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Journal of Neuropsychology (2017), 11, 26–39
©2015 The British Psychological Society
www.wileyonlinelibrary.com
Word recognition in Alzheimer’s disease: Effects of
semantic degeneration
Fernando Cuetos
1
, Noem
ı Arce
1
, Carmen Mart
ınez
2
and
Andrew W. Ellis
3,
*
1
University of Oviedo, Spain
2
Cabue~
nes Hospital, Gij
on, Spain
3
University of York, UK
Impairments of word recognition in Alzheimer’s disease (AD) have been less widely
investigated than impairments affecting word retrieval and production. In particular, we
know little about what makes individual words easier or harder for patients with AD to
recognize. We used a lexical selection task in which participants were shown sets of four
items, each set consisting of one word and three non-words. The task was simply to point
to the word on each trial. Forty patients with mild-to-moderate AD were significantly
impaired on this task relative to matched controls who made very few errors. The
number of patients with AD able to recognize each word correctly was predicted by the
frequency, age of acquisition, and imageability of the words, but not by their length or
number of orthographic neighbours. Patient Mini-Mental State Examination and
phonological fluency scores also predicted the number of words recognized. We
propose that progressive degradation of central semantic representations in AD
differentially affects the ability to recognize low-imageability, low-frequency, late-
acquired words, with the same factors affecting word recognition as affecting word
retrieval.
Two of the cognitive problems most commonly reported in the early stages of
Alzheimer’s disease (AD) are difficulty remembering recent events (episodic memory)
and difficulty understanding and producing words (semantic memory; see Altmann &
McClung, 2008; Lambon Ralph et al., 2001; Taler & Phillips, 2008; Verma & Howard,
2012). Problems with word retrieval have been studied intensively through the use of
picture naming and other tasks (Chertkow & Bub, 1990; Chertkow, Bub, & Caplan,
1992; Cuetos, Martinez, Martinez, Izura, & Ellis, 2003; Hodges, Salmon, & Butters, 1991,
1992; Martin & Fedio, 1983). Problems with word recognition and comprehension in
AD have also been documented and, like the problems in word retrieval, have mainly
been attributed to impairments affecting the mental representations of concepts,
including the semantic representations of familiar objects. Deficits in word recognition
remain, however, underinvestigated compared with deficits in word retrieval and
production.
Evidence for a semantic basis to the lexical problems in AD includes the following: (1)
the ability of patients to name specific objects is predicted by the amount of semantic
*Correspondence should be addressed to Andrew W. Ellis, Department of Psychology, University of York, York YO10 5DD, UK
(email: andy.ellis@york.ac.uk).
DOI:10.1111/jnp.12077
26
information they retain about those objects (Garrard, Lambon Ralph, Patterson, Pratt, &
Hodges, 2005; Lambon Ralph, Patterson, & Hodges, 1997); (2) semantic naming errors
(such as naming penguin as duck, or cow as milk) predominate in the early stages of the
disease (Barbarotto, Capitani, Jori, Laiacona, & Molinari, 1998; Cuetos, Gonz
alez-Nosti, &
Mart
ınez, 2005; Cuetos, Rodr
ıguez-Ferreiro, Sage, & Ellis, 2012; Hodges et al., 1991; Huff,
Corkin, & Growden, 1986; Martin & Fedio, 1983; Rodr
ıguez-Ferreiro, Davies, Gonz
alez-
Nosti, Barb
on, & Cuetos, 2009); and (3) distinctive semantic information about concepts
is lost sooner than information that is shared with many other concepts (Flanagan,
Copland, Chenery, Byrne, & Angwin, 2013) with the result that patients can generate
fewer atypical members of particular categories than typical members (Perri, Zannino,
Caltagirone, & Carlesimo, 2012; Sailor, Antoine, Diaz, Kuslansky, & Kluger, 2004).
