Category-specific semantic deficits in Alzheimer's disease: A semantic priming study

Neuropsychologia 46 (2008) 935–946

Category-specific semantic deficits in Alzheimer’s disease: A semantic priming study Mireia Hern´andez a,b , Albert Costa a,b,∗ , Montserrat Juncadella c , N´uria Sebasti´an-Gall´es a,b , Ram´on Re˜ne´ c a

GRNC, Parc Cient´ıfic Universitat de Barcelona & Hospital Sant Joan de D´eu, Spain b Departament de Psicologia B` asica, Universitat de Barcelona, Spain c Unitat de Diagn` ostic i Tractament de Dem`encies, Servei de Neurologia de l’Hospital Universitari de Bellvitge, Spain Received 8 August 2007; received in revised form 17 October 2007; accepted 24 November 2007 Available online 14 January 2008

Abstract Category-specific semantic deficits in individuals suffering brain damage after relatively focal lesions provide an important source of evidence about the organization of semantic knowledge. However, whether Alzheimer’s disease (AD), in which the brain damage is more widespread, affects semantic categories to a different extent is still controversial. In the present study, we assess this issue by means of the semantic priming technique. AD patients with a mild impairment of their semantic knowledge showed comparable priming effects to that of controls for the categories of animals and artifacts. Interestingly, however, patients with a moderate impairment of their semantic knowledge showed a normal priming effect for animals but a very reduced priming effect (if any) for artifacts. These results reveal that AD may affect the semantic knowledge of different semantic categories to a different extent. The implications of this observation for current theoretical accounts of semantic representation in the brain are discussed. © 2007 Elsevier Ltd. All rights reserved. Keywords: Category-specific semantic deficits; Semantic knowledge; Semantic priming; Alzheimer’s disease

1. Introduction Theories of the organization of semantic knowledge in the brain have paid much attention to the presence of categoryspecific semantic deficits in brain damaged individuals (Basso, Capitani, & Laiacona, 1988; Borgo & Shallice, 2001; Caramazza & Shelton, 1998; Caramazza, Hillis, Rapp, & Romani, 1990; De Renzi & Lucchelli, 1994; Farah & McClelland, 1991; Farah, Hammond, Mehta, & Radcliff, 1989; Hillis & Caramazza, 1991; Kensinger, Siri, Cappa, & Corkin, 2003; Moss & Tyler, 2000; Pietrini et al., 1988; Sacchett & Humphreys, 1992; Sartori & Job, 1988; Silveri & Gainotti, 1988; Sirigu, Duhamel, & Poncet, 1991; Warrington & McCarthy, 1983, 1987; Warrington & Shallice, 1984). Category-specific semantic deficits refer to a disproportionate semantic impairment for members of one spe-

∗

Corresponding author at: Departament de Psicologia B`asica, Universitat de Barcelona, P. Vall d’Hebron 171, 08035 Barcelona, Spain. E-mail address: [email protected] (A. Costa). 0028-3932/$ – see front matter © 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2007.11.018

cific semantic category in the context of a relative sparing of members of other semantic categories. The majority of these cases showed a disadvantage for living things compared to nonliving things, and just a few cases have described the opposite pattern (relative sparing of artifacts in comparison to animals; see Capitani, Laiacona, Mahon, & Caramazza, 2003; for an extensive review of the reported cases). Although the existence of category-specific semantic deficits is well accepted, their origin is still under dispute (see Carmazza & Mahon, 2006 for an overview of this debate). Studying the semantic breakdown in Alzheimer’s disease (AD) may potentially provide a rich source of information for testing the different hypotheses about how semantic information is organized in the brain. In this context, whether or not category-specific semantic deficits emerge in the course of AD may be crucial. The main goal of this research is to provide new experimental evidence regarding the presence of category-specific semantic deficits in AD. We do so by assessing the pattern of semantic priming effects for different semantic categories in AD patients with different degrees of semantic memory impairment.

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The breakdown of the semantic system is one of the most common symptoms in patients with AD (e.g., Hodges, Salmon, & Butters, 1992). However, whether this semantic breakdown in AD results in category-specific semantic deficits is still a matter of debate. Whereas some studies suggest that living things may be more impaired than artifacts (Daum, Riesch, Sartori, & Birbaumer, 1996; Mazzoni, Moretti, Lucchini, Vista, & Muratorio, 1991; Montanes, Goldblum, & Boller, 1995; Silveri, Daniele, Giustolisi, & Gianotti, 1991; Zannino, Perri, Carlesimo, Pasqualetti, & Caltagirone, 2002), other studies have not found any category-specific semantic deficit (Gainotti, Di Betta, & Silveri, 1996; Hodges et al., 1992; Montanes, Goldblum, & Boller, 1996; Perri et al., 2003; Tippett, Grossman, & Farah, 1996). This contrasting pattern might be due, in part, to methodological differences across studies. For example, several researchers have raised concerns about the comparability of the materials from the different semantic categories, with the members of the more affected category being less frequent, less familiar, and less prototypical than those of the better preserved category (Funnell & Sheridan, 1992; Stewart, Parkin, & Hunkin, 1992; Tippett et al., 1996). These concerns are exacerbated by the fact that, in some of the studies, control participants performed at ceiling levels, therefore making it difficult to assess potential differences in difficulty between the materials used in the various semantic categories (e.g., Albanese, 2007; Laws, Leeson, & Gale, 2003; Moreno-Mart´ınez & Laws, 2007; Perri et al., 2003; Zannino, Perri, Pasqualetti, Caltagirone, & Carlesimo, 2006). In contrast, other authors have claimed that the number of reported AD cases with category-specific semantic deficits may have been underestimated. On this view, the failure to detect category-specific semantic deficits in group analyses might be due to individual category-specific semantic deficits in opposite directions (e.g., Garrard, Patterson, Watson, & Hodges, 1998; Gonnerman, Andersen, Devlin, Kempler, & Seidenberg, 1997). Another example of how methodological factors may affect the detectability of category-specific deficits refers to the distribution of gender in a given sample. In a meta-analytic review of category naming in AD, Laws, Adlington, Gale, MorenoMart´ınez, and Sartori (2007)1 found an imbalance in the naming scores for males and females across categories. This is important given that other studies have found females to be better at naming fruits than males, and worse at naming tools (Gainotti, 2005). Another important problem when assessing the semantic knowledge of AD patients is the global cognitive impairment they suffer as a consequence of the diffuse nature of the disease. Thus, the diminished cognitive abilities of AD patients may interfere with semantic memory tasks that rely on con-

