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Biological Psychology 93 (2013) 246– 254

Contents lists available at SciVerse ScienceDirect

Biological Psychology

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lectrophysiological correlates of looking at paintings and its associationith art expertise

.Y. Panga, M. Nadalb, J.S. Müller-Paulc, R. Rosenbergd, C. Kleine,f,∗

Institute for Psychology, University of Leipzig, GermanyDepartment of Psychology, University of the Balearic Islands, SpainDepartment of Cognitive Biology, University of Vienna, AustriaDepartment of History of Art, University of Vienna, AustriaSchool of Psychology, Bangor University, United KingdomDepartment of Child and Adolescent Psychiatry and Psychotherapy, University of Freiburg, Germany

r t i c l e i n f o

rticle history:eceived 22 July 2011ccepted 31 October 2012vailable online 15 November 2012

a b s t r a c t

This study investigated the electrocortical correlates of art expertise, as defined by a newly developed,content-valid and internally consistent 23-item art expertise questionnaire in N = 27 participants thatvaried in their degree of art expertise. Participants viewed each 50 paintings, filtering-distorted versions

eywords:EGRPxpertiseisual art

of these paintings and plain colour stimuli under free-viewing conditions whilst the EEG was recordedfrom 64 channels. Results revealed P3b-/LPC-like bilateral posterior event-related potentials (ERP) thatwere larger over the right hemisphere than over the left hemisphere. Art expertise correlated negativelywith the amplitude of the ERP responses to paintings and control stimuli. We conclude that art expertiseis associated with reduced ERP responses to visual stimuli in general that can be considered to reflectincreased neural efficiency due to extensive practice in the contemplation of visual art.

. Introduction

Visual art is one of the most widespread forms of artisticxpression, and is created in one form or another by virtually alluman cultures. Ever since Fechner’s (1876) seminal work, themnipresence of art in people’s daily lives has stimulated thempirical investigation of this anthropologic universal (Averillt al., 1998; Silvia, 2005). Current psychological models underscorehe role of the complex interaction of mnemonic–cognitive andvaluative–affective processes in the appreciation of art (Ledert al., 2004). Researchers have attempted in recent years to char-cterise some of the attributes of art that contribute to aestheticppraisals such as “liking” (e.g., Jacobsen and Höfel, 2002, 2003;andau et al., 2006; Locher et al., 1999; Nodine et al., 1993), as wells the influence of distortions in the composition of paintings onarticipants’ aesthetic valuation (Locher et al., 1999; Vartanian andoel, 2004). Furthermore, neuro-aesthetic research has begun to

nveil the complex patterns of neural activation that accompanyesthetic experience (Ramachandran and Hirstein, 1999; Nadalnd Pearce, 2011), including its perceptual (Brown et al., 2006;

∗ Corresponding author at: School of Psychology, University of Wales, Bangor, Therigantia Building, Penrallt Road, Bangor, Gwynedd LL57 2AS, Wales, UK.el.: +44 01248 38 8351; fax: +44 01248 38 2599.

E-mail address: c.klein@bangor.ac.uk (C. Klein).

301-0511/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.biopsycho.2012.10.013

© 2012 Elsevier B.V. All rights reserved.

Calvo-Merino et al., 2010a; Cela-Conde et al., 2009; Cupchik et al.,2009; Koelsch et al., 2006; Lacey et al., 2011; Vartanian and Goel,2004), cognitive-evaluative (Cela-Conde et al., 2004; Cupchik et al.,2009; Jacobsen et al., 2006) and motivational (Blood and Zatorre,2001; Brown et al., 2004; Cupchik et al., 2009; Ishai et al., 2007;Kawabata and Zeki, 2004; Kirk et al., 2009a; Koelsch et al., 2006;Salimpoor et al., 2011; Vartanian and Goel, 2004) components.

