Brain and Cognition 69 (2009) 306–315
Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Brain correlates of aesthetic expertise: A parametric fMRI study Ulrich Kirk a,b,*, Martin Skov a, Mark Schram Christensen a,c, Niels Nygaard d a
Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Kettegaard Allé 30, DK-2650 Hvidovre, Denmark Wellcome Laboratory of Neurobiology, Anatomy Department, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK Department of Exercise and Sport Sciences, University of Copenhagen, The Panum Institute, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark d Institute for Architecture and Aesthetics, Aarhus School of Architecture, Norreport 20, DK-8000 Aarhus C, Denmark b c
a r t i c l e
i n f o
Article history: Accepted 1 August 2008 Available online 9 September 2008 Keywords: Neuroaesthetics Expertise, orbitofrontal cortex Subcallosal cingulate gyrus Architecture Faces
a b s t r a c t Several studies have demonstrated that acquired expertise inﬂuences aesthetic judgments. In this paradigm we used functional magnetic resonance imaging (fMRI) to study aesthetic judgments of visually presented architectural stimuli and control-stimuli (faces) for a group of architects and a group of non-architects. This design allowed us to test whether level of expertise modulates neural activity in brain areas associated with either perceptual processing, memory, or reward processing. We show that experts and non-experts recruit bilateral medial orbitofrontal cortex (OFC) and subcallosal cingulate gyrus differentially during aesthetic judgment, even in the absence of behavioural aesthetic rating differences between experts and non-experts. By contrast, activity in nucleus accumbens (NAcc) exhibits a differential response proﬁle compared to OFC and subcallosal cingulate gyrus, suggesting a dissociable role between these regions in the reward processing of expertise. Finally, categorical responses (irrespective of aesthetic ratings) resulted in expertise effects in memory-related areas such as hippocampus and precuneus. These results highlight the fact that expertise not only modulates cognitive processing, but also modulates the response in reward related brain areas. Ó 2008 Elsevier Inc. All rights reserved.
1. Introduction In psychological models of aesthetic experience it is generally assumed that art-related expertise inﬂuences subjects’ preference for works of art (Leder, Belke, Oeberst, & Augustin, 2004). Indeed, a substantial number of behavioural studies have conﬁrmed that level of expertise modulates the aesthetic evaluation of art objects (Eysenck & Castle, 1970; Gordon, 1951/1952, 1956; Hekkert, Peper, & van Wieringen, 1994, Hekkert & van Wieringen, 1996a, 1996b; O’Hare, 1976; Schmidt, McLaughlin, & Leighten, 1989). It is therefore likely that art experts use different neural processes for determining aesthetic evaluation than non-experts. The question we wish to raise here is whether this putative difference in aesthetic evaluation can be detected as a difference in neural activity through the use of functional magnetic resonance imaging (fMRI). It has been shown by imaging experiments that acquired expertise is associated with changes in brain structures underlying perceptual and memory processes, even on a macro-anatomical scale. For example, in a study using voxel-based morphometry analysis, Maguire and colleagues (2000) found that grey matter volume in the posterior hippocampus of London taxi drivers is greater than
* Corresponding author. Address: Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Kettegaard Allé 30, DK-2650 Hvidovre, Denmark. E-mail address: [email protected]
(U. Kirk). 0278-2626/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.bandc.2008.08.004
in age-matched controls, and that the size of this increase correlates positively with time spent taxi driving. Furthermore, several experiments have demonstrated that musicians, after years of playing, respond differently to musical inputs as compared to non-musicians (for a review, see Schlaug, 2003). For example, in a recent fMRI study, Bangert and colleagues (2006) compared brain activity in groups of musicians and non-musicians as they passively listened to a piano sequence and found elevated activity in the musicians in regions of the temporal lobe associated with auditory processing, and in frontal regions associated with motor control. Several neuroimaging studies have investigated cortical areas that are recruited when subjects make aesthetic evaluations from a variety of stimulus modalities such as paintings (Cela-Conde et al., 2004; Kawabata & Zeki, 2004; Vartanian& Goel, 2004), music (Blood & Zatorre 2001; Blood, Zatorre, Bermudez, & Evans, 1999; Koelsch, Fritz, von Cramon, Müller, & Friederici, 2006; Brown, Martinez, & Parsons, 2004; Menon & Levitin, 2005), faces (Aharon et al., 2001; Nakamura et al., 1998; O’Doherty et al. 2003; Winston, O’Doherty, Kilner, Perrett, & Dolan, 2007) and geometrical ﬁgures (Jacobsen, Schubotz, Höfel, & Cramon, 2006). Taken together, these studies suggest that the computation of aesthetic preferences for objects predominantly relies on the activity of cortical and subcortical areas implicated in the processing of reward; especially striatum, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) (for a review, see Skov, in press.) It is therefore important to inves-
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
tigate whether expertise inﬂuences aesthetic evaluation through the modulation of neural activity in these areas. Since the medial OFC is not only found to correlate with subjective hedonic value in most of the studies mentioned above, but have also been demonstrated to be involved in coding stimulus value of a variety of other sensory modalities, including taste (O’Doherty et al., 2001; Small, Zatorre, Dagher, Evans, & Jones-Gotman, 2001; Small et al., 2003), olfactory (Anderson et al., 2003; Gottfried, Deichmann, Winston, & Dolan, 2002; Rolls, Kringelbach, & de Araujo, 2003), and somatosensory (Rolls, O’Doherty et al., 2003), we hypothesized that this region would reﬂect a modulation of aesthetic assessment according to level of expertise. To accomplish this experimental aim, naïve subjects (i.e. subjects professing to have no great interest or expertise in art or architecture) and expert subjects (i.e. graduate students in architecture and professional architects) were asked to rate the aesthetic value of a series of images containing both buildings and faces during an event-related fMRI paradigm (see Fig. 1). We hypothesized that the expert-speciﬁc conditions (i.e. building images) would signiﬁcantly affect both aesthetic ratings and neural activity differentially in the two groups. Since earlier psychometric studies have found that people in different cultures, and of both sexes, tend to agree as to which faces are attractive (Langlois et al., 2000), we predicted the two groups’ aesthetic ratings and neural processing would not differentiate for face images. 2. Experimental methods 2.1. Subjects A total of 24 healthy volunteers (11 experts/13 non-experts; 6 female experts/7 female non-experts; experts mean age: 30.8 years; age range 26–42 years; non-experts mean age: 27.2 years; age range 22–32 years; all subjects were right-handed) were scanned. We excluded two subjects (both male non-experts) from the analysis for clinical reasons. The experts were recruited from architectural ofﬁces and schools where they were graduate or post-graduate students. Non-experts were all undergraduate or
graduate students with no formal education in any art-related ﬁeld. Written informed consent was obtained from all subjects and ethical approval (KF-01-131/03) was obtained before the experiment. All subjects had normal or corrected-to-normal vision, and none had a history of neurological or psychiatric disorders. 2.2. Stimulus set Visual achromatic stimuli belonging to two categories, buildings and faces, were used as stimulus material. One hundred and sixty-eight building stimuli were selected from various online resources. The surrounding of the building image was shaded so that the building was in focus for each stimulus. This was accomplished in Photoshop (version 7.0, Adobe, USA). Any image noticeably distorted (e.g., proportion and illumination) by this process was excluded from the stimulus pool. Building stimuli were presented with a resolution of 600 pixels in height and varying width with a maximum of 1024 pixels. Prior to scanning the building stimuli were exposed to an aesthetic judgment scale in a behavioural pilot study by a separate cohort of subjects (7 experts/6 non-experts; 3 female experts/3 female non-experts; experts mean age 34.3 years; age range 27–44 years; non-experts mean age 29.2 years; age range 27–30 years). Level of appeal was measured using an aesthetic rating scale from 1 to 5, where 1 was deﬁned as ‘‘very unappealing” and 5 as ‘‘very appealing”. The stimuli conformed to a balanced distribution in the frequency of each rating bin between experts and non-experts. To investigate whether there were differences between the two groups for rating-speciﬁc stimuli, i.e. whether there was an image-wise difference between experts and non-experts for buildings, further analyses were applied. The building stimuli were selected according to two sub-classes: a formal/stylistic sub-classiﬁcation (‘modernist’ and ‘non-modernist’ architecture) and a typological sub-classiﬁcation (‘private’ and ‘public’ architecture). This was done in order to further control for a potential skewed preference distribution between the groups; for instance, experts might all prefer modernist and non-experts might all prefer non-modernist buildings. However, this potentially confounding effect did not amount to signiﬁcant differences between stimuli sub-classes across groups in subsequent statistical analyses (F(7, 40) = 1.78; p > .1). The face database was provided by the Max-Planck Institute for Biological Cybernetics in Tuebingen, Germany. 