Recognition of category-related visual stimuli in Parkinson's disease: Before and after pharmacological treatment

Neuropsychologia 45 (2007) 2931–2941

Recognition of category-related visual stimuli in Parkinson’s disease: Before and after pharmacological treatment S. Righi a,∗ , M.P. Viggiano a , M. Paganini b , S. Ramat c , P. Marini c a

Dipartimento di Psicologia, Universit`a degli Studi di Firenze, Via s. Niccol`o 93, 50125 Firenze, Italy b Clinica Neurologica II, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy c Clinica Neurologica I, Dipartimento di Scienze Neurologiche e Psichiatriche, Universit` a degli Studi di Firenze, Azienda Ospedaliero Universitaria Careggi, Firenze, Italy Received 11 October 2006; received in revised form 1 June 2007; accepted 8 June 2007 Available online 16 June 2007

Abstract Visual-sensory dysfunctions and semantic processing impairments are widely reported in Parkinson’s disease (PD) research. The present study investigated the category-specific deficit in object recognition as a function of both the semantic category and spatial frequency content of stimuli. In the first experiment, the role of dopamine in object-recognition processing was assessed by comparing PD drug na¨ıve (PD-DN), PD receiving levodopa treatment (PD-LD), and control subjects. Experiment 2 consisted of a retest session for PD drug na¨ıve subjects after a period of pharmacological treatment. All participants completed an identification task which displayed animals and tools at nine levels of filtering. Each object was revealed in a sequence of frames whereby the object was presented at increasingly less-filtered images up to a complete version of the image. Results indicate an impaired identification pattern for PD-DN subjects solely for animal category stimuli. This differential pharmacological therapy effect was also confirmed at retest (experiment 2). Thus, our data suggest that dopaminergic loss has a specific role in category-specific impairment. Two possible hypotheses are discussed that may account for the defective recognition of semantically different objects in PD. © 2007 Elsevier Ltd. All rights reserved. Keywords: Semantic category; Parkinson’s disease; Object recognition; Spatial filtering

1. Introduction An increasing body of compelling evidence suggests that Parkinson’s disease (PD) patients are affected by a wide range of neuropsychological impairments. Visual–spatial information processing deficits have frequently been reported (CroninGolomb & Amick, 2001; Johnson et al., 2004; Keijsers, Admiraal, Cools, Bloem, & Gielen, 2005; for a review see Antal, Bandini, Keri, & Bodis-Wollner, 1998; Bodis-Wollner, 2003). Specifically, PD might differentially affect processes leading to object recognition, ranging from physical input analysis and single-feature binding to complete stimuli identification (Amick, Cronin-Golomb, & Gilmore, 2003; Amick, Schendan, Ganis, & Cronin-Golomb, 2006; Bodis-Wollner, 1990; Crevits & De Ridder, 1997; Davidsdottir, Cronin-Golomb, & Lee,

∗

Corresponding author. Tel.: +39 055 2491628. E-mail address: [email protected] (S. Righi).

0028-3932/$ – see front matter © 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2007.06.002

2005; Diederich, Raman, Leurgans, & Goetz, 2002; Flowers & Robertson, 1995; Laatu, Revonsuo, Pihko, Portin, & Rinne, 2004; Pieri, Diederich, Raman, & Goetz, 2000). Specific loss of contrast sensitivity for high and medium spatial frequencies has frequently been reported in early visual-sensory processing (Bodis-Wollner, 1990; Bodis-Wollner & Onofrj, 1987; Bulens, Meerwaldt, van der Wildt, & Keemink, 1986; Diederich et al., 2002; Flowers & Robertson, 1995; Harris, Atkinson, Lee, Nithi, & Fowler, 2003; Harris, Calvert, & Phillipson, 1992; Kupersmith, Shakin, Siegel, & Lieberman, 1982; Masson, Mestre, & Blin, 1993; Mestre, Blin, Serratrice, & Pailhous, 1990). Moreover, higher level visual–perceptual impairment on several visual tasks has also been observed. PD seems to particularly affect the process that leads to the integration of visual information into a perceptual whole (configural processing), as has emerged from visual closure tasks (Cousins, Hanley, Davies, Turnbull, & Playfer, 2000), embedded figure tests (Flowers & Robertson, 1995) and tasks requiring the discrimination of familiar objects from a corresponding scrambled stimuli (object

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detection tasks) (Laatu et al., 2004). Consistent with configural processing impairment results, a growing body of literature has reported face recognition deficits (Cousins et al., 2000; Dewick, Hanley, Davies, Playfer, & Turnbull, 1991; Haeske-Dewick, 1996; Hovestadt, De Jong, & Meerwaldt, 1987). Within object-recognition studies the semantic nature of the stimuli has not been extensively investigated. As well known (Caramazza & Shelton, 1998; Forde & Humphreys, 2002; Chao, Haxby, & Martin, 1999; Paz-Caballero, Cuetos, & Dobarro, 2006; Warrington & Shallice, 1984), the recognition is influenced by the semantic nature of the stimuli and a remarkable main dissociation between living and non-living things has been well established. There is a paucity of research on the processing of semantically different visual objects in PD subjects. To date, there are only two studies on this subject which were conducted by Antal, Keri, Kincses et al. (2002), Antal, Keri, Dibo et al. (2002). The electrophysiological procedures employed in these studies revealed a reduced response (N150) for visually presented scenes containing animals compared to those without animals. On the basis of these findings (Antal, Keri, Kincses et al., 2002; Antal, Keri, Dibo et al., 2002) and considering that PD can produce both sensory-visual and cognitive-perceptual impairments, it could be valuable to verify whether semantically different stimuli might be differentially affected by PD. The hypothesis of differential impairment for semantically different stimuli might also be consistent with recent categorization theories and relative empirical data. According to the perceptual–functional theory (Warrington & Shallice, 1984), identification of living stimuli (which is predominantly constrained by perceptual features such as shape, color, texture, etc.) might be more difficult for PD patients compared to that of non-living objects (which predominantly relies on the functional attributes of the object such as its use, the context, etc.). Anatomical and functional dissociations in the processing of objects from different semantic categories have been wellestablished in neuropsychological (Caramazza & Mahon, 2003; Caramazza & Shelton, 1998; De Renzi & Lucchelli, 1994; Forde & Humphreys, 2002; Hillis & Caramazza, 1991; Moss & Tyler, 2000; Sacchett & Humphreys, 1992; Warrington & Shallice, 1984), electrophysiological (Paz-Caballero et al., 2006; Sim & Kiefer, 2005; Sitnikova, West, Kuperberg, & Holcomb, 2006; West & Holcomb, 2002) and neuroimaging studies (Chao, Haxby, & Martin, 1999; Damasio, Grabowski, Tranel, Hichwa, & Damasio, 1996; Martin, Wiggs, Ungerleider, & Haxby, 1996; Okada et al., 2000; Perani et al., 1995). The aim of the present work is to investigate whether PD affects the visual recognition of semantically different objects (living and non-living). Specifically we propose to verify, whether (and if so, to what extent) the spatial-frequency content of the stimuli (bottom up variable) plays a differential role in the recognition of objects from different semantic categories (top-down variable). The differential weight of visual-sensory variables, such as spatial frequency content, upon identification of living and non-living objects has recently been reported for young and elderly subjects (Vannucci, Viggiano, & Argenti, 2001; Viggiano, Costantini, Vannucci, & Righi, 2004; Viggiano, Vannucci, & Righi, 2004; Viggiano, Righi, & Galli, 2006).

