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Task-related temporal and topographical changes of cortical activity during ultra-rapid visual categorization
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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / b r a i n r e s
Research Report
Task-related temporal and topographical changes of cortical activity during ultra-rapid visual categorization Tamás Zsigmond Kincses a,b,c,⁎ , Zoltan Chadaide a,b , Edina T. Varga a,b , Andrea Antal b , Walter Paulus b a
Department of Neurology, University of Szeged, Szeged, Hungary Department of Clinical Neurophysiology, Georg-August University of Göttingen, Göttingen, Germany c FMRIB Centre, Department of Clinical Neurology, University of Oxford, Oxford, UK b
A R T I C LE I N FO
AB S T R A C T
Article history:
The aim of our study was to provide electrophysiological evidence about the modulation of
Accepted 5 July 2006
the categorization process by task requirements in the human brain. Event-related
Available online 22 August 2006
potentials (ERP) were recorded during three different categorization tasks using matched stimulus sets. In all cases, the subjects were required to differentiate between “animal” and
Keywords:
“non-animal” stimuli. In the first task (two-choice task), they were asked to press
Visual categorization
corresponding buttons to each stimulus types. The second task was a go/no-go paradigm,
Natural scene
only animal stimuli required motor response. The third task was a counting task;
Event-related potential
participants had to count the animal stimuli without any motor response. The reaction
go/no-go
times in the go/no-go paradigm were significantly shorter. ERP differences between animal
Human
and non-animal pictures in the go/no-go task also appeared earlier and were localized at
Dipole source fitting
more posterior scalp positions compared to the two-choice task. Comparing animal responses in the two-choice task and in the go/no-go paradigm, we found a significant difference in the 130- to 170-ms time window over the fronto-central, centro-parietal regions. Similar differences were found between the responses to animal pictures in the two-choice task and in the counting paradigm. We used brain electric source analysis (BESA) algorithm on difference waves to localize the best fitting dipoles and determine the localization of brain areas contributing to scalp potential differences. The results show that different task requirements evoke different activity in the medial part of the temporal pole. The data we provided here draw attention to the careful handling of results obtained from categorization experiments, because different task requirements can affect the early categorization process itself. © 2006 Elsevier B.V. All rights reserved.
⁎ Corresponding author. FMRIB Centre, Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK. Fax: +44 1865 222717. E-mail address: [email protected] (T. Zsigmond Kincses). URL: http://www.fmrib.ox.ac.uk/~tkincses (T. Zsigmond Kincses). 0006-8993/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2006.07.044
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1.
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Introduction
The meaningful organization of the surrounding environment, categorization, plays a crucial role in our everyday life. Many neuroimaging and electrophysiological studies have provided evidence for a discrete categorical organization of the brain. In particular, there are specific representations of different categories in the occipito-temporal cortex and surrounding areas, such as faces in the lateral occipito-temporal cortex (LOC) and the fusiform face area (Allison et al., 1994a,b, 1999; Clark et al., 1996; Goffaux et al., 2003; Kanwisher, 2000; Kanwisher et al., 1997; Ojemann et al., 1992; Puce et al., 1996; Schendan et al., 1998), human body representation in the LOC (Downing et al., 2001), animals in the right fusiform cortex (Kawashima et al., 2001), buildings in the right lingual sulcus (Aguirre et al., 1998), man-made tools in the left posterior middle temporal cortex (Moore and Price, 1999) and plants in the right lateral occipital cortex (Kawashima et al., 2001). Although the abovementioned studies provided detailed data about the spatial representation of the different categories in the brain, the temporal dynamics of the categorization process is less well explored. Several studies of the categorization of color pictures of natural scenes reported a robust difference between the event-related potentials (ERP)s evoked by animal and non-animal pictures, developing at 150 ms after the stimulus onset (Thorpe et al., 1996). Later investigations proved that this was not due to an exclusive, ‘hard-wired’ processing of the evolutionary important animal category, because the same kind of potential difference could have been obtained with different supra-ordinal categories also (animals vs. vehicles) (VanRullen et al., 2001). Additionally, Delorme et al. have showed that chromatic information does not play a defining role in the early decision making processes, which point-out the dominant role of the magno-cellular pathway (Delorme et al., 2000). It has been shown that complex natural images can be processed and categorized in parallel without the need for sequential attention (Rousselet et al., 2002). Furthermore, it seems that familiarity or expertise has no effect on this kind of ultra-rapid categorization: 3 weeks of extensive training failed to decrease reaction times (Fabre-Thorpe et al., 2001). Because of the high time constraint stated by the obtained reaction times and the very early electrophysiological differences between responses evoked by pictures containing different categories, the authors suggested that mainly feedforward connections are involved in the underlying neuronal processes. It was found that the early category specific potential differences were unaffected in Alzheimer's disease (AD) despite the clinically severe symptoms and cognitive decline. The results are surprising because it is evident from former studies that in AD visual information processing is impaired due to the pathology of cortico-cortical pathways in the ventral occipito-temporal stream (Hof and Bouras, 1991; Hof et al., 1997; Leuchter et al., 1992). On the other hand, in Parkinson's disease (PD) in which the cortico-striatal pathways are the most affected structures, the early electrophysiological signs of the categorization processes have disappeared, the responses evoked by animal and non-animal pictures were
similar in the given early time window (Antal et al., 2000, 2001, 2002a). On the basis of these studies, the authors argued that the long cortico-cortical pathways, damaged in AD has a minor role, whereas the cortico-striatal network, known to be one of the main neural assemblies of the feature extraction processes (Ashby et al., 1998; Kropotov and Etlinger, 1999), is crucial in the ultra-rapid visual categorization. In the abovementioned studies the main difference between the experimental setups used by the different groups were the different motor output requirements. Thorpe's group used go/no-go paradigm; subjects were asked to release a button when they saw a picture of an animal (Thorpe et al., 1996). In other studies, the subjects were required to press the relevant buttons for animal and non-animal pictures (Antal et al., 2000, 2001, 2002a,b, 2003). The question arises of whether the categories in the two different paradigms are processed in the same way. If the two different categories are processed differently, we expected to see different brain activation patterns. Specifically, when subjects have to make a go/no-go decision, one expects the reaction times to be shorter, because the information needed to be extracted to identify an animal, probably needs less time than analyze a picture in much fuller details to decide between two categories. To investigate the effect of the task requirements on the categorization process, we have obtained ERPs in the following three paradigms in a multi-channel setup. (i) In the two-choice task, subjects were required to categorize animal and nonanimal pictures and press one of the two respective buttons. (ii) In the go/no-go task, subjects only had to press a button if they have seen an animal; no response was needed for the non-animal category. (iii) In the counting task, subjects had to count pictures containing animals without any response excluding the effect of the forthcoming motor output.
2.
Results
2.1.
Behavioral data
The average reaction times are shown in Table 1. The t-test revealed significant differences between the animal and nonanimal pictures in the two-choice paradigm (p < 0.05), less time required to react to animal pictures. The reaction times of the animal categories were also significantly different between the two-choice and go/no-go paradigms (p < 0.05). The go/nogo paradigm required less time to react on an animal picture than when the subjects had to make a differential response for the different categories. The accuracy did not differ throughout the sessions (p > 0.05).
Table 1 – Mean reaction times (ms) calculated from the individual median reaction times Animal
Two-choice task Go/no-go task
Non-animal
Mean (ms)
SD
Mean (ms)
SD
359.18 329.43
140.93 139.57
401.81 –
172.81 –
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2.2.