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In their meta-analytic review Laws et al. (2007) also found that using color stimuli increases the impairment of naming in AD. Although the reason for the impact of color in the naming performance of AD is still unknown, some authors propose that it is due to the color vision impairment present in many AD patients (see Cronin-Golomb, Sugiura, Corkin, & Growdon, 1993; Kurylo et al., 1994; Pache et al., 2003; Rizzo, Anderson, Dawson, & Nawrot, 2000; Wijk, Berg, Sivik, & Steen, 1999).

scious attentional processes such as the intentional retrieval of stored information (i.e., explicit tasks, questionnaires and naming tasks). That is, in some cases a semantic impairment assessed with these tools may stem from a failure to access intact semantic information, rather than from damage to semantic knowledge per se. A possible way to circumvent this problem is to use a measure of semantic knowledge that does not require explicit access to semantic information, such as semantic priming. The semantic priming effect refers to the facilitation in the recognition of a target word (e.g., tiger) produced by the prior presentation of a semantically related prime word (e.g., cat) compared with the prior presentation of a semantically unrelated prime word (e.g., chair; Fischler, 1977; Meyer & Schvaneveldt, 1971; Neely, 1977). This effect is considered to be the consequence of the automatic activation spreading in the semantic network produced by the presentation of the prime word. That is, the semantically related prime would pre-activate the target, making its subsequent recognition faster (Collins & Loftus, 1975). Note, that the precise mechanism that leads to semantic priming is, in principle, orthogonal to the organization of the semantic system. For example, priming would be predicted both if one assumes a holistic representation of concepts that are linked according to their semantic relationship, or if one assumes a more distributed system in which concepts are represented by semantic features shared with other concepts. In the present study, we contrast the pattern of semantic priming for two semantic categories (animals and artifacts) in two groups of AD patients suffering from different degrees of semantic memory impairment, and one control group. Whether the severity of semantic memory impairment should affect the pattern of semantic priming for these two categories differentially depends on the specific theoretical assumptions about the internal structure of these semantic categories. Recent proposals about the internal structure of semantic categories emphasized on the notion of shared vs. distinctive semantic properties (Devlin, Gonnerman, Andersen, & Seidenberg, 1998; Durrant-Peatfield, Tyler, Moss, & Levy, 1997; Tyler, Moss, Durrant-Peatfield, & Levy, 2000). Shared properties are features present in many members of the category (e.g., having legs or a tail in the case of animals). Distinctive properties are features present in a reduced subset of the category members (e.g., having a trunk). Furthermore, shared properties tend to be highly correlated (e.g., if an entity has legs it would have eyes), which supposedly makes them less vulnerable to damage. In this framework, category-specific semantic deficits may result from the differential distribution of shared and distinctive properties for different semantic categories: living things would have more shared than distinctive properties, while artifacts would have fewer shared properties and more distinctive ones. Thus, random damage to the semantic system should affect distinctive properties to a larger degree in the case of artifacts and shared properties more in the case of living things. And, given the assumption that shared properties are more resistant to damage, moderate damage to the semantic system should result in a disproportionate impairment for those concepts whose representations rely more on distinctive properties (e.g., artifacts; Devlin et al., 1998).

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However, following the crucial assumption that highly correlated properties are more resistant to damage, other authors make different predictions about the semantic loss across different semantic categories (Durrant-Peatfield et al., 1997; Tyler et al., 2000). These authors embrace the assumption that living things have more shared properties while artifacts have more distinctive ones. However, they further argue that the correlation between the form (visual properties) and the function of concepts plays a preponderant role in the representation of conceptual knowledge. Crucially this correlation involves a different sort of properties for living things and artifacts. For living things, the correlation between form and function tends to involve shared properties (biological functions and perceptual properties; e.g., can see > has eyes), since there are fewer distinctive properties of living things and they tend to be weakly correlated. In contrast, the form-function correlation for artifacts tends to involve distinctive properties (e.g., if it has a blade, it is likely to be used to cut things; if it has prongs, it is likely to serve to prick things), since their shared properties are fewer and weakly correlated. Thus, with the assumptions that (a) highly correlated properties are more resistant to damage, (b) form and function correlation plays a preponderant role, and (c) form and function correlation involves different sort of properties for living things and artifacts, the authors argue that the distinctive properties of living things are the most likely to be damaged, because they are less correlated. Thus, according to this view living things will tend to be more affected than artifacts in moderate stages of semantic memory impairment (Durrant-Peatfield et al., 1997; Tyler et al., 2000). Considering these different views, the robustness of the different type of properties (shared vs. distinctive) is crucial to predict the pattern of semantic priming effects across semantic categories. If distinctive properties are damaged then semantic priming should be very large for those representations with few distinctive properties. This is because the difference between two items with few distinctive properties would be blurred, and the large number of shared properties would allow priming to arise. For example, if the property “mane” is damaged then the prime “lion” becomes very close (certainly closer than if the property is not damaged) to the target “tiger”. In this scenario, we may expect to find an abnormal increase in the magnitude of the priming effect (hyperpriming) for semantic categories with few distinctive properties and many shared ones—animals (see Giffard et al., 2001, 2002; for similar arguments regarding the increased similarity between prime and target related to AD and its relationship with hyperpriming; see also Zannino et al., 2006 for related arguments concerning the role the similarity between concepts in semantic deficits). Interestingly, the same damage may have a different effect for concepts that have many distinctive properties but few shared ones. For these items, damage to distinctive properties would hamper the recognition of the prime (to a larger extent than in the case of living things). Furthermore, given that such items only have a few properties shared with other items of the same category, semantic priming would be reduced. For example, if the properties “blade” and “cut” are damaged then the prime “knife” may not exert a priming effect on the target “axe” as the few shared properties between the