Specifically, consistent with the sensory-functional hypothesis (Warrington & Shallice, 1984), the present study investigates whether the visual-sensory impairment of PD patients produces any deterioration in the processing of living objects (since their recognition requires increased visual–perceptual information). Another interesting issue concerns whether or not a defective recognition process for living objects is only present in PD drug na¨ıve patients (and not in PD patients under pharmacological treatment) since (a) dopaminergic therapy can improve visual-sensory processing (Hunt, Sadun, & Bassi, 1995; Hutton & Morris, 2001) and (b) dopamine may have a specific role in the modulation of signal-to-noise ratio for salient perceptual inputs (Boussaoud & Kermadi, 1997; Horvitz, 2002; Kawagoe, Takikawa, & Hikosaka, 1998; Nicola, Surmeier, & Malenka, 2000). To test our hypothesis, stimuli from two main semantic categories (living: animals; non-living: tools) at different levels of spatial filtering were presented using a coarse-to-fine order procedure, which gradually integrated spatial frequency information (from the most blurred image to the complete figure). Moreover, we assessed both PD drug na¨ıve and PD under pharmacological treatment patients to clarify the effect of dopamine deficiency on the semantic impairment. 2. Experiment 1 2.1. Method 2.1.1. Participants Two groups of idiopathic, non-demented, PD patients and a control group were assessed. The groups comprised: 20 PD drug na¨ıve patients (PD-DN) (12 males, 8 females), 15 PD patients under treatment with L-DOPA (PD-LD) (9 males, 6 females) and 50 elderly healthy volunteers (24 males, 26 females). Demographical and disease-related characteristics of the sample are reported in Table 1. Each patient was evaluated in two distinct, approximately 1-h long, assessment sessions. There was 1 week between the sessions. In the first session clinical (UPDRS III, Fahn & Elton, 1987; Hoehn–Yahr Scale, Hoehn & Yahr, 1967) and neuropsychological (Mini Mental Status Examination, MMSE; Folstein, Folstein, & McHugh, 1975) data were collected. A standard contrast sensitivity test chart (Nomura, Ando, Niino, Shimokata, & Miyake, 2003) was also administered. In the second evaluation session subjects were administered the computerized testing which is described in Sections 2.1.2 and 3.1.2. All the comparisions of demographic and clinical data were performed by using t-test assuming independent samples. There were no differences between controls and PD patients, for age, education level and Mini Mental Status Examination (MMSE) scores (p = n.s.). All subjects were right-handed and had a normal or corrected to normal vision. The PD-DN and the PD-LD groups did not differ on the age of onset of the disease, the UPDRS III score and Hoehn–Yahr Scale score (p = n.s.). The duration of the disease (months) was greater in PD-LD subjects than in PD-DN patients (t = 4.04, d.f. = 33, p < 0.001). Informed written consent was obtained from all participants. 2.1.2. Stimuli and procedure Contrast sensitivity was measured using a standard contrast sensitivity test chart (Nomura et al., 2003) for different sine-wave gratings that varied in spatial frequency (from 3 to 18 cycles/deg), contrast (from 5 to 100%), and orientation (vertical or tilted ±15◦ from vertical). Subjects were individually requested to indicate the orientation of the bars. The final reliable contrast value was adopted as the contrast sensitivity for each spatial frequency. Stimuli consisted of photographs of real life objects (36 animals and 30 tools), taken from a standardized set of pictures (Viggiano, Costantini et al.,

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Table 1 The mean (M) and the standard deviation (S.D.) of the demographical and disease-related characteristics of the samples PD-DN (N = 20)

Age (years) Education (years) Duration of disease (months) Onset of disease (age) L-DOPA therapy duration (months) Dose of L-DOPA die (mg) Hoehn–Yahr stage UPDRS III MMSE

PD-LD (N = 15)

Controls (N = 50)

M

S.D.

M

S.D.

M

S.D.

64.8 8.9 29.4 62.1

9.8 4.3 18.4 10.9

9.9 3.8

0.4 6.6 1.6

8.1 3.5 46.1 8.2 26.0 231.2 0.5 12.4 1.1

66.7 9.2

1.3 12.7 28.3

68.5 7.2 67.2 63.1 45.6 569.0 1.6 15.7 28.3

28.4

1.39

Fig. 1. (a) Spectrum amplitude of vertical and horizontal spatial frequencies (SF) of an animal and a tool. (b) Amplitude frequency response of the lowest and the highest resolution filters.

2004; Viggiano, Vannucci et al., 2004). Stimuli were presented at nine different levels of spatial filtering following a coarse-to-fine order that gradually integrated spatial information. The stimuli were blurred by removing the spatial frequency ranges from the spectrum of the image (Fig. 1a). This filtering process created a multi-resolution representation of the scanned images. Given an image resolution of 300 dpi, the maximum reproducible image frequency (fmax ), is equal to 150 cycles/in. Hence, to get the multi-resolution representation, we filtered the original image using eight different filters characterized by different bandwidths in the range (0 − fmax ). Fig. 1b shows the frequency response of the lowest and highest resolution filters, respectively. The plot is normalized to fmax (e.g., a frequency of 0.5 stands for 0.5fmax ). Two stimuli were presented in the training session. Each stimulus was presented in the canonical orientation (Viggiano & Vannucci, 2002) on a CRT which was computer controlled. The images had a similar mean size and mean luminance. Images were exposed for 200 ms with a variable ISI ranging from 1 to 2 s. On average the picture subtended a visual angle of 7.5◦ high by 7.5◦ wide. Each picture was presented in an ascending sequence of nine levels of filtering, starting from the most blurred (level 1), and adding new ranges of High

Spatial Frequencies (HSFs), through to the origin resolution version (level 9). Each stimulus was displayed at all the nine resolution levels regardless of the level at which it was actually identified (Viggiano & Kutas, 1998, 2000) (Fig. 2). The presentation order of the stimuli was randomized for each participant. Eye movements were recorded using a web cam. Subjects were asked to press one of two buttons (located in the sagittal plane). They were asked to press the first if they identified the object (“Yes, I identify it”) and the other if they did not (“No, I don’t identify it”). After the identification response, the stimuli stream was stopped and subjects were asked to name the stimulus. Feedback on the correctness of the name was always given (“right” or “wrong”). Non-recognized or incorrectly named pictures, which did not correspond to the dominant or non-dominant names of the given picture (Viggiano, Costantini et al., 2004; Viggiano, Vannucci et al., 2004), were considered to be errors.1 The subject was told that the response was “wrong” and the stimulus stream was re-run from the

1 For example if the dominant name of the animal picture was “crayfish”, the non-dominant names of “shrimp” or “shellfish” or “scampi” were also consid-

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Fig. 2. Example of an ascending sequence of nine filtering levels of two stimuli (scissors and dog). subsequent resolution level so that the subject could try to identify the stimulus again. Responses that agreed with the dominant or non-dominant names of the picture (Viggiano, Costantini et al., 2004; Viggiano, Vannucci et al., 2004) were considered correct and were included in the correct identification analysis. The subject was told that the response was “right” for correct responses and a new stimulus was then administered to the subject. For each category (animals and tools separately) the correct identifications, at each Spatial-Frequency Filtering level, were converted in the percentage of correct identifications applying the formula: (number of correct identification at each Spatial-Frequency Filtering level × 100)/total number of presented pictures. Moreover, for each category (animals and tools separately) the percentage of errors was computed applying the formula: 100 − (percentage of correct identifications at each Spatial-Frequency Filtering level).

