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Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data
Brain and Cognition 87 (2014) 52–56
Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data Oshin Vartanian a,⇑, Martin Skov b,c a
University of Toronto—Scarborough, Toronto, ON, Canada Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark c Decision Neuroscience Research Group, Copenhagen Business School, Copenhagen, Denmark b
a r t i c l e
i n f o
Article history: Accepted 8 March 2014 Available online 4 April 2014 Keywords: Visual art Aesthetics Emotion Default mode network Scene perception
a b s t r a c t Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative metaanalysis of fifteen experiments using the activation likelihood estimation (ALE) method was conducted. As predicted, viewing paintings was correlated with activation in a distributed system including the occipital lobes, temporal lobe structures in the ventral stream involved in object (fusiform gyrus) and scene (parahippocampal gyrus) perception, and the anterior insula—a key structure in experience of emotion. In addition, we also observed activation in the posterior cingulate cortex bilaterally—part of the brain’s default network. These results suggest that viewing paintings engages not only systems involved in visual representation and object recognition, but also structures underlying emotions and internalized cognitions. Ó 2014 Elsevier Inc. All rights reserved.
1. Introduction Two theoretical models have proposed that aesthetic experience associated with exposure to works of art arises as a function of the engagement of a distributed set of perceptual, cognitive, and emotional processes (Chatterjee, 2003; Leder, Belke, Oeberst, & Augustin, 2004). Uncovering the neural systems that underlie this distributed functional architecture is one of the major goals of the field of neuroaesthetics (Skov & Vartanian, 2009). Descriptive reviews of studies to date have indicated that aesthetic experience in response to viewing artworks is indeed a function of a distributed set of brain areas, each of which is hypothesized to underlie a different component process modulated by task demands (see, e.g., Cela-Conde et al., 2011; Nadal, Munar, Capó, Rosselló, & Cela-Conde, 2008). For example, whereas explicit instruction to focus on subjective emotions while viewing artworks is more likely to activate structures underlying the experience of visceral emotion (e.g., anterior insula), explicit instruction to examine the objects that make up scenes in paintings is more likely to activate
⇑ Corresponding author. Address: Department of Psychology, University of Toronto—Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada. E-mail address: [email protected] (O. Vartanian). http://dx.doi.org/10.1016/j.bandc.2014.03.004 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.
structures underlying visuospatial processing such as the parietal lobes (Cupchik, Vartanian, Crawley, & Mikulis, 2009). Recently, Brown, Gao, Tisdelle, Eickhoff, and Liotti (2011) conducted a large-scale meta-analysis of neuroimaging studies of positive-valence aesthetic appraisal across sensory modalities. Their aim was to highlight regions reliably activated during appraisal of the valence of perceived objects in the visual, auditory, gustatory or olfactory domains. They were motivated by their search for the core processes underlying aesthetic evaluation. As a result, although some studies that had used paintings as stimuli were also included, the selected studies had necessarily used tasks involving aesthetic evaluation, thereby excluding studies in which paintings had been used to study sensory processing, decision making alone, or passive viewing. In contrast, here we subject data from functional magnetic resonance imaging (fMRI) studies in which participants viewed paintings to a quantitative meta-analysis. Our aim was different from Brown et al.’s in that we were motivated to reveal brain regions activated reliably as a function of exposure to paintings regardless of variation in task demands (e.g., passive viewing, active ratings, etc.). Given that paintings constitute a key stimulus set across studies of neuroaesthetics, isolating the neural structures that are activated in response to viewing them will be a useful tool in teasing apart task-related and stimulus-related effects in future studies.