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two items may not be enough for the effect to arise. Thus, we would expect the magnitude of the semantic priming effect to be abnormally reduced (hypopriming) for the members of this category—artifacts. Note however, that these predictions depend on the precise theory that one adopts. For example, if we follow Devlin et al. (1998), hypopriming for artifacts is expected, given that the less robust properties are the distinctive ones. In contrast, according to Durrant-Peatfield et al. (1997) and Tyler et al. (2000), given that distinctive properties of artifacts are supposed be very robust to damage, we should observe a normal priming effect for artifacts. As advanced, we tested two different groups of AD patients: (a) patients with very mild semantic memory impairment, and (b) patients with a moderate impairment of the semantic system. To classify the patients into the two groups we assessed their semantic memory impairment by means of a questionnaire. 2. Methods 2.1. Subjects Thirty-six patients diagnosed with mild to moderate AD (19 females and 17 males; mean age = 72.3 years, S.D. = 8.9; mean years of education = 6.5, S.D. = 4), and 21 control participants (12 females and 9 males; mean age = 70.7 years, S.D. = 6.1; mean years of education = 7.5, S.D. = 3.3) participated in this study. The mean scores on the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) were 23 for the AD group (S.D. = 3.8), and 28 (S.D. = 1.7) for the control group. All patients met the clinical criteria established by the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (McKhann et al., 1984) for probable Alzheimer’s disease. Their medical history, neurological examination, brain imaging and laboratory tests provided assurance that their dementia symptoms cannot be attributed to an illness other than AD. All patients suffered from mild dementia [Global Dementia Stating (Reisberg, Ferris, de Leon, & Crook, 1982), GDS = 4], except for two patients in the moderate group who were in a moderate stage (GDS = 5). The clinical profile of the patients was mainly characterized by episodic memory impairment and temporal disorientation. In addition to these disorders, other cognitive dysfunctions were present differently across patients (e.g., executive dysfunction). Control subjects were recruited from among the patients’ relatives and in clubs for retired people. Any with a history of neurological or psychiatric disorders or who obtained a score of less than 24 in the Mini-Mental State Examination (MMSE) were excluded. Next we first describe the questionnaire and the criteria used to classify the patients in the two groups and the methodology used in the experimental investigation on semantic priming.

2.2. Description of the semantic memory questionnaire A Spanish version of the verbal questionnaire developed by Laiacona, Barbarotto, Trivelli, and Capitani (1993a) to assess semantic memory was administered to all participants. The questionnaire consisted of 30 items for living things (10 animals, 10 fruit, and 10 vegetables) and 30 items for artifacts (10 tools, 10 furniture, and 10 vehicles) from Snodgrass and Vanderwart’s set (Snodgrass & Vanderwart, 1980). For each item (e.g., elephant) there were six questions in a forced-choice format with three alternatives. These questions requested different types of information (shared, distinctive perceptual, and distinctive functional). Each correct response counted as 1 point leading to a maximum score of 360 points. 2.2.1. Type of semantic information requested in the questionnaire Two of the questions requested superordinate information: one of them concerning semantic category (e.g., is it an animal, a plant or an object?) and the other

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intra-category type of concept (e.g., is it a tree a vegetable or a fruit?). Two other questions requested subordinate perceptual information (e.g., is it round, oblong or conical?), one of them confronting two items of the same semantic category (e.g., is a horse bigger than a dog?). The last two questions requested subordinate functional information including no perceptual property: one of them concerning information about the use of an object (e.g., Does a cow give milk, eggs, or wool?), and the other concerning encyclopaedic information (e.g., Does a cow live in the farm, in the granary, or in the forest?). In order to keep the same terminology we used in the introduction when describing the existing theories about the pattern of category-specific semantic deficits in AD we renamed the type of questions as follows: superordinate questions (including both semantic category and intra-category) were renamed as “shared” questions, subordinate perceptual questions (including both multiple choice and confronting) were renamed as “distinctive perceptual”; and the subordinate functional (including both about the use and concerning encyclopaedic information) were renamed as “distinctive functional”. 2.2.2. Intrinsic properties of items Previous studies have shown that scores for living things tend to be lower than for artifacts. This difference has often been attributed to an imbalance in the values that some variables take for these different categories, living things usually being less frequent, familiar, and prototypical (see Barbarotto, Capitani, & Laiacona, 1996; Capitani, Laiacona, & Barbarotto, 1993; Laiacona, Barbarotto, Trivelli, & Capitani, 1993b). In order to examine whether this bias in the materials was also present in our questionnaire, frequency ratings of the stimulus words in the Spanish lexicon were obtained from Sebasti´an-Gall´es, Cuetos, Mart´ı, and Carreiras (2000); familiarity ratings were obtained by asking 22 elderly healthy participants to rate how usual the concept of the given item was in their realm of experience on a 5-point scale (5 = very familiar); prototypicality ratings of the stimulus were obtained from Battig and Montague (1969); and the level of difficulty of the questions were obtained by asking 10 young, well-educated participants to rate the difficulty of each question on a 5-point scale (5 = very difficult). Living things were found to be less frequent (living things mean: 4.59, S.D.: 3.5; artifacts mean: 26.89, S.D.: 42,25; t(358) = 7.06, P = .0001;), less familiar (living things mean: 4.49, S.D.: 0.75; artifacts mean: 6.04, S.D.: 0.92; t(358) = 17.57, P = .0001) and less prototypical (living things mean: 127.97, S.D.: 128.16; artifacts mean: 181.6, S.D.: 144.43; t(358) = 3.73, P = .0001) than those of artifacts. Also, questions about living things were more difficult than those about artifacts (living things mean: 1.69, S.D.: 0.6; artifacts mean: 1.45, S.D.: 0.44; t(358) = 4.26, P = .0001). We also found that distinctive functional questions were more difficult than the distinctive perceptual ones (functional mean: 1.94, S.D.: 0.61; perceptive mean: 1.57, S.D.: 0.34; t(238) = 5.78, P = .0001). Finally, both distinctive functional and distinctive perceptual questions were more difficult than shared questions (shared mean: 1.21, S.D.: 0.32; distinctive functional versus shared: t(238) = 11.57, P = .0001; distinctive perceptual versus shared: t(238) = 8.4, P = .04).