One of the issues addressed in this paper is the extent to which the same properties of
words predict success or failure in word retrieval and word recognition by patients with
AD. Two factors that have emerged as consistent predictors of object naming accuracy in
AD are word frequency and age of acquisition (AoA), with patients being better at
retrieving and producing high-frequency names and names learned early in life than low-
frequency names and names learned later in life (Cuetos, Rodr
ıguez-Ferreiro et al., 2012;
Rodr
ıguez-Ferreiro et al., 2009; Tippett, Meier, Blackwood, & Diaz-Asper, 2007).
Theoretical interpretations of those effects have proposed that the semantic represen-
tations of concepts learned early in life and activated with high frequency thereafter are
richer and/or easier to access and that the semantic representations of concepts are
learned later in life and activated less often. Richer, more accessible semantic
representations survive the early stages of AD better than poorer, less accessible
representations and are, for example, able to drive successful name retrieval in patients in
the mild-to-moderate stages of AD (see Ellis, 2011; for a review). The apparent lack of
influence of the complexity of object pictures or the length of object names on picture
naming in patients with AD has been interpreted as indicating that the primary
impairment is semantic rather than, for example, perceptual or phonological (Albanese,
2007; Cuetos et al., 2005; Cuetos, Rodr
ıguez-Ferreiro et al., 2012; Rodr
ıguez-Ferreiro
et al., 2009; Silveri, Cappa, Mariotti, & Puopolo, 2002; Tippett et al., 2007).
One problem with investigating word recognition in AD and other neuropsychological
conditions is that the tasks used to assess recognition and comprehension often involve
presenting more than one word or picture at a time; for example, asking patients to point
to the picture that matches a target word from a set of alternatives, or indicate whether
pairs of words have similar or dissimilar meanings. When multiple words or pictures are
presented, it becomes difficult to separate the properties of the target words from the
properties of the other words or pictures that accompany them. The lexical decision task
has sometimes been employed in an effort to overcome these problems. In lexical
decision, participants are shown a sequence of words interleaved with invented non-
words (i.e., strings of letters that look like words but happen not to be; for example, flupe
or quentole). In the traditional form of the lexical decision task, one word or non-word is
presented at a time and the participant is required to indicate whether each item is a word
or not. There is no requirement to demonstrate any understanding of the meanings of the
words presented. Using visual presentation of written words and non-words, Chertkow
and Bub (1989, 1990), Chertkow et al. (1992), Cuetos et al. (2003) and Du~
nabeitia,
Mar
ın, and Carreiras (2009) reported that patients with AD showed similar levels of
accuracy in lexical decision as healthy, matched controls. In contrast, Madden, Welsh-
Bohmer, and Tupler (1999) reported less accurate lexical decision in patients with AD
Word recognition in Alzheimer’s disease 27
using written presentation while Glosser, Kohn, Friedman, Sands, and Grugan (1997)
reported less accurate performance when the stimuli were spoken words and non-words.
We will argue here that patients with AD are impaired at lexical decision relative to
controls because they fail to recognize some words with which they were once familiar
(and which matched controls continue to recognize). We believe that there are at least
three reasons why previous studies have sometimes failed to observe reduced levels of
accuracy in patients with AD. First, some studies have used object names as the words in
their lexical decision tasks (Chertkow & Bub, 1989; Chertkow et al., 1992; Cuetos et al.,
2003). By definition, object names are high in imageability. They tend also to be learned
relatively early in life. If word recognition in patients with AD is better for high-
imageability, early-acquired words than for low-imageability, late-acquired words, then
object names may be relatively easy for patients with AD to recognize and therefore not
particularly well suited to identifying problems with word recognition.
A second issue was highlighted by Madden et al. (1999) who found a decrease in
lexical decision accuracy in patients with AD relative to controls that was greater for non-
words than for words. That is, patients appeared able to recognize most of the words
correctly while miscategorising many non-words as words. This may indicate that when
patients with AD are unsure as to whether an item is a word or a non-word, they tend to
categorize it as a word. A Yes bias of this sort will sustain a spuriously high level of
apparently correct responses to words and will only be revealed in the standard lexical
decision task if overall accuracy among the patients with AD is not at ceiling and if error
rates to non-words are also analysed. Madden et al. (1999) addressed this issue using a
signal detection analysis which demonstrated a substantial bias towards responding Yes to
non-words, but also an impaired sensitivity to words over and above the change in
response bias.