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O. Vartanian, M. Skov / Brain and Cognition 87 (2014) 52–56 Table 1 List of studies included in the meta-analysis. Study
N
Peaks
Analysis
Task
Kawabata and Zeki (2004) Vartanian and Goel (2004) Fairhall and Ishai (2008) Kirk, Skov, Hulme, Christensen, and Zeki (2009) Cupchik et al. (2009) Wiesmann and Ishai (2010) Lebreton, Jorge, Michel, Thirion, and Pessiglione (2009) Harvey et al. (2010) Lacey et al. (2011) Kirk, Harvey, and Montague (2011) Huang, Bridge, Kemp, and Parker (2011) Ishizu and Zeki (2012a) Vessel et al. (2012) Ishizu and Zeki (2012b) Silveira et al. (2012)
10 12 12 14 16 24 20 87 8 40 14 21 16 21 15
3 9 12 7 4 11 26 6 15 5 6 2 19 13 16
Contrast Parametric Contrast Contrast Contrast Contrast Contrast Parametric Contrast Parametric Contrast Parametric Contrast Contrast Contrast
Aesthetic judgment Aesthetic judgment Recognition Aesthetic judgment Active viewing Recognition Mixed judgment Passive viewing Active viewing Passive viewing Unrestricted Aesthetic judgment Active viewing Aesthetic judgment Affective judgment
Note. N = number of participants. Peaks = Foci of activation for selected contrast or parametric analysis. Aesthetic judgment = making a preference or beauty judgment, passive viewing = viewing not coupled with instruction to form an attitude, active viewing = viewing coupled with instruction to form an attitude, mixed judgment = making aesthetic and other judgments, recognition = memory task, unrestricted = subjects instructed to view each image as they pleased, affective judgment = judging the extent to which one is affected by the painting.
We focused on the visual modality and paintings specifically for two reasons. First, we were able to locate a sufficient number of fMRI studies in this area to enable a meta-analysis. Second, both models discussed above (Chatterjee, 2003; Leder et al., 2004) are based primarily on vision. For this latter reason, we were able to make predictions regarding the involvement of specific neural structures across studies. First, we hypothesized that viewing paintings would activate regions of the visual cortex involved in processing of early, intermediate, and late visual features that underlie painting perception, including color and form (Chatterjee, 2003; Greenlee & Tse, 2008; Wandell, Dumoulin, & Brewer, 2009). Second, we hypothesized that structures involved in the perception of objects and spaces would also be activated, specifically structures in the ventral stream tuned towards object recognition (Grill-Spector & Sayres, 2008; Kanwisher & Yovel, 2009; Mishkin, Ungerleider, & Macko, 1983; Ungerleider & Mishkin, 1982). Third, it is almost universally assumed that a primary objective of art is to evoke affective responses in the viewer, although whether the brain’s emotion and reward systems would be activated across studies with varying instructions remains an open question. Conveniently, the structures known to play a role in emotion and reward are well established (Montague & Berns, 2002), including the nucleus accumbens (Aharon et al., 2001), the ventral striatum (Kampe, Frith, Dolan, & Frith, 2001), the orbitofrontal cortex (O’Doherty et al., 2003; Winston, O’Doherty, Kilner, Perrett & Dolan, 2006), and the insula (see Di Dio & Gallese, 2009). Therefore, our third and exploratory hypothesis was whether viewing paintings would activate the brains’ reward and/or emotion systems. 2. Material and methods Studies were selected by conducting Boolean searches in PubMed using the terms ‘‘painting’’, ‘‘art’’, ‘‘aesthetic’’, ‘‘beauty’’, ‘‘MRI’’, ‘‘brain’’, and ‘‘neuroimaging’’ in February 2014. This set of papers was augmented by others in which participants viewed paintings under non-aesthetic conditions. Extracted fMRI studies were subsequently checked to ensure that (a) they involved viewing paintings,1 (b) they were comprised of neurologically healthy and adult participants, (c) the analyses were whole brain rather than 1 For this meta-analysis we only selected studies that used paintings, resulting in the exclusion of fMRI studies which had used sculptures or geometric patterns as stimuli (e.g., Di Dio, Macaluso, & Rizzolatti, 2007; Jacobsen, Schubotz, Höfel, & von Cramon, 2005).