2.3. Criteria to classify patients in two experimental groups The criteria to classify the patients in one of the two experimental groups were the following: a) those patients with a total score not lower than 2 S.D. relative to control participants (mean = 343.8, S.D. = 7) were included in the mild AD group (mean = 337.6, S.D. = 3), and b) those patients with a total score lower than 3 S.D. relative to control participants were included in the moderate AD group (mean = 305, S.D. = 19.2). The two groups of AD patients each included 18 participants (mild: 11 females and 7 males; mean age = 71.7 years, S.D. = 9; mean years of education = 6.6, S.D. = 4; mean score on the MMSE = 24, S.D. = 3.1; and the moderate: 8 females and 10 males; mean age = 73.2 years, S.D. = 9; mean years of education = 6.4, S.D. = 4; mean score on the MMSE = 22, S.D. = 4.3). There were no differences in age or years of education between the three groups. Note that the classification criteria refer to the degree of semantic memory impairment and do not take into account participants’ scores in the MMSE. So, when referring to moderate and mild AD groups we refer to their semantic memory impairment and not to their global cognitive impairment produced by AD

and measured by MMSE. In fact, the difference between the two groups in their global cognitive impairment (as measured by the MMSE) was not significant (P < .10), suggesting that the two groups were relatively comparable in their cognitive decline.

2.4. Description of the semantic priming task In this experiment, the participants’ task is to decide whether the target is an existing Spanish word (lion) or not (lioc)—a lexical decision task. 2.4.1. Materials Sixty words (30 denoting animals and 30 denoting artifacts) were used as targets. For each target word a semantically related (co-ordinate) word and an unrelated word were used as primes. The primes (e.g., tiger) that served as related for a target of a given semantic category (e.g., lion), served also as unrelated primes for a target of the other semantic category (e.g., hammer). Thus, the same primes were used in the two semantic categories (once as a related and once as an unrelated prime), and the same targets were used in the related and unrelated conditions. Targets and primes in all conditions were comparable in frequency, familiarity and length (see Appendix A). To prevent effects of the participant’s expectancy about the nature of the target in a given trial, the number of related trials was reduced, by including 90 unrelated word pairs that served as fillers. This led to 150 trials in which the target was a word (60 experimental trials + 90 filler trials), therefore requiring a positive response. Another 150 trials in which the target was a non-word were created (the non-words were all pronounceable and conformed to the phonotactics of Spanish). The primes for these non-word targets were also words. Thus, in total, each participant was presented with 300 trials: (a) 60 experimental trials that required a positive response (30 semantically related and 30 semantically unrelated), (b) 90 filler trials that required a positive response (semantically unrelated pairs), and (c) 150 trials that had a non-word as a target, and therefore required a negative response. The experiment was administered in two different sessions 2 days apart, each session containing two blocks. Targets and primes were not presented twice in the same session. Blocks lasted 5 min each and had 75 trials. The distribution of the pairs (animal/artifact, related/unrelated and word/non-word) was balanced across blocks. There were six different lists of stimuli assigned in a pseudorandom fashion from subject to subject. In each list the order in which the trials were presented was randomized following these restrictions: (a) to avoid a response bias related to context no more than five trials requiring the same response occurred in a row, (b) experimental trials were never placed at the beginning of a list to allow participants to warm up, and (c) a semantically related pair was always followed by an unrelated pair to reduce the development of strategies and also to avoid possible interfering effects of residual activation from the previous semantically related trial. 2.4.2. Procedure The experiment was run individually and controlled by the software DMDX (Forster & Forster, 2003). Each trial had the following structure. First, a fixation point was presented for 500 ms and followed by a prime word for 200 ms. Second, after a blank interval of 50 ms, the target stimulus appeared and remained visible until a response was given or after a deadline of 5000 ms. All stimuli were presented on the center of the computer screen in Arial font (size 36). The experimenter controlled the beginning of each trial by pressing a button on the computer keyboard. Participants were asked to press the ‘yes’ key with their dominant hand (the right hand for all the participants) if the target corresponded to a Spanish word, and to press the ‘no’ key with their other hand if the target did not correspond to a Spanish word. The ‘yes’ key was always placed to the right of the participant and the ‘no’ key to the left. Participants were instructed to respond as fast as possible. In order to familiarize participants with the task, a list of 30 practice trials (unrelated word pairs and word/non-word pairs) was administered before the experiment proper. 2.4.3. Data analyses Trials with reaction times (RTs) longer than 3S.D. from each participant’s RT mean were excluded from the analyses (controls: 1.51%, mild: 2.7%, moderate:

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Table 1 Panel A: Mean (and S.D.) scores obtained by controls and AD patients in the questionnaire broken down by type of question (shared, distinctive perceptual and distinctive functional) for both living things and artifacts. The maximum score for each cell is 60. Panel B: Total mean (and S.D.) scores obtained by controls and AD patients for each type of question (shared, distinctive perceptual, distinctive functional, living things and artifacts), and total score obtained in the questionnaire Panel A

Type of question Living things

Group Control Mild Moderate

Shared 58.8 (1.4) 57.4 (1.8) 51.4 (5.2)

Panel B

Totals

Group

Shared

Control Mild Moderate

118.5 (1.7) 117.2 (2.1) 108 (8.2)

Artifacts Perceptual 57 (1.7) 54.5 (2.2) 49.5 (3.7)

Functional

Shared

Perceptual

Functional

53.1 (2.3) 52.7 (1.6) 45.1 (5.2)

59.7 (0.6) 59.8 (0.6) 56.6 (3.3)

57.3 (2) 56.9 (2.1) 52.1 (3.3)

57.9 (1.9) 56.3 (2) 50.3 (4.5)

Perceptual

Functional

Living things

Artifacts

Total score

114.3 (3.1) 111.4 (2.8) 101.6 (5.7)

111 (3.5) 109 (2.2) 95.4 (7.9)