168 face stimuli were selected; half of the stimuli were female faces. Stimuli were rated by a separate group of subjects (n = 10/4 females; mean age 28.4 years; age range 26–30 years) for level of appeal in a behavioural pilot study prior to scanning. Level of appeal was rated using the same aesthetic rating scale as described above. One hundred and sixty-eight faces were selected from the high, middle and low ends of the appeal ratings in order to obtain a balanced distribution. The face stimuli were masked in order to remove hair and were adjusted to be of equal size and luminance by using Photoshop (version 7.0, Adobe, USA). The faces were centred in a 588 600 pixel black background and presented at a screen resolution of 1024 768 pixels. 2.3. Experimental paradigm
Fig. 1. Experimental paradigm. A ﬁxation cross was shown for 1000 ms followed by stimulus presentation with a duration of 3000 ms in which subjects were instructed to indicate the level of aesthetic appeal by means of button-press on a scale from 5 (highest appeal) to 1 (lowest appeal). Examples of stimuli used in the scanning sessions are displayed.
The experimental protocol consisted of an event-related design in which subjects were scanned while being presented with each of the 168 face stimuli and the 168 building stimuli in a pseudorandom order, making a total of 336 presentations. On each trial, a ﬁxation cross was presented for 1000 ms on a grey background followed by a stimuli presentation for 3000 ms. Subjects were instructed to press one of ﬁve buttons on a response key-pad with their right hand to indicate their aesthetic judgment (1 = very unappealing, 5 = very appealing). Randomly interspersed with the
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
stimuli presentations were 56 null event trials (grey screen). Total scanning-time per subject was 26 min. in one session. The long paradigm lasting 26 min could have potentially given rise to fatigue in the subjects. However, we did not observe any deviations in behavioural differences when the ﬁrst part of the scanning was compared with the last part of the scanning. In particular the trial to trial judgment variability was assessed as a measure of fatigue, under the assumption that fatigued subjects would tend to evaluate similarly from trial to trial when they were fatigued. Missing trials and the reaction time were also used as indirect measures, and these did not change either. Prior to scanning, subjects were informed that the study was concerned with investigating aesthetic judgments, but no reference was made to the experimental aims. After the scanning task was complete, subjects were presented with the stimuli again, this time outside the scanner, where they rated each stimulus for familiarity. Familiarity ratings were entered into the design matrix as regressors of no interest. Stimuli were presented and responses collected using Eprime (Psychology Software Tools, Inc.). The stimuli were backprojected via a LCD projector onto a transparent screen positioned over the subjects’ head and viewed through a tilted mirror ﬁxed to the head coil. 2.4. fMRI data acquisition The functional imaging was conducted by using a 3 Tesla scanner (Siemens, Magnetom Trio, Erlangen, Germany) to acquire gradient T2* weighted gradient echo (GR) echo planar images (EPI) to maximize the blood oxygen level-dependent (BOLD) contrast (echo-time, TE = 30 ms; repetition time, TR = 2400 ms; ﬂip angle, FA = 90°). The EPI sequence was optimized in order to reduce signal drop-out in OFC (Deichmann, Gottfried, Hutton, & Turner, 2003). Each functional image was acquired in an interleaved way, beginning with 2nd slice (slice No. 2,4,. . .,40, 1,3,. . .,39) when counted from the bottom, comprising 40 axial slices each 3.0 mm thick, consisting of a 64 64 matrix with an in-plane resolution of 3 3 mm. This gave near whole-brain coverage, excluding inferior parts of the cerebellum. Each session consisted of 654 volumes. The subjects’ pulse and respiration were recorded using an MRI-compatible pulse oximeter, and a respiration belt, both sampled at 50 Hz. After the functional scan, a T1 weighted MPRAGE structural sequence was acquired, using a phased array head coil to provide high-resolution anatomical detail. 2.5. fMRI data analysis Image pre-processing and data analysis was performed using SPM2 (Wellcome Department of Imaging Neuroscience, London, UK). The EPI images were spatially realigned (Friston et al., 1995). This was followed by temporal realignment, which corrected for slice-time differences using the middle slice as reference slice. Images were then normalized to the Montreal Neurological Institute (MNI) EPI-template provided in SPM2. Finally, a spatial ﬁltering was performed by applying a Gaussian smoothing kernel of 8 mm FWHM (full width at half-maximum). Following pre-processing a general linear model (GLM) was applied to the time course data, where each event was modelled with a separate single impulse response function time-locked to middle stimulus time and then convolved with the canonical haemodynamic response function (HRF), including its temporal and dispersion derivatives in order to capture small variations in the onset and width of the BOLD responses. A parametric regression analysis was used (Buchel, Holmes, Rees, & Friston, 1998) that allowed us to model on/off, linear and non-linear haemodynamic responses using orthogonalized polynomial expansion functions. This was performed for each of the two
stimulus conditions using subject-speciﬁc aesthetic ratings in order to model a potential parametric modulation of aesthetic ratings. The on/off or 0th order parametric regression analysis allows inferences to be made about variations in the response across the two subject groups independent of the aesthetic ratings. First-level analysis was performed on each subject to generate a single mean parameter corresponding to each term of the polynomial expansion. In order to correct for the structured noise induced by respiration and cardiac pulsation we included RETROICOR (RETROspective Image based CORrection method) nuisance covariates in the design matrix (Glover, Li, & Ress, 2000). These regressors are a Fourier expansion of the aliased cardiac and respiratory oscillations. We included six regressors for respiration and ten regressors for cardiac pulsation. We also included 24 regressors that remove residual movement artefacts with spin history effects, which have been shown to remain even after image realignment (Friston, Williams, Howard, Frackowiak, & Turner, 1996). This set of nuisance regressors have also been shown to reduce inter and intra subject variation signiﬁcantly (Lund, Nørgaard, Rostrup, Rowe, & Paulson, 2005). Having all four types of nuisance regressors in the design improves the assumption of independently and identically distributed errors (Lund, Madsen, Sidaros, Luo, & Nichols, 2006). For the analysis we also applied a high pass ﬁlter with a cut-off frequency at 1/128 Hz. This high pass ﬁlter removes any temporal drift that oscillates slower than once every 128 s, and it will therefore remove slowly varying drift caused by hardware instabilities. The statistical parametric maps were entered into a second-level, random effects analysis (RFX) accounting for the between subject variance. Experts and non-experts were treated as separate groups in an ANOVA model using the beta-estimates of the two groups and the two stimuli conditions for the linear and the quadratic expansions. Equal variance was not assumed, thus SPM2’s options for non-sphericity correction was applied (Glaser & Friston, 2004). Using t-contrasts allowed us to test for correlations of the fMRI BOLD signal and the parameters of interest performed as on/off, linear and non-linear parametric modulations, respectively. Reported p-values were set at a threshold of p < .001, uncorrected, unless otherwise stated. In order to correct for multiple comparisons in the medial orbitofrontal cortex, a region in which activation was predicted on the basis of our a priori hypothesis, we used small volume corrections (SVC) (Worsley et al., 1996) constraining our analysis to this region using a sphere with a 10 mm radius. We used the coordinates reported in Kawabata and Zeki (2004) for medial OFC. Before using SVC, we transformed coordinates given by Kawabata and Zeki (2004) from Talairach space to MNI space (http://www.mrc-cbu.cam.ac.uk). The coordinates of all activations are reported in MNI space.