exclusively region-of-interest (ROI), and (d) the complete list of activation peaks (i.e., foci) was published in the paper or made available to us. This resulted in fifteen experiments, involving a total of 330 participants and 166 peaks of activation (Table 1). 2.1. Activation likelihood estimation Our meta-analysis was conducted using the activation likelihood estimation method (ALE) (Turkeltaub, Eden, Jones, & Zeffiro, 2002). ALE is a quantitative meta-analysis technique that highlights brain regions that are activated reliably across studies. Much like traditional meta-analytic approaches, ALE’s advantages include ‘‘seeing the ‘‘landscape’’ of a research domain, keeping statistical significance in perspective, minimizing wasted data, becoming intimate with the data summarized, (and) asking focused research questions’’ (Rosenthal & DiMatteo, 2001, p. 59). In addition, the method has been shown to provide a reliable means for conducting coordinate-based meta-analyses of functional imaging data (Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012). We believe that meta-analyses and qualitative reviews are complementary, jointly providing windows into common and nuanced aspects of a domain, respectively. ALE’s approach involves comparing activation likelihoods calculated from observed activation foci with a null distribution of randomly generated activation likelihoods. It pools peak activation coordinates across studies that have investigated an effect of interest (Laird et al., 2005). For this meta-analysis all coordinates were renormalized to Talairach space using the icbm2tal transformation (Lancaster et al., 2007) implemented in the GingerALE 2.0 toolbox (http://brainmap.org; Research Imaging Center of the University of Texas Health Science Center, San Antonio, TX). The resulting coordinates were used to generate ‘‘activation likelihoods’’ for each voxel in the brain. For each focus, ALE computes each voxel as a function of its distance from that focus using a three-dimensional Gaussian probability density function centered at its coordinates. This generates vectors of values for each voxel representing probabilities of belonging to specific foci. These values are assumed to be independent such that the existence of one focus does not give information about whether another focus will occur. The vector values are combined with the addition rule for log-probabilities, yielding ALE statistics. Thus, the ALE statistic represents the probability of a certain voxel to belong to any of the included foci. Significance tests are conducted by comparing the ALE statistic in each voxel with a null distribution, generated via repeatedly
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O. Vartanian, M. Skov / Brain and Cognition 87 (2014) 52–56
calculating ALE statistics from randomly placed activation foci. This null distribution is then used to estimate the threshold resulting for a given false discovery rate (FDR). Finally, a cluster threshold (minimum spatial extent of significant contiguous clusters) can be applied. Here the FDR was set to p < .01, and the minimum cluster size to 50 contiguous voxels. To ensure that the assumption of independence was not violated, we selected only one contrast from each study, comparing the main judgment or viewing condition with a baseline (or rest) condition. In two cases the foci involved a parametric analysis of brain activation co-varying with preference ratings (Harvey, Kirk, Denfield, & Montague, 2010; Vartanian & Goel, 2004). ALE can handle contrast and parametric foci within the same analysis. 3. Results and discussion As predicted, viewing paintings activated a distributed network of structures in the brain (Table 2). Supporting our first hypothesis, viewing paintings activated areas in the visual cortex including the lingual gyrus and the middle occipital gyrus, as well as the fusiform gyrus (Table 2 and Fig. 1). These activations can be attributed to the processing of various early, intermediate, and late visual features of the stimuli embedded within paintings, including orientation, shape, color, grouping, and categorization (see Chatterjee, 2003; Greenlee & Tse, 2008; Wandell et al., 2009). Although not located in the occipital lobes, the inferior temporal cortex (Table 2 and Fig. 1) has a well-established role in visual representation of form and color (Gross, 1992), and likely contributes to that process here. Activation was also observed in the precuneus (Fig. 2), which we attribute to visuospatial exploration of pictorial stimuli. Specifically, the precuneus has been activated in studies that facilitated spatial exploration of paintings (see Cupchik et al., 2009; Fairhall & Ishai, 2008). Supporting our second hypothesis, we observed activation in the fusiform gyrus and the parahippocampal gyrus (Table 2 and Fig. 3). The fusiform gyrus is involved in object perception and recognition (Grill-Spector & Sayres, 2008), and its activation here likely represents the detection of objects within paintings (e.g., faces) (Kanwisher & Yovel, 2009). The parahippocampal gyrus is involved in the perception and recognition of places (Epstein & Kanwisher, 1998), which explains its involvement while viewing paintings rich in representations of scenes (e.g., landscapes). Our analysis also activated the anterior temporal lobe (i.e., superior temporal gyrus) (Fig. 4). Recent evidence suggests that this anterior region of the temporal lobes is involved not just in semantic
Fig. 1. Viewing paintings activated the middle occipital gyrus, the lingual gyrus, the inferior temporal cortex, the insula and the putamen.