168.9 (4.1) 164.6 (2.9) 146 (11.3)

174.9 (3.3) 172.9 (3.3) 159 (9.3)

343.8 (7) 337.6 (3) 305 (19.2)

2.7%). Errors and RTs were submitted to an ANOVA with ‘Group of Participants (control, mild, moderate)’ as between-participants factor, and with ‘Condition (related, unrelated) and ‘Category (animal, artifact)’ as within-subject factors. In a subsequent analysis, we assessed the magnitude of the priming effects taking into consideration the differences in general RTs between the groups. That is, we estimated the magnitude of the priming effect for each participant in each category by subtracting the RTs in the unrelated condition from the related condition and dividing the result by the average RT in the unrelated condition. This approach reduces the impact of the overall RT differences between groups in the detection of differences in the magnitude of the priming effects (Burke, White, & Diaz, 1987). The resulting percentages were submitted to an ANOVA with ‘Group of Participants (control, mild, moderate)’ as a between-participants factor, and with ‘Category (animal, artifact)’ as a within-subject factor.

3. Results 3.1. Semantic memory questionnaire Control participants had a higher score than AD patients for both semantic categories (living things: controls mean = 168.9, S.D. = 4.1; AD mean = 155.3, S.D. = 12.6; F(1, 55) = 22.78, M.S.E. = 105.9, P = .0001); artifacts: controls mean = 174.9, S.D. = 3.3; AD mean = 165,9, S.D. = 9.9; F (1, 55) = 16.76, M.S.E. = 66.6, P = .0001). Also, they scored higher in all types of questions (shared: controls mean = 118.5, S.D. = 1.7; AD mean = 112.5, S.D. = 7,4; F (1, 55) = 12.87, M.S.E. = 36.48, P = .001; distinctive perceptual: controls mean = 114.3, S.D. = 3.1; AD mean = 106.5, S.D. = 6.7; F (1, 55) = 25.34, M.S.E. = 31.88, P = .0001; distinctive functional: controls mean = 111, S.D. = 3.5; AD mean = 102.2, S.D. = 8.9; F(1, 55) = 18.95, M.S.E. = 55.2, P = .0001). The difference in the mean scores between the mild and the control groups was significant, as it was between the mild and moderate groups. Thus, these criteria confirmed that patients were classified in two well-defined and non-overlapping groups in terms of their semantic memory impairment. Note also the presence of a significant positive correlation (r = 0.4; P = .02) between the total score of the questionnaire and the MMSE for AD patients, indicating that the performance in the questionnaire is related to the amount of global cognitive impairment.

Besides the difference in the total score between the AD groups, the pattern of responses for the two groups was very similar (see Table 1). Both groups exhibited a lower performance in questions related to: (a) living things vs. artifacts (mild: F (1, 17) = 52.68, M.S.E. = 14.88, P = .0001; moderate: F (1, 17) = 38.24, M.S.E. = 42.18, P = .0001), (b) distinctive properties vs. shared ones (mild: F (1, 17) = 27853.67, M.S.E. = 4.01, P = .0001; moderate: F (1, 17) = 5741.45, M.S.E. = 16.18, P = .0001), and (c) distinctive functional properties vs. perceptual ones (mild: F (1, 17) = 8.02, M.S.E. = 7.01, P = .012; moderate: F (1, 17) = 17.65, M.S.E. = 19.74, P = .001). However, this pattern is essentially the same one as that observed in the control group (see Table 1): living things vs. artifacts: F (1, 20) = 140.05, M.S.E. = 3.72, P = .0001); shared vs. distinctive: F (1, 20) = 7794.66, M.S.E. = 15.39, P = .0001; distinctive perceptual vs. distinctive functional: F (1, 20) = 28.65, M.S.E. = 3.96, P = .0001). This similarity between the pattern of results across participants reflects the presence of an imbalance in the difficulty of the questions included in the questionnaire (see Funnell & Sheridan, 1992; Laiacona et al., 1993a, 1993b; Stewart et al., 1992; Tippett et al., 1996, for similar arguments; Section 2.2.2. for experimental support for this claim). Note that the fact that the difference between categories was larger for the moderate group than for the other two groups (F (2, 54) = 6.93, M.S.E. = 5.76, P = .002) is difficult to interpret. This is because a slight imbalance in the difficulty of the questions that leads to a poorer performance with animals than with artifacts for controls, may lead to a more pronounced difference between categories for patients. 3.1.1. Multiple regression analyses of group performances As previously described in Section 2.2.2, living things were at a disadvantage in the values of the variables word frequency, familiarity, prototypicality and question difficulty (assessed by a different group of participants). To assess whether the difference in these values may account for the lower performance in living things we conducted a step-wise regression analysis. In this analysis conducted for each group independently, we entered

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as the dependent variable the average of each question for each item, and as independent variables: the frequency, familiarity and prototypicality, question difficulty, and category membership. The independent variable that accounted for the largest amount of variance was familiarity for all groups (controls: R2 change = 0.2; F(1, 358) = 90.17, M.S.E. = 0.01, P = .0001; mild: R2 change = 0.27; F (1, 358) = 130.45, M.S.E. = 0.01, P = .0001; moderate: R2 change = 0,3; F (1, 358) = 152.27, M.S.E. = 0.02, P = .0001). For the two subgroups of AD patients, the other variable that contributed to explain the variance in the dependent variable was difficulty of questions (mild: R2 change = 0.03; F (2, 357) = 75.62, M.S.E. = 0.01, P = .0001; moderate: R2 change = 0.05, F (2, 357) = 94.98, M.S.E. = 0.02, P = .0001). Importantly, the independent variable of ‘Category’ did not contribute to explain the variance in the dependent variable. Further logistic regression analyses considering the score of each participant for each question as a dependent variable did not reveal any systematic effect of the variable category. 3.1.2. Logistic regression analyses of single subject’s performance In the following analyses we explore whether the performance of the patients in the different groups was relatively homogeneous. To do so, we carried out logistic regression analysis of the individual participants’ performance. This allowed us to assess whether the group average stems from a homogeneous pattern across participants or from a different potential category effects in different directions that cancel each other out (see Gonnerman et al., 1997; Garrard et al., 1998). In these analyses, the dependent variable was the performance accuracy of each participant on the single items of the