Event-related potentials
2.2.1.
First experiment: two-choice task
Fig. 1A shows the grand averaged potentials and difference waveforms at frontal, fronto-central and parieto-central positions. The t-test executed on the grand averages showed
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the earliest difference between animal and non-animal waveforms at 178 ms after the stimulus onset at the Fp2 electrode site (178–224 ms). Similar responses were found at the Fpz (180–214 ms), Fp1 (196–208 ms), AF3 (196–216 ms), AF7 (192–202 ms), AF4 (206–216 ms) electrode positions, with non-animal responses being more negative than animal
Fig. 1 – Grand average potentials for animal (. . . .) and non-animal (… …) pictures and their differences (thick line). The upper waves are pools of fronto-central, middle central, and lower centro-parietal electrodes. Shaded periods are significant deviations of the two responses. (B) Two-choice task. (C) Go/no-go task. Positivity is downward. Best fitting dipole pairs are presented on head schemes: two-choice task (A), go/no-go task (D). Details in Table 2.
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responses. Preceding these intervals, there was no significant deviation of the two potentials. The ANOVA conducted on the N1 peak amplitudes revealed a significant main effect of electrode position (F(61,854) = 43.23, p < 0.001). The effect of stimulus (animal vs. non-animal) was only marginally significant (F(1,14) = 3.83, p = 0.070). The remaining interaction (electrode location vs. stimulus type) was insignificant. The amplitudes of the N70-P100 components did not differ significantly between the categories (p = 0.641).
2.2.2.
Animal vs. animal (1. vs. 2. experiment)
Comparing the obtained responses of animal pictures in the two-choice vs. go/no-go tasks with t-test, we found that the processing of the same kind of stimulus in the two different paradigms starts to deviate at a very early phase (Fig. 2A). Animal pictures in the two-choice task evoked more negative potentials than in the go/no-go paradigm at Cz (136–160 ms), FC1 (140–164 ms), C1 (134–160 ms), CP1 (128–158 ms), FC3 (142– 160 ms), C3 (136–158 ms), CP3 (128–156 ms), FCz (140–160 ms), CPz (130–152 ms), FC2 (144–162 ms), C2 (138–162 ms), CP2 (144– 158 ms), FC4 (144–166 ms), C4 (144–168 ms), CP4 (148–158 ms).
2.2.5.
Animal vs. animal (3. vs. 2. experiment)
The early parts of the animal responses in the counting and go/no-go task were similar; there was no significant difference between the two responses in the first 220 ms.
2.2.7.
Non-animal vs. non-animal (1. vs. 2. experiment)
In the early time scale, there was no difference between the non-animal responses in the two-choice vs. go/no-go paradigm.
2.2.8.
Non-animal vs. non-animal (1. vs. 3. experiment)
At around 130 ms, there were slight differences between the two potentials, none of them reaching the level of statistical significance. Responses for non-animal pictures in the twochoice task were more negative at Cz (120–134 ms), FC1 (150– 162 ms), C1 (120–140 ms), CP1 (122–138 ms), CP3 (122– 136 ms), FCz (144–162 ms), CPz (120–134 ms), FC2 (150– 162 ms), C2 (150–160 ms).
2.2.9.
Non-animal vs. non-animal (3. vs. 2. experiment)
The early time course of the non-animal responses did not differ in the counting and in the go/no-go tasks.
2.3.
Source analyzes
Equivalent dipoles were first fitted to time windows where differential activity was significant in the ERP analyzes. Then additional sources were fitted if it was possible to explain more then 80% of the variance in earlier phase of the response (90–220 ms). Table 2 summarizes the results. The dipole source pairs are depicted on Fig. 1 and Fig. 2.
Third experiment: counting task
When the subjects had to count the animal pictures, the early ERPs corresponding to the two categories differed in the first two time windows only at C2 electrode site (166–184 ms) (Fig. 3). The ANOVA conducted on the peak amplitudes of the N70 showed no effect of stimulus type (p = 0.974).
2.2.4.
2.2.6.
Second experiment: go/no-go task
Fig. 1B shows the grand averaged potentials and difference waveforms at frontal, fronto-central and parieto-central positions. The first significant difference between the animal and non-animal pictures appeared at 144 ms in the right central, centro-parietal region (C2: 144–178 ms, C4: 144–176 ms, CP4: 144–158 ms). The two responses became significantly different soon after at other central, fronto-central electrode positions also (Cz: 150–170 ms, FC1: 150–170 ms, C1: 150–170 ms, C3: 150– 170 ms, FC2: 150–178 ms, FC3: 150–170 ms). The grand average potentials became significantly different at FCz, FC4 electrode somewhat later, but still very early (156–168 ms, 156–176 ms respectively). Additionally further differences developed after 175 ms in other electrode positions and in the earlier mentioned also (C1: 176–190 ms, FC1: 176–202 ms, FCz: 176– 204 ms; FC2: 182–204 ms, FC4: 184–192 ms, F4: 184–196 ms, C2: 184–196 ms, P2: 184–204 ms, AF3: 184–200 ms, CP1: 184–202 ms, Cz: 184–202 ms; AFz: 190–204 ms, CPz: 192–204 ms; AF7: 208– 218 ms, CP3: 208–218 ms, Pz: 208–218 ms). The ANOVA carried out on the N1 peak amplitudes showed significant main effect of stimulus (F(1,14) = 12.86, p < 0.005) and electrode location (F(61,854) = 34.9, p < 0.001). The stimulus type and electrode location interaction was not significant (p = 0.516). The primary visually evoked potentials (N70, P100) were not different between animal and non-animal pictures (p = 0.991).
2.2.3.
following times and electrode positions: Cz (118–136 ms), Pz (118–128 ms), C1 (118–134 ms), CP1 (118–136 ms), CP3 (118– 134 ms), P3 (124–132 ms), CPz (118–138 ms), C2 (126–138 ms), CP2 (118–130 ms), P2 (120–128 ms), C4 (150–158 ms), P4 (120– 130 ms). Pooled responses are shown on Fig. 2B.
Animal vs. animal (1. vs. 3. experiment)
The responses evoked by animal pictures from the two-choice task and from the counting task differed significantly in the
2.3.1.
First experiment: two-choice task
We fitted symmetric dipoles to the animal non-animal difference waves in the 176- to 218-ms time window. The resulting dipole pairs (dipoles 3 and 4 on Fig. 1C) are located in the temporo-occipital junction. At this interval, the variance not explained by the dipole pairs was only 8.2%. Table 2 shows the Talairach coordinates.
2.3.2.
Second experiment: go/no-go task
The source analysis conducted on the differential responses yielded very similar source location in the go/no-go task in the occipito-temporal junction. On the other hand, the sources were about 1 cm apart from the sources found in the two target task. The fitting interval for these sources starts somewhat earlier (144 ms).
2.3.3.
Animal vs. animal (1. vs. 2. experiment)
Symmetrical sources accounted for more then 94% of the variance when fitted in the 136- to 168-ms time window. The symmetrical sources were at the medial side of the temporal poles.
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Fig. 2 – Grand average potentials for animal stimuli and their differences (thick line). (B) Two-choice task (. . . .) and counting task (… …). (C) Two-choice task (. . . .) and Go/no-go task (… …). Shaded periods are significant deviations of the two responses. The upper waves are pools of fronto-central, middle central, and lower centro-parietal electrodes. Positivity is downward. Best fitting dipole pairs on head schemes. (A) Two-choice vs. counting task. (D) Two-choice task vs. go/no-go task. Details in Table 2.