3. Results 3.1. Behavioural results We ﬁrst inspected the two groups’ behavioural responses, i.e. aesthetic ratings, collected during scanning (see Fig. 2). A twoway ANOVA with two factor levels (buildings, faces) and groups (experts, non-experts) revealed signiﬁcant differences between stimulus conditions (F(1, 10) = 54.42; p < 2 10 7) (see Fig. 2A), but no signiﬁcant differences between groups (F(1, 10) = 1.89; p > .1). Furthermore, no signiﬁcant interactions between stimulus conditions and groups was observed (F(1, 10) = 1.44; p > .2). The same analysis was applied to the reaction-time data (RT) collected during scanning. However no signiﬁcant differences were found between groups (F(1, 10) = 0.45; p > .5). Analysing the mean ratings
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
Fig. 2. Behavioural responses collected during scanning. (A) Mean aesthetic ratings for the two stimulus conditions and both subject groups. The mean rating for building stimuli for experts was 3.29 (SD = 0.21) and for non-experts 3.28 (SD = 0.32). For face stimuli the mean rating for experts was 2.7 (SD = 0.25) and for non-experts 2.79 (SD = 0.33). (B) Mean reaction times (RT) for stimulus conditions and subject groups. Examination of RTs revealed that average RT for building stimuli for experts was 2003 ms (SD = 357.4) and for non-experts 2011 ms. (SD = 562.4). The average response time for face stimuli for experts was 1810 ms (SD = 256.6) and for non-experts 1696 ms (SD = 440.4). Response latencies between groups and stimulus conditions did not differ signiﬁcantly in a one-way ANOVA (F(3, 40) = 1.48; p < .23). (C) Distribution of ratings across the ﬁve rating bins for building stimuli, where each rating bin is bears a scale from 5 (high appeal) to 1 (low appeal) (x-axis) and the response frequency across subjects in percent is shown (y-axis). Error bars indicate SD. (D) Distribution of ratings across the ﬁve rating bins for face stimuli. Error bars indicate SD.
of the two stimulus classes, these results suggest that experts and non-experts did not display signiﬁcantly different behaviour in making an aesthetic judgment of either faces or buildings. We next inspected whether experts and non-experts differed in the frequency of each rating bin (see Fig. 2C and D), as such a difference might not be reﬂected when inspecting the mean aesthetic ratings (see Fig. 2A). Moreover, such a potential difference in the distribution of ratings could also have consequences for interpreting the fMRI results, since a different distribution in the frequency of rating bins between groups could potentially account for differences in the linear ﬁts between the two groups. No signiﬁcant differences were found between groups in the frequency of each rating bin for face stimuli (see Fig. 2D). For building stimuli (see Fig. 2C) no signiﬁcant differences between groups was observed for rating bin 4 and 5 (reﬂecting high appeal) and bin 1 and 2 (reﬂecting low appeal). However, the middle rating bin resulted in a signiﬁcant difference between groups (two sample t = 3.02; df = 10; p < .01). Finally, we looked at the variance of RTs between the two groups. Although we found no signiﬁcant differences between the two groups in mean ratings and RTs, it was possible that a difference between groups would be reﬂected in greater variance in RTs between the groups. However, we found no such difference in variance across groups (F(1, 4) = 0.73; p > .4), rating bin (F(1, 4) = 0.24;
p > .9), or stimulus type (F(1, 4) = 2.25; p > .1). Likewise, we observed no signiﬁcant interactions (group rating, group stimulus, and group rating stimulus). 3.2. fMRI results 3.2.1. Correlation between the BOLD signal and linear aesthetic ratings To test whether architectural expertise modulated brain activity associated with making aesthetic judgments a 1st order parametric regression model using the subject-speciﬁc behavioural responses was applied. We deﬁned the expertise effect as the interaction between the two subject groups and the two stimulus conditions [Expert_Build–Expert_Faces] [Non-Expert_Build– Non-Expert_Faces]. This analysis allowed us to focus on voxels for which the difference between the responses for the two stimulus conditions varied across the two subject groups. The interaction revealed signiﬁcant activations in bilateral subcallosal cingulate gyrus ( 4, 30, 2; z = 4.47; p < .05, corrected for multiple comparisons using false discovery rate, FDR; 14, 40, 2; z = 4.32; p < .05, FDR) (see Fig. 3). When we performed small volume corrections (SVC) we observed signiﬁcant activity in bilateral medial OFC ( 8, 30, 20; z = 3.83; p < .05, FDR, SVC; 6, 34, 16; z = 3.40; p < .05, FDR, SVC). These signiﬁcant interactions were further
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
Fig. 3. The ﬁgure shows areas where the BOLD signal correlates with a linear ﬁt for the interaction [Expert_Build–Expert_Faces] [Non-Expert_Build–Non-Expert_Faces]. The upper left panel shows activation in left subcallosal cingulate gyrus. The upper right panel shows parameter estimates for subcallosal cingulate gyrus ( 4, 30, 2), where the xaxis reﬂects the experimental conditions and the y-axis shows BOLD signal changes. The lower left panel shows activation in left medial OFC and the lower right panel shows parameter estimates from the hottest voxels in medial OFC ( 8, 30, 20). Activations are overlaid on sagittal sections of the canonical SPM structural image. Activations are displayed at p < .001, uncorrected. Error bars indicate 90% conﬁdence interval.