While many people across many cultures undoubtedly share aliking for visual art, empirical studies have identified a number ofsystematic inter-individual differences in people’s appreciation ofart that can be related to theoretical models of aesthetic experience.Leder et al. (2004) comprehensive model of aesthetic appreciationincludes a number of component processes that refer to long-term memory, and hence implicit or explicit knowledge, in whichindividuals obviously differ. One of these sources of systematicinter-individual differences is expertise. It has been shown that artexperts and non-experts differ in various aspects of art perception,including the way they explore and perceptually organise paintings(Hekkert and van Wieringen, 1996; Kristjanson and Antes, 1989;Schmidt et al., 1989), the way they process complexity (Reber et al.,2004), and their aesthetic preferences and judgements (Hekkertand van Wieringen, 1996; McWhinnie, 1968; Nodine et al., 1993).

The investigation of expertise is relevant for the study of art,and beyond, because expertise and the accumulated effects oflong-term training are accompanied by changes in brain processesinvolved in domain-relevant tasks (Gauthier et al., 2010) and can

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hus serve as a “natural model” of neural plasticity (Bukach et al.,006; Münte et al., 2002; Rossion et al., 2004, 2007; Scott et al.,008; Tanaka and Curran, 2001). In addition, systematic experi-ental task modifications during the contemplation of paintings

nspired by explicit theoretical models of aesthetic experience suchs Leder et al. (2004) model, could empirically link the effects ofraining and expertise to the various components of the complexmalgam of cognitive, evaluative and affective processes that char-cterise aesthetic experience.

It is thus reasonable to expect art experts to differ fromon-experts in certain aspects of brain activity while processingrt-related stimuli and performing art-related tasks. Such dif-erences have indeed been shown with various neuroscienceechniques (Solso, 2001), including the EEG. A study in solvinghess problems, for instance, has revealed that experts as comparedo non-experts, exhibited higher EEG synchronisation in general,ncluding higher delta band synchrony in more posterior corticalegions, as well as stronger right hemispheric dominance (Volket al., 2002). Volke and colleagues’ (1999, as cited in Volke et al.,002) work showed that increased EEG coherence correlated withimple, well-mastered, and largely automatically performed tasks,hereas decreased coherence correlated with difficult and poorlyastered tasks. Bhattacharya and Petsche (2002) found higher EEG

hase synchronisation, particularly in the delta and gamma bandsn the right hemisphere and in more posterior brain regions, whenrtists were asked to imagine a painting after viewing it. Theseesults are in accordance with the aforementioned findings of Volket al. (2002) study, and suggests the interesting possibility that con-emplating paintings may be simpler for art experts, who, owing toheir greater efficiency in solving tasks in their particular domain,

ight rely on more well-mastered and effortless cognitive and per-eptual processes.

However, considering the art expertise literature, it is interest-ng to note that virtually all of the aforementioned studies definedrt “experts” according to their formal education background ororking experience in fine art or art history, and “non-experts”

s people who have equivalent educational level in areas unre-ated to art (such as psychology undergraduates) but have noormal art training. Such differentiations between (art) expertsnd non-experts render art expertise a (quasi-) categorical variablee.g., artists and non-artists, Bhattacharya and Petsche, 2002, 2005;ristjanson and Antes, 1989; art-trained and untrained, Nodinet al., 1993; experts and non-experts, Hekkert and van Wieringen,996; experts and novices, Schmidt et al., 1989). Such classifica-ions, however, can be considered as the results of an artificialichotomisation of an otherwise continuous quantitative variable:he degree of study or practice. Smith and Smith’s (2006) Aestheticluency Scale and Chatterjee et al. (2010) Art Experience Ques-ionnaire, which yield a continuous measure for participants’ artxpertise, constitute attempts to overcome this drawback. Silvia’s2007) results suggest that such quantitative measures representalid means to measure the degree of expertise, and avoid otherroblems derived from defining expertise based solely on themount of experience or work (Farrington-Darby and Wilson, 2006;hanteau et al., 2003).