A simple way to eliminate response bias is to modify the lexical decision task to a
version in which participants are presented on each trial with (for example) one word and
one non-word and are asked to indicate which item is the real word. With a requirement to
pick out the word on every trial, response biases are removed. Baddeley, Emslie, and
Nimmo-Smith’s (1993) ‘Spot the Word’ test involved just such a two-alternative forced-
choice version of lexical decision (Baddeley & Crawford, 2012). Using that test, Law and
O’Carroll (1998) found a trend towards more errors in patients with AD compared with
controls, while Beardsall and Huppert (1997) reported more errors in patients with mild-
to-moderate AD than in patients with minimal dementia. But although these studies dealt
with the issue of response bias, they illustrate a third potenti al problem. If lexical selection
or lexical decision tasks use half words and half non-words, participants have a 50%
chance of making a correct response on each trial even if they actually have no idea which
of the stimuli are words and which are non-words. A 50% chance rate makes accuracy
scores relatively insensitive to impairments. The lexical selection task can be made more
sensitive by increasing the number of non-words that accompany each word target,
thereby reducing the probability of selecting the real word purely by chance. Cuetos,
Herrera, and Ellis (2010) presented three non-words with each real word in a lexical
selection task, thereby reducing the chance of a fortuitously correct response to 25%.
Patients with mild-to-moderate AD correctly selected the real word on an average of 88%
of trials, while matched controls selected the word on an average of 99% of trials. Although
well above chance, patients with AD performed significantly worse than controls.
If the problems affecting word recognition in AD share a common origin with the
problems affecting word retrieval (e.g., impairment to semantic representations affecting
both word recognition and production), we would expect the same factors to influence
28 Fernando Cuetos et al.
lexical selection as have been shown to influence word retrieval. As noted above, one of
the factors consistently found to influence word retrieval by patients with AD in naming
tasks is AoA: All other things being equal, patients are better able to name objects with
early- than late-acquired names (Albanese, 2007; Catling, South, & Dent, 2013; Cuetos,
Rodr
ıguez-Ferreiro et al., 2012; Holmes, Fitch, & Ellis, 2006; Marquez, Cappa, & Sartori,
2011; Rodr
ıguez-Ferreiro et al., 2009; Silveri et al., 2002; Tippett et al., 2007). Effects of
AoA have also been observed when patients are asked to generate words belonging to
different semantic categories (Forbes-McKay, Ellis, Shanks, & Venneri, 2005; Sailor,
Zimmerman, & Sanders, 2011). In addition, Perri et al. (2012) found that patients with
mild AD were able to generate more semantic features for early- than late-acquired
concepts. Cuetos, Rodr
ıguez-Ferreiro et al. (2012) found worse lexical selection
performance for late- than early-acquired words in the lexical selection task using sets
of words matched on frequency, imageability, and other factors. AoA would therefore
appear to be one property of words and concepts that affects the accuracy of both word
retrieval and word recognition in patients with AD.
A number of recent studies have reported effects of word frequency on object naming
in patients with AD that were independent of the effects of AoA and other factors (Cuetos,
Rodr
ıguez-Ferreiro et al., 2012; Rodr
ıguez-Ferreiro et al., 2009; Tippett et al., 2007). We
therefore predicted an effect of word frequency on lexical selection in patients with AD
independent of the effects of any other factors. It is hard to evaluate the effects of
imageability on object naming because objects tend to score highly on imageability, so the
range of values available for manipulation is limited. Failures to find the effects of
imageability on naming accuracy in patients with AD (Albanese, 2007; Cuetos et al., 2005;
Cuetos, Rodr
ıguez-Ferreiro et al., 2012; Rodr
ıguez-Ferreiro et al., 2009) cannot form the
basis of firm predictions regarding possible effects in lexical selection where a much
wider range of imageability values can be employed. Two studies have, however, reported
effects of imageability/concreteness in patients with AD using alternative tasks.