the non-parametric Mann–Whitney U-test. Compared to controls PD-DN subjects (Fig. 3) shown a statistically significant decrease in contrast sensitivity for 3 cycles/deg (p < 0.001), 6 cycles/deg (p < 0.006), 9 cycles/deg (p < 0.004), 12 cycles/deg (p < 0.04) and 15 cycles/deg (p < 0.04). Compared to controls PD-LD subjects (Fig. 3) shown a statistically significant decrease in contrast sensitivity for 3 cycles/deg (p < 0.004), 6 cycles/deg (p < 0.03), 9 cycles/deg (p < 0.01) and 15 cycles/deg

2.2. Results 2.2.1. Contrast sensitivity measurement Since the data were from a non-normal distribution (Kolmogorov–Smirnov Goodness-of-Fit Test: lower bound p = 0.200) the contrast sensitivity thresholds for each spatial frequency for the three groups was statistically assessed using

ered as correct, but any other responses were considered as errors. Likewise for tool pictures, if the dominant name of the picture was “pincers”, the nondominant names of “pliers” or “wire cutters” were also considered as correct, but any other responses were considered as errors.

Fig. 3. Percentage of participants who identified correctly the grating orientation at the lower contrast value (K = 5) for various spatial frequencies.

S. Righi et al. / Neuropsychologia 45 (2007) 2931–2941

(p < 0.03) (Fig. 3). Statistically significant differences between PD-DN and PD-LD patients did not emerge. 2.2.2. Identification task 2.2.2.1. Errors. Errors included non-recognized or incorrectly named pictures. A repeated-measure ANOVA was conducted on the percentage of errors with two levels of Category factor (animals and tools) and three levels of Group factor (PD-DN, PD-LD and controls). Results emerged using non-parametric Kruskall–Wallis test were equivalent to those of ANOVA, hence, ANOVA results were described. For animals the mean percentage of errors was 2.88% in controls, 2.55% in PD-DN and 3.24% in PD-LD. For tools mean percentage of errors was 2.00% in controls, 2.89% in PD-DN and 2.25% in PD-LD. The ANOVA did not indicate any significant differences. 2.2.2.2. Correct identifications. A mixed-design ANOVA was conducted on percentage of correct identifications at each level of spatial filtering with three levels of Group factor (PD-DN, PD-LD and controls), two levels of Category factor (animals and tools) and nine levels of Spatial-Frequency Filtering factor (from L1 to L9). To correct violations of the sphericity assumption, inherent in repeated-measure designs with more than two levels, the Greenhouse–Geisser correction was applied and adjusted degrees of freedom rounded to the nearest whole number are reported. Non-parametric Kruskall–Wallis test produced results that were equivalent to those of ANOVA. Hence, ANOVA results were reported. The factor Spatial-Frequency Filtering, was significant F(4,310) = 117.65, p < 0.0001. Planned contrasts for main effects (pairwise comparisons), computed by using the Sidak adjustment for multiple comparisons, showed that the higher percentage of identifications occurred at intermediate levels of filtering, L4 (mean = 23.98%) and L5 (21.16%), which did not differ from each other (p = n.s.). Specifically, L4 had the higher percentage of correct recognitions compared to all the other spatial filtering levels (except L5) (L1: p < 0.0001; L2: p < 0.0001; L3: p < 0.001; L6: p < 0.0001; L7: p < 0.0001; L8: p < 0.0001; L9: p < 0.0001). Moreover, L5 had an higher percentage of correct recognitions compared to all the other spatial filtering levels (L1: p < 0.0001; L2: p < 0.0001; L6: p < 0.0001; L7:

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p < 0.0001; L8: p < 0.0001; L9: p < 0.0001), except L3 (p = n.s.) and L4 (p = n.s.). The interaction Category × Spatial-Frequency Filtering × Group was significant, F(9,375) = 2.63, p < 0.005. To solve this interaction two mixed-design ANOVAs were performed independently for animals and tools, with three levels of Group factor (PD-DN, PD-LD and controls) and nine levels of Spatial-Frequency Filtering factor (from L1 to L9). As shown in Fig. 4, for tools the three groups did not differ in pattern of identification (interaction Spatial-Frequency Filtering × Group, F(8,342) = 0.53, p = n.s.). For animals different pattern of identification emerged (interaction Spatial-Frequency Filtering × Group, F(8,318) = 2.99, p < 0.003). To solve the interaction an Oneway ANOVA has been performed. Differences between groups emerged at L4 [F(2,82) = 4.53, p < 0.014) and at L5 [F(2,82) = 6.71, p < 0.002). The post-hoc comparison performed with Duncan’s test showed that the PD-DN group had a significantly lower percentage of recognitions at L4 compared to PD-LD patients (p < 0.05) and controls (p < 0.05), that not differ from each other. Moreover, at L5, Duncan’s test showed that the PD-DN group, compared to PD-LD group (p < 0.05) and controls (p < 0.05) (that not differ from each other), had an higher percentage of recognitions. This result means that the PD-DN group needed more high spatial-frequency content (the greatest percentage of identifications was at L5) to identify animals with respect to the other two groups, that did not differ from each other and that had the highest percentage of recognition at L4. 2.3. Discussion PD-LD patients and controls had the same pattern of results: the greatest percentage of correct identifications was for both animals and tools at L4 of filtering. These data are consistent with a recent study on a same-age population (Viggiano et al., 2006). It is remarkable that the identification pattern of PD-DN patients differs from others only for animals; the PD-DN subjects needed a greater amount of physical information to correctly identify the animals. This data cannot be attributed to sensory deficits since the two groups of PD patients (PD-LD and PDDN) had the same contrast sensitivity performance. Since the PD patient groups (PD-DN and PD-LD subjects) did not differ on clinical (age of onset of the disease, UPDRS III score,

Fig. 4. Percentage of correct responses for animals and tools at different level of spatial filtering in PD patients (PD-DN, drug na¨ıve patients; PD-LD, under treatment with L-DOPA) and controls.