Fig. 2. Viewing paintings activated the precuneus.
Table 2 List of structures activated in the meta-analysis. Structure
BA
Lingual gyrus Middle occipital gyrus
17 17 19 19 19 19 37 37 36 37 38 13 13 30 30 7 –
Fusiform gyrus
Parahippocampal gyrus Inferior temporal gyrus Superior temporal gyrus Insula Posterior cingulate cortex Precuneus Putamen
x
y 17 22 33 33 27 28 40 31 23 43 41 39 34 8 12 27 19
Cluster size (mm3)
z 91 88 79 75 59 57 45 41 40 68 17 18 17 52 51 62 7
0 0 12 15 10 10 18 14 9 2 15 5 4 11 10 45 0
1776 3928 592 336 376 216 304 2560 544 160 216 80 392 520 176 120 72
Note. BA = Brodmann area. All coordinates are listed in Talairach space (see text).
Fig. 3. Viewing paintings activated the fusiform gyrus and the parahippocampal gyrus.
memory—including our knowledge of objects—but also in higherorder conceptual integration of information in relation to objects (e.g., how does a knife function?) (Bonner & Price, 2013; Patterson,
O. Vartanian, M. Skov / Brain and Cognition 87 (2014) 52–56
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& Schooler, 2009), activated when we ‘‘maximize the utility of moments when we are not otherwise engaged by the external world’’ (Buckner, Andrews-Hanna, & Schacter, 2008, p. 1). This finding offers an interesting angle to the study of paintings by highlighting a process that many consider essential to deep appreciation of artworks, namely a focus on inner emotions and thoughts (see Vessel, Starr, & Gabrielle, 2012). Finally, it is important to note that structures involved in visual perception can also contribute to the computation of preferences. For example, not only is the parahippocampal cortex involved in scene perception, but its activity while viewing scenes is correlated with pleasure (Biederman & Vessel, 2006; Yue, Vessel, & Biederman, 2007). In fact, discovering the contributions of the brain’s perceptual apparatus to preference formation holds one of the great promises for the field of neuroaesthetics. 4. Conclusion Fig. 4. Viewing paintings activated the anterior temporal pole (superior temporal gyrus) and the fusiform gyrus.
Nestor, & Rogers, 2007; Peelen & Caramazza, 2012). The activation of this region indicates that the perception of paintings might trigger higher-order semantic analysis of the represented objects beyond mere recognition. With our third hypothesis we set out to explore whether viewing paintings would activate structures involved in emotion and/or reward processing. Indeed, we observed activation in the anterior insula bilaterally (Table 2 and Fig. 1). The anterior insula is known to play a critical role in emotional processing (Craig, 2010), and is part of the brain’s core affective system (Barrett, Mesquita, Ochsner, & Gross, 2007). In addition, there was activation in the putamen (Table 2 and Fig. 1). This structure in the basal ganglia is reliably activated by the anticipation of rewards (Liu, Hairston, Schrier, & Fan, 2011), and its activation here could signal the perceived rewarding properties of paintings. The involvement of the anterior insula and the putamen could be indicators of their contribution to ‘‘continuous affective evaluation’’ (Leder et al., 2004), potentially leading to a conscious evaluation during the later stages of aesthetic information processing. Somewhat unexpectedly, we also observed activation in the posterior cingulate cortex bilaterally (Fig. 5). This region has emerged as a key component of the brain’s ‘default network’ (Mason et al., 2007; see also Christoff, Gordon, Smallwood, Smith,
Fig. 5. Viewing paintings activated the posterior cingulate cortex and the fusiform gyrus.