questionnaire. The independent variables were the frequency, familiarity and prototypicality, question difficulty, and category membership. The results of these individual logistic regression analyses revealed that the independent variable of ‘Category’ contributed to the difference between living things and artifacts only in two participants (JG and VM) of the mild AD group (JG: 89% living things correct, 99% artifacts correct, Wald (1) = 6.61, P = .01; VM: 92% living things correct, 94% artifacts correct, Wald (1) = 5.37, P = .02), and in two participants (JF and PC) of the moderate AD group (JF: 63% living things correct, 82% artifacts correct, Wald (1) = 5.87, P = .02; PC: 87% living things correct, 91% artifacts correct, Wald (1) = 5.24, P = .02). For the rest of the participants, their performance in the questionnaire was not predicted by semantic category. Hence, the two groups of AD participants seem to show a homogeneous performance on the questionnaire, performance that is not predicted by semantic category. 3.2. Semantic priming effects across semantic categories in AD No significant main effects nor interactions were observed in the error analysis of experimental trials (see Table 2). In addition, there were no differences between the three groups of participants in the overall percentage of errors (controls: 4.9%, mild AD: 7%, moderate AD: 7.6%; F(2, 54) = 1.4, M.S.E. = 28.45, P = .27), revealing that AD participants understood the instructions and were able to perform the task properly. In the analyses of RTs the main effects of the variables “Condition” and “Group of Participants” were significant (F (1, 54) = 6.31, M.S.E. = 5178.39, P = .015; and F (1,

Table 2 Panel A: Mean (and S.D.) percentage of errors of each group of participants on related and unrelated conditions in the semantic priming task, both for animal and artifact type of target. Panel B: Mean (and S.D.) reaction times of each group of participants on related and unrelated conditions in the semantic priming task, both for animal and artifact type of target. Panel C: Mean (and S.D.) percentage of priming effects broken by semantic category and group of participants Condition

Group of participants Control

Mild

Moderate

Panel A: Errors Related Animal Artifact

1.27 (4.5) 2.22 (6.09)

2.96 (4.7) 2.59 (3.35)

2.96 (6.56) 3.33 (5.24)

Unrelated Animal Artifact

0.32 (1.46) 0.95 (3.19)

1.48 (3.65) 3.7 (5.7)

1.85 (3.83) 2.2 (5.1)

Panel B: RTs Animal Related Unrelated

797 (184) 841 (195)

1116 (409) 1157 (394)

1264 (309) 1339 (349)

Artifact Related Unrelated

808 (186) 852 (200)

1127 (371) 1195 (390)

1337 (396) 1340 (356)

4 (5.1) 5 (5.9)

5.2 (4.6) 0.7 (6.6)

Panel C: % Priming Animal Artifact

5.1 (4.8) 4.8 (5.9)

M. Hern´andez et al. / Neuropsychologia 46 (2008) 935–946

54) = 12.65, M.S.E. = 394418.89, P = .0001, respectively). Also, the variable “Category” was significant (F (1, 54) = 54.59, M.S.E. = 2187.36, P = .0001). Importantly, there was a threeway interaction between these three variables (F (2, 54) = 4.27, M.S.E. = 2799.57, P = .019). When analyzing the effects for each group independently, a significant interaction between “Category” and “Condition” was observed for the moderate AD group (F (1, 17) = 5.32, M.S.E. = 4404.87, P = .03), indicating that the priming effect was larger for the category animals than for the category artifacts. In fact, the semantic priming effect for the category “artifacts” was not significant (3 ms, F(1, 17) < 1). This interaction between “Category” and “Condition” was not present in any of the two other groups (Mild: F(1, 17) = 1.58, M.S.E. = 2145.83, P = .23; and Control; F(1, 20) < 1), indicating that the priming effect for the two categories was of comparable magnitude for these participants. The analyses of the percentage of the priming effect considering the overall RTs (see data analyses) led to the following results. The main effects of ‘Group of Participants’ (F(2, 54) = 1.6, M.S.E. = 27.38, P = .21) and ‘Category’ (F(1, 54) < 1) were not significant. However, there was a marginal interaction between the two variables (F(2, 54) = 2.64, M.S.E. = 33.68, P = .08). When analyzing the results for each group separately, no differences in the magnitude of priming between animals and artifacts for the mild AD group and the Control group (both Fs < 1) were observed. However, and consistent with the results of the previous analyses, for the moderate AD group the percentage of the priming effect was larger for the category animals than for the category artifacts (F(1, 17) = 5.64, M.S.E. = 32.3, P = .03; see Fig. 1). In fact the percentage of priming effect for artifacts was near to 0. A closer look at the results reveals that the magnitude of the semantic priming was similar for all categories and for all

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groups, with one exception: the magnitude of the semantic priming for artifacts in the moderate AD group was significantly lower. Convergent results consistent with the notion that the degree of semantic memory impairment affects the magnitude of the priming effect differentially for animals and artifacts come from the following two observations. First, the total score in the questionnaire for the AD patients correlated positively with magnitude of the semantic priming for artifacts (r = 0.32; P = .05). That is, the more preserved the semantic system (as indexed by the questionnaire) the larger the priming effect for artifacts. This analysis is also important because it minimizes any potential effect that a misclassification of individuals in the two different AD groups may have had in the group analyses. Second, the total score in the questionnaire did not correlate with the magnitude of the semantic priming for animals (r = −0.07; P = .69). That is, priming for animals was not affected by the degree of semantic memory impairment (at least as it is indexed by the questionnaire). Finally, we examined whether those four patients for whom the independent variable of ‘Category’ contributed to predict their performance on the semantic memory questionnaire (see Section 3.1.2) showed a reduced percentage of priming for animals. To do so, we calculated a semantic asymmetry index for each participant by subtracting the percentage of priming obtained for artifacts from that obtained for animals. The cut-off point established was the controls’ mean semantic asymmetry index plus 3S.D. (control’s mean: −.90, S.D.: 89, off-off: 267; Perri et al., 2003). All the four patients obtained a semantic asymmetry index within the normal range (JG: −3, VM: 189, JF: −68, PC: −114). Overall, these results reveal the existence of an abnormally reduced (if any) semantic priming effect for artifacts for AD patients with a moderate semantic memory impairment. Interestingly, this hypopriming for artifacts is observed in the context of a normal magnitude of semantic priming for animals. 4. General discussion

Fig. 1. Magnitude of the priming effect (%) broken by group of participants and by semantic category. Error bars represent standard error. The magnitude of the priming effect was estimated for each participant in each category by subtracting the RTs in the unrelated condition from the related condition and dividing the result by the average RT in the unrelated condition.