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Hoehn–Yahr Scale score) or neuropsychological (MMSE) characteristics, but only on the administration of pharmacological therapy, the different recognition performance between PD-DN and PD-LD could be attributed to a pharmacological therapy effect. Moreover, the effect of pharmacological therapy could suggest a specific role of dopaminergic deficits in recognition of semantically different objects, which is consistent with previous research (Antal, Keri, Kincses et al., 2002; Antal, Keri, Dibo et al., 2002). To confirm the role of dopaminergic deficiency in animal identification we re-tested the same PD-DN patients after a period of pharmacological therapy. 3. Experiment 2 3.1. Method 3.1.1. Participants Ten to fifteen months from the first test session (experiment 1), 16 PDDN patients (Re-tested Parkinson Disease under Drug Therapy, RPD-DT) (nine males, seven females) and 16 controls (Re-tested Controls, RC) (eight males, eight females) were re-assessed. At the time of the re-test session all the RPDDT subjects were under pharmacological therapy: eight subjects were under L-DOPA therapy (RPD-LD) (three males, five females) and seven subjects (six males, two females) were under dopaminergic agonist therapy (RPD-DA) (see Table 2 for the demographic and disease-related characteristic of the samples). The same assessment procedure of experiment 1 was used: patients were assessed in two sessions which were 1 week apart. In the first session, medical (UPDRS III and Hoehn–Yahr Scale), neuropsychological (MMSE) and perceptual (contrast sensitivity test chart) evaluations were conducted. In the second session the same computerized testing that was used in experiment 1 was administered. Test and retest session comparisons (performed by using t-test for paired measurements) indicated that, in spite of the drug therapy, the RPD-DT subjects did not differ on the MMSE (p = n.s.), the Hoehn–Yahr Scale score (p = n.s.) nor on the UPDRS III score, (p = n.s.). A possible explanation for the lack of a significant improvement in the UPDRS III (motor) scores is the very mild motor impairment of PD-DN patients in the test session (mean = 11.31) and consequently the lack of a relationship between a clear clinical improvement and a statistically significant improvement in retest session (mean = 9.31). All the comparisions of demographic data were performed by using t-test assuming independent samples. There were no significant differences between RC and RPD subjects for age, education level and MMSE scores (p = n.s.). The RPD-LD and RPD-DA groups did not differ from each other for the age of onset of the disease, disease duration, therapy duration (months), UPDRS III score, Hoehn–Yahr Scale score, and MMSE score (p = n.s.).

3.1.2. Stimuli and procedure The same contrast sensitivity test chart (Nomura et al., 2003) from experiment 1 was used to assess the subjects. The same stimuli, apparatus and procedure from experiment 1 were used.

3.2. Results To determine whether, and to what extent, the different pharmacological therapies (L-DOPA and dopaminergic agonists therapy) have a differential effect on identification performance, a preliminary analysis was conducted on the RPD-DT data. For both RPD-LD and RPD-DA groups, group subject performance in experiment 1 was compared with groups performance in experiment 2. 3.2.1. RPD-LD versus RPD-DA 3.2.1.1. Contrast sensitivity measurement. To exclude “a priori” differences in RPD-LD and RPD-DA groups, the contrast sensitivity at each spatial frequency (3, 6, 9, 12, 15, 18 cycles/deg) of the two groups was statistically assessed by Mann–Whitney U-test both in test (experiment 1) and in retest phase (experiment 2). Statistically significant differences between RPD-LD and RPD-DA did not emerge at test or retest sessions (for each spatial frequency p = n.s.). Two groups (RPDLD and RPD-DA) did not differ in contrast sensitivity thresholds for each spatial frequency (3, 6, 9, 12, 15, 18 cycles/deg) neither at initial testing (i.e. experiment 1) nor following different pharmacological treatments (experiment 2). Therefore we proceeded to compare the contrast sensitivity performances in test (experiment 1) and retest (experiment 2) within each group (RPD-LD and RPD-DA) by using the paired-sample Wilcoxon signed ranks test. Statistically significant differences at each spatial frequencies did not emerge (3, 6, 9, 12, 15, 18 cycles/deg; p = n.s.) for either group. Hence, in both groups (RPD-LD and RPD-DA) the contrast sensitivity thresholds for each spatial frequency (3, 6, 9, 12, 15, 18 cycles/deg) did not change in relation to pharmacological treatment. Overall, the lack of difference in contrast sensitivity between PD groups means that we can not attribute between group differences in errors or correct identifications to differences in basic visual function.

Table 2 The mean (M) and the standard deviation (S.D.) of the demographical and disease-related characteristics of the samples RPD-LD (N = 8)

Age (years) Education (years) Duration of disease (months) Onset of disease (age) Therapy duration (months) Dose of therapy die (mg) Hoehn–Yahr stage UPDRS III MMSE a

RPD-DA (N = 8)

RC (N = 16)

M

S.D.

M

S.D.

M

S.D.

67.0 8.8 45.0 58.4 9.8 306.3 1.4 12.25 28.6

9.8 3.9 17.6 7.6 1.5 99.1 0.4 5.4 1.3

60.8 10.3 37.7 63.3 11.7

7.7 4.3 13.9 12.4 2.8

68.4

8.0

29.1

1.1

a

1.1 10.71 28.8

0.3 4.7 0.57

One subject was under cabergoline (1 mg/die); two under pergolide (M = 3 mg/die; S.D. = 0); four under pramipexole (M = 2.46 mg/die; S.D. = 0.3); one under sumanirole (48 mg/die).

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3.2.1.2. Identification task. Errors. Errors included nonrecognized or incorrectly-named pictures. A repeated-measure ANOVA was conducted on the percentage of errors with two levels of Session factor (test and re-test), two levels of Category factor (animals and tools) and two levels of Group factor (RPDLD and RPD-DA). No significative differences emerged. Using Mann–Whitney non-parametric test no significative differences were observed. Correct identifications. A mixed-design ANOVA was conducted on percentage of correct identification at each level of spatial filtering with two levels of Group factor (RPD-LD and RPD-DA), two levels of Session factor (test and re-test), two levels of Category factor (animals and tools) and nine levels of Spatial-Frequency Filtering factor (from L1 to L9). To correct violations of the sphericity assumption, the Greenhouse–Geisser correction was applied and adjusted degrees of freedom rounded to the nearest whole number are reported. Mann–Whitney U non-parametric test produced results comparable to those of ANOVA. Only ANOVA results were reported. The main effect of Group was not significant, F(1,14) = 0.69; p = n.s. The main effect of Spatial-Frequency Filtering was significant, F(4,50) = 123.50, p < 0.0001; planned contrasts for main effects (pairwise comparisons) computed by using the Sidak adjustment for multiple comparisons, showed that most of identifications occurred at L4 (mean = 28.12%) that significantly differed from all the other levels (L1: p < 0.0001; L2: p < 0.0001; L3: p < 0.0009; L5: p < 0.0001; L6: p < 0.0001; L7: p < 0.0001; L8: p < 0.0001; L9: p < 0.0001). The interaction Session × Spatial-Frequency Filtering was significant, F(3,43) = 18.27, p < 0.001. t-Test showed that the correct recognitions occurred at lower levels of the Spatial-Frequency Filtering variable (L1, t = −3.13, d.f. = 15, p < 0.007; L2, t = −5.29, d.f. = 15, p < 0.001; L3, t = −3.02, d.f. = 15, p < 0.01; L4, t = −4.15, d.f. = 15, p < 0.001) in re-test compared to test session. The interaction Session × Category × Spatial-Frequency Filtering was significant, F(4,61) = 4.53, p < 0.001; t-test for paired-measurements showed that only for animal category correct identifications were higher at lower levels of the Spatial-Frequency Filtering variable (the recognitions occurred previously) in retest respect to test session (L3, t = −5.34, d.f. = 15, p < 0.0001; L4, t = −7.20, d.f. = 15, p < 0.0001). No interaction with Group factor was significant. 3.2.2. RPD-DT versus RC Since RPD-LD and RPD-DA groups did not differ from each other, the whole group of RPD-DT patients has been compared with controls. 3.2.2.1. Contrast sensitivity measurement. Mann–Whitney Utest was used to compare the contrast sensitivity performance for the two groups (RPD-DT and RC). Significant differences did not emerge neither in test nor in retest. Hence, contrast sensitivity threshold for each spatial frequency (3, 6, 9, 12, 15, 18 cycles/deg) did not differ in two groups (RPD-DT and RC). Within each group (RPD-DT and RC) the contrast sensitivity performance in test and retest was compared by using the paired-sample Wilcoxon signed ranks test. Significant differ-