investigated by examining contrasts of parameter estimates. Fig. 3 describes the subject-averaged parameter estimates from the peak activations at the group level in subcallosal cingulate gyrus and medial OFC, which show the marked difference between experts and non-experts for building stimuli. Experts had greater activation in subcallosal cingulate gyrus and medial OFC when making aesthetic judgments of buildings, while activation levels for the same voxels for face stimuli in both groups were essentially balanced, suggesting that the observed interaction effects depend on acquired expertise of architecture. Interestingly, the subcallosal cingulate gyrus correlated positively with aesthetic judgments for experts while for non-experts it was negatively correlated. However, both groups correlated positively in OFC, while only the activation level was signiﬁcant for experts compared to nonexperts. A potentially confounding effect in this paradigm would have been introduced if experts had any prior knowledge of the building stimuli, so that the cortical differences between groups reﬂected recognition effects rather than aesthetic judgment, speciﬁcally in medial OFC (Frey& Petrides, 2002). To control for this we regressed familiarity data (collected post-scanning for building stimuli) onto brain activity. SVC was applied, constraining our analysis to the medial OFC activation ( 8, 30, 22 and 6, 34, 16). However, this analysis did not produce any supra-threshold voxels at p < .001, uncorrected (not shown), indicating that familiarity effects did not contribute to the results in medial OFC. The likely explanation for this is simply that only very few buildings were recognized by the experts, and hence, any effects of familiarity would be modelled out by the applied high pass ﬁlter. When we performed the inverse interaction, which should highlight areas exhibiting an elevated parametric response to buildings over faces in non-experts compared to experts, [NonExpert_Build–Non-Expert_Faces] [Expert_Build–Expert_Faces], we
found that no areas correlated signiﬁcantly with such a response proﬁle (p < .001, uncorrected). In order to formally identify possible common brain areas between the two subject groups that scaled linearly with preference responses for building stimuli we performed a conjunction between experts and non-experts. This analysis did not reveal any signiﬁcant voxels (p < .001, uncorrected), supporting our hypothesis that processes involved in aesthetic evaluation are differentially modulated in the two groups. 3.2.2. Correlation between the BOLD signal and non-linear aesthetic ratings We furthermore modelled 2nd order polynomial expansions of the subject-speciﬁc aesthetic judgments. This analysis makes it possible to seek evidence for brain activity that correlates signiﬁcantly with a positive 2nd order non-linear response proﬁle, which has the form of a u-shaped function (where responses are maximal for appealing and unappealing stimuli compared to neutrally rated stimuli) to account for additional variance not captured by the linear 1st order term. An interaction analysis using this non-linear order term [Non-Expert_Build–Non-Expert_Faces] [Expert_Build– Expert_Faces] and [Expert_Build–Expert_Faces] [Non-Expert_Build–Non-Expert_Faces] did not produce any signiﬁcant activity (p < .001, uncorrected). In order to search for common areas with a non-linear response proﬁle for building stimuli regardless of group we performed a conjunction analysis. One region in the ventral striatum, namely the left nucleus accumbens (NAcc) ( 10, 10, 4; z = 4.94; p < .008, FDR), and also a small cluster in the left anterior thalamus ( 14, 4, 12; z = 4.75; p < .008, FDR), were both signiﬁcant in the conjunction analysis. In order to further investigate the role of NAcc and the anterior thalamus we employed a conjunction analysis including both stimulus conditions and both groups, but the result did not meet a corrected threshold. We therefore applied SVC using
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
the clusters from the building-speciﬁc conjunction, and found that left NAcc ( 10, 8, 4; z = 3.42; p < .05, FDR, SVC) was signiﬁcantly more active in both stimuli conditions in both groups (see Fig. 4). The activation in left anterior thalamus did not survive SVC. These results suggest that left NAcc plays a role in encoding high and low aesthetic values that is not modulated by expertise or stimulus modality. 3.2.3. Correlation between the BOLD signal and expertise irrespective of aesthetic ratings Finally, in order to identify voxels that responded differentially in the two groups per se—i.e., irrespective of aesthetic rating—an interaction analysis using regressors from a zero-order parametric regression analysis was conducted. An interaction analysis [Expert_Build–Expert_Faces] [Non-Expert_Build–Non-Expert_Faces] showed distinct speciﬁcity for buildings compared to faces in experts relative to non-experts in bilateral hippocampus, left precuneus and cerebellum (see Fig. 5 and Table 1). In the converse interaction [Non-Expert_Build–Non-Expert_Faces] [Expert_Build–Expert_Faces] we observed signiﬁcant activations in bilateral calcarine gyrus bilateral and fusiform gyrus located adjacent to the collateral sulcus and inferior lingual gyrus posterior to the parahippocampal gyrus (see Fig. 6 and Table 1). We furthermore conducted a conjunction analysis using the building-speciﬁc main effects for both groups [Expert_Build–Expert_Faces] and [Non-Expert_Build–Non-Expert_Faces]. We observed bilateral activation of the parahippocampal place area (PPA) [30, 42, 14; 30, 46, 10, FDR] (see Supplementary material) that has been found to respond selectively to houses, landscapes and other environmental sceneries (Epstein & Kanwisher, 1998). 4. Discussion
Fig. 5. The upper panel display activation of the left hippocampus from the interaction [Expert_Build–Expert_Faces] [Non-Expert_Build–Non-Expert_Faces] using the zero-order parametric analysis that display voxels activated irrespective of aesthetic rating. The lower panel shows parameter estimates for the left hippocampus, where the x-axis reﬂects the experimental conditions, and the y-axis shows BOLD signal changes. Activation is displayed at p < .001, uncorrected. Error bars indicate 90% conﬁdence interval.
Table 1 Summary of interaction effects for the parametric regression analysis irrespective of aesthetic ratings Brain region
The present experiment extends other studies of expertise to suggest that acquired expertise not only impacts on cognitive and perceptual systems (Bangert et al., 2006; Maguire et al.,
Number of voxels
[Expert_Build–Expert_Faces] [Non-Expert_Build–Non-Expert_Faces] R hippocampus 38, 28, 8 3.60 18 L hippocampus 26, 14, 14 3.29 17 34, 16, 20 L precuneus 6, 54, 22 3.61 37 R cerebellum 16, 62, 16 3.57 26 26, 68, 24 3.44 13 [Non-Expert_Build–Non-Expert_Faces] [Expert_Build–Expert_Faces] R fusiform gyrus 32, 54, 4 3.35 9 L fusiform gyrus 30, 56, 0 3.73 42 R calcarine gyrus 18, 58, 16 3.59 21 L calcarine gyrus 18, 64, 14 3.79 30 R pons 16, 18, 30 4.41 69 Activations are shown at (p < .001, uncorrected). L, left hemisphere; R, right hemisphere.
Fig. 4. The upper panel shows activation in left NAcc using the 2nd order non-linear term. The lower panel shows the parameter estimates in both groups and for both stimulus conditions in left NAcc ( 10, 8, 4) where the x-axis reﬂects the experimental conditions consisting of both groups and both stimulus conditions, and the y-axis shows BOLD signal changes. Activations are overlaid on sagittal, coronal and axial sections of the canonical SPM structural image. Activation is displayed at p < .008, FDR). Error bars indicate 90% conﬁdence interval.