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Table 2 – Talariach coordinates of dipoles calculated from the difference of the animal and non-animal responses and from the difference of the animal responses Fitting time (ms)
Talairach coordinates x-loc
Residual variance
y-loc
z-loc
Two-choice task, animal vs. non-animal Dipole 1 176–218 −43.6 −31.74 Dipole 2 176–218 +43.6 −31.74
+15.74 +15.74
7.5% 7.5%
go/no-go task, animal vs. non-animal Dipole 1 144–218 −42.83 Dipole 2 144–218 +42.83
+25.30 +25.30
4.2% 4.2%
Two-choice vs. go/no-go task, animal stimuli Dipole 1 136–168 −23.9 +12.8 Dipole 2 136–168 +23.9 +12.8
−10.1 −10.1
5.8% 5.8%
Two-choice vs. counting task, animal stimuli Dipole 1 118–158 −17.3 +13.2 Dipole 2 118–158 +17.3 +13.2
−10.0 −10.0
6.2% 6.2%
2.3.4.
−33.21 −33.21
Animal vs. animal (1. vs. 3. experiment)
Very similar source positions were yielded, and the time window was somewhat earlier (118–158 ms).
3.
Discussion
Our results provide evidence about task-dependent modulation of the early categorization processes. Categorizing the same type of pictures required less time when the task was a go/no-go paradigm, than in the two-choice task. Regarding to the electrophysiological data, when the subjects only had to decide about the presence or absence of animals in the go/nogo paradigm, the first electrophysiological signs of the categorization process appeared earlier than in the two target task. We could identify a different topographical distribution of the two responses. The different timing and topography reveals that different neural processing is involved in the categorization process when the task requirements are changed. The time required developing different responses for the two types of pictures was very short in both the two-choice task and in the go/no-go task. However, the difference in the go/no-go task appeared remarkably earlier. At least three different parts of the information processing–decision making network can be responsible for this time delay. Deciding about the presence of a category member forces the brain to extract important features from the sensory input and if the information is enough, the decision can be made and the motor output can be initialized. When response is needed for two different categories, more information has to be collected about the image to fulfil the increased requirements of the decision making process. One other possible source of the delay is the extra-charge on the working memory. If the subjects had to choose from two possible different responses in the two-choice task, not only one (animal), but an additional (non-animal) category had to be kept in the working memory. The third source of the delay can be the more complex motor
output, as in the two-choice task, two possible motor output have to be mapped to two different categories. Goal-directed behavior requires information processing, maintaining information in the working memory, ignoring non-relevant distractions, selecting between the possible responses, inhibiting inappropriate responses, and initiating the appropriate one. Several studies have emphasized the importance of the prefrontal and parietal cortices in goaldirected behavior (Bunge et al., 2002; Hazeltine et al., 2000; Hollerman et al., 2000; Owen et al., 1990). Instead of the common co-activation of the two areas in goal-directed behavior, due to the strong interconnections, recent studies point out the different role of the two areas in tasks where goal directed behavior is required. Imaging studies of verbal working memory have provided evidence of the functional dissociation between the two regions, such that prefrontal cortex is thought to be involved in actively rehearsing phonological information stored in parietal cortex (Bunge et al., 2001; Jonides et al., 1998; Paulesu et al., 1993). Bunge et al. in a recent study, in which subjects performed a variant of Eriksen flanker task (Eriksen and Eriksen, 1974), provided evidence that the parietal cortex is involved in activating possible responses on the basis of the input from the sensory cortices and that the prefrontal cortex is recruited when there is a need to select a particular response (Bunge et al., 2000). In our study, comparing the electrophysiological responses for animal pictures in the two-choice and the go/ no-go task, we found differences in the early time course at widespread regions, but mostly at centro-parietal locations. One possible explanation for such findings is that the parietal cortex keeps two motor response representations ready in the two-choice task and only one in the go/no-go task. The early parts of the electrophysiological responses evoked by animals in the go/no-go paradigm and in the counting task were similar. In the counting task as well as in the go/no-go task, there was only one important category subjects had to deal with. One can argue that even if types of the required responses were different in the two tasks (button press for animals vs. counting the animal pictures), the parietal cortex did not have to maintain two different responses for two categories as in the two-choice task. Interestingly, in the counting task, the animal and nonanimal responses differed only at C2 electrode position. One can claim that this is due to reduced attention, and subjects not fully cooperating with the task. However, this is unlikely as the overall performance in detection of the animal pictures did not differ through the task, and if subjects detected an incorrect number of animal pictures, they reported similar number of pictures were difficult to categorize. These pictures usually contained animals in a peripheral position or in an environment where the figure-ground segregation was more difficult. One possible explanation for the smaller difference between target and non-target responses can be that the time constraints were less strict in the counting task, as there was no immediately measurable motor response which would have forced the subjects to respond as fast as possible. Johnson and co-workers have examined the differences between the two alternative force choice and the go/no-go tasks in a rapid natural scene categorization task (Johnson and Olshausen, 2003). The authors described only minor
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differences in timing of the earliest differences. In the go/nogo task the target and non-target responses started to differ only about 10 ms later than in two alternative force choice task. It is possible that the stricter statistical criteria for detection of the significant differences may account for the differences between the two results. Interestingly, the authors conclude that these early differences (starting between 135– 150 ms in their study) which are not related to the reaction times, represent differences in early visual processing. As reported in their study and by earlier studies as well, the low level (power spectrum) features of the animal and non-animal pictures were different (Torralba and Oliva, 2003). Using the cued target task, the authors showed that differences reliably contingent upon recognition appear between 150 and 300 ms and have an onset which covaries with the reaction time. If the early potential differences in our study represents similar low level feature differences, we can suspect that modulation of these early electrophysiological representations by the task requirements are derived from the top-down modulation of the information processing. The distinct target templates (one vs. two target templates in the go/no-go and the two target task) may integrate with the bottom-up information processing differently according to the task requirements resulting in dissimilar ERPs. An interesting question could be how these category templates are formulated. Theoretically the category membership can be decided mainly based on rules (Smith et al., 1998), or similarity (Grossman et al., 2001; Smith et al., 1998). We can speculate that when subjects had to decide between two different categories, they relied more on rules describing the two categories, and when only the animal category required a response, subjects compared the presented picture with a prototype or with known exemplar(s) of the category stored in the memory. These different mechanisms could lead to different activation of related brain areas. In line with our hypothesis, a recent fMRI study (Grossman et al., 2001) reported greater recruitment of the left dorsolateral prefrontal cortex and a unique recruitment of the right ventral frontal cortex and the thalamus in the rule based categorization while the similarity based categorization was associated with right inferior parietal activation. These separate neural mechanisms may be responsible for the differences we described. Although we have no direct evidence about the different categorization processes involved in the two tasks it would at least partly explain our former results about natural scene categorization in PD patients. We previously have provided evidence about the categorization deficit in PD in the same two-choice task (Antal et al., 2000, 2001). From earlier studies, we know that rule based categorization shows a selective impairment in PD (Ashby et al., 1998) which may be responsible for the described deficit. Whether the deficiency in PD is selective for only the two-choice task, is still an open question and yet to be answered. The source analysis yielded sources for the differential responses in the animal non-animal and in the two-choice tasks in different localization. These sources correlate well with former results from fMRI and source analysis data published recently with a similar experimental setup (Delorme et al., 2004). On the other hand, the category-related activation pattern of the occipito-temporal junction, showed
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strong modulation by the tasks requirements. In contrast to our results, Delorme et al. (2004) found that task requirements can change only the timing and not the spatial localization of the sources activated by the task. The authors showed that, in the tasks they used, the same visual pathway and the same decisional brain area were involved regardless of the task. The difference in the two studies probably originates from the different paradigms and the different task. In the abovementioned study, an “animal” categorization task and a single-photograph recognition task were alternated, as opposed to our experiments where both tasks were categorization tasks with different response requirements. The result of the source analysis has to be discussed carefully. As the inverse problem has no solution, the sources we have obtained here cannot be absolutely related to the activation patterns, and the sources derived from the differential potentials are virtual sources, not real ones. On the other hand, the method is used frequently because it can give a good representation of the task modulation of the activity pattern. Examining our results an other question is, whether the described modulation of categorization is specific for the animal category. Previously VanRullen and his co-workers showed that reaction times did not differ in tasks categorizing animals and artificial objects (VanRullen et al., 2001). Johnson and Olshausen (2003) described bigger ERP differences for entry level categorization than for superordinate level categorization, but the components were the later, P300-related potentials evoked by cued target task. Antal compared evoked potentials from a similar go/no-go categorization task and a spatial frequency discrimination task. In the discrimination task, the differential response reached the level of significance only at two frontal electrode positions between 140 and 150 ms. In a parallel study (Kincses et al., submitted), we examined the task demands on spatial frequency discrimination task. Even though the categorization and discrimination task are not directly comparable because of the supposedly different underlying neural mechanisms, we have found interesting similarities. Comparing the responses evoked by target stimuli in the go/no-go and in the two target tasks, we have found an earlier difference above the left parietal areas, and a later broad difference over the frontal electrode positions, which strengthens the hypothesis about the differential role of the parietal and frontal cortex in the goal directed behavior. In summary, we have found different temporal and topographical distribution of animal and non-animal categories in healthy subjects when the task requirements were different. The results we have provided here draw attention to handling the data obtained in categorization experiments carefully, because the task requirements may strongly influence the categorization process itself.