Based on these considerations, the present study pursued theollowing aims and hypotheses. First, considering aesthetic experi-nce as the product of a complex amalgam of cognitive, evaluativend motivational processes (possibly) rising during the free andffortless viewing of paintings for pleasure (Kant’s “Wohlgefallen”),e realised a free viewing condition without the addition of certain

ognitive, evaluative or motivationally relevant tasks and assured

articipants attention for the presented stimuli by pre-announcinguestions about the stimuli at the end of the session. Second, weompared individuals with varying degrees of art training andxperience with art with regard to their electro-cortical correlates

ology 93 (2013) 246– 254 247

during the contemplation of art. Here, we predict lower electro-cortical activity in those with greater art expertise as an expressionof increased neural efficiency due to practice. Third, in order totackle the specificity of neural responses to visual art, we employedtwo types of control stimuli that differ from visual art either by“dissolving” its constituting elements by filtering, or by realisingvisual stimulation in one of its simplest manners as coloured plainvisual fields. While the original paintings contain all visual ele-ments that contribute to their aesthetic value, with the filtering weeliminated recognisable visual elements but preserved the over-all form and colour palette. It has been shown that the processingof stimuli in which the compositional structure is changed differsfrom the processing of paintings with “intact” compositional struc-ture (e.g., Locher et al., 1999; Vartanian and Goel, 2004). Plain colourscreens were used as a third experimental stimulus type, as plaincolour stimuli lack even the faintest information regarding formand pattern. For this comparison we therefore predict a right-sidedpredominance of electro-cortical activity and greater amplitudesfor paintings than for control stimuli (see also next point). Fourth,referring to the aforementioned expertise-related EEG literatureas well as further studies using visual stimuli that are relevanthere (Cuthbert et al., 2000; Keil et al., 2002), we focus our anal-yses on the ERP components at posterior locations as our “regionof interest”. These ERP components can be expected to resemblethe P300 and the Late Positive Complex (LPC) and are thus relevantfor models of aesthetic experience that stress the importance ofmemory processes (such as recognition and long-term memory) inthe appreciation of visual art (Leder et al., 2004). These processesshould be most pronounced in response to the original paintingswith their recognisable compositional elements and structure. Inaddition to these theoretical rationales of focusing on posteriorLPC-like potentials, this topographical focus minimises the poten-tial confounds of proper EEG activity with residual eye movementartefacts due to volume conduction to negligible.

2. Methods

2.1. Participants

On the basis of a recently developed art expertise questionnaire that focusesof elements of formal art and musical education (see Appendix 1), N = 27 partici-pants (mean age: 24.48 ± 5.60, range: 19–40 years; 17 females; 22 right-handed;visual art expertise sum score: 27.13 ± 11.76, range: 9.0–48.75) were selected froma larger screening sample with N = 346 undergraduate students. The questionnaireconsisted of 44 closed-ended questions, in which the first 23 were related to visualart education and the remaining ones to education in music. Of these, questions9, 17 and 24 were not included in the computation of the art expertise sum scoreas these are nominal variables without numerical information. For the other 20art-related questions, the answer scores were summed up. This questionnaire dif-fers from similar measures (see Section 1) in covering more areas of art expertise(formal art education, art preferences and specific interests) in greater detail (alsocovering rather elaborated skills such as specific art analysis techniques to differ-entiate individuals in the upper expertise range). Questions about education in artand the time contributed to visual art contemplation weighted the most. An expertin the history of art (RR) and an expert in the study of art expertise (MN) have con-sidered these items as content valid for the construct of art expertise. The internalconsistency of the 22 questions which contributed to the visual art expertise scoreswas very high (Cronbach’s alpha = .906). Participants in both groups were native orat least proficient English speakers (25 European, 1 Indian, 1 South-American). Allof the participants had normal or corrected-to-normal vision. All participants gaveinformed written consent before they participated in questionnaire screening andEEG recording sessions.

2.2. Apparatus

The experiment took place in sound-attenuated and electrically shielded EEGlab at Bangor University. BrainAmps DC amplifiers (sampling rate: 500 Hz, onlineband pass: DC-250 Hz; resolution: 0.1 �V, Brain Products, UK) and EasyCaps (Falk

Minow Systems, Munich) were used to record the EEG from 64 positions of the inter-national 10-10 system (American Electroencephalographic Society, 1991), includingF9 and F10 near the outer canthi for EOG measurement. The reference electrode andground electrode were placed at Cz and AF4, respectively. Electrode impedanceswere kept below 5 k�. The EEG was recorded on a Pentium 3 PC using Brain Vision

248 C.Y. Pang et al. / Biological Psychology 93 (2013) 246– 254

Fig. 1. Examples of different genres of art (portrait, genre work, landscape and still life) are shown in their original (left) and filtered (right) versions.Bronzino, “Untitled (portrait of Bia Medici)”. Available at http://zh.wikipedia.org/wiki/File:Angelo Bronzino 037.jpg, de Hooch, “Mutter an der Wiege”. Available ath alche“ 0%B9