Rissenberg and Glanzer (1987) reported that the impairment shown by patients with
AD at producing words in response to definitions was substantially greater when the
target words were abstract than when they were concrete while Peters, Majerus, De
Baerdemaeker, Salmon, and Collette (2009) found better immediate serial recall of high-
than low-imageability words in patients with AD. Effects of imageability (or concreteness)
are generally regarded as indicators of semantic involvement in particular tasks, the
assumption being made that the limited perceptual experience associated with abstract
words causes their semantic representations to be less detailed than the representations of
concrete words (Hoffman, Jones, & Lambon Ralph, 2013). If the underlying deficit in
patients with AD is primarily semantic, and if imageability is a semantic variable, then we
would predict an effect of imageability on lexical selection in the present study.
Some words in the language resemble many other words, while others are more
distinctive in their appearance. One measure of distinctiveness is orthographic
neighbourhood size (N), defined as the number of other words that differ from a target
word by a single letter (Coltheart, Davelaar, Jonasson, & Besner, 1977). Distinctive words
have low Nvalues, while typical words have higher values. The only study we are aware of
that has investigated the effects of Non object naming in patients with AD is by Rodr
ıguez-
Ferreiro et al. (2009) who found no significant effect. Du~
nabeitia et al. (2009) compared
lexical decision accuracy and reaction times to words with many or few orthographic
neighbours in patients with AD and in controls. Both groups made more errors to low than
high Nwords, but the difference in favour of the high Nwords was smaller in the patients
with AD and not significant. That is, in terms of its effect on accuracy, the effect of Nwas
Word recognition in Alzheimer’s disease 29
reduced in patients with AD compared with controls. Du~
nabeitia et al. (2009) also
reported comparable effects of Non reaction times in patients with AD and in controls.
Accuracy of responses to non-words was not reported, so the possibili ty of a Yes bias in the
patients cannot be excluded.
Finally, studies of word retrieval in AD have consistently failed to find effects of word
length. When word frequency and other factors are taken into account, patients are as
likely to be able to retrieve and produce a long object name correctly as a short name
(Cuetos, Rodr
ıguez-Ferreiro et al., 2012; Rodr
ıguez-Ferreiro et al., 2009; Silveri et al.,
2002; Tippett et al., 2007). We are not aware of any previous investigations of the
influence of length on word recognition in patients with AD, but if the principal deficit in
AD is semantic rather than perceptual, orthographic, or phonological, there is no reason
to expect an effect of length on lexical selection performance in patients with AD.
The stimuli of interest in the present study were 150 words that varied on word
frequency, AoA, imageability, N, and length. These were chosen to be words that the
controls were expected to know. On each trial of the lexical selection task, one written
word was accompanied by three non-words that were matched to the word on number of
letters and syllables. Participants were simply asked to indicate which item in the array
was a real word. The participants were 40 patients with mild-to-moderate AD and 25
matched controls. We predicted that the patients with AD would have difficulty
recognizing the more low-frequency, late-acquired, and low-imageability words. We did
not expect to find an effect of length. The literature was ambiguous as to whether an
effect of Nmight be observed.
In presenting the results, we report one set of correlations which show the
relationships across patients between age, years of education, Mini-Mental State
Examination (MMSE) scores, scores on two fluency tasks, and lexical selection accuracy.