4.
Experimental procedures
4.1.
Subjects
The study involved 8 subjects (mean (SD) age: 28.7 (6.4) years, range: 21–40 years, 5 men) fulfilling the following criteria: clear ocular media; no evidence of retinal pathology; no history of
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diabetes, alcoholism or mental deterioration; visual acuity greater than 0.9 with or without correction. Before participation, subjects were informed about all aspects of the experiments and gave an informed consent. We conformed with the Declaration of Helsinki, and the Ethics Committee of the University of Göttingen approved the study.
4.1.1.
Stimuli
The stimulus battery included approximately 3000 color photographs of complex natural images, containing either an animal or a non-animal item. All stimuli were available from commercial databases and were matched for average luminance. Animals included birds, mammals, reptiles, fishes, and insects. Non-animals were pictures of mountains, forests, rivers, lakes, buildings, flowers, fruits, and urban scenes with people, vehicles, and other artificial objects. All stimuli subtended a vertical visual angle of 10° and a horizontal visual angle of 15 degrees from a viewing distance of 1 m. A small fixation dot was present in the middle of the video screen, controlled by a PC. The luminance of the stimulus area (80 cd/m2) and the background luminance (8 cd/m2) were held constant throughout the experiments.
4.1.2.
Products GmbH, Munich, Germany) and were analyzed offline. No digital filters were used. The data obtained were rereferenced to the average of all electrodes. Baseline correction was calculated on the basis of the 100 ms prestimulus interval. After visual inspection segments containing EOG or extensive muscle artifacts were removed, and correct responses were averaged.
4.1.4.
Data analysis
To assess the time dynamics and the onset of differences of the obtained electrical responses, we have used the method described by Rugg et al. (1995). Grand average curves were obtained for both animal and non-animal stimuli in each experimental session. We compared two grand average curves by two-sample t-test. The relevant t-scores from the two
Procedures
Participants were asked to fixate on the small dot in the middle of the screen. The sequence of trials was initiated by the subject by pressing one of the two response buttons. The first trial began 2 s thereafter. One trial consisted of the presentation of a single stimulus for 33 ms (two frames at 60 Hz vertical refresh rate). The inter-stimulus interval was 2 s. Stimuli were presented in a randomized order. The probabilities of animal and non-animal items were equal. A training block was given before the test to ensure that each subject understood the task. In each experiment, the subjects were presented with 250 stimuli. The subjects were required to respond as fast and as precisely as they could. In the two-choice task, subjects were asked to decide whether the presented picture belongs to the animal or to the non-animal category, by pressing one of the two response buttons in 1000 ms after the presentation of the stimulus. The second experiment was a go/no-go paradigm, which required the subject to press the left button only if the presented picture contained an animal(s), and to withhold the response if an animal was not seen. As in the first task, the responses were collected in the 1000-ms time window after the stimulus onset. All motor responses were performed with right hand. In the third task, the subjects had to count the number of animal pictures during the session and report it at the end. All recordings were made in one session. The task sequence was counterbalanced across subjects.
4.1.3.
Electrophysiological recordings
ERPs were recorded on 64 channels, using 64 ring electrodes (inner diameter: 6 mm, outer diameter: 12 mm) (EasyCap; Falk and Minlow). The electrodes were placed in accordance with the extended international 10/20 system. Cz reference was used for recording. The ground was placed 2 cm ahead from the tragus of the right ear. Data were collected with the sampling rate of 500 Hz with a BrainAmp system (Brain
Fig. 3 – Grand average potentials evoked by animal (. . . .) and non-animal(… …) pictures in the counting task. Thick lines stand for the difference between the two categories. Note that the original, unfiltered data is represented. There were no significant differences between the two curves only at C2 position for short period of time.
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curves were calculated for each data points. The two curves were concluded to be significantly different at a given time interval if a single t-score and at least 5 consecutive data points indicated significant differences. With a sampling frequency of 500 Hz this corresponds to at least 10 ms. We concentrated on the early processes, e.g., time window between 60–220 ms. The peak amplitudes and latencies of the primary evoked potentials and the formerly described N1 component were measured. N1 was defined as the major negative deflection of the ERPs in the time window subtending from 150 to 220 ms. The N70 component was the main negative wave in the time window of 60 to 120 ms. The obtained peak amplitudes and latencies were treated with repeated measure analysis of variance (ANOVA). The details of the significant main effects and interactions were further analyzed with Tukey's HSD test. The median response times were calculated for each subject in each experimental setup. The averages of the obtained reaction times were compared with t-tests.
4.2.
Source analyzes
The obtained grand average difference waves were 35 Hz lowpass filtered and treated with brain electric source analyzes algorithm (BESA 5.0.4 Megis GmBh, München) (Scherg and Berg, 1996). We used a 4-shell ellipsoidal model. Symmetrical dipoles were fitted to time periods where significant ERP differences were calculated. Additional symmetric dipoles explaining at least 80% of total variance were fitted to intervals, where ERP waveforms deviated markedly, although not statistically. In all cases, symmetric dipoles were obtained because residual variance was smaller when two dipoles fitted (Fig. 3).
Acknowledgments This project was supported bt the VW Foundation (AA, WP) and the Marie Curie Training Site Fellowship (ZTK, ZC, ETV). We thank Dr. Rose Bosnell for the English corrections.