2000), but also modulates the response of brain areas associated with the processing of reward. However, the processing of reward has been linked to several brain areas, including the ventral tegmental area, ventral striatum, amygdala and OFC (for a review, see McClure, York, & Montague, 2004), and our results show that only parts of this system are modulated by expertise during aesthetic judgment. In contrast to expertise effects observed in OFC and subcallosal cingulate gyrus, we found that activity in left NAcc was elevated in both groups and stimuli conditions in response to appealing and non-appealing stimuli. The response proﬁle of medial OFC in both groups exhibited a positive linear correlation with aesthetic ratings. However, when compared to each other the increase was signiﬁcantly higher in the experts than in the non-experts. The fact that the medial part of OFC shows sensitivity to the magnitude of aesthetic value is in accordance with studies on reward processing showing that the relative reward value of stimuli is reﬂected by the amplitude of
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
Fig. 6. The ﬁgure displays the interaction [Non-Expert_Build–Non-Expert_Faces] [Expert_Build–Expert_Faces] from the zero-order parametric analysis that shows brain areas activated, irrespective of the actual aesthetic ratings. The upper panel displays a bilateral activation of the fusiform gyrus on slices where zcoordinates for the three slices are ascending from 6, 4 and 2, respectively. Evident on the slices are the collateral sulcus just lateral to the activation in the fusiform gyrus. The lower panel displays the corresponding parameter estimates in the right fusiform gyrus. The x-axis reﬂects the experimental conditions. The y-axis shows BOLD signal changes. Activations are displayed at p < .001, uncorrected. Error bars indicate 90% conﬁdence interval.
neural activity in OFC (Kringelbach, 2005; Tremblay & Schultz, 1999). For instance, in studies comparing subjects ingesting food in states of hunger and satiety a contrast of these two states reveals different neural responses in OFC, indicating that OFC neurons codes reward aspects of a stimulus rather than sensory aspects (Kringelbach, O’Doherty, Rolls, & Andrews, 2003). Furthermore, several studies suggest that the medial aspect of the human OFC represents the hedonic attributes involved in preference judgments of various stimulus types (Aharon et al., 2001; Anderson et al., 2003; Blood et al., 1999; Gottfried et al., 2002; Kawabata et al., 2004; O’Doherty et al., 2001; O’Doherty et al., 2003; Rolls, Kringelbach, O’Doherty, Rolls, & Andrews, 2003; Rolls, O’Doherty, et al., 2003; Small et al., 2001, 2003). The implication of these previous ﬁndings for the present results is that medial OFC may be engaged under conditions where behavioural decision making based on stimulus reward value is required (Bechara, Damasio, & Damasio, 2000; Wallis, 2007). Recently, several studies have demonstrated that medial OFC responses can be modulated by top-down information such as knowledge of the price of a wine (Plassmann et al., 2008), brand information (McClure, York et al., 2004), and visual word descriptors inﬂuence preference for odours (de Araujo, Rolls, Velazco, Margot, & Cayeux, 2005). The novelty of our results is that the representation of stimulus value, or possibly intrinsic motivation, in medial OFC varies with expertise level to such an extent that the experts displayed higher activation to the building stimuli than the non-experts, but not to the control-stimuli, i.e. faces. In contrast to OFC, voxels in the subcallosal part of the anterior cingulate gyrus responded inversely to high and low ratings in the experts compared to non-experts. This result supports the growing recognition that anterior cingulate gyrus and OFC contribute distinct component processes to decision making (Rushworth, Behrens, Rudebeck, & Walton, 2007). The anterior aspect of the cingulate gyrus forms an anatomical interface between the OFC and premotor cortex. Since it also receives afferents from subcorti-
cal dopaminergic neurons and inputs from dorsolateral prefrontal cortex, it has been suggested that the anterior cingulate integrates the affective drive and action strategies for the purpose of selecting appropriate motor responses, i.e. making decisions how to act (Paus, 2001). Another possibility might be that subcallosal cingulate gyrus activity reﬂects the subjects’ monitoring of their own emotional state (Ochsner et al., 2004), whereby appealing buildings are more arousing to the experts than to the non-experts. Indeed, the subcallosal aspect of the cingulate gyrus has previously been implicated in such forms of emotional processing, including the recall of happy autobiographical memories (Lane, Reinman, Ahern, Schwartz, & Davidson, 1997) and attending to emotionally stimulating words (Maddock, Garrett, & Buonocore, 2003). Interestingly, decreasing musical dissonance, associated with an elevated experience of pleasure (Blood et al., 1999) and passive listening to unfamiliar, pleasant musical compared to a rest condition (Brown et al., 2004) has also been shown to produce enhanced activity in subcallosal cingulate gyrus. Finally, as pupillometry data was not recorded in the present study we are unable to assess whether or not such effects would have detected differences between the two groups. Indeed there is evidence for involvement of the subcallosal cingulate in generating and monitoring autonomic interoceptive states (Critchley, 2004). It is notable that while activity in OFC and the subcallosal cingulate gyrus were sensitive to the level of expertise, the behavioural responses did not parallel this difference in neural activation between the two groups. As a number of previous behavioural studies have found an effect of expertise on aesthetic evaluation to be robust, our result was somewhat surprising (Eysenck & Castle, 1970; Gordon, 1951/1952; Gordon, 1956; O’Hare, 1976; Schmidt et al., 1989). One possible reason for this discrepancy between our study and earlier ones could be the method of comparison used in our study. It is conceivable that comparing means of rating is not sufﬁciently sensitive to detect differences in experts’ and non-experts’ ratings. Experts and nonexperts are known to respond differentially to dimensions of stimulus qualities such as craftsmanship and quality (Hekkert & van Wieringen, 1996b), or chromatic versus achromatic versions of paintings (Hekkert & van Wieringen, 1996a). It is conceivable that our set of buildings stimuli lack perceptual properties that systematically inﬂuence the differential aesthetic assessment of experts and non-experts. On the other hand, the fact that we did not observe a behavioural difference in the two groups’ responses to the building stimuli strengthens the neural result. If both a difference in behaviour and neural activity between the two groups had been observed, the difference in neural activity might have been confounded by the differences in behavioural responses. In the present situation where the two groups differ in neural activity but not in behaviour we can be sure that what we observe is a true group difference. Our ﬁnding of a positive bivalent response in the ventral striatum, speciﬁcally the left NAcc, in both subject groups and to both stimuli types replicates and extends previous ﬁndings that NAcc and OFC play different functional roles in reward processing (Knutson, Fong, Adams, Varner, & Hommer, 2001; O’Doherty et al., 2002; O’Doherty et al., 2003; Tremblay& Schultz, 1999; Watanabe, 1999). Whereas the OFC is thought to process reward outcomes, the NAcc is generally believed to subserve the prediction of reward and to compute the variance between reward expectation and the actual reward (for reviews, see Knutson & Cooper, 2005; Montague, Hyman, & Cohen, 2004). Although earlier results have found non-linear response proﬁles in the NAcc as discussed below, to our knowledge there has been no previous descriptions of NAcc activity with negative aesthetic ratings. Electrophysiological recordings in animals have demonstrated that NAcc neurons increase their response to positive reward prediction errors (situations that are
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