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Contents lists available at ScienceDirect
Brain and Cognition journal homepage: www.elsevier.com/locate/b&c
Neural correlates of viewing paintings: Evidence from a quantitative meta-analysis of functional magnetic resonance imaging data Oshin Vartanian a,⇑, Martin Skov b,c a
University of Toronto—Scarborough, Toronto, ON, Canada Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark c Decision Neuroscience Research Group, Copenhagen Business School, Copenhagen, Denmark b
a r t i c l e
i n f o
Article history: Accepted 8 March 2014 Available online 4 April 2014 Keywords: Visual art Aesthetics Emotion Default mode network Scene perception
a b s t r a c t Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative metaanalysis of fifteen experiments using the activation likelihood estimation (ALE) method was conducted. As predicted, viewing paintings was correlated with activation in a distributed system including the occipital lobes, temporal lobe structures in the ventral stream involved in object (fusiform gyrus) and scene (parahippocampal gyrus) perception, and the anterior insula—a key structure in experience of emotion. In addition, we also observed activation in the posterior cingulate cortex bilaterally—part of the brain’s default network. These results suggest that viewing paintings engages not only systems involved in visual representation and object recognition, but also structures underlying emotions and internalized cognitions. Ó 2014 Elsevier Inc. All rights reserved.
1. Introduction Two theoretical models have proposed that aesthetic experience associated with exposure to works of art arises as a function of the engagement of a distributed set of perceptual, cognitive, and emotional processes (Chatterjee, 2003; Leder, Belke, Oeberst, & Augustin, 2004). Uncovering the neural systems that underlie this distributed functional architecture is one of the major goals of the field of neuroaesthetics (Skov & Vartanian, 2009). Descriptive reviews of studies to date have indicated that aesthetic experience in response to viewing artworks is indeed a function of a distributed set of brain areas, each of which is hypothesized to underlie a different component process modulated by task demands (see, e.g., Cela-Conde et al., 2011; Nadal, Munar, Capó, Rosselló, & Cela-Conde, 2008). For example, whereas explicit instruction to focus on subjective emotions while viewing artworks is more likely to activate structures underlying the experience of visceral emotion (e.g., anterior insula), explicit instruction to examine the objects that make up scenes in paintings is more likely to activate
⇑ Corresponding author. Address: Department of Psychology, University of Toronto—Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada. E-mail address: [email protected] (O. Vartanian). http://dx.doi.org/10.1016/j.bandc.2014.03.004 0278-2626/Ó 2014 Elsevier Inc. All rights reserved.
structures underlying visuospatial processing such as the parietal lobes (Cupchik, Vartanian, Crawley, & Mikulis, 2009). Recently, Brown, Gao, Tisdelle, Eickhoff, and Liotti (2011) conducted a large-scale meta-analysis of neuroimaging studies of positive-valence aesthetic appraisal across sensory modalities. Their aim was to highlight regions reliably activated during appraisal of the valence of perceived objects in the visual, auditory, gustatory or olfactory domains. They were motivated by their search for the core processes underlying aesthetic evaluation. As a result, although some studies that had used paintings as stimuli were also included, the selected studies had necessarily used tasks involving aesthetic evaluation, thereby excluding studies in which paintings had been used to study sensory processing, decision making alone, or passive viewing. In contrast, here we subject data from functional magnetic resonance imaging (fMRI) studies in which participants viewed paintings to a quantitative meta-analysis. Our aim was different from Brown et al.’s in that we were motivated to reveal brain regions activated reliably as a function of exposure to paintings regardless of variation in task demands (e.g., passive viewing, active ratings, etc.). Given that paintings constitute a key stimulus set across studies of neuroaesthetics, isolating the neural structures that are activated in response to viewing them will be a useful tool in teasing apart task-related and stimulus-related effects in future studies.