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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / b r a i n r e s
Research Report
Task-related temporal and topographical changes of cortical activity during ultra-rapid visual categorization Tamás Zsigmond Kincses a,b,c,⁎ , Zoltan Chadaide a,b , Edina T. Varga a,b , Andrea Antal b , Walter Paulus b a
Department of Neurology, University of Szeged, Szeged, Hungary Department of Clinical Neurophysiology, Georg-August University of Göttingen, Göttingen, Germany c FMRIB Centre, Department of Clinical Neurology, University of Oxford, Oxford, UK b
A R T I C LE I N FO
AB S T R A C T
Article history:
The aim of our study was to provide electrophysiological evidence about the modulation of
Accepted 5 July 2006
the categorization process by task requirements in the human brain. Event-related
Available online 22 August 2006
potentials (ERP) were recorded during three different categorization tasks using matched stimulus sets. In all cases, the subjects were required to differentiate between “animal” and
Keywords:
“non-animal” stimuli. In the first task (two-choice task), they were asked to press
Visual categorization
corresponding buttons to each stimulus types. The second task was a go/no-go paradigm,
Natural scene
only animal stimuli required motor response. The third task was a counting task;
Event-related potential
participants had to count the animal stimuli without any motor response. The reaction
go/no-go
times in the go/no-go paradigm were significantly shorter. ERP differences between animal
Human
and non-animal pictures in the go/no-go task also appeared earlier and were localized at
Dipole source fitting
more posterior scalp positions compared to the two-choice task. Comparing animal responses in the two-choice task and in the go/no-go paradigm, we found a significant difference in the 130- to 170-ms time window over the fronto-central, centro-parietal regions. Similar differences were found between the responses to animal pictures in the two-choice task and in the counting paradigm. We used brain electric source analysis (BESA) algorithm on difference waves to localize the best fitting dipoles and determine the localization of brain areas contributing to scalp potential differences. The results show that different task requirements evoke different activity in the medial part of the temporal pole. The data we provided here draw attention to the careful handling of results obtained from categorization experiments, because different task requirements can affect the early categorization process itself. © 2006 Elsevier B.V. All rights reserved.
⁎ Corresponding author. FMRIB Centre, Department of Clinical Neurology, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK. Fax: +44 1865 222717. E-mail address: [email protected] (T. Zsigmond Kincses). URL: http://www.fmrib.ox.ac.uk/~tkincses (T. Zsigmond Kincses). 0006-8993/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2006.07.044
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1.
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Introduction
The meaningful organization of the surrounding environment, categorization, plays a crucial role in our everyday life. Many neuroimaging and electrophysiological studies have provided evidence for a discrete categorical organization of the brain. In particular, there are specific representations of different categories in the occipito-temporal cortex and surrounding areas, such as faces in the lateral occipito-temporal cortex (LOC) and the fusiform face area (Allison et al., 1994a,b, 1999; Clark et al., 1996; Goffaux et al., 2003; Kanwisher, 2000; Kanwisher et al., 1997; Ojemann et al., 1992; Puce et al., 1996; Schendan et al., 1998), human body representation in the LOC (Downing et al., 2001), animals in the right fusiform cortex (Kawashima et al., 2001), buildings in the right lingual sulcus (Aguirre et al., 1998), man-made tools in the left posterior middle temporal cortex (Moore and Price, 1999) and plants in the right lateral occipital cortex (Kawashima et al., 2001). Although the abovementioned studies provided detailed data about the spatial representation of the different categories in the brain, the temporal dynamics of the categorization process is less well explored. Several studies of the categorization of color pictures of natural scenes reported a robust difference between the event-related potentials (ERP)s evoked by animal and non-animal pictures, developing at 150 ms after the stimulus onset (Thorpe et al., 1996). Later investigations proved that this was not due to an exclusive, ‘hard-wired’ processing of the evolutionary important animal category, because the same kind of potential difference could have been obtained with different supra-ordinal categories also (animals vs. vehicles) (VanRullen et al., 2001). Additionally, Delorme et al. have showed that chromatic information does not play a defining role in the early decision making processes, which point-out the dominant role of the magno-cellular pathway (Delorme et al., 2000). It has been shown that complex natural images can be processed and categorized in parallel without the need for sequential attention (Rousselet et al., 2002). Furthermore, it seems that familiarity or expertise has no effect on this kind of ultra-rapid categorization: 3 weeks of extensive training failed to decrease reaction times (Fabre-Thorpe et al., 2001). Because of the high time constraint stated by the obtained reaction times and the very early electrophysiological differences between responses evoked by pictures containing different categories, the authors suggested that mainly feedforward connections are involved in the underlying neuronal processes. It was found that the early category specific potential differences were unaffected in Alzheimer's disease (AD) despite the clinically severe symptoms and cognitive decline. The results are surprising because it is evident from former studies that in AD visual information processing is impaired due to the pathology of cortico-cortical pathways in the ventral occipito-temporal stream (Hof and Bouras, 1991; Hof et al., 1997; Leuchter et al., 1992). On the other hand, in Parkinson's disease (PD) in which the cortico-striatal pathways are the most affected structures, the early electrophysiological signs of the categorization processes have disappeared, the responses evoked by animal and non-animal pictures were
similar in the given early time window (Antal et al., 2000, 2001, 2002a). On the basis of these studies, the authors argued that the long cortico-cortical pathways, damaged in AD has a minor role, whereas the cortico-striatal network, known to be one of the main neural assemblies of the feature extraction processes (Ashby et al., 1998; Kropotov and Etlinger, 1999), is crucial in the ultra-rapid visual categorization. In the abovementioned studies the main difference between the experimental setups used by the different groups were the different motor output requirements. Thorpe's group used go/no-go paradigm; subjects were asked to release a button when they saw a picture of an animal (Thorpe et al., 1996). In other studies, the subjects were required to press the relevant buttons for animal and non-animal pictures (Antal et al., 2000, 2001, 2002a,b, 2003). The question arises of whether the categories in the two different paradigms are processed in the same way. If the two different categories are processed differently, we expected to see different brain activation patterns. Specifically, when subjects have to make a go/no-go decision, one expects the reaction times to be shorter, because the information needed to be extracted to identify an animal, probably needs less time than analyze a picture in much fuller details to decide between two categories. To investigate the effect of the task requirements on the categorization process, we have obtained ERPs in the following three paradigms in a multi-channel setup. (i) In the two-choice task, subjects were required to categorize animal and nonanimal pictures and press one of the two respective buttons. (ii) In the go/no-go task, subjects only had to press a button if they have seen an animal; no response was needed for the non-animal category. (iii) In the counting task, subjects had to count pictures containing animals without any response excluding the effect of the forthcoming motor output.
2.
Results
2.1.
Behavioral data
The average reaction times are shown in Table 1. The t-test revealed significant differences between the animal and nonanimal pictures in the two-choice paradigm (p < 0.05), less time required to react to animal pictures. The reaction times of the animal categories were also significantly different between the two-choice and go/no-go paradigms (p < 0.05). The go/nogo paradigm required less time to react on an animal picture than when the subjects had to make a differential response for the different categories. The accuracy did not differ throughout the sessions (p > 0.05).
Table 1 – Mean reaction times (ms) calculated from the individual median reaction times Animal
Two-choice task Go/no-go task
Non-animal
Mean (ms)
SD
Mean (ms)
SD
359.18 329.43
140.93 139.57
401.81 –
172.81 –
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2.2.