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ences did not emerged at each spatial frequency (3, 6, 9, 12, 15, 18 cycles/deg; p = n.s.) for both groups (RPD-DT and RC). The result suggests that the contrast sensitivity thresholds did not change within each group (RPD-DT and RC) either in test or retest sessions. The lack of difference in contrast sensitivity between PD group and controls means that eventually present differences in performance can not be attributed to differences in basic visual function. 3.2.2.2. Identification task. Errors. Errors included nonrecognized or incorrectly named pictures. A repeated-measure ANOVA was conducted on the percentage of errors with two levels of Session factor (test and re-test), two levels of Category factor (animals and tools) and two levels of Group factor (RPD-DT and RC). No significative differences emerged. The same results emerged using non-parametric procedures (Mann–Whitney U-test). Correct identifications. A mixed-design ANOVA was performed on percentage of correct identifications at each level of spatial filtering with two levels of Group factor (RPD-DT and RC), two levels of Session factor (test and re-test), two levels of Category factor (animals and tools) and nine levels of SpatialFrequency Filtering factor (from L1 to L9). To correct violations of the sphericity assumption, the Greenhouse–Geisser correction was applied and adjusted degrees of freedom rounded to the nearest whole number are reported. The results from nonparametric Mann–Whitney U-test were comparable to those of ANOVA. Hence, only ANOVA results were reported. The main effect of Group was not significant, F(1,30) = 2.92, p = n.s. The main effect of Spatial-Frequency Filtering was significant, F(3,100) = 166.20, p < 0.0001; Planned contrasts for main effects (pairwise comparisons) computed by using the Sidak adjustment for multiple comparisons, showed that the higher percentage of identifications occurred at L4 (mean = 28.20%) that significantly differed from all the other levels (L1: p < 0.0001; L2: p < 0.0001; L3: p < 0.0001; L5: p < 0.0001; L6: p < 0.0001; L7: p < 0.0001; L8: p < 0.0001; L9: p < 0.0001). The interaction Session × Spatial-Frequency Filtering was significant, F(4,117) = 20.71, p < 0.0001; t-test for paired-measurements showed that the recognition occurred at lower Spatial-Frequency Filtering levels (L1, t = −3.92, d.f. = 31, p < 0.0004; L2, t = −7.04, d.f. = 31, p < 0.0001; L3, t = −34.73, d.f. = 31, p < 0.0001; L4, t = −3.24, d.f. = 31, p < 0.002) in re-test compared to test session. The interaction Session × Category × Spatial Frequency Filtering × Group was significant, F(5,136) = 4.69, p < 0.001. To solve this interaction the difference between the percentage of correct responses at retest and test session, respectively, was calculated for each level from 1 to 4. These four values were cumulated to obtain a synthetic index of the increment of performance in re-test session. On this index a mixed-design ANOVA was conducted, with two levels of Category factor (animals and tools) and two levels of Group factor (RPD-DT and RC). Moreover, to clarify whether the contrast sensitivity performance may play a role in recognition performance, the difference between the retest and test contrast sensitivity threshold for each spatial frequencies (3, 6, 9, 12, 15, 18 cycles/deg) was introduced in ANOVA as a Covari-

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S. Righi et al. / Neuropsychologia 45 (2007) 2931–2941

Fig. 5. Percentage of correct responses for animals in test and re-test sessions in: (a) RC, Re-tested Controls; (b) RPD-DT, Re-tested Parkinson Disease under Drug Therapy. The area in grey represents the differences between re-test and test.

ate with six levels (D3, D6, D9, D12, D15, D18). The interaction Category × Group was significant, F(1,24) = 9.75, p < 0.005. For the tools, the RPD-DT group had the same increase of correct responses of the controls. Instead, as shown in Fig. 5, for the animals the RPD-DT patients had at L4 an increment of correct recognition greater than the controls. Since no covariate or interaction with the covariate was significant (all p = n.s.) it is possible to exclude any contrast sensitivity threshold difference effect on recognition performance. Moreover, to clarify whether the improvement in animal recognition in the RPD-DT patients at re-test was related to motor performance, a Pearson’s r correlation was calculated between the synthetic index of animal recognition performance and the difference between the UPDRS III (motor) score from the re-test and test sessions. Since the Pearson’s r correlation was not significant (p = n.s.), it is possible to conclude that animal recognition improvement is independent of motor performance. 3.3. Discussion In the re-test session (experiment 2), recognition performances improved for all subjects for both categories (animals and tools). Most remarkable, the RPD-DT group had a significant improvement in the recognition of animals in the re-test session with respect to controls. In the test session (experiment 1) the RPD-DT patients (at that time PD-DN patients) had a greater percentage of recognition at level 5 of filtering for animals. The same subjects (RPD-DT) recognized the highest percentage of animals at level 4 of filtering in the re-test session, which was the same level where controls and PD-LD patients had the greatest percentage of identification in experiment 1. Therefore, to correctly identify the animals, the RPD-DT patients needed a greater amount of high spatial frequencies in the test session compared to controls and PD-LD subjects, while in the re-test RPD-DT subjects returned to levels comparable to controls. Noteworthy, the improvement in animal recognition in the RPD-DT patients at re-test was not related either to contrast sensitivity threshold effect or motor performance improvement. Since the retest session took place after a period of pharmacological treatment, the RPD-DT improvement could be due to drug therapy, independently of the drug used (L-DOPA or

dopaminergic agonists). Actually the drug therapy overlaps with the progression of the disease. Retesting the PD-LD group (experiment 1) would have been one way to assess the effects of PD progression on recognition performance. However, this was not possible in the present experiment because the dosage of antiparkinsonian therapy is increased over time in order to adequately control clinical symptoms. 4. General discussion The first aim of the present study was to test whether PD produces impairments in the processing of semantically different objects. More specifically, we were interested in investigating whether, and to what extent, (a) the spatial-frequencies content of the stimuli plays a specific role in the category-specific deficits, and (b) the dopamine deficiency contributes to the semantic impairment that is eventually present in PD patients. Our results show defective semantic processing in PD-DN patients only for animal stimuli. Indeed, while the PD-LD patients had a performance comparable to controls for both stimuli categories, the PD-DN patients needed a greater amount of physical information (high spatial frequencies) to correctly identify the animals, but not to recognize the tools (experiment 1). Noteworthy, this category-related impairment seems likely to be associated with dopamine deficiency, since it only emerged in PD-DN patients and was completely recovered by pharmacological treatment (L-Dopa and dopaminergic agonist drugs) that restores dopamine levels. In fact, after a period of pharmacological treatment the PD-DN patients (the RPD-DT subjects in experiment 2) exhibited an animal recognition performance wholly comparable to the controls (RC). Noteworthy, the improvement in animal recognition in the RPD-DT patients at re-test was not related to motor performance improvement. An important issue is that the improvement of RPD-DT in animal identification unlikely may to be attributed to a dopaminergic action at a sensory-physical level (bottom-up process). Indeed, if dopamine improved the information processing at the level of spatial frequency streams, we would expect the performance in animal recognition to proceed at the same rate as that of the spatial frequencies perception. This did not happen, since both PD-LD and PD-DN patients exhibited the same perfor-