The main goal of this research was to assess the presence of category-specific semantic deficits in AD. As discussed in the Introduction, the extent to which the semantic breakdown in AD affects semantic categories differently is still controversial, and consequently further experimental evidence is needed to clarify this issue. With this objective we assessed the pattern of semantic priming effects for two semantic categories (animals and artifacts) and for three different groups of participants: a) a control group, b) a group of patients diagnosed with AD with a mild semantic deficit, and c) a group of patients diagnosed with AD with a moderate semantic deficit. Patients were assigned to the two groups following their responses to a semantic memory questionnaire. The experimental investigation led to several interesting results. First, semantic priming for the category animals was similar for the three groups of participants. Second, semantic priming for the category artifacts was similar for the control and mild AD groups (and actually similar to the category ani-

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M. Hern´andez et al. / Neuropsychologia 46 (2008) 935–946

mals). Third, and more importantly, AD patients with a moderate impairment of the semantic system did not show semantic priming for artifacts. The first two results indicate that semantic priming is not affected at the very early stages of semantic memory breakdown. Thus, to the extent that semantic priming taps into semantic knowledge, we can conclude that such knowledge is not impaired enough to affect the presence of semantic priming in AD patients with very mild effects of their semantic memory. Note that this conclusion needs in principle to be restricted to the classification criterion according to which we assigned the participants to each group. As the criterion was the participants’ scores in a semantic memory questionnaire, the conclusion must be qualified in terms of the semantic memory impairment and not in terms of the standard index of the AD stage. However, it is also worth noting that the average score of the group in the MMSE was 24, indicating that most of the patients were at the very early stages of the disease. More interesting for our purposes here, is the pattern of priming effects for the moderate AD group. These patients showed normal semantic priming for animals but a very reduced one (if any) for artifacts. This hypopriming for artifacts suggests that the breakdown of the semantic system in AD is not homogeneous, but rather it affects different semantic categories to a different degree. This observation supports the presence of categoryspecific semantic deficits in AD. Furthermore, the fact that different semantic categories are disrupted to different degrees seems at odds with the notion that AD patients do not have a semantic memory impairment, but rather that their problems stem from a general decline in working memory or attentional demands. If that were to be the case, such problems should affect all semantic categories similarly. Prior to our investigation, many studies have used the priming technique to study the semantic system of AD patients (Alathari, Ngo, & Dopkins, 2004; Balota & Duchek, 1991; Balota, Watson, Duchek, & Ferraro, 1999; Chertkow, Bub, & Seidenberg, 1989; Chertkow et al., 1994; Giffard et al., 2001, 2002; Margolin, Pate, & Friedrich, 1996; Nebes, Brady, & Huff, 1989; Nebes, Martin, & Horn, 1984; Ober & Shenaut, 1988; Ober, Shenaut, Jagust, & Stillman, 1991; Perri et al., 2003; Salmon, Shimamura, Butters, & Smith, 1988; Silveri et al., 1996). However, to our knowledge, there is only one study that has contrasted the pattern of priming effects across semantic categories in a group of AD patients (Perri et al., 2003). This study found no differences in the magnitude of semantic priming across semantic categories. The inconsistency between this study and ours may be due to important methodological differences. First, unlike our experiment, Perri et al. did not classify the AD patients according to the magnitude of their semantic memory impairment. To do so appears to be crucial, given the differential pattern of results between the mild and the moderate AD groups tested in our study. Thus, the lack of differences between semantic categories in their study might be due to a too heterogeneous sample of AD patients. Second, another important difference between these two studies concerns the materials used to elicit semantic priming. While in our experiment, the semantic relationship used to elicit semantic priming was that of co-ordination, in

Perri et al.’s experiment both co-ordination (e.g., onion-garlic) and association (e.g., pepper-red) relationships were used. As shown by Giffard et al. (2001, 2002) the magnitude of semantic priming in AD patients is not homogeneous for these two different semantic relationships. Hence, a direct comparison of these two studies is problematic, and further research is needed to understand the origin of this contrasting pattern of results. In the Introduction, we derived contrasting predictions about the pattern of semantic priming effects in AD from several theoretical proposals (Devlin et al., 1998; Durrant-Peatfield et al., 1997; Tyler et al., 2000). Our results fit well, in part, with Devlin et al.’s (1998) proposal according to which when the degree of semantic memory impairment is not yet severe, artifacts may be more affected than animals. This prediction is predicated on the following assumptions: (a) artifacts have more distinctive properties than living things, and (b) distinctive properties are most vulnerable to brain damage. The hypopriming observed for artifacts in the moderate AD group is consistent with the combination of these two assumptions, and it is at odds with the prediction that at early stages of semantic memory impairment living things should be more affected than artifacts (DurrantPeatfield et al., 1997; Tyler et al., 2000). Further support for the higher vulnerability of distinctive properties relative to shared properties has been provided by Giffard et al. (2001, 2002). In this study, AD patients that showed more problems in consciously retrieving distinctive properties in comparison to shared ones, revealed a hyperpriming effect. The authors interpreted this effect as revealing that distinctive properties are affected by brain damage first while the shared ones remain unaffected until more advanced stages of semantic memory impairment. As a consequence of damaging distinctive properties, concepts become progressively more similar, leading to hyperpriming. Our results fit well, in part, with Giffard et al.’s (2001, 2002) view. If shared properties are more resistant to damage and animals have more shared properties than artifacts, then we should expect to find a larger priming effect for animals than for artifacts, as we observed for the moderate AD group. However, following this rationale one would have expected to find a difference in the magnitude of priming for animals between the mild and the moderate AD groups, the magnitude of the priming being larger for the moderate AD group. This is because damage to distinctive properties would have increased the similarity between members of this category. Hence, it appears that the distinction between damage to shared and distinctive properties is not enough to capture the whole set of results. A possible way to accommodate the lack of hyperpriming for animals in our study is to assume that shared properties may help the retrieval of less accessible distinctive properties due to brain damage (see Rapp & Caramazza, 1993; for an argument about the increase in the activation threshold of properties of concepts as a consequence of the impairment of the semantic system). For example, the concurrent activation of shared properties may spread to the distinctive ones increasing the probability that these, less available distinctive properties, are accessed. In this scenario, the large amount