Event-related potentials
2.2.1.
First experiment: two-choice task
Fig. 1A shows the grand averaged potentials and difference waveforms at frontal, fronto-central and parieto-central positions. The t-test executed on the grand averages showed
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the earliest difference between animal and non-animal waveforms at 178 ms after the stimulus onset at the Fp2 electrode site (178–224 ms). Similar responses were found at the Fpz (180–214 ms), Fp1 (196–208 ms), AF3 (196–216 ms), AF7 (192–202 ms), AF4 (206–216 ms) electrode positions, with non-animal responses being more negative than animal
Fig. 1 – Grand average potentials for animal (. . . .) and non-animal (… …) pictures and their differences (thick line). The upper waves are pools of fronto-central, middle central, and lower centro-parietal electrodes. Shaded periods are significant deviations of the two responses. (B) Two-choice task. (C) Go/no-go task. Positivity is downward. Best fitting dipole pairs are presented on head schemes: two-choice task (A), go/no-go task (D). Details in Table 2.
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responses. Preceding these intervals, there was no significant deviation of the two potentials. The ANOVA conducted on the N1 peak amplitudes revealed a significant main effect of electrode position (F(61,854) = 43.23, p < 0.001). The effect of stimulus (animal vs. non-animal) was only marginally significant (F(1,14) = 3.83, p = 0.070). The remaining interaction (electrode location vs. stimulus type) was insignificant. The amplitudes of the N70-P100 components did not differ significantly between the categories (p = 0.641).
2.2.2.
Animal vs. animal (1. vs. 2. experiment)
Comparing the obtained responses of animal pictures in the two-choice vs. go/no-go tasks with t-test, we found that the processing of the same kind of stimulus in the two different paradigms starts to deviate at a very early phase (Fig. 2A). Animal pictures in the two-choice task evoked more negative potentials than in the go/no-go paradigm at Cz (136–160 ms), FC1 (140–164 ms), C1 (134–160 ms), CP1 (128–158 ms), FC3 (142– 160 ms), C3 (136–158 ms), CP3 (128–156 ms), FCz (140–160 ms), CPz (130–152 ms), FC2 (144–162 ms), C2 (138–162 ms), CP2 (144– 158 ms), FC4 (144–166 ms), C4 (144–168 ms), CP4 (148–158 ms).
2.2.5.
Animal vs. animal (3. vs. 2. experiment)
The early parts of the animal responses in the counting and go/no-go task were similar; there was no significant difference between the two responses in the first 220 ms.
2.2.7.
Non-animal vs. non-animal (1. vs. 2. experiment)
In the early time scale, there was no difference between the non-animal responses in the two-choice vs. go/no-go paradigm.
2.2.8.
Non-animal vs. non-animal (1. vs. 3. experiment)
At around 130 ms, there were slight differences between the two potentials, none of them reaching the level of statistical significance. Responses for non-animal pictures in the twochoice task were more negative at Cz (120–134 ms), FC1 (150– 162 ms), C1 (120–140 ms), CP1 (122–138 ms), CP3 (122– 136 ms), FCz (144–162 ms), CPz (120–134 ms), FC2 (150– 162 ms), C2 (150–160 ms).
2.2.9.
Non-animal vs. non-animal (3. vs. 2. experiment)
The early time course of the non-animal responses did not differ in the counting and in the go/no-go tasks.
2.3.
Source analyzes
Equivalent dipoles were first fitted to time windows where differential activity was significant in the ERP analyzes. Then additional sources were fitted if it was possible to explain more then 80% of the variance in earlier phase of the response (90–220 ms). Table 2 summarizes the results. The dipole source pairs are depicted on Fig. 1 and Fig. 2.
Third experiment: counting task
When the subjects had to count the animal pictures, the early ERPs corresponding to the two categories differed in the first two time windows only at C2 electrode site (166–184 ms) (Fig. 3). The ANOVA conducted on the peak amplitudes of the N70 showed no effect of stimulus type (p = 0.974).
2.2.4.
2.2.6.
Second experiment: go/no-go task
Fig. 1B shows the grand averaged potentials and difference waveforms at frontal, fronto-central and parieto-central positions. The first significant difference between the animal and non-animal pictures appeared at 144 ms in the right central, centro-parietal region (C2: 144–178 ms, C4: 144–176 ms, CP4: 144–158 ms). The two responses became significantly different soon after at other central, fronto-central electrode positions also (Cz: 150–170 ms, FC1: 150–170 ms, C1: 150–170 ms, C3: 150– 170 ms, FC2: 150–178 ms, FC3: 150–170 ms). The grand average potentials became significantly different at FCz, FC4 electrode somewhat later, but still very early (156–168 ms, 156–176 ms respectively). Additionally further differences developed after 175 ms in other electrode positions and in the earlier mentioned also (C1: 176–190 ms, FC1: 176–202 ms, FCz: 176– 204 ms; FC2: 182–204 ms, FC4: 184–192 ms, F4: 184–196 ms, C2: 184–196 ms, P2: 184–204 ms, AF3: 184–200 ms, CP1: 184–202 ms, Cz: 184–202 ms; AFz: 190–204 ms, CPz: 192–204 ms; AF7: 208– 218 ms, CP3: 208–218 ms, Pz: 208–218 ms). The ANOVA carried out on the N1 peak amplitudes showed significant main effect of stimulus (F(1,14) = 12.86, p < 0.005) and electrode location (F(61,854) = 34.9, p < 0.001). The stimulus type and electrode location interaction was not significant (p = 0.516). The primary visually evoked potentials (N70, P100) were not different between animal and non-animal pictures (p = 0.991).
2.2.3.
following times and electrode positions: Cz (118–136 ms), Pz (118–128 ms), C1 (118–134 ms), CP1 (118–136 ms), CP3 (118– 134 ms), P3 (124–132 ms), CPz (118–138 ms), C2 (126–138 ms), CP2 (118–130 ms), P2 (120–128 ms), C4 (150–158 ms), P4 (120– 130 ms). Pooled responses are shown on Fig. 2B.
Animal vs. animal (1. vs. 3. experiment)
The responses evoked by animal pictures from the two-choice task and from the counting task differed significantly in the
2.3.1.
First experiment: two-choice task
We fitted symmetric dipoles to the animal non-animal difference waves in the 176- to 218-ms time window. The resulting dipole pairs (dipoles 3 and 4 on Fig. 1C) are located in the temporo-occipital junction. At this interval, the variance not explained by the dipole pairs was only 8.2%. Table 2 shows the Talairach coordinates.
2.3.2.
Second experiment: go/no-go task
The source analysis conducted on the differential responses yielded very similar source location in the go/no-go task in the occipito-temporal junction. On the other hand, the sources were about 1 cm apart from the sources found in the two target task. The fitting interval for these sources starts somewhat earlier (144 ms).
2.3.3.
Animal vs. animal (1. vs. 2. experiment)
Symmetrical sources accounted for more then 94% of the variance when fitted in the 136- to 168-ms time window. The symmetrical sources were at the medial side of the temporal poles.
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Fig. 2 – Grand average potentials for animal stimuli and their differences (thick line). (B) Two-choice task (. . . .) and counting task (… …). (C) Two-choice task (. . . .) and Go/no-go task (… …). Shaded periods are significant deviations of the two responses. The upper waves are pools of fronto-central, middle central, and lower centro-parietal electrodes. Positivity is downward. Best fitting dipole pairs on head schemes. (A) Two-choice vs. counting task. (D) Two-choice task vs. go/no-go task. Details in Table 2.