better than expected) and decrease their response to negative prediction errors (Apicella, Ljungberg, Scarnati, & Schultz, 1991; Schultz, Apicella, Ljungberg, Romo, & Scarnati, 1993). Neuroimaging work has subsequently replicated these ﬁndings (e.g., O’Doherty et al., 2006; Seymour, Daw, Dayan, Singer, & Dolan, 2007; Spicer et al., 2007). However, recently another hypothesis has been put forth, suggesting that activations of the ventral striatum are not sensitive to errors in prediction, but rather encode salience dimensions of the stimulus. This idea heralds from imaging studies where the ventral striatum has been shown to correlate with prediction errors regardless of valence (Jensen et al., 2007; Zink, Martin-Skurski, Chappelow, & Berns, 2004). A recent fMRI study by Cooper and Knutson (2008), though, suggests that the ventral striatum may compute the interaction of valence and salience, depending upon the context wherein motivational behaviour takes place. Directly comparing degrees of valence and salience, Cooper and Knutson found that both the valence and the salience of anticipated incentives correlated with NAcc activation. More speciﬁcally, in this study when outcomes were uncertain and salience high, NAcc activation increased for anticipated loss and gain, whereas NAcc activation increased for anticipated gain and decreased for anticipated loss when outcomes were certain and salience low. In our study the experimental set-up might be seen as similar to the ﬁrst situation (uncertain outcome and high salience). However, we neither manipulated the salience of the pictures nor the relation between anticipation and reward, so this remains conjecture. Since it is possible that the subjects covertly anticipated the reward outcome of the upcoming stimuli based on the recently transpired judgment act, we cannot rule out the possibility that the NAcc activation reﬂects prediction error signalling. The zero-order parametric regression analysis identiﬁed brain regions showing a typological response to the two stimulus conditions irrespective of aesthetic ratings. Hence, this analysis detects differences in the two groups’ response to the stimulus material beyond those differences speciﬁcally related to making an aesthetic judgment. Inspection of the parameter estimates in signiﬁcant voxels from the interaction showed that experts had signiﬁcantly more activity in hippocampus, precuneus and cerebellum relative to non-experts for expert-stimuli (buildings) compared to control-stimuli (faces). Precuneus is often reported to play a role in integrating the current input with prior established knowledge (Fletcher et al., 1995; Maguire, Frith, & Morris, 1999) and in episodic memory retrieval (Krause et al., 1999). Our data suggests that the demand on the precuneus is higher in experts when perceiving expert-stimuli, as building conditions presumably depend more on connections between retrieved information and prior knowledge for this group relative to non-experts. The hippocampus has also been consistently implicated in episodic memories (Brown & Aggleton, 2001; Eichenbaum, Schoenbaum, Young, & Bunsey, 1996; Eichenbaum, Yonelinas, & Ranganath, 2007). Hippocampus activation has been associated with conditions where subjects correctly recollect contextual information compared to conditions where they do not (Cansino, Maquet, Dolan, & Rugg, 2002). Our ﬁndings support these data and suggest that the hippocampus and precuneus may be selectively engaged during memory retrieval in experts. As we have ruled out familiarity effects in the data, experts may have attempted to organize new information into a framework of prior knowledge and use this information to guide and bias aesthetic judgments. The possibility that the hippocampus and the precuneus are speciﬁcally involved in biasing preference judgments based on recruitment of episodic memory has also been suggested by other studies (Jacobsen et al., 2006; McClure et al., 2004). Speciﬁcally, a characteristic of episodic memories in the present study might be increased encoding of associations in experts (Eichenbaum et al., 2007) responding to expert-stimuli, which is dissociable from
stimuli recognized based on familiarity (Eldridge, Knowlton, Furmanski, Bookheimer, & Engel, 2000). For the converse interaction analysis we found no activation in areas involved in episodic memory formation. However, we found activity in regions of the visual cortex and in the ventral temporal cortex, such as the calcarine gyrus and in the fusiform gyrus. The bilateral activation of the fusiform gyrus is interesting as this region, although distinctly demarcated by the collateral sulcus, is anatomically closely located to an area straddling the anterior end of lingual gyrus which has been deemed the location of a building-sensitive region (Aguirre, Zarahn, & D’Esposito, 1998). Further evidence for building selectivity shows that the medial portion of the fusiform gyrus, including the collateral sulcus, demonstrates greater fMRI signal change in response to buildings as compared to faces and chairs (Ishai, Ungerleider, Martin, Schouten, & Haxby, 1999). There is disagreement about the exact anatomical location of a building-sensitive region, but it seems to include both inferior lingual gyrus and the fusiform gyrus surrounding the collateral sulcus. Our activation, clearly located in the fusiform gyrus, is, however, distinct from the face-sensitive region within the fusiform gyrus (Kanwisher, McDermott, & Chun, 1997), which is located inferior and lateral to our building-sensitive voxels. This is furthermore evidenced by the parameter estimates in the building-sensitive region of the fusiform gyrus, where it is shown that this area is unresponsive to face stimuli in both groups (see Fig. 6). The region we observed in the fusiform gyrus is located just adjacent to the parahippocampal gyrus. Several neuroimaging studies have demonstrated that the posterior portion of the parahippocampal gyrus is involved in the representation of large-scale places and scenes (Epstein & Kanwisher, 1998; Maguire, Frith, Burgess, Donnett, & O’Keefe, 1998). It is noteworthy that we observed activity in the PPA in the conjunction analysis between [buildings > faces] for experts and non-experts. Evident in this conjunction is also activation, beyond the PPA, of the entire ventral temporal cortex and visual cortex (see Supplementary material), suggesting that the response to buildings is not restricted to the region that responds maximally to that object category located in the fusiform gyrus. However, this effect may also be driven by differences in visual stimulation across the two stimulus conditions, because building trials were presented with varying pixel width. The fact that building stimuli activate the entire ventral temporal cortex, albeit to varying degrees, suggests, in agreement with other reports (Ishai et al., 1999), that the representation of buildings in this portion of the cortex may be feature-based rather than building-sensitive per se. Such an interpretation may account for the differential activation of the fusiform gyrus between experts and nonexperts evident in the interaction analysis. The demand on this portion of the fusiform gyrus is higher for non-experts compared to experts presumably due to experts’ recruitment of episodic memory, whereas non-experts are more sensitive to speciﬁc perceptual features of building stimuli, which ﬁnds further support by the relative stronger activation in the calcarine gyrus in non-experts relative to experts. In conclusion, we have demonstrated that expertise modulates brain areas to both aesthetic processing and to cognitive or typological processing irrespective of aesthetic ratings. Speciﬁcally, our new discovery is that the representation of stimulus value in medial OFC and bilateral subcallosal cingulate gyrus is modulated by expertise. We found that only some regions associated with the processing of reward are modulated by expertise (OFC, subcallosal cingulate gyrus), while activity in NAcc was typical of both experts and non-experts, suggesting that these regions play different roles in reward processing. Furthermore, we have demonstrated that experts and non-experts differ in their neural response to expertise stimuli per se, irrespective of aesthetic ratings. This typological response was observed bilaterally in the hippocampus and precu-
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315