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O. Vartanian, M. Skov / Brain and Cognition 87 (2014) 52–56 Table 1 List of studies included in the meta-analysis. Study
N
Peaks
Analysis
Task
Kawabata and Zeki (2004) Vartanian and Goel (2004) Fairhall and Ishai (2008) Kirk, Skov, Hulme, Christensen, and Zeki (2009) Cupchik et al. (2009) Wiesmann and Ishai (2010) Lebreton, Jorge, Michel, Thirion, and Pessiglione (2009) Harvey et al. (2010) Lacey et al. (2011) Kirk, Harvey, and Montague (2011) Huang, Bridge, Kemp, and Parker (2011) Ishizu and Zeki (2012a) Vessel et al. (2012) Ishizu and Zeki (2012b) Silveira et al. (2012)
10 12 12 14 16 24 20 87 8 40 14 21 16 21 15
3 9 12 7 4 11 26 6 15 5 6 2 19 13 16
Contrast Parametric Contrast Contrast Contrast Contrast Contrast Parametric Contrast Parametric Contrast Parametric Contrast Contrast Contrast
Aesthetic judgment Aesthetic judgment Recognition Aesthetic judgment Active viewing Recognition Mixed judgment Passive viewing Active viewing Passive viewing Unrestricted Aesthetic judgment Active viewing Aesthetic judgment Affective judgment
Note. N = number of participants. Peaks = Foci of activation for selected contrast or parametric analysis. Aesthetic judgment = making a preference or beauty judgment, passive viewing = viewing not coupled with instruction to form an attitude, active viewing = viewing coupled with instruction to form an attitude, mixed judgment = making aesthetic and other judgments, recognition = memory task, unrestricted = subjects instructed to view each image as they pleased, affective judgment = judging the extent to which one is affected by the painting.
We focused on the visual modality and paintings specifically for two reasons. First, we were able to locate a sufficient number of fMRI studies in this area to enable a meta-analysis. Second, both models discussed above (Chatterjee, 2003; Leder et al., 2004) are based primarily on vision. For this latter reason, we were able to make predictions regarding the involvement of specific neural structures across studies. First, we hypothesized that viewing paintings would activate regions of the visual cortex involved in processing of early, intermediate, and late visual features that underlie painting perception, including color and form (Chatterjee, 2003; Greenlee & Tse, 2008; Wandell, Dumoulin, & Brewer, 2009). Second, we hypothesized that structures involved in the perception of objects and spaces would also be activated, specifically structures in the ventral stream tuned towards object recognition (Grill-Spector & Sayres, 2008; Kanwisher & Yovel, 2009; Mishkin, Ungerleider, & Macko, 1983; Ungerleider & Mishkin, 1982). Third, it is almost universally assumed that a primary objective of art is to evoke affective responses in the viewer, although whether the brain’s emotion and reward systems would be activated across studies with varying instructions remains an open question. Conveniently, the structures known to play a role in emotion and reward are well established (Montague & Berns, 2002), including the nucleus accumbens (Aharon et al., 2001), the ventral striatum (Kampe, Frith, Dolan, & Frith, 2001), the orbitofrontal cortex (O’Doherty et al., 2003; Winston, O’Doherty, Kilner, Perrett & Dolan, 2006), and the insula (see Di Dio & Gallese, 2009). Therefore, our third and exploratory hypothesis was whether viewing paintings would activate the brains’ reward and/or emotion systems. 2. Material and methods Studies were selected by conducting Boolean searches in PubMed using the terms ‘‘painting’’, ‘‘art’’, ‘‘aesthetic’’, ‘‘beauty’’, ‘‘MRI’’, ‘‘brain’’, and ‘‘neuroimaging’’ in February 2014. This set of papers was augmented by others in which participants viewed paintings under non-aesthetic conditions. Extracted fMRI studies were subsequently checked to ensure that (a) they involved viewing paintings,1 (b) they were comprised of neurologically healthy and adult participants, (c) the analyses were whole brain rather than 1 For this meta-analysis we only selected studies that used paintings, resulting in the exclusion of fMRI studies which had used sculptures or geometric patterns as stimuli (e.g., Di Dio, Macaluso, & Rizzolatti, 2007; Jacobsen, Schubotz, Höfel, & von Cramon, 2005).