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ttp://fr.wikipedia.org/wiki/Fichier:Pieter de Hooch 016.jpg, Corinth, “Ostern am WDer Wasserbehälter”. Available at http://bg.wikipedia.org/wiki/%D0%A4%D0%B0%D

ecorder Version 1.04 (Brain Products, UK). The experimental stimuli were editedy Adobe Photoshop CS3. They were presented with the “Presentation” softwareNeurobehavioral Systems, USA) on a 21′′-TFT monitor in 1024 × 768 pixels resolu-ion. BESA Version 5.1.6 by MEGIS Software was then used to perform ocular artefactorrection, EEG data segmentation, and all further EEG analysis. BPlot Version 1.4.0.9MEGIS Software) was used to generate EEG data visualisations and maps. Statisticalnalysis was conducted using SPSS 13.0 for Windows.

.3. Stimuli

Fifty representational Western paintings of high professional standards in fourifferent categories (14 portraits, 11 landscapes, 18 genre works, and 7 still lives,ee Fig. 1) were presented under two stimulus types: in their original appearancend filtered. The original stimuli were created by resizing paintings into maxi-um dimension which fitted in a 1024 × 768 pixels pure black background without

istorting the original dimension (Fig. 1). The filtered paintings were created bypplying the four filters: median noise, Gaussian blur, mosaic, or glass distortionAdobe Photoshop CS3). Depending on the features of the paintings, different filtersere applied until the structure and details of the paintings were dissolved but the

verall forms were retained. We did not apply the same filter to every painting inrder to avoid specific effect related to the filter. The plain colour stimulus type wasreated by filling 50 different randomly selected colours on the 1024 × 768 pixelsackground. Each stimulus type included 50 different stimuli, thus a total of 150timuli were used in the study. These 150 stimuli were randomly divided into 3

nsee”. Available at http://it.wikipedia.org/wiki/File:Lovis Corinth 007.jpg, Chardin,%D0%BB:Jean-Baptiste Sim%C3%A9on Chardin 009.jpg.

sets. Each set consisted of 50 stimuli, and these 3 sets of stimuli were presented toall participants in a single fixed order.

In order to minimise the confounding influence of differential familiarity withthe paintings in the two groups only relatively unknown paintings were used. Famil-iarity with the paintings in their original form was assessed at the end of the labsession (see below).

2.4. Procedure

At the beginning of the session, the laboratory was shown to the participants andthe equipment and the different tasks were explained. If a participant agreed to takepart in the experiment, s/he gave her/his written consent and testing could begin.The electrode cap was mounted, electrodes were attached and electrode impedanceswere brought to below 5 k�. Next, participants were prompted with arrows pointingto the left, right, up or down as well as with a schematic picture of an eye to producehorizontal and vertical saccades to visually-marked positions outside the monitor oreye blinks (“calibration”). The proper (quasi-) experiment consisted of two separateparts, the order of which was counterbalanced, namely presentation of (a) art stimuli(35 min duration) and (b) music stimuli (35 min duration). Results of the music study

will be reported elsewhere.

During the visual art study, each stimulus was presented only once accordingto the following procedure. At the beginning of each trial, a white fixation crossappeared in the middle of the screen for 6.0 s, followed by an art stimulus thatwas presented for 6.0 s. After stimulus offset, the fixation point appeared again and

C.Y. Pang et al. / Biological Psychology 93 (2013) 246– 254 249

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ig. 2. Grand average ERP curves displaying the relationship between art expertise ahe sample is artificially dichotomised in low-scorers versus high-scorers.

emained there for another 6.0 s before the next trial. Participants were asked toarefully attend to the stimuli as they would be asked about them at the end of theession. After the experiment, participants were shown the paintings used as stimulind asked to report if they had ever seen the paintings before the experiment. Afterhis task, participants were asked to fill in the Edinburgh Handedness InventoryOldfield, 1971) and to indicate their age.