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mance in the contrast sensitivity threshold while only the PD-DN subjects needed a greater amount of high spatial frequencies to correctly recognize the animals. Furthermore, the pharmacological treatment produced a recovery in animal recognition but not in contrast sensitivity perception. It is further noteworthy that the contrast sensitivity that was introduced as a covariate in the test-retest analysis did not interact with performance. Above all, our data suggest that dopamine depletion plays a role in the recognition of animal stimuli and acts at higher levels of visual–perceptual and visual–cognitive processing. The neural areas (substantia nigra, cortico-striatal basal ganglia pathway, ventral–tegmental area) affected by dopamine loss in PD might be directly involved in animal processing, as has been suggested by previous studies (Antal, Keri, Kincses et al., 2002; Antal, Keri, Dibo et al., 2002). One possible explanation for this is that the PD-DN patients’ animal recognition impairment could be related to defective analysis and binding of the visual attributes of the stimuli. Such an explanation is consistent with the perceptual–functional theory (Warrington & Shallice, 1984) which states that identification of living objects primarily relies on visual–perceptual features. This hypothesis is supported by evidence indicating that PD can produce impairments in the global configuration analysis (Cousins et al., 2000) and the attentive processing of visual characteristics of the stimuli (Horowitz, Choi, Horvitz, Cote, & Mangels, 2006; Lieb et al., 1999). Furthermore, it has been demonstrated that the dopaminergic neurons of the caudate nucleus are important for the extraction of physical features (Antal et al., 2003; Kropotov & Etlinger, 1999) and the modulation of visual working memory (Kemps, Szmalec, Vandierendonck, & Crevits, 2005; Stoffers, Berendse, Deijen, & Wolters, 2003). Additionally, the possible association between impaired visual–perceptual processing and dopaminergic depletion might also be related to the specific role of dopamine which serves to adjust the information flow through cortico-striatal basal ganglia circuits by modulating the signal-to-noise ratio of sensory information and enhancing signals for salient perceptive inputs (Boussaoud & Kermadi, 1997; Horowitz et al., 2006; Kawagoe et al., 1998; Nicola et al., 2000). Altogether this evidence supports the hypothesis that the dopaminergic neurons of the neostriatum constitute an integrative region for category-relevant features in animal recognition. This hypothesis is further backed by primate studies (Vogels, 1999) that have shown multiple input pathways that converge in neostriatal areas from primary sensory areas (Brown, 1992) and the infero-temporal cortex (IT), a region that neuroimaging studies have firmly implicated as being involved in the recognition of living objects (Chao & Martin, 2000; Chao, Weisberg, & Martin, 2002; Martin et al., 1996). Moreover, considering the rich system of interconnections between the basal ganglia and the prefrontal cortex via the fronto-striatal pathway, it is possible that defective top-down processing could also play a specific role in the animal recognition impairment observed in PD-DN patients. Bar (2003) has suggested that a partially analysed version of the visual inputs (i.e. mainly formed by low spatial frequencies) is very rapidly projected from the primary visual cortex to the prefrontal cortex, where multiple hypotheses (top-down process) about

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the stimuli are activated. This shortcut would be very helpful in the fast recognition of dangerous stimuli for adaptive survival. According to Bar’s suggestion and taking into account the “similar-dissimilar hypothesis” (Humphreys, Riddoch, & Quinlan, 1988), the animal recognition impairment observed in PD-DN patients could be attributed to the presence of multiple competing implicit hypotheses for this semantic category. Indeed, unlike tools, animals constitute a fairly homogeneous stimuli set that share several visual–perceptual features (i.e., had four legs, a head, ears, tail), and are more difficult to recognise as a single exemplar. Consistent with this hypothesis, several recent studies using multi-priming paradigms with verbal stimuli (Angwin, Chenery, Copland, Murdoch, & Silburn, 2005; Arnott, Chenery, Murdoch, & Silburn, 2001), have suggested that the semantic deficits of PD patients are specifically linked to the presence of semantic competitors. Moreover, the role of dopaminergic networks in both (a) the modulation of the semantic activation time course (Seger & Cincotta, 2006) and (b) the selection/suppression of conflicting inputs (Fielding, GeorgiouKaristianis, Millist, & White, 2006; Seiss & Praamstra, 2006; Wylie, Stout, & Bashore, 2005) has been well documented. In addition, the dopaminergic neurons of the dorsolateral prefrontal cortex (DLPFC) and the dorsal striatum (regions affected at an early stage of PD) (Owen, 2004) seem to be the substrates that are most involved in set shifting (Cools, 2006; Ravizza & Ciranni, 2002) and active manipulation of information within memory (Owen, 2004) as well as in top-down representation stability during the categorization tasks (Keri, 2003). In conclusion, our data suggest that dopamine deficiency interferes with the dynamic interplay between sensory/physical and cognitive processes that gives rise to correct object identification. The fact that the PD-DN patients needed more physical information to recognize the living objects (animals) might be explained by two different hypotheses. The deficits can be attributed to both an impaired bottom-up processing of the analysis and integration of the visual–perceptual attributes of the stimuli, or to a defective top-down processing that produces an overload in semantic activation. In other words, the two hypotheses do not contradict each other since the dopaminergic substrates may modulate the flow of visual–perceptual input, thereby enhancing task-relevant signals and inhibiting irrelevant information (Horvitz, 2002; Horowitz et al., 2006) to facilitate the decision process regarding the multiple conflicting responses (Desimone & Duncan, 1995). Further research is required to delineate the extent to which each hypothesis interacts in this equation and clarify the exact nature of the impairment underlying the semantic defect of PD subjects.

References Amick, M. M., Cronin-Golomb, A., & Gilmore, G. C. (2003). Visual processing of rapidly presented stimuli is normalized in Parkinson’s disease when proximal stimulus strength is enhanced. Vision Research, 43, 2827– 2835. Amick, M. M., Schendan, H. E., Ganis, G., & Cronin-Golomb, A. (2006). Fronto-striatal circuits are necessary for visuo-motor transformation: Mental rotation in Parkinson’s disease. Neuropsychologia, 44, 339–349.

