disease: A longitudinal study with clinical implications. American Journal ofAlzheimer’s Disease and Other Dementias, 12, 258–271. doi: 10.1177/153331759701200604

*Ripich, D. N., Carpenter, B. D., & Ziol, E. W. (2000). Conversational cohesion patterns in men andwomen with Alzheimer’s disease: A longitudinal study. International Journal of Language andCommunication Disorders, 35, 49–64. doi: 10.1080/136828200247241

*Ripich, D. N., & Terrell, B. Y. (1988). Patterns of discourse cohesion and coherence in Alzheimer’sdisease. Journal of Speech and Hearing Disorders, 53, 8–15. doi: 10.1044/jshd.5301.08

*Ripich, D. N., Vertes, D., Whitehouse, P., Fulton, S., & Ekelman, B. (1991). Turn-taking and speechact patterns in the discourse of senile dementia of the Alzheimer’s type patients. Brain andLanguage, 40, 330–343. doi: 10.1016/0093-934X(91)90133-L

*Sajjadi, S. A., Patterson, K., Tomek, M., & Nestor, P. J. (2012). Abnormalities of connected speech insemantic dementia vs Alzheimer’s disease. Aphasiology, 26, 847–866. doi: 10.1080/02687038.2012.654933

Salthouse, T. A. (2017). Contributions of the individual differences approach to cognitive aging. TheJournals of Gerontology Series B: Psychological Sciences and Social Sciences, 72, 7–15. doi:10.1093/geronb/gbw069

*Singh, S., Bucks, R. S., & Cuerden, J. M. (2001). Evaluation of an objective technique for analysingtemporal variables in DAT spontaneous speech. Aphasiology, 15, 571–583. doi: 10.1080/02687040143000041

*Smith, S. R., Chenery, H. J., & Murdoch, B. E. (1989). Semantic abilities in dementia of theAlzheimer type. II. Grammatical semantics. Brain and Language, 36, 533–542. doi: 10.1016/0093-934X(89)90084-9

*Tomoeda, C. K., Bayles, K. A., Trosset, M. W., Azuma, T., & McGeagh, A. (1996). Cross-sectionalanalysis of Alzheimer disease effects on oral discourse in a picture description task. AlzheimerDisease and Associated Disorders, 10, 204–215.

*Van Lancker Sidtis, D., Choi, J., Alken, A., & Sidtis, J. J. (2015). Formulaic language in Parkinson’sdisease and Alzheimer’s disease: Complementary effects of subcortical and cortical dysfunction.Journal of Speech, Language, and Hearing Research, 58, 1493–1507. doi: 10.1044/2015_JSLHR-L-14-0341

Van Rhee, H. J., Suurmond, R., & Hak, T. (2015). User manual for meta-essentials: Workbooks formeta-analysis (Version 1.0). Rotterdam, The Netherlands: Erasmus Research Institute ofManagement. Retrieved from www.erim.eur.nl/research-support/meta-essentials

Wilson, D. B. (2001). Practical meta-analysis effect size calculator. Retrieved from http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php

*Zimmerer, V. C., Wibrow, M., & Varley, R. A. (2016). Formulaic language in people with probableAlzheimer’s disease: A frequency-based approach. Journal of Alzheimer’s Disease, 53, 1145–1160.doi: 10.3233/JAD-160099

*Zraick, R. I., Carr, P. B., Gregg, B. A., Smith-Olinde, L., Ghormley, C., & Hutton, T. J. (2011).Information units produced by persons with Mild Alzheimer’s disease during a picture descrip-tion task. Journal of Medical Speech-Language Pathology, 19, 37–49.

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