.5. Primary and statistical data analysis

EEG data analysis was accomplished with BESA and BPlot (MEGIS software,unich). In the first data analysis step, the MSEC method (Berg and Scherg, 1994)as applied to the “calibration” data. The MSEC method differs from traditional EOG

orrection methods in that eye activity is described by components that are empir-cally determined separately for each individual. The components are treated likeipole sources which model the EEG activity. While both eye and cortical activityre assumed to be recorded by all applied channels, albeit to a different degrees, theopography of eye versus cortical activity, as described by source vectors, differs. Theye source vectors were determined empirically by applying a Principal Componentnalysis (PCA) on co-variances of all time points of the calibration data. While theesulting source vector defines the topography of the eye activity, the source wave-orm describes the magnitude of the source over time. In order to correct the EEGor eye movement artefacts, the source wave rather than the EOG signal is sub-racted from each EEG channels in proportions which are defined by the respectiveource component. Three types of eye movement were corrected: vertical and hor-zontal eye movements, and blinks. In addition, the source waveform identifies theye activity. By averaging these waveforms along with the EEG, an estimate of theime-locked eye activity is obtained. The efficacy of the eye activity correction washecked by visual inspection, and trials still containing artefacts were rejected.

A trial was defined as the interval starting 1000 ms before and ending 6000 msfter stimulus onset, thus including a 1000 ms pre-stimulus baseline. After seg-entation, the raw data were re-referenced to the common average reference

nd 0.1–150.0 Hz zero phase band-pass filtered (both slopes set to 12 db/Octave).resentation stimulus triggers were used to differentiate the three experimentaltimulus types: original painting, modified painting, and plain colour. Accepted tri-ls of the same stimulus type were averaged for each participant. Finally, grandverages were then created for high-scorers and low-scorers from the individualverages. Individual and average waveforms and topographical maps were visuallynspected to identify the ERP components following stimulus onset as well as theiratency ranges and topographies. The latency ranges were (a) 245–390 ms and (b)00–525 ms. For the first component, peaks were found based on the P6 electrodeithin the latency range 245–390 ms due to great variation among the latencies

f the peak for each participant. The mean amplitudes of the ERP during 25 msround the peak for each stimulus type were used for statistical analysis. Eight elec-rode channels (P3, P4, P5, P6, PO3, PO4, PO7 and PO8) covered the main activityn these latency ranges and were for this reason used for statistical analysis. Theopographical maps are based on the BESA standard montage of 81 channels.

P amplitudes at parietal leads and for the three experimental conditions separately.

Repeated-measures ANCOVAs were conducted with the SAS “Proc GLM” withType III sums of squares using the amplitude of the 8 selected channels as depend-ent measures and “EXPERTISE” (the visual art expertise sum score, see above) aspredictor in the regression part of the ANCOVA as well as “LATENCY” (245–390 ms;500–525 ms), “STIMULUS TYPE” (original painting; filtered painting; plain colour),“HEMISPHERE” (left- versus right-sided electrodes), and “ELECTRODE” (P3/P4;P5/P6; PO3/PO4; PO7/PO8) as the within-subject factors in the ANOVA part of theANCOVA. Although “EXPERTISE” has been considered as a continuous variable bothconceptually and in the ANCOVA, in order to display the relationship between artexpertise and ERP activity, the following two types of topographical maps were cre-ated. Firstly, as the dichotomising of an otherwise continuous variable, the entiresample was split up into two groups (henceforth called “high-scorers” and “low-scorers” for the sake of brevity and to indicate that these participants were quitehighly experienced or little experienced, respectively) in order to show the properERP topographies in those scoring high versus low in art expertise (see Figs. 2 and 3).Secondly, for each ERP component and condition separately, we determined the cor-relation between the amplitude in each of the EEG channels and the art expertisesum score. The topography of these correlations is shown in Fig. 4. After excluding4 participants with poor data quality, data of 11 “high-scorers” (visual art expertisesum score: 37.84 ± 6.15 (ranging from 29.00 to 48.75); 7 females, 9 right-handed,mean age: 24.8 ± 6.0 years (19–39 years)) and 16 “low-scorers” (visual art expertisesum score: 16.42 ± 3.90 (ranging from 9.00 to 22.25); 10 females, 13 right-handed,mean age: 24.1 ± 5.5 years (19–40 years)) remained for these secondary analy-ses. The cut-off point for this dichotomisation was based on the expertise scoredistribution of the larger screening sample, and the group difference in visual artexpertise was highly significant (t(15.495) = −10.230, p < .001). The number of paint-ings known to the participants was very low and did not differ between groups(high-scorers: 1.8 ± 2.4; low-scorers: 2.1 ± 2.9; t(25) = 0.23, p = .82).