2940

S. Righi et al. / Neuropsychologia 45 (2007) 2931–2941

Angwin, A. J., Chenery, H. J., Copland, D. A., Murdoch, B. E., & Silburn, P. A. (2005). Summation of semantic priming and complex sentence comprehension in Parkinson’s disease. Cognitive Brain Research, 25, 78–89. Antal, A., Bandini, F., Keri, S., & Bodis-Wollner, I. (1998). Visuo-cognitive dysfunctions in Parkinson’s disease. Clinical Neuroscience, 5, 147–152. Antal, A., Keri, S., Kincses, T., Kalman, J., Dibo, G., Benedek, G., et al. (2002). Corticostriatal circuitry mediates fast-track visual categorization. Cognitive Brain Research, 13, 53–59. Antal, A., Keri, S., Dibo, G., Benedek, G., Janka, Z., Vecsei, L., et al. (2002). Electrophysiological correlates of visual categorization: Evidence for cognitive dysfunctions in early Parkinson’s disease. Cognitive Brain Research, 13, 153–158. Antal, A., Keri, S., Kincses, T., Dibo, G., Szabo, A., Benedek, G., et al. (2003). Dopaminergic contributions to visual categorization of natural scenes: Evidences from Parkinson’s disease. Journal of Neural Transmission, 110, 757–770. Arnott, W. L., Chenery, H. J., Murdoch, B. E., & Silburn, P. A. (2001). Semantic priming in Parkinson’s disease: Evidence for delayed spreading activation. Journal of Clinical Experimental Neuropsychology, 23, 502–519. Bar, M. (2003). A cortical mechanism for triggering top-down facilitation in visual object recognition. Journal of Cognitive Neuroscience, 154, 600–609. Bodis-Wollner, I. (1990). Visual deficits related to dopamine deficiency in experimental animals and Parkinson’s disease patients. Trends in Neuroscience, 13, 296–302. Bodis-Wollner, I. (2003). Neuropsychological and perceptual defects in Parkinson’s disease. Parkinsonism & Related Disorders, 9, S83–S89. Bodis-Wollner, I., & Onofrj, M. (1987). The visual system in Parkinson’s disease. Advance in Neurology, 45, 323–327. Boussaoud, D., & Kermadi, I. (1997). The primate striatum: Neuronal activity in relation to spatial attention versus motor preparation. European Journal of Neuroscience, 9, 2152–2168. Brown, L. L. (1992). Somatotopic organization in rat striatum: Evidence for a combinational map. Proceeding of National Academy of Science, 89, 7403–7407. Bulens, C., Meerwaldt, J. D., van der Wildt, G. J., & Keemink, C. J. (1986). Contrast sensitivity in Parkinson’s disease. Neurology, 36, 1121–1125. Caramazza, A., & Mahon, B. Z. (2003). The organization of conceptual knowledge: The evidence from category-specific semantic deficits. Trends in Cognitive Science, 7, 354–361. Caramazza, A., & Shelton, J. R. (1998). Domain-specific knowledge systems in the brain the animate-inanimate distinction. Journal of Cognitive Neuroscience, 10, 1–34. Chao, L. L., & Martin, A. (2000). Representation of manipulable man-made objects in the dorsal stream. Neuroimage, 12, 478–484. Chao, L. L., Haxby, J. V., & Martin, A. (1999). Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nature Neuroscience, 10, 913–919. Chao, L. L., Weisberg, J., & Martin, A. (2002). Experience-dependent modulation of category-related cortical activity. Cerebral Cortex, 12, 545–551. Cools, R. (2006). Dopaminergic modulation of cognitive function-implications for L-DOPA treatment in Parkinson’s disease. Neuroscience & Biobehavioral Reviews, 30, 1–23. Cousins, R., Hanley, J. R., Davies, A. D., Turnbull, C. J., & Playfer, J. R. (2000). Understanding memory for faces in Parkinson’s disease: The role of configural processing. Neuropsychologia, 38, 837–847. Crevits, L., & De Ridder, K. (1997). Disturbed striato-prefrontal mediated visual behaviour in moderate to severe parkinsonian patients. Journal of Neurology, Neurosurgery and Psychiatry, 63, 296–299. Cronin-Golomb, A., & Amick, M. (2001). Spatial abilities in aging, Alzheimer’s disease and Parkinson’s disease. In F. Boller & S. Cappa (Eds.), Handbook of neuropsychology (2nd ed., Vol. 6, pp. 119–143). Amsterdam: Elsevier. Damasio, H., Grabowski, T. J., Tranel, D., Hichwa, R. D., & Damasio, A. R. (1996). A neural basis for lexical retrieval. Nature, 380, 499–505. Davidsdottir, S., Cronin-Golomb, A., & Lee, A. (2005). Visual and spatial symptoms in Parkinson’s disease. Vision Research, 45, 1285–1296. De Renzi, E., & Lucchelli, F. (1994). Are semantic systems separately represented in the brain? The case of living category impairment. Cortex, 30, 3–25.

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222. Dewick, H. C., Hanley, J. R., Davies, A. D., Playfer, J., & Turnbull, C. (1991). Perception and memory for faces in Parkinson’s disease. Neuropsychologia, 29, 785–802. Diederich, N. J., Raman, R., Leurgans, S., & Goetz, C. G. (2002). Progressive worsening of spatial and chromatic processing deficits in Parkinson disease. Archives of Neurology, 59, 1249–1252. Fahn, S., & Elton, R. L. (1987). Unified Parkinson’s disease rating scale. In S. Fahn, C. D. Marsden, D. Calne, & M. Goldstein (Eds.), Recent developments in Parkinson’s disease (pp. 153–163). Florham Park, NY: MacMillian Care Information. Fielding, J., Georgiou-Karistianis, N., Millist, L., & White, O. (2006). Temporal variation in the control of goal-directed visuospatial attention in basal ganglia disorders. Neuroscience Research, 54, 57–65. Flowers, K. A., & Robertson, C. (1995). Perceptual abnormalities in Parkinson’s disease: top-down or bottom-up processes? Perception, 24, 1201–1221. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198. Forde, E. M. E., & Humphreys, G. W. (2002). Category specificity in brain and mind. New York: Psychology Press. Haeske-Dewick, H. C. (1996). Are perception and memory for faces influenced by a specific age at onset factor in Parkinson’s disease? Neuropsychologia, 34, 315–320. Harris, J. P., Atkinson, E. A., Lee, A. C., Nithi, K., & Fowler, M. S. (2003). Hemispace differences in the visual perception of size in left hemiParkinson’s disease. Neuropsychologia, 41, 795–807. Harris, J. P., Calvert, J. E., & Phillipson, O. T. (1992). Processing of spatial contrast in peripheral vision in Parkinson’s disease. Brain, 115, 1447–1457. Hillis, A. E., & Caramazza, A. (1991). Category-specific naming and comprehension impairment: A double dissociation. Brain, 114, 2081–2094. Hoehn, M. M., & Yahr, M. D. (1967). Parkinsonism: Onset, progression and mortality. Neurology, 17, 427–442. Horowitz, T. S., Choi, W. Y., Horvitz, J. C., Cote, L. J., & Mangels, J. A. (2006). Visual search deficits in Parkinson’s disease are attenuated by bottom-up target salience and top-down information. Neuropsychologia, 44, 1962–1977. Horvitz, J. C. (2002). Dopamine gating of glutamatergic sensorimotor and incentive motivational input signals to the striatum. Behavioral Brain Research, 137, 65–74. Hovestadt, A., De Jong, G. J., & Meerwaldt, J. D. (1987). Visuospatial impairment in Parkinson’s disease: Does it exist? Journal of Neurology, Neurosurgery and Psychiatry, 50, 1560–1561. Humphreys, G. W., Riddoch, M. J., & Quinlan, P. T. (1988). Cascade processes in picture identification. Cognitive Neuropsychology, 5, 67–104. Hunt, L. A., Sadun, A. A., & Bassi, C. J. (1995). Review of the visual system in Parkinson’s disease. Optometric Vision Science, 72, 92–99. Hutton, J. T., & Morris, J. L. (2001). Vision in Parkinson’s disease. Advance in Neurology, 86, 279–288. Johnson, A. M., Almeida, Q. J., Stough, C., Thompson, J. C., Singarayer, R., & Jog, M. S. (2004). Visual inspection time in Parkinson’s disease: Deficits in early stages of cognitive processing. Neuropsychologia, 42, 577–583. Kawagoe, R., Takikawa, Y., & Hikosaka, O. (1998). Expectation of reward modulates cognitive signals in the basal ganglia. Nature Neuroscience, 1, 411–416. Keijsers, N. L., Admiraal, M. A., Cools, A. R., Bloem, B. R., & Gielen, C. C. (2005). Differential progression of proprioceptive and visual information processing deficits in Parkinson’s disease. European Journal of Neuroscience, 21, 239–248. Kemps, E., Szmalec, A., Vandierendonck, A., & Crevits, L. (2005). Visuo-spatial processing in Parkinson’s disease: Evidence for diminished visuo-spatial sketch pad and central executive resources. Parkinsonism and Related Disorders, 11, 181–186. Keri, S. (2003). The cognitive neuroscience of category learning. Brain Research Review, 43, 85–109. Kropotov, J. D., & Etlinger, S. C. (1999). Selection of actions in the basal ganglia-thalamocortical circuits: Review and model. International Journal of Psychophysiology, 31, 197–217.