exclusively region-of-interest (ROI), and (d) the complete list of activation peaks (i.e., foci) was published in the paper or made available to us. This resulted in fifteen experiments, involving a total of 330 participants and 166 peaks of activation (Table 1). 2.1. Activation likelihood estimation Our meta-analysis was conducted using the activation likelihood estimation method (ALE) (Turkeltaub, Eden, Jones, & Zeffiro, 2002). ALE is a quantitative meta-analysis technique that highlights brain regions that are activated reliably across studies. Much like traditional meta-analytic approaches, ALE’s advantages include ‘‘seeing the ‘‘landscape’’ of a research domain, keeping statistical significance in perspective, minimizing wasted data, becoming intimate with the data summarized, (and) asking focused research questions’’ (Rosenthal & DiMatteo, 2001, p. 59). In addition, the method has been shown to provide a reliable means for conducting coordinate-based meta-analyses of functional imaging data (Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012). We believe that meta-analyses and qualitative reviews are complementary, jointly providing windows into common and nuanced aspects of a domain, respectively. ALE’s approach involves comparing activation likelihoods calculated from observed activation foci with a null distribution of randomly generated activation likelihoods. It pools peak activation coordinates across studies that have investigated an effect of interest (Laird et al., 2005). For this meta-analysis all coordinates were renormalized to Talairach space using the icbm2tal transformation (Lancaster et al., 2007) implemented in the GingerALE 2.0 toolbox (http://brainmap.org; Research Imaging Center of the University of Texas Health Science Center, San Antonio, TX). The resulting coordinates were used to generate ‘‘activation likelihoods’’ for each voxel in the brain. For each focus, ALE computes each voxel as a function of its distance from that focus using a three-dimensional Gaussian probability density function centered at its coordinates. This generates vectors of values for each voxel representing probabilities of belonging to specific foci. These values are assumed to be independent such that the existence of one focus does not give information about whether another focus will occur. The vector values are combined with the addition rule for log-probabilities, yielding ALE statistics. Thus, the ALE statistic represents the probability of a certain voxel to belong to any of the included foci. Significance tests are conducted by comparing the ALE statistic in each voxel with a null distribution, generated via repeatedly
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calculating ALE statistics from randomly placed activation foci. This null distribution is then used to estimate the threshold resulting for a given false discovery rate (FDR). Finally, a cluster threshold (minimum spatial extent of significant contiguous clusters) can be applied. Here the FDR was set to p < .01, and the minimum cluster size to 50 contiguous voxels. To ensure that the assumption of independence was not violated, we selected only one contrast from each study, comparing the main judgment or viewing condition with a baseline (or rest) condition. In two cases the foci involved a parametric analysis of brain activation co-varying with preference ratings (Harvey, Kirk, Denfield, & Montague, 2010; Vartanian & Goel, 2004). ALE can handle contrast and parametric foci within the same analysis. 3. Results and discussion As predicted, viewing paintings activated a distributed network of structures in the brain (Table 2). Supporting our first hypothesis, viewing paintings activated areas in the visual cortex including the lingual gyrus and the middle occipital gyrus, as well as the fusiform gyrus (Table 2 and Fig. 1). These activations can be attributed to the processing of various early, intermediate, and late visual features of the stimuli embedded within paintings, including orientation, shape, color, grouping, and categorization (see Chatterjee, 2003; Greenlee & Tse, 2008; Wandell et al., 2009). Although not located in the occipital lobes, the inferior temporal cortex (Table 2 and Fig. 1) has a well-established role in visual representation of form and color (Gross, 1992), and likely contributes to that process here. Activation was also observed in the precuneus (Fig. 2), which we attribute to visuospatial exploration of pictorial stimuli. Specifically, the precuneus has been activated in studies that facilitated spatial exploration of paintings (see Cupchik et al., 2009; Fairhall & Ishai, 2008). Supporting our second hypothesis, we observed activation in the fusiform gyrus and the parahippocampal gyrus (Table 2 and Fig. 3). The fusiform gyrus is involved in object perception and recognition (Grill-Spector & Sayres, 2008), and its activation here likely represents the detection of objects within paintings (e.g., faces) (Kanwisher & Yovel, 2009). The parahippocampal gyrus is involved in the perception and recognition of places (Epstein & Kanwisher, 1998), which explains its involvement while viewing paintings rich in representations of scenes (e.g., landscapes). Our analysis also activated the anterior temporal lobe (i.e., superior temporal gyrus) (Fig. 4). Recent evidence suggests that this anterior region of the temporal lobes is involved not just in semantic
Fig. 1. Viewing paintings activated the middle occipital gyrus, the lingual gyrus, the inferior temporal cortex, the insula and the putamen.