neus, and suggests that experts may integrate current input into a framework of prior knowledge and use this information to organize aesthetic judgments. Acknowledgments We thank Prof. S. Zeki, Dr. O.J. Hulme and Dr. T. Lund for helpful discussions. Prof. C. Frith, Dr. M. Self, Dr. V. Cardin and Dr. T. Ramsøy provided useful comments on the manuscript. P. Neckelmann prepared the stimulus material. U. Kirk was supported by a Ph.D. scholarship from the Danish Medical Research Council; M. Skov was supported by Hvidovre Hospital’s research foundation; M.S. Christensen was supported by a Ph.D. scholarship from the Faculty of Science, University of Copenhagen; N. Nygaard was supported by a Ph.D. scholarship by the Danish Research Council for the Humanities. The MR-scanner was donated by the Simon Spies Foundation. Appendix A. Supplementary data The ﬁgure displays the building conjunction from the zero-order parametric analysis derived from the building-speciﬁc main effects for both groups [Expert_Build–Expert_Faces] and [Non-Expert_Build–Non-Expert_Faces]. Glass-brain activation is displayed at FDR-corrected threshold. Supplementary data associated with this article can be found, in the online version, at doi:10.1016/ j.bandc.2008.08.004. References Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). An area within the human ventral cortex sensitive to ‘building’ stimuli: Evidence and implications. Neuron, 21, 373–383. Aharon, I., Etcoff, N., Ariely, D., Chabris, C. F., O’Connor, E., & Breiter, H. C. (2001). Beautiful faces have variable reward value: fMRI and behavioural evidence. Neuron, 32, 537–551. Anderson, A. K., Christoff, K., Stappen, I., Panitz, D., Ghahremani, D. G., Glover, G., et al. (2003). Dissociated neural representations of intensity and valence in human olfaction. Nature Neuroscience, 6, 196–202. Apicella, P., Ljungberg, T., Scarnati, E., & Schultz, W. (1991). Responses to reward in dorsal and ventral striatum. Experimental Brain Research, 85, 491–500. Bangert, M., Peschel, T., Schlaug, G., Rotte, M., Drescher, D., & Hinrichs, H. (2006). Shared networks for auditory and motor processing in professional pianists: Evidence from fMRI conjunction. Neuroimage, 30, 917–926. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Decision making and the orbitalfrontal cortex. Cerebral Cortex, 10, 295–307. Blood, A. J., & Zatorre, R. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proceedings of the National Academy of Sciences of the United States of America, 98, 11818–11823. Blood, A. J., Zatorre, R. J., Bermudez, P., & Evans, A. C. (1999). Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nature Neuroscience, 2, 382–387. Brown, M. W., & Aggleton, J. P. (2001). Recognition memory: What are the roles of the perirhinal cortex and hippocampus? Nature Review Neuroscience, 2, 51–61. Brown, S., Martinez, M. J., & Parsons, L. M. (2004). Passive music listening spontaneously engages limbic and paralimbic systems. NeuroReport, 15, 2033–2037. Buchel, C., Holmes, A. P., Rees, G., & Friston, K. J. (1998). Characterizing stimulus response functions using nonlinear regressors in parametric fMRI experiments. Neuroimage, 8, 140–148. Cansino, S., Maquet, P., Dolan, R. J., & Rugg, M. D. (2002). Brain activity underlying encoding and retrieval of source memory. Cerebral Cortex, 12, 1048–1056. Cela-Conde, C. J., Marty, G., Maestú, F., Ortiz, T., Munar, E., Fernández, A., et al. (2004). Activation of the prefrontal cortex in the human visual aesthetic perception. Proceedings of the National Academy of Sciences of the United States of America, 101, 6321–6325. Cooper, J. C., & Knutson, B. (2008). Valence and salience contribute to nucleus accumbens activation. Neuroimage, 39, 538–547. Critchley, H. D. (2004). The human cortex responds to an interoceptive challenge. Proceedings of the National Academy of Sciences of the United States of America, 101, 6333–6334. de Araujo, I. E., Rolls, E. T., Velazco, M. I., Margot, C., & Cayeux, I. (2005). Cognitive modulation of olfactory processing. Neuron, 46, 671–679. Deichmann, R., Gottfried, J. A., Hutton, C., & Turner, R. (2003). Optimized EPI for fMRI studies of the orbitofrontal cortex. Neuroimage, 19, 430–441. Eichenbaum, H., Schoenbaum, G., Young, B., & Bunsey, M. (1996). Functional organization of the hippocampal memory system. Proceedings of the
National Academy of Sciences of the United States of America, 93, 13500–13507. Eichenbaum, H., Yonelinas, A. P., & Ranganath, C. (2007). The medial temporal lobe and recognition memory. Annual Reviews of Neuroscience, 21, 123–152. Eldridge, L. L., Knowlton, B. J., Furmanski, C. S., Bookheimer, S. Y., & Engel, S. A. (2000). Remembering episodes: A selective role for the hippocampus during retrieval. Nature Neuroscience, 11, 1148–1152. Epstein, R., & Kanwisher, N. (1998). A cortical representation of the local visual environment. Nature, 392, 598–601. Eysenck, H. J., & Castle, M. (1970). Training in art as a factor in the determination of preference judgments for polygons. British Journal of Psychology, 61, 65–81. Fletcher, P. C., Frith, C. D., Grasby, P. M., Shallice, T., Frackowiak, R. S. J., & Dolan, R. J. (1995). Brain systems for encoding and retrieval of auditory-verbal memory: An in vivo study in humans. Brain, 118, 401–416. Frey, S., & Petrides, M. (2002). Orbitofrontal cortex and memory formation. Neuron, 36, 171–176. Friston, K. J., Ashburner, J., Frith, C. D., Poline, J. B., Heather, J. D., & Frackowiak, R. S. J. (1995). Spatial registration and normalization of images. Human Brain Mapping, 2, 165–189. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346–355. Glaser, D., & Friston, K. J. (2004). Variance components. In R. S. J. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, & S. Zeki, et al. (Eds.), Human brain function (pp. 781–792). Elsevier Academic Press. Glover, G. H., Li, T. Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44, 162–167. Gordon, D. A. (1951/1952). Methodology in the study of art evaluation. Journal of Aesthetics and Art Criticism, 10, 338–352. Gordon, D. A. (1956). Individual differences in the evaluation of art and the nature of art standards. Journal of Educational Research, 50, 17–30. Gottfried, J. A., Deichmann, R., Winston, J. S., & Dolan, R. J. (2002). Functional heterogeneity in human olfactory cortex: An event-related functional magnetic resonance imaging study. Journal of Neuroscience, 22, 10819–10828. Hekkert, P., Peper, L. E., & van Wieringen, P. C. W. (1994). The effect of verbal instruction and artistic background on the aesthetic judgment of rectangles. Empirical Studies of the Arts, 12, 185–203. Hekkert, P., & van Wieringen, P. C. W. (1996a). The impact of level of expertise on the evaluation of original and altered versions of post-impressionistic paintings. Acta Psychologia, 94, 117–131. Hekkert, P., & van Wieringen, P. C. W. (1996b). Beauty in the eye of expert and nonexpert beholders: A study in the appraisal of art. American Journal of Psychology, 109, 389–407. Ishai, A., Ungerleider, L. G., Martin, A., Schouten, J. L., & Haxby, J. V. (1999). Distributed representation of objects in the human visual pathway. Proceedings of the National Academy of Sciences of the United States of America, 96, 9379–9384. Jacobsen, T., Schubotz, R. I., Höfel, L., & Cramon, D. Y. (2006). Brain correlates of aesthetic judgment of beauty. Neuroimage, 29, 276–285. Jensen, J., Smith, A. J., Willeit, M., Crawley, A. P., Mikulis, D. J., Vitcu, I., et al. (2007). Separate brain regions code for salience vs valance during reward prediction in humans. Human Brain Mapping, 28, 294–302. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. Journal of Neuroscience, 17, 4302–4311. Kawabata, H., & Zeki, S. (2004). Neural correlates of beauty. Journal of Neurophysiology, 91, 1699–1705. Knutson, B., & Cooper, J. C. (2005). Functional magnetic resonance imaging of reward prediction. Current Opinion of Neurobiology, 18, 411–417. Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L., & Hommer, D. (2001). Dissociation of reward anticipation and outcome with event-related fMRI. NeuroReport, 12, 3683–3687. Koelsch, S., Fritz, T., von Cramon, D. Y., Müller, K., & Friederici, A. D. (2006). Investigating emotion with music: An fMRi study. Human Brain Mapping, 27, 239–250. Krause, B. J., Schmidt, D., Mottaghy, F. M., Taylor, J., Halsband, U., Herzog, H., et al. (1999). Episodic retrieval activates the precuneus irrespective of the imagery content of word pair associates: A PET-study. Brain, 122, 255–263. Kringelbach, M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews Neuroscience, 6, 691–702. Kringelbach, M. L., O’Doherty, J., Rolls, E. T., & Andrews, C. (2003). Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. Cerebral Cortex, 13, 1064–1071. Lane, R. D., Reinman, E. M., Ahern, G. L., Schwartz, G. E., & Davidson, R. J. (1997). Neuroanatomical correlates of happiness, sadness, and disgust. The American Journal of Psychiatry, 154, 926–933. Langlois, J. H., Kalakanis, L., Rubenstein, A. J., Larson, A., Hallam, M., & Smoot, M. (2000). Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, 126, 390–423. Leder, H., Belke, B., Oeberst, A., & Augustin, D. (2004). A model of aesthetic appreciation and aesthetic judgments. British Journal of Psychology, 95, 489–508. Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W. L., & Nichols, T. E. (2006). Non-white noise in fMRI: Does modeling have an impact? Neuroimage, 29, 54–66. Lund, T. E., Nørgaard, M. D., Rostrup, E., Rowe, J. B., & Paulson, O. B. (2005). Motion or activity: Their role in intra- and inter-subject variation in fMRI. Neuroimage, 26, 960–964.