We then present an item analysis showing the correlations across words between
frequency, AoA, imageability, etc. and the accuracy with which the patients with AD as a
group responded to those words. The main statistical analysis is, however, a mixed-effects
multiple regression. Where a conventional multiple regression would take a mean level of
accuracy for each word across patients and use it to look at the effects of word properties
like frequency and length, a mixed-effects multiple regression takes every response from
every participant to every word (i.e., 40 patients with AD 9150 words =6,000 data
points) and allows the researcher to analyse the effects of participant and stimulus
characteristics in the same analysis which is more sensitive to effects than a traditional
multiple regression (cf. Cuetos, Rodr
ıguez-Ferreiro et al., 2012; Gonz
alez-Nosti, Barb
on,
Rodr
ıguez-Ferreiro, & Cuetos, 2013). Our mixed-effects analysis combined participant
MMSE and fluency scores with frequency, AoA, and the other lexical variables.
Methods
Participants
Forty patients with probable dementia of Alzheimer’s type (35 female and 5 male aged 66–
91 years) took part in the study. The patients were selected on the basis of their medical
history, information from a knowledgeable informant, a CT or MRI scan, and a
neuropsychological evaluation which included the MMSE (Folstein, Folstein, & McHugh,
1975) along with tests of semantic fluency (naming as many animals as possible in 1 min)
and phonological fluency (producing as many words beginning with ‘s’ as possible in
1 min). The diagnosis of probable AD was made according to the Neurological and
30 Fernando Cuetos et al.
Communicative Disorders and Stroke –Alzheimer’s Disease and Related Disorders
(NINCDS-ARDA criteria; McKhann et al., 1984; revised by McKhann et al., 2011).
Patients also met the DSM-IV criteria for dementia of the Alzheimer’s type (American
Psychiatric Association, 2000). The study was approved by the Ethics Committee of the
Hospital Cabue~
nes, Gij
on. Informed consent was obtained from all participants and from
patients’ caregivers prior to the study.
The controls were 25 healthy adult volunteers (21 female and 4 male) matched to the
patients on age and years of education (Table 1). None had a psychiatric history, sensory
deficiencies, or medical conditions that could impair performance on the neuropsycho-
logical tests. Patients with AD differed significantly from the controls on MMSE score, t
(63) =10.62, p<.001, semantic fluency, t(63) =8.77, p<.001, and phonological
fluency, t(63) =6.45, p<.001, but not on age, t(63) =1.10, p=.276, or years of
education, t(63) =0.07, p=.946.
Materials
One hundred and fifty Spanish words were selected for this study covering a range of
values for AoA, word frequency, imageability, length, and N. The words were taken from
the ratings study by Cuetos, Samartino, and Ellis (2012) and were items that a high
proportion of adults in that study (30 healthy adults aged 61–85 years) recognized as
familiar. AoA ratings were taken from the Cuetos, Samartino et al.’s (2012) study where
they were obtained using a scale from 1 =learned before the age of 2 years to
8=learned after the age of 20. Such ratings have been shown to correlate highly with
objective measures of AoA (Brysbaert & Ellis, 2017; Ellis, 2011). Word frequency values
were taken from the Subtlex-Esp database (Cuetos, Gonz
alez-Nosti, Barb
on, & Brysbaert,
2011) which is based on the frequencies with which words occur in a corpus of
41.5 million words taken from contemporary film subtitles. Subtitle frequencies have
been found to predict performance in word recognition experiments better than
frequency counts based solely on written texts (Brysbaert & New, 2009). The words had a
mean of 14.45 occurrences per million words of Spanish (range 0.19–311.85).
Imageability values were taken from the LEXESP database (Sebasti
an, Mart
ı, Carreiras,
& Cuetos, 2000) where adult participants rated words on a 7-point scale from 1 =very
hard to conjure up a mental image to 7 =very easy to conjure up a mental image. The
words had a mean imageability value of 5.00 (range 2.07–6.67). Values of Nwere taken
from P
erez, Alameida, and Cuetos (2003). The words had a mean of 2.11 neighbours
(range 0–17). Finally, word length was measured as the number of letters in each word
Table 1. Summary of participant characteristics and scores on the MMSE, semantic fluency, and
phonological fluency tasks
Age Years of education MMSE Semantic fluency Phonological fluency
Patients with AD
Mean 78.70 8.10 19.88 7.15 5.23
SD 5.98 3.20 4.23 3.44 3.57
Controls
Mean 77.20 8.16 29.08 15.88 10.96
SD 4.13 3.83 1.15 4.56 3.32
Note.AD=Alzheimer’s disease, MMSE =Mini-Mental State Examination, SD =standard deviation.