In addition to the proper test statistics (F-, t-values), significance levels andHuynh–Feldt’s epsilon values are reported. Unless stated otherwise, the ANCOVAresults of the non-normalised data remained significant after data normalisation.

3. Results

Fig. 2 shows the temporal course for those eight electrodesthat were used for statistical analyses in art high-scorers and low-scorers for the three experimental stimulus types. As can be seenin Fig. 2 (grand average curves) and Fig. 3 (grand average maps),the experimental stimuli elicited ERP components that resemblein terms of latency and topography the Late Positive Complex

(LPC) or P3b that is typically seen after the attentive processingof visual stimuli. Plain colours elicited smaller amplitudes thanfiltered paintings, which in turn elicited smaller amplitudes thanthe original paintings. It can be seen in Figs. 2 and 3 that both the

250 C.Y. Pang et al. / Biological Psychology 93 (2013) 246– 254

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ig. 3. Grand average ERP maps displaying the relationship between art expertise anonditions and the two analysed components. The sample is artificially dichotomis

ositive deflections and the differences between the three stim-

lus types in these deflections exhibited topographical maximat posterior positions. While Figs. 2 and 3 suggest a negative cor-elation between art expertise and ERP amplitudes, this negativeelationship is directly shown in Fig. 4. This figure reveals that for

topographies as well as differences in ERP topographies for the three experimentalow-scorers versus high-scorers.

the first ERP component, greater art expertise is associated with

lower ERP amplitudes at right-posterior leads. For the second ERPcomponent and the original and filtered paintings, the negativeERP-expertise correlation is more symmetrical over the twohemispheres.

C.Y. Pang et al. / Biological Psychology 93 (2013) 246– 254 251

Fig. 4. Correlation maps showing the relationship between ERP amplitudes and the continuous art expertise sum score for the two components and the three conditionss ts negT of the

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eparately. Note that these maps do not show the ERPs as such. Red colour represenhe scale on the right side displays the size of the correlations. (For interpretation

f the article.)

The statistical analyses of the first ERP component revealed a sig-ificant STIMULUS TYPE × HEMISPHERE × ELECTRODE interactionF3.767,94.179 = 3.858, p = .007, ε = .628, eta2 = .134) that can be furtherpecified by its subordinate interaction and main effects (STIM-LUS TYPE × HEMISPHERE: F1.894,47.355 = 7.113, p = .002, ε = .947,ta2 = .222; STIMULUS TYPE × ELECTRODE: F4.283,107.069 = 4.140,

= .003, ε = .714, eta2 = .142; STIMULUS TYPE: F1.698,42.444 = 11.923, < .001, ε = .849, eta2 = .323; HEMISPHERE: F1.000,25.000 = 13.100, = .001, ε = 1.000, eta2 = .344; ELECTRODE: F2.525,63.133 = 7.081, = .001, ε = .842, eta2 = .221). Together, these effects point to aopographical distribution that favoured the right hemisphere asell as lateral parietal and mesial parieto-occipital leads and

howed an amplitude reduction from original paintings over fil-

ered paintings to plain colours at all positions (see Table 1). Whilehis interaction remained significant after data normalisation, thenderlying interaction of STIMULUS TYPE with HEMISPHERE justailed conventional significance level (p = .059).

able 1aired t-tests results for the comparison of different stimulus types.