M. Hern´andez et al. / Neuropsychologia 46 (2008) 935–946

of shared properties may compensate for moderate damage to the distinctive properties of animals resulting in a normal priming effect for this category, rather than the predicted hyperpriming effect. However, moderate damage to the distinctive properties would have more dramatic effects for artifacts, given that these concepts do not have many shared properties, and consequently the activation of the shared properties cannot help the retrieval of distinctive properties. Admittedly, this is a tentative explanation that needs to be tested in future research. Finally, it is worth pointing out that other studies have come to different conclusions regarding the interaction of semantic deficit and category impairment (e.g., Garrard et al., 1998; Garrard et al., 2001; Zannino et al., 2002). For example, by means of a naming task and a semantic memory questionnaire, Zannino et al. (2002), found that about 20% of their mild to moderate AD patients had greater difficulty with living things while no participant demonstrated the reverse pattern. The origin of this sharp discrepancy between the results may stem from the different techniques used to explore the semantic knowledge of AD patients. To perform the picture naming task participants have to identify a particular concept among possible alternative semantic neighbours. A crucial issue in this context is whether the different semantic categories have similar sizes of semantic neighbourhoods, or in other words, whether living things are more confusable than artifacts. If that were to be the case, we would expect living things to be more affected by a general impairment in the semantic system. This imbalance in the confusability of the different semantic categories will certainly lead to different effects in the priming paradigm than in naming, where participants do not need to isolate a specific concept to perform the lexical decision task. Further research

Target’s semantic category

is needed to clarify the origin of these contrasting patterns of results.

5. Conclusion The results of the present study indicate that category-specific semantic deficits are present in AD, at moderate stages of the semantic memory impairment. In particular, our study reveals that artifacts seem to be especially impaired in comparison to animals. Given the diffuse brain damage associated with AD, we argued that the category-specific semantic deficit in AD is likely to reveal the intrinsic structure of semantic properties across categories and in particular the distribution of different types of semantic properties across them.

Acknowledgments This research was supported by three grants from the Spanish Government (SEJ-2005/Consolider/IMSERSO) and by the McDonnell grant “Bridging Mind Brain and Behavior”. Mireia Hern´andez was supported by a Pre-doctoral fellowship from the Catalan Government. Requests for reprints should be addressed to Albert Costa. The authors are grateful to Bradford Mahon, Iva Ivanova, Anna Leonard Cook for their comments on previous versions of this manuscript.

Appendix A. Stimuli used in the semantic priming experiment

Spanish

English

Prime

Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Animal Artifact Artifact Artifact Artifact Artifact Artifact Artifact

943

Target

Related

Unrelated

Pato Tigre Gorila ´ Aguila Cangrejo Foca Ara˜na Cabra Elefante Abeja Zorro Conejo Cerdo Le´on Caracol Coche Tren Pincel Sierra Alicates Bicicleta Vaso

Pincel Tren Coche Sierra Vaso Bicicleta Tenedor Alicates Silla Mecedora Escoba Cepillo Clavo Cazo Barco Gorila Tigre Pato ´ Aguila Cabra Foca Cangrejo

Cisne Pantera Oso Buitre Langosta Delf´ın Hormiga Oveja Jirafa Mosca Lobo Ardilla Vaca Gato Gusano Cami´on Autob´us L´apiz Hacha Tijeras Pat´ın Taza

Prime

Target

Related

Unrelated

Duck Tigre Gorilla Tagle Crab Seal Spider Goat Elephant Bee Fox Rabbit Pig Lion Snail Car Train Paintbrush Saw Pliers Bicycle Glass

Paintbrush Train Car Saw Glass Bicycle Fork Pliers Chair Rocking chair Broom Brush Nail Saucepan Ship Gorilla Tigre Duck Tagle Goat Seal Crac

Swan Panther Bear Vulture Lobster Dolphin Ant Sheep Giraffe Fly Wolf Squirrel Cow Cat Worm Truck Bus Pensil Axe Scissors Skate Cup

944 Artifact Artifact Artifact Artifact Artifact Artifact Artifact Artifact

M. Hern´andez et al. / Neuropsychologia 46 (2008) 935–946 Tenedor Silla Escoba Cepillo Mecedora Barco Clavo Cazo

Ara˜na Elefante Zorro Conejo Abeja Caracol Cerdo Le´on

Cuchara Taburete Fregona Peine Sill´on Avi´on Tuerca Sart´en

Unpaired t-test indicated that targets in the two categories were matched in word frequency (animals = 8.53, artifacts = 11.57; t(28) = 0.71, P = .48), familiarity (animals = 3.52, artifacts = 4.14; t(28) = 0.51, P = .61;) and length (animals = 5.53, artifacts = 5.93; t(28) = 0.87, P = .39). Unpaired t-test indicated that primes in the two categories were matched in: (a) word frequency (animals = 12.61, artifacts = 5.93; t(28) = 1.39, P = .18), familiarity (animals = 4.69, artifacts = 5.11; t(28) = 0.45, P = .65), and length (animals = 5.56, artifacts = 5.81; t(28) = 0.76, P = .46). Unpaired t-test indicated that semantically related pairs in both categories had a similar association strength (animals = 0.01, artifacts = 0.02; t(28) = 1.11, P = .28; Fern´andez, Diez, Alonso, & Beato, 2004).

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