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Table 2 – Talariach coordinates of dipoles calculated from the difference of the animal and non-animal responses and from the difference of the animal responses Fitting time (ms)
Talairach coordinates x-loc
Residual variance
y-loc
z-loc
Two-choice task, animal vs. non-animal Dipole 1 176–218 −43.6 −31.74 Dipole 2 176–218 +43.6 −31.74
+15.74 +15.74
7.5% 7.5%
go/no-go task, animal vs. non-animal Dipole 1 144–218 −42.83 Dipole 2 144–218 +42.83
+25.30 +25.30
4.2% 4.2%
Two-choice vs. go/no-go task, animal stimuli Dipole 1 136–168 −23.9 +12.8 Dipole 2 136–168 +23.9 +12.8
−10.1 −10.1
5.8% 5.8%
Two-choice vs. counting task, animal stimuli Dipole 1 118–158 −17.3 +13.2 Dipole 2 118–158 +17.3 +13.2
−10.0 −10.0
6.2% 6.2%
2.3.4.
−33.21 −33.21
Animal vs. animal (1. vs. 3. experiment)
Very similar source positions were yielded, and the time window was somewhat earlier (118–158 ms).
3.
Discussion
Our results provide evidence about task-dependent modulation of the early categorization processes. Categorizing the same type of pictures required less time when the task was a go/no-go paradigm, than in the two-choice task. Regarding to the electrophysiological data, when the subjects only had to decide about the presence or absence of animals in the go/nogo paradigm, the first electrophysiological signs of the categorization process appeared earlier than in the two target task. We could identify a different topographical distribution of the two responses. The different timing and topography reveals that different neural processing is involved in the categorization process when the task requirements are changed. The time required developing different responses for the two types of pictures was very short in both the two-choice task and in the go/no-go task. However, the difference in the go/no-go task appeared remarkably earlier. At least three different parts of the information processing–decision making network can be responsible for this time delay. Deciding about the presence of a category member forces the brain to extract important features from the sensory input and if the information is enough, the decision can be made and the motor output can be initialized. When response is needed for two different categories, more information has to be collected about the image to fulfil the increased requirements of the decision making process. One other possible source of the delay is the extra-charge on the working memory. If the subjects had to choose from two possible different responses in the two-choice task, not only one (animal), but an additional (non-animal) category had to be kept in the working memory. The third source of the delay can be the more complex motor
output, as in the two-choice task, two possible motor output have to be mapped to two different categories. Goal-directed behavior requires information processing, maintaining information in the working memory, ignoring non-relevant distractions, selecting between the possible responses, inhibiting inappropriate responses, and initiating the appropriate one. Several studies have emphasized the importance of the prefrontal and parietal cortices in goaldirected behavior (Bunge et al., 2002; Hazeltine et al., 2000; Hollerman et al., 2000; Owen et al., 1990). Instead of the common co-activation of the two areas in goal-directed behavior, due to the strong interconnections, recent studies point out the different role of the two areas in tasks where goal directed behavior is required. Imaging studies of verbal working memory have provided evidence of the functional dissociation between the two regions, such that prefrontal cortex is thought to be involved in actively rehearsing phonological information stored in parietal cortex (Bunge et al., 2001; Jonides et al., 1998; Paulesu et al., 1993). Bunge et al. in a recent study, in which subjects performed a variant of Eriksen flanker task (Eriksen and Eriksen, 1974), provided evidence that the parietal cortex is involved in activating possible responses on the basis of the input from the sensory cortices and that the prefrontal cortex is recruited when there is a need to select a particular response (Bunge et al., 2000). In our study, comparing the electrophysiological responses for animal pictures in the two-choice and the go/ no-go task, we found differences in the early time course at widespread regions, but mostly at centro-parietal locations. One possible explanation for such findings is that the parietal cortex keeps two motor response representations ready in the two-choice task and only one in the go/no-go task. The early parts of the electrophysiological responses evoked by animals in the go/no-go paradigm and in the counting task were similar. In the counting task as well as in the go/no-go task, there was only one important category subjects had to deal with. One can argue that even if types of the required responses were different in the two tasks (button press for animals vs. counting the animal pictures), the parietal cortex did not have to maintain two different responses for two categories as in the two-choice task. Interestingly, in the counting task, the animal and nonanimal responses differed only at C2 electrode position. One can claim that this is due to reduced attention, and subjects not fully cooperating with the task. However, this is unlikely as the overall performance in detection of the animal pictures did not differ through the task, and if subjects detected an incorrect number of animal pictures, they reported similar number of pictures were difficult to categorize. These pictures usually contained animals in a peripheral position or in an environment where the figure-ground segregation was more difficult. One possible explanation for the smaller difference between target and non-target responses can be that the time constraints were less strict in the counting task, as there was no immediately measurable motor response which would have forced the subjects to respond as fast as possible. Johnson and co-workers have examined the differences between the two alternative force choice and the go/no-go tasks in a rapid natural scene categorization task (Johnson and Olshausen, 2003). The authors described only minor
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differences in timing of the earliest differences. In the go/nogo task the target and non-target responses started to differ only about 10 ms later than in two alternative force choice task. It is possible that the stricter statistical criteria for detection of the significant differences may account for the differences between the two results. Interestingly, the authors conclude that these early differences (starting between 135– 150 ms in their study) which are not related to the reaction times, represent differences in early visual processing. As reported in their study and by earlier studies as well, the low level (power spectrum) features of the animal and non-animal pictures were different (Torralba and Oliva, 2003). Using the cued target task, the authors showed that differences reliably contingent upon recognition appear between 150 and 300 ms and have an onset which covaries with the reaction time. If the early potential differences in our study represents similar low level feature differences, we can suspect that modulation of these early electrophysiological representations by the task requirements are derived from the top-down modulation of the information processing. The distinct target templates (one vs. two target templates in the go/no-go and the two target task) may integrate with the bottom-up information processing differently according to the task requirements resulting in dissimilar ERPs. An interesting question could be how these category templates are formulated. Theoretically the category membership can be decided mainly based on rules (Smith et al., 1998), or similarity (Grossman et al., 2001; Smith et al., 1998). We can speculate that when subjects had to decide between two different categories, they relied more on rules describing the two categories, and when only the animal category required a response, subjects compared the presented picture with a prototype or with known exemplar(s) of the category stored in the memory. These different mechanisms could lead to different activation of related brain areas. In line with our hypothesis, a recent fMRI study (Grossman et al., 2001) reported greater recruitment of the left dorsolateral prefrontal cortex and a unique recruitment of the right ventral frontal cortex and the thalamus in the rule based categorization while the similarity based categorization was associated with right inferior parietal activation. These separate neural mechanisms may be responsible for the differences we described. Although we have no direct evidence about the different categorization processes involved in the two tasks it would at least partly explain our former results about natural scene categorization in PD patients. We previously have provided evidence about the categorization deficit in PD in the same two-choice task (Antal et al., 2000, 2001). From earlier studies, we know that rule based categorization shows a selective impairment in PD (Ashby et al., 1998) which may be responsible for the described deficit. Whether the deficiency in PD is selective for only the two-choice task, is still an open question and yet to be answered. The source analysis yielded sources for the differential responses in the animal non-animal and in the two-choice tasks in different localization. These sources correlate well with former results from fMRI and source analysis data published recently with a similar experimental setup (Delorme et al., 2004). On the other hand, the category-related activation pattern of the occipito-temporal junction, showed
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strong modulation by the tasks requirements. In contrast to our results, Delorme et al. (2004) found that task requirements can change only the timing and not the spatial localization of the sources activated by the task. The authors showed that, in the tasks they used, the same visual pathway and the same decisional brain area were involved regardless of the task. The difference in the two studies probably originates from the different paradigms and the different task. In the abovementioned study, an “animal” categorization task and a single-photograph recognition task were alternated, as opposed to our experiments where both tasks were categorization tasks with different response requirements. The result of the source analysis has to be discussed carefully. As the inverse problem has no solution, the sources we have obtained here cannot be absolutely related to the activation patterns, and the sources derived from the differential potentials are virtual sources, not real ones. On the other hand, the method is used frequently because it can give a good representation of the task modulation of the activity pattern. Examining our results an other question is, whether the described modulation of categorization is specific for the animal category. Previously VanRullen and his co-workers showed that reaction times did not differ in tasks categorizing animals and artificial objects (VanRullen et al., 2001). Johnson and Olshausen (2003) described bigger ERP differences for entry level categorization than for superordinate level categorization, but the components were the later, P300-related potentials evoked by cued target task. Antal compared evoked potentials from a similar go/no-go categorization task and a spatial frequency discrimination task. In the discrimination task, the differential response reached the level of significance only at two frontal electrode positions between 140 and 150 ms. In a parallel study (Kincses et al., submitted), we examined the task demands on spatial frequency discrimination task. Even though the categorization and discrimination task are not directly comparable because of the supposedly different underlying neural mechanisms, we have found interesting similarities. Comparing the responses evoked by target stimuli in the go/no-go and in the two target tasks, we have found an earlier difference above the left parietal areas, and a later broad difference over the frontal electrode positions, which strengthens the hypothesis about the differential role of the parietal and frontal cortex in the goal directed behavior. In summary, we have found different temporal and topographical distribution of animal and non-animal categories in healthy subjects when the task requirements were different. The results we have provided here draw attention to handling the data obtained in categorization experiments carefully, because the task requirements may strongly influence the categorization process itself.