Mean amplitude ± SD (�V) Original – filtered

Original Filtered Blank colour t p Coh

Left 2.72 ± 2.8 1.54 ± 3.0 0.10 ± 1.8 3.614* .001 .7Right 4.81 ± 4.6 2.86 ± 4.3 0.53 ± 2.4 5.359* <.001 1.0P3 2.38 ± 2.8 1.39 ± 2.9 0.22 ± 1.6 2.905* .007 .5P4 4.64 ± 4.3 2.69 ± 3.8 0.96 ± 2.0 5.787* <.001 1.1P5 2.07 ± 2.9 1.22 ± 2.9 -0.38 ± 2.1 2.556* .017 .4P6 4.76 ± 4.8 3.35 ± 4.7 -0.29 ± 2.5 3.270* .003 .6PO3 3.87 ± 3.5 2.16 ± 3.6 0.78 ± 1.9 4.759* <.001 .9PO4 5.69 ± 5.3 3.13 ± 4.8 1.58 ± 3.2 7.288* <.001 1.4PO7 2.56 ± 2.9 1.38 ± 3.2 -0.23 ± 2.1 3.058* .005 .5PO8 4.14 ± 4.7 2.27 ± 4.8 -0.14 ± 2.5 3.641* .001 .6

* Significance determined according to Holm (1979); df = 26; latency range 245–390 m

ative ERP-expertise correlations, green colour positive ERP-expertise correlations.references to colour in this figure legend, the reader is referred to the web version

The ANCOVA revealed furthermore a significant associationbetween EXPERTISE SCORE and the amplitudes of the firstERP component, in particular at bilateral parieto-occipitalelectrodes (see Fig. 4) (EXPERTISE SCORE × ELECTRODE:F2.525,63.133 = 4.396, p = .010, eta2 = .150; EXPERTISE SCORE:F1,25 = 7.723, p = .010, eta2 = .236; ELECTRODE: F2.525,63.133 = 7.081,p = .001, eta2 = .221) and over the right hemisphere (EXPERTISESCORE × HEMISPHERE: F1.000,25.000 = 7.427, p = .012, eta2 = .229;EXPERTISE SCORE: F1,25 = 7.723, p = .010, eta2 = .236; HEMISPHERE:F1.000,25.000 = 13.100, p = .001, eta2 = .344). Greater art expertise wasthus associated with lower electro-cortical amplitude at parietalleads (P3/P4: r = −.365, p = .061; P5/P6: r = −.416, p = .031), parieto-occipital leads (PO3/PO4: r = −.504, p = .007; PO7/PO8: r = −.541,

p = .004) and more so over the right hemisphere (r = −.546, p = .003)than over the left hemisphere (r = −.277, p = .162). The interactionof STIMULUS TYPE and EXPERTISE SCORE was not significant forthe first component.

Original – blank colour Filtered – blank colour

en’s d t p Cohen’s d t p Cohen’s d

00 5.403* <.001 1.097 3.049* .005 .65339 6.660* <.001 1.603 3.863* .001 .90570 4.005* <.001 .815 2.177* .039 .46454 5.577* <.001 1.349 3.011* .006 .70894 4.752* <.001 .934 3.076* .005 .61730 6.604* <.001 1.492 4.974* <.001 1.10327 5.599* <.001 1.258 2.704* .012 .65763 6.343* <.001 1.521 2.634* .014 .58787 5.963* <.001 1.196 3.478* .002 .72398 6.634* <.001 1.621 3.489* .002 .823

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Like the first component, the second component was moreronounced over the right hemisphere as compared to the leftHEMISPHERE: F1.000,25.000 = 18.738, p < .001, ε = 1.000, eta2 = .428)nd larger for original paintings as opposed to filtered paintingsr plain colours (STIMULUS TYPE: F1.787,44.678 = 27.627, p < .001,

= .894, eta2 = .525). While the effect of stimulus type was some-hat differently pronounced for the different bilateral electrodeairs (STIMULUS TYPE × ELECTRODE: F5.087,127.172 = 2.947, p = .014,

= .848, eta2 = .105), large effects were found for all of them (F-alues ranging between 21.7 at P3/P4 and 28.4 at PO7/PO8). Unlikehe first component, the hemisphere asymmetry of the second com-onent did not interact with the type of stimulus being presentedr with art expertise. Nevertheless, also for this component, thereas a general negative relationship between art expertise and ERP

mplitude (EXPERTISE SCORE: F1,25 = 8.331, p = .008, eta2 = .250;ee also Fig. 4). The interaction of STIMULUS TYPE and EXPER-ISE SCORE was not significant for the second component (p = .059,ta2 = .111).