Fig. 2. Viewing paintings activated the precuneus.
Table 2 List of structures activated in the meta-analysis. Structure
BA
Lingual gyrus Middle occipital gyrus
17 17 19 19 19 19 37 37 36 37 38 13 13 30 30 7 –
Fusiform gyrus
Parahippocampal gyrus Inferior temporal gyrus Superior temporal gyrus Insula Posterior cingulate cortex Precuneus Putamen
x
y 17 22 33 33 27 28 40 31 23 43 41 39 34 8 12 27 19
Cluster size (mm3)
z 91 88 79 75 59 57 45 41 40 68 17 18 17 52 51 62 7
0 0 12 15 10 10 18 14 9 2 15 5 4 11 10 45 0
1776 3928 592 336 376 216 304 2560 544 160 216 80 392 520 176 120 72
Note. BA = Brodmann area. All coordinates are listed in Talairach space (see text).
Fig. 3. Viewing paintings activated the fusiform gyrus and the parahippocampal gyrus.
memory—including our knowledge of objects—but also in higherorder conceptual integration of information in relation to objects (e.g., how does a knife function?) (Bonner & Price, 2013; Patterson,
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& Schooler, 2009), activated when we ‘‘maximize the utility of moments when we are not otherwise engaged by the external world’’ (Buckner, Andrews-Hanna, & Schacter, 2008, p. 1). This finding offers an interesting angle to the study of paintings by highlighting a process that many consider essential to deep appreciation of artworks, namely a focus on inner emotions and thoughts (see Vessel, Starr, & Gabrielle, 2012). Finally, it is important to note that structures involved in visual perception can also contribute to the computation of preferences. For example, not only is the parahippocampal cortex involved in scene perception, but its activity while viewing scenes is correlated with pleasure (Biederman & Vessel, 2006; Yue, Vessel, & Biederman, 2007). In fact, discovering the contributions of the brain’s perceptual apparatus to preference formation holds one of the great promises for the field of neuroaesthetics. 4. Conclusion Fig. 4. Viewing paintings activated the anterior temporal pole (superior temporal gyrus) and the fusiform gyrus.
Nestor, & Rogers, 2007; Peelen & Caramazza, 2012). The activation of this region indicates that the perception of paintings might trigger higher-order semantic analysis of the represented objects beyond mere recognition. With our third hypothesis we set out to explore whether viewing paintings would activate structures involved in emotion and/or reward processing. Indeed, we observed activation in the anterior insula bilaterally (Table 2 and Fig. 1). The anterior insula is known to play a critical role in emotional processing (Craig, 2010), and is part of the brain’s core affective system (Barrett, Mesquita, Ochsner, & Gross, 2007). In addition, there was activation in the putamen (Table 2 and Fig. 1). This structure in the basal ganglia is reliably activated by the anticipation of rewards (Liu, Hairston, Schrier, & Fan, 2011), and its activation here could signal the perceived rewarding properties of paintings. The involvement of the anterior insula and the putamen could be indicators of their contribution to ‘‘continuous affective evaluation’’ (Leder et al., 2004), potentially leading to a conscious evaluation during the later stages of aesthetic information processing. Somewhat unexpectedly, we also observed activation in the posterior cingulate cortex bilaterally (Fig. 5). This region has emerged as a key component of the brain’s ‘default network’ (Mason et al., 2007; see also Christoff, Gordon, Smallwood, Smith,
Fig. 5. Viewing paintings activated the posterior cingulate cortex and the fusiform gyrus.
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