Word recognition in Alzheimer’s disease 31
(mean 6.15; range 4–9). During item selection, an effort was made to reduce the
intercorrelations between the variables while maintaining a reasonable spread of values
on each variable.
Four hundred and fifty non-words were generated, three for each target word. The
non-words were pronounceable, word-like letter strings which could be words in Spanish
but happen not to be (e.g., lefa, milusa, and pasorento). The three non-words that
accompanied each target word were matched to the target on the number of letters and
syllables. To check that there were no confounds between the properties of the target
words and the characteristics of the non-words that accompanied them, we calculated the
mean length, N, and bigram frequencies of the non-words used on each trial. The
correlations between the mean length, N, and bigram frequencies of the non-words and
the frequency, AoA, and imageability of the accompanying words were all non-significant.
It is unlikely, therefore, that any effect of frequency, AoA, or imageability found for the
recognition of real words was an indirect consequence of differences in the ease or
difficulty of rejecting the non-words that were presented with the words on each trial.
The three non-words and one word were presented on the screen of a laptop in a
square formation (two above and two below) using black, lower case font on a white
background. Words were distributed evenly across the four possible positions, with a real
word occurring at each position either 37 or 38 times.
Procedure
Participants were tested individually in a quiet room in the Neurology Unit of the
Cabue~
nes Hospital, Gij
on. The experimenter first explained the lexical selection task
using several examples. When the experimenter was satisfied that the participant
understood the task, the experimental stimuli were presented one set at a time. On each
trial, participants were asked to look at the four alternatives and indicate which was the
real word by pointing to it. Occasionally, participants recognized the real word quickly
and named it rather than pointing to it. Those responses were accepted, with the patient
being reminded of the requirement to point to the real words rather than name them. To
minimize non-verbal cues, the experimenter stood to the side and slightly behind the
patient, noting the response on each trial by marking the chosen item on a response sheet
which showed the four alternatives. No feedback was given during the experiment. The
MMSE and fluency tasks were administered on the same day as the lexical selection task.
Results
As expected, performance of the controls on the lexical selection task was close to ceiling.
Controls identified a mean of 147.4 of the 150 words correctly (98.3%), with 20 of the 25
controls responding correctly to 147 or more of the words. The patients with AD
identified a mean of 123.2 words correctly (82.1%). That is well above the chance rate of
25% but significantly worse than the accuracy of the controls, t(63) =6.46, p<.001.
Having established that the patients with AD were impaired on the lexical selection task,
further analyses focused on the patient data.
Table 2 shows the correlations between age, years of education, MMSE, semantic
fluency, phonological fluency, and lexical selection for the patients with AD. Lexical
selection scores correlated significantly with number of years of education, MMSE,
semantic fluency, and phonological fluency.
32 Fernando Cuetos et al.
Table 3 shows the correlations across items between the word properties (frequency,
AoA, etc.) and the number of patients with AD who selected each word correctly. Lexical
selection scores for the 150 words correlated significantly with word frequency, AoA, and
imageability, but not with Nor length.