S. Righi et al. / Neuropsychologia 45 (2007) 2931–2941 Kupersmith, M. J., Shakin, E., Siegel, I. M., & Lieberman, A. (1982). Visual system abnormalities in patients with Parkinson’s disease. Archives of Neurology, 39, 284–286. Laatu, S., Revonsuo, A., Pihko, L., Portin, R., & Rinne, J. O. (2004). Visual object recognition deficits in early Parkinson’s disease. Parkinsonism and Related Disorders, 10, 227–233. Lieb, K., Brucker, S., Bach, M., Els, T., Lucking, C. H., & Greenlee, M. W. (1999). Impairment in preattentive visual processing in patients with Parkinson’s disease. Brain, 122, 303–313. Martin, A., Wiggs, C. L., Ungerleider, L. G., & Haxby, J. V. (1996). Neural correlates of category-specific knowledge. Nature, 379, 649–652. Masson, G., Mestre, D., & Blin, O. (1993). Dopaminergic modulation of visual sensitivity in man. Fundaments of Clinical Pharmacology, 7, 449–463. Mestre, D., Blin, O., Serratrice, G., & Pailhous, J. (1990). Spatiotemporal contrast sensitivity differs in normal aging and Parkinson’s disease. Neurology, 40, 1710–1714. Moss, H. E., & Tyler, L. K. (2000). A progressive category-specific semantic deficit for non-living things. Neuropsychologia, 38, 60–82. Nicola, S. M., Surmeier, J., & Malenka, R. C. (2000). Dopaminergic modulation of neuronal excitability in the striatum and nucleus accumbens. Annual Review of Neuroscience, 23, 185–215. Nomura, H., Ando, F., Niino, N., Shimokata, H., & Miyake, Y. (2003). Agerelated change in contrast sensitivity among Japanese adults. Japanese Journal of Ophthalmology, 47, 299–303. Okada, T., Tanaka, S., Nakai, T., Nishizawa, S., Inui, T., Sadato, N., et al. (2000). Naming of animals and tools: A functional magnetic resonance imaging study of categorical differences in the human brain areas commonly used for naming visually presented objects. Neuroscience Letters, 296, 33–36. Owen, A. M. (2004). Cognitive dysfunction in Parkinson’s disease: The role of fronto-striatal circuitry. The Neuroscientist, 10, 525–537. Paz-Caballero, D., Cuetos, F., & Dobarro, A. (2006). Electrophysiological evidence for a natural/artefactual dissociation. Brain Research, 1067, 189–200. Perani, D., Cappa, S. F., Bettinardi, V., Bressi, S., Gorno Tampini, M., Matarrese, M., et al. (1995). Different neural systems for the recognition of animals and man-made tools. Neuroreport, 6, 1637–1641. Pieri, V., Diederich, N. J., Raman, R., & Goetz, C. G. (2000). Decreased color discrimination and contrast sensitivity in Parkinson’s disease. Journal of the Neurological Sciences, 172, 7–11. Ravizza, S. M., & Ciranni, M. A. (2002). Contributions of the prefrontal cortex and basal ganglia to set shifting. Journal of Cognitive Neurosciences, 14, 472–483. Sacchett, C., & Humphreys, G. W. (1992). Calling a squirrel a squirrel but a canoe a wigwam: A category-specific deficit for artefactual objects and body parts. Cognitive Neuropsychology, 9, 73–86.

2941

Seger, C. A., & Cincotta, C. M. (2006). Dynamics of frontal, striatal, and hippocampal systems during rule learning. Cerebral Cortex, 16, 1546–1555. Seiss, E., & Praamstra, P. (2006). Time-course of masked response priming and inhibition in Parkinson’s disease. Neuropsychologia, 44, 869–875. Sim, F. J., & Kiefer, M. (2005). Category-related brain activity to natural categories is associated with the retrieval of visual features: Evidence from repetition effects during visual and functional judgments. Cognitive Brain Research, 24, 260–273. Sitnikova, T., West, W. C., Kuperberg, G. R., & Holcomb, P. J. (2006). The neural organization of semantic memory: Electrophysiological activity suggests feature-based segregation. Biological Psychology, 71, 326–340. Stoffers, D., Berendse, H. W., Deijen, J. B., & Wolters, E. Ch. (2003). Deficits on Corsi’s block-tapping task in early stage Parkinson’s disease. Parkinsonism & Related Disorders, 10, 107–111. Vannucci, M., Viggiano, M. P., & Argenti, F. (2001). Identification of spatially filtered stimuli as function of the semantic category. Cognitive Brain Research, 12, 475–478. Viggiano, M. P., & Kutas, M. (1998). The covert interplay between perception and memory: Event-related potential evidence. Electroencephalography and Clinical Neurophysiology, 108, 435–439. Viggiano, M. P., & Kutas, M. (2000). Covert and overt identification of fragmented objects. Journal of Experimental Psychology General, 129, 107– 125. Viggiano, M. P., Costantini, A., Vannucci, M., & Righi, S. (2004). Hemispheric asymmetry for spatially filtered stimuli belonging to different semantic categories. Cognitive Brain Research, 20, 519–524. Viggiano, M. P., Righi, S., & Galli, G. (2006). Category-specificity visual recognition has affected by aging and profession. Archives of Gerontology and Geriatrics, 42, 329–338. Viggiano, M. P., & Vannucci, M. (2002). Drawing and identifying objects in relation to semantic category and handedness. Neuropsychologia, 8, 1482–1487. Viggiano, M. P., Vannucci, M., & Righi, S. (2004). A new standardized set of ecological pictures for experimental and clinical research on visual object processing. Cortex, 40, 491–509. Vogels, R. (1999). Categorization of complex visual images by rhesus monkeys. Part II. Single-cell study. European Journal of Neuroscience, 11, 1239–1255. Warrington, E. K., & Shallice, T. (1984). Category specific semantic impairments. Brain, 107, 829–854. West, W. C., & Holcomb, P. J. (2002). Event-related potentials during discourselevel semantic integration of complex pictures. Cognitive Brain Research, 13, 363–375. Wylie, S. A., Stout, J. C., & Bashore, T. R. (2005). Activation of conflicting responses in Parkinson’s disease: Evidence for degrading and facilitating effects on response time. Neuropsychologia, 7, 1033–1043.