. Discussion

The aim of the present study was to investigate the relationshipetween art expertise, as measured through a newly developeduestionnaire, and the electro-cortical correlates of processingaintings. To that end, the participants of our study freely con-emplated unfamiliar paintings of different genres in their originalr filtered versions as well as plain colours. This study revealedhe following main results. First, under free viewing conditionshe stimuli used here elicited a P3b-like posterior positive deflec-ion. Second, processing original paintings elicited greater posteriorRP amplitudes than processing filtered paintings or plain colours.hird, the electro-cortical correlates of art processing were moreronounced over the right than over the left hemisphere. Fourth,reater art expertise was associated with smaller ERP amplitudest parieto-occipital sites and over the right hemisphere during anearly” processing stage (around 300 ms) and more broadly poste-ior at a “late” processing stage (around 500 ms). Fifth, this negativexpertise-amplitude relationship generalised to filtered paintingsnd even plain colour fields.

Ad (1). The P3b and the Late Positive Complex (LPC; Friedmant al., 1978) that often follows it belong to the most intensively andxtensively researched ERP components, and it is beyond the scopef this article to review this literature. Suffice to say, that the P3bas been associated with the “context updating” or contextual “clo-ure” in working memory (e.g., Donchin and Coles, 1988; Verleger,988) and has been shown to index the retrieval of knowledge from

ong-term memory (Koester and Prinz, 2007). As outlined in Sec-ion 1, current models of art appreciation have emphasised theole of knowledge in long-term memory (Leder et al., 2004). Theere presence of this component, thus suggests that the partic-

pants of our study were attentively processing the stimuli theyere exposed to during our free-viewing condition.

Ad (2). The P3b/LPC waveform exhibited the largest amplitudesor paintings and the smallest for plain colours. Various previoustudies have shown that degradation of visual stimuli cause reduc-ions in the P3b amplitude (reviewed in Kok, 2001). These findingsre in line with the positive relationship between informationransmission and P3b amplitude as specified in Johnson’s (1986)riarchic Model of the P3b. Applying filtering techniques intendedo degrade the painting stimuli by dissolving their constituent ele-

ents, a technique that was put to an extreme by removing theselements altogether in the plain colour stimuli. The posterior pos-

tive deflection reported in the present paper sensitively reflectedhese experimental manipulations. The original painting stimuli,owever, were not only the least degraded ones; with their specificontent, that is visual art, they conveyed a kind of information the

ology 93 (2013) 246– 254

processing in working memory of which can be assumed to elicitemotional reward and arousal to some extent (Chatterjee, 2004;Leder et al., 2004). The present results therefore not only confirmresults of a study that used stimuli similar to the ones used here(Vartanian and Goel, 2004), but also results of studies that useddifferent kinds of emotionally relevant stimuli (e.g., Cuthbert et al.,2000; Keil et al., 2002).

Ad (3). The posterior ERP reported here were not only sensi-tive to the experimental task manipulations, they also exhibiteda clear right-hemispheric preponderance that is in line with pre-vious publications on aesthetic judgement (Höfel and Jacobsen,2007; Jacobsen and Höfel, 2003) or the processing of metaphorsand emotional memories (Bhattacharya and Petsche, 2002). Oneof the important implications of the posterior, right-sided pre-dominance of the ERP components for paintings, their degraded(filtered) versions and even plain colour stimuli is that the under-lying electro-cortical generators are not unique to the processing ofvisual art. While this conclusion may read peculiar at first glance, itmust be stated that more than a decade of neuroimaging of visualart and aesthetic appreciation has yielded no evidence that evolu-tion has produced any anatomic “module” or neurophysiologicalprocess that is unique to the processing of visual art (Chatterjee,2004, 2011; Skov, 2010; Zaidel, 2005). Our findings here thussupport the notion that the appreciation of visual art relies oncognitive and neural processes involved in processing other kindsof visual stimuli (Aglioti et al., 2012; Calvo-Merino et al., 2005,2008, 2010a,b; Harvey et al., 2010; Kirk, 2008; Kirk et al., 2009a,b,2011; Salimpoor et al., 2011; Skov, 2010). This finding is importantbecause it argues against the popular belief that because “art is spe-cial” there should be “some special brain process” associated withart or aesthetics.

Ad (4). The greater the art expertise