4.
Experimental procedures
4.1.
Subjects
The study involved 8 subjects (mean (SD) age: 28.7 (6.4) years, range: 21–40 years, 5 men) fulfilling the following criteria: clear ocular media; no evidence of retinal pathology; no history of
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diabetes, alcoholism or mental deterioration; visual acuity greater than 0.9 with or without correction. Before participation, subjects were informed about all aspects of the experiments and gave an informed consent. We conformed with the Declaration of Helsinki, and the Ethics Committee of the University of Göttingen approved the study.
4.1.1.
Stimuli
The stimulus battery included approximately 3000 color photographs of complex natural images, containing either an animal or a non-animal item. All stimuli were available from commercial databases and were matched for average luminance. Animals included birds, mammals, reptiles, fishes, and insects. Non-animals were pictures of mountains, forests, rivers, lakes, buildings, flowers, fruits, and urban scenes with people, vehicles, and other artificial objects. All stimuli subtended a vertical visual angle of 10° and a horizontal visual angle of 15 degrees from a viewing distance of 1 m. A small fixation dot was present in the middle of the video screen, controlled by a PC. The luminance of the stimulus area (80 cd/m2) and the background luminance (8 cd/m2) were held constant throughout the experiments.
4.1.2.
Products GmbH, Munich, Germany) and were analyzed offline. No digital filters were used. The data obtained were rereferenced to the average of all electrodes. Baseline correction was calculated on the basis of the 100 ms prestimulus interval. After visual inspection segments containing EOG or extensive muscle artifacts were removed, and correct responses were averaged.
4.1.4.
Data analysis
To assess the time dynamics and the onset of differences of the obtained electrical responses, we have used the method described by Rugg et al. (1995). Grand average curves were obtained for both animal and non-animal stimuli in each experimental session. We compared two grand average curves by two-sample t-test. The relevant t-scores from the two
Procedures
Participants were asked to fixate on the small dot in the middle of the screen. The sequence of trials was initiated by the subject by pressing one of the two response buttons. The first trial began 2 s thereafter. One trial consisted of the presentation of a single stimulus for 33 ms (two frames at 60 Hz vertical refresh rate). The inter-stimulus interval was 2 s. Stimuli were presented in a randomized order. The probabilities of animal and non-animal items were equal. A training block was given before the test to ensure that each subject understood the task. In each experiment, the subjects were presented with 250 stimuli. The subjects were required to respond as fast and as precisely as they could. In the two-choice task, subjects were asked to decide whether the presented picture belongs to the animal or to the non-animal category, by pressing one of the two response buttons in 1000 ms after the presentation of the stimulus. The second experiment was a go/no-go paradigm, which required the subject to press the left button only if the presented picture contained an animal(s), and to withhold the response if an animal was not seen. As in the first task, the responses were collected in the 1000-ms time window after the stimulus onset. All motor responses were performed with right hand. In the third task, the subjects had to count the number of animal pictures during the session and report it at the end. All recordings were made in one session. The task sequence was counterbalanced across subjects.
4.1.3.
Electrophysiological recordings
ERPs were recorded on 64 channels, using 64 ring electrodes (inner diameter: 6 mm, outer diameter: 12 mm) (EasyCap; Falk and Minlow). The electrodes were placed in accordance with the extended international 10/20 system. Cz reference was used for recording. The ground was placed 2 cm ahead from the tragus of the right ear. Data were collected with the sampling rate of 500 Hz with a BrainAmp system (Brain
Fig. 3 – Grand average potentials evoked by animal (. . . .) and non-animal(… …) pictures in the counting task. Thick lines stand for the difference between the two categories. Note that the original, unfiltered data is represented. There were no significant differences between the two curves only at C2 position for short period of time.
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curves were calculated for each data points. The two curves were concluded to be significantly different at a given time interval if a single t-score and at least 5 consecutive data points indicated significant differences. With a sampling frequency of 500 Hz this corresponds to at least 10 ms. We concentrated on the early processes, e.g., time window between 60–220 ms. The peak amplitudes and latencies of the primary evoked potentials and the formerly described N1 component were measured. N1 was defined as the major negative deflection of the ERPs in the time window subtending from 150 to 220 ms. The N70 component was the main negative wave in the time window of 60 to 120 ms. The obtained peak amplitudes and latencies were treated with repeated measure analysis of variance (ANOVA). The details of the significant main effects and interactions were further analyzed with Tukey's HSD test. The median response times were calculated for each subject in each experimental setup. The averages of the obtained reaction times were compared with t-tests.
4.2.
Source analyzes
The obtained grand average difference waves were 35 Hz lowpass filtered and treated with brain electric source analyzes algorithm (BESA 5.0.4 Megis GmBh, München) (Scherg and Berg, 1996). We used a 4-shell ellipsoidal model. Symmetrical dipoles were fitted to time periods where significant ERP differences were calculated. Additional symmetric dipoles explaining at least 80% of total variance were fitted to intervals, where ERP waveforms deviated markedly, although not statistically. In all cases, symmetric dipoles were obtained because residual variance was smaller when two dipoles fitted (Fig. 3).
Acknowledgments This project was supported bt the VW Foundation (AA, WP) and the Marie Curie Training Site Fellowship (ZTK, ZC, ETV). We thank Dr. Rose Bosnell for the English corrections.
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