When the predictor variables are correlated with each other as well as with the
dependent variable (number of patients selecting each word correctly), some form of
regression analysis is required to determine which predictor variables are exerting
genuinely independent effects on the dependent variable. We employed a mixed-effects
multiple regression model implemented through the lme4 package in R (R Development
Core Team, 2012, version 2.15). The model had patients and items as random intercepts
and the predictors as fixed effect factors or covariates (cf. Baayen, Davidson, & Bates,
2008; Kuperman, Schreuder, Bertram, & Baayen, 2009). The patient characteristics
employed as predictors were MMSE scores and scores on the semantic and phonological
fluency tasks. The lexical predictors were word frequency, AoA, imageability, length, and
N. Log values of the predictors were used to reduce skew. Collinearity was assessed by
calculating the variance inflation factor (VIF) for each predictor. VIF provides a measure of
how much larger the variance of a particular coefficient is than it would have been if that
predictor was completely uncorrelated with the other predictors, with VIF values >4
Table 2. Correlations between age, years of education, MMSE, semantic fluency, phonological fluency,
and lexical selection for the patients with AD
Age Years of Educ. MMSE Sem. fluency Phon. fluency Lexical selection
Age –.307 .064 .015 .048 .036
Years of education –.156 .082 .107 .377*
MMSE –.589*** .410** .688***
Semantic fluency –.605*** .433**
Phonological fluency –.455**
Lexical selection –
Notes. Educ. =education, MMSE =Mini-Mental State Examination, Sem. =Semantic, Phon. =Phono-
logical.
*p<.05; **p<.01; ***p<.001.
Table 3. Correlations among the predictor variables and between the predictor variables and the
number of patients with AD who selected each word correctly in the lexical selection test
AoA Freq Imag Length NLexical selection
AoA –.281*** .531*** .143 .244** .431***
Frequency –.029 .029 .099 .234**
Imageability –.152 .121 .344***
Length –.648*** .057
N–.134
Lexical selection –
Notes. AoA =age of acquisition, Freq =Frequency, Imag =Imageability, N=Number of orthographic
neighbours.
*p<.05; **p<.01; ***p<.001.
Word recognition in Alzheimer’s disease 33
generally taken to indicate potential problems with multicollinearity (Cohen, Cohen,
West, & Aiken, 2003). The VIF values for the predictors in this study ranged from 1.275 to
1.714, indicating that multicollinearity was not a problem and that the analysis was valid.
The analysis found significant effects of MMSE, Z=4.55, p<.001, and phonological
fluency, Z=2.25, p<.05, among the participant variables and word frequency,
Z=3.28, p<.001, AoA, Z=6.21, p<001, and imageability, Z=4.26, p<.001,
among the lexical variables. In the context of these other predictors, the effects of
semantic fluency score, Z=0.52, p=.600, neighbourhood size (N), Z=0.39,
p=.692, and word length, Z=0.56, p=.574, did not approach significance.
Discussion
The words employed in the present study were chosen to be ones that the healthy
controls were expected to recognize (and therefore, by inference, words that the patients
with AD would have been able to recognize before the onset of their illness). As expected,
control performance was at or close to ceiling while the patients with AD identified
significantly fewer real words than the controls. We note that the patients with AD in our
study were predominantly female. That would be expected from the longer life
expectancy of women, but a meta-analysis by Irvine, Laws, Gale, and Kondel (2012) found
that cognitive functions are more severely and more widely affected in women with AD
than in men, even allowing for differences in age, education, or dementia severity. The
controls in the present study had the same proportion of men and women as the patients
with AD.
The lexical selection task eliminates the problem of response bias that can affect
traditional lexical decision and reduces the probability of making a correct response by
chance. The finding that lexical selection was impaired in the patients with AD supports
previous demonstrations of impaired lexical decision/selection in AD (Beardsall &
Huppert, 1997; Cuetos et al., 2010; Glosser et al., 1997; Madden et al., 1999) and
suggests that reports of near-normal lexical decision accuracy in patients with AD may
have been due to a combination of response biases, high chance rates, and the use of
relatively easy, high-imageability words.