U. Kirk et al. / Brain and Cognition 69 (2009) 306–315 Maddock, R. J., Garrett, A. S., & Buonocore, M. H. (2003). Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task. Human Brain Mapping, 18, 30–41. Maguire, E. A., Frith, C. D., Burgess, N., Donnett, J. G., & O’Keefe, J. (1998). Knowing where things are: Parahippocampal involvement in encoding object locations in virtual large-scale space. Journal of Cognitive Neuroscience, 10, 61–76. Maguire, E. A., Frith, C. D., & Morris, R. G. M. (1999). The functional neuroanatomy of comprehension and memory: The importance of prior knowledge. Brain, 122, 1839–1850. Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proceedings of the National Academy of Sciences of the United States of America, 97, 4414–4416. McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004). Neural correlates of behavioral preference for culturally familiar drinks. Neuron, 44, 379–387. McClure, S. M., York, M. K., & Montague, P. R. (2004). The neural substrates of reward processing in humans: The modern role of fMRI. Neuroscientist, 10, 260–268. Menon, V., & Levitin, D. J. (2005). The rewards of music listening: Response and physiological connectivity of the mesolimbic system. Neuroimage, 28, 175–184. Montague, P. R., Hyman, S., & Cohen, J. D. (2004). Computational roles for dopamine in behavioural control. Nature, 431, 760–767. Nakamura, K., Kawashima, R., Nagumo, S., Ito, K., Sugiura, M., Kato, T., et al. (1998). Neuroanatomical correlates of the assessment of facial attractiveness. NeuroReport, 9, 753–757. Ochsner, K. N., Knierim, K., Ludlow, D. H., Hanelin, J., Ramachandran, T., Glover, G., et al. (2004). Reﬂecting upon feelings: An fMRI study of neural systems supporting the attribution of emotion to self and other. Journal of Cognitive Neuroscience, 16, 1746–1772. O’Doherty, J. P., Buchanan, T. W., Seymour, B., & Dolan, R. J. (2006). Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron, 49, 157–166. O’Doherty, J. P., Deichmann, R., Critchley, H., & Dolan, R. J. (2002). Neural responses during anticipation of a primary taste reward. Neuron, 33, 815–926. O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., & McGlone, F. (2001). Representation of pleasant and aversive taste in the human brain. Journal of Neurophysiology, 85, 1315–1321. O’Doherty, J., Winston, J., Critchley, H., Perrett, D., Burt, D. M., & Dolan, R. J. (2003). Beauty in a smile; the role of medial orbitiofrontal cortex in facial attractiveness. Neuropsychologia, 41, 147–155. O’Hare, D. P. A. (1976). Individual differences in perceived similarity and preference for visual art: A multidimensional scaling analysis. Perception and Psychophysics, 20, 445–452. Paus, T. (2001). Primate anterior cingulate cortex: Where motor control, drive and cognition interface. Nature Reviews Neuroscience, 2, 417–424.
Plassmann, H., O’Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences of the United States of America, 105, 1050–1054. Rolls, E. T., Kringelbach, M. L., & de Araujo, I. E. (2003). Different representations of pleasant and unpleasant odours in the human brain. European Journal of Neuroscience, 18, 695–703. Rolls, E. T., O’Doherty, J., Kringelbach, M. L., Francis, S., Bowtell, R., & McGlone, F. (2003). Representations of pleasant and painful touch in the human orbitofrontal and cingulate cortices. Cerebral Cortex, 13, 308–317. Rushworth, M. F., Behrens, T. E., Rudebeck, P. H., & Walton, M. E. (2007). Contrasting roles for cingulate and orbitofrontal cortex in decisions and social behaviour. Trends in Cognitive Science, 14, 168–176. Schlaug, G. (2003). The brain of musicians. In I. Peretz & R. Zatorre (Eds.), The cognitive neuroscience of music (pp. 366–381). Oxford: Oxford University Press. Schmidt, J. A., McLaughlin, J. P., & Leighten, J. (1989). Novice strategies for understanding paintings. Applied Cognitive Psychology, 3, 65–72. Schultz, W., Apicella, P., Ljungberg, T., Romo, R., & Scarnati, E. (1993). Rewardrelated activity in the monkey striatum and substantia nigra. Progress in Brain Research, 99, 227–235. Seymour, B., Daw, N., Dayan, P., Singer, T., & Dolan, R. (2007). Differential encoding of losses and gains in the human striatum. Journal of Neuroscience, 27, 4826–4831. Skov, M. (in press). The pleasure of art. In M. Kringelbach & K. Berridge (Eds.), Pleasures of the brain. Oxford: Oxford University Press. Small, D. M., Gregory, M. D., Mak, Y. E., Gitelman, D., Mesulam, M. M., & Parrish, T. (2003). Dissociation of neural representation of intensity and affective valuation in human gestation. Neuron, 39, 701–711. Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., & Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate. Brain, 124, 1720–1733. Spicer, J., Galvan, A., Hare, T. A., Voss, H., Glover, G., & Casey, B. J. (2007). Sensitivity of the nucleus accumbens to violations in expectation of reward. Neuroimage, 34, 455–461. Tremblay, L., & Schultz, W. (1999). Relative reward preference in primate orbitofrontal cortex. Nature, 398, 704–708. Vartanian, O., & Goel, V. (2004). Neuroanatomical correlates of aesthetic preference for paintings. NeuroReport, 15, 893–897. Wallis, J. D. (2007). Orbitofrontal cortex and its contribution to decision-making. Annual Reviews of Neuroscience, 30, 31–56. Watanabe, M. (1999). Attraction is relative not absolute. Nature, 398, 661–662. Winston, J. S., O’Doherty, J., Kilner, J. M., Perrett, D. I., & Dolan, R. J. (2007). Brain systems for assessing facial attractiveness. Neuropsychologia, 45, 195–206. Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K., & Evans, A. C. (1996). A uniﬁed statistical approach for determining signiﬁcant signals in images of cerebral activation. Human Brain Mapping, 4, 58–73. Zink, C. F., Martin-Skurski, M. E., Chappelow, J. C., & Berns, G. S. (2004). Human striatal responses to monetary reward depend on saliency. Neuron, 42, 509–517.