Having established that the patients with AD failed to recognize words they would
previously have known, further analyses focused on the patient data. The mixed-effects
regression analysis found that lexical selection accuracy was predicted by the MMSE and
phonological fluency scores of the patients and by the frequency, AoA, and imageability of
the words. In the context of those predictors, the effects of semantic fluency scores and
the length and Nvalues of words were not significant. Worse performance at word
recognition in more severe patients with lower MMSE scores is to be expected (Cuetos
et al., 2010). The effect of AoA, with better recognition of early- than late-acquired words,
replicates the finding of Cuetos et al. (2010). The demonstration of an additional and
independent effect of word frequency is new in this context, but independent effects of
frequency and AoA have been observed in studies of object naming by patients with AD
(Cuetos, Samartino et al., 2012; Rodr
ıguez-Ferreiro et al., 2009; Tippett et al., 2007). The
observed effect of imageability is in line with reported effects of that variable in other tasks
(Peters et al., 2009; Rissenberg & Glanzer, 1987). Taken together, the effects of
frequency, AoA, and imageability are consistent with the hypothesis that a central
semantic impairment underlies much of the difficulty that patients have in both word
recognition and production.
34 Fernando Cuetos et al.
We chose words for our lexical selection task that we expected healthy controls to
know because we see little point in demonstrating that patients with AD are unable to
recognize little-known words that controls are also unable to recognize. A connection
might nevertheless be made between impaired word recognition in patients with AD and
normal word recognition in healthy adults. The connection is that the same factors that
cause patients with AD to be unable to recognize once-familiar words also appear to affect
the speed with which healthy adults can recognize words. Effects of word frequency,
AoA, and imageability have been reported on the speed with which healthy adults
respond correctly to words in lexical decision (Cortese & Khanna, 2007; Cortese &
Schock, 2013; Gonz
alez-Nosti et al., 2013). This suggests a general rule whereby words
that healthy adults recognize correctly but slowly are the words that patients with AD fail
to recognize at all. We propose that the mechanism underlying these effects is that
impoverished semantic representations in healthy adults mean that low-frequency, late-
acquired, and low-imageability words are recognized relatively slowly in lexical decision.
Those impoverished representations are more vulnerable to the degenerative effects of
AD than are the richer representations of high-frequency, early-acquired, and high-
imageability words. Words whose semantic representations have degenerated beyond a
certain point no longer seem familiar and cannot be easily distinguished from non-words
in the lexical selection task (Ellis, 2011).
We found no effect of orthographic neighbourhood size on lexical selection
accuracy in our AD group. Studies of object naming in patients with AD have also failed
to find the effects of N(Cuetos, Samartino et al., 2012; Rodr
ıguez-Ferreiro et al., 2009;
Tippett et al., 2007). In contrast, Du~
nabeitia et al. (2009) found the effects of Non
lexical decision speed and accuracy both in patients with AD and in controls. Findings
from other studies have been mixed with respect to the effects of Non lexical decision
speed in healthy adults (Cortese & Khanna, 2007; Cortese & Schock, 2013; Gonz
alez-
Nosti et al., 2013). Balota, Cortese, Sergent-Marshall, Spieler, and Yapp (2004) found
no effects of Non lexical decision speed or accuracy in young adults but effects on
both speed and accuracy in older adults. This is an issue where further research is
needed, including exploring different measures of the similarity between word forms.
Despite this apparent anomaly, the general rule that slow recognition in healthy adults
converts into recognition failure in patients with AD is, we believe, supported by the
available evidence.
In addition to finding no significant effects of word length and Non lexical selection
accuracy in our patient group, we found no effect of semantic fluency scores. Studies have
indicated that the probability that patients with AD will retrieve particular words in the
semantic fluency task is influenced by both the frequency and AoA of those words (Binetti
et al., 1995; Forbes-McKay et al., 2005; Marczinski & Kertesz, 2006; Sailor et al., 2011). It
may be that when frequency and AoA are included as predictors of word recognition
accuracy along with MMSE and phonological fluency, little or no variance remains for
semantic fluency to account for.
Acknowledgements
This research was supported by grant PSI2012-31913 from the Spanish Government.
Word recognition in Alzheimer’s disease 35
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Received 13 July 2014; revised version received 13 May 2015
Word recognition in Alzheimer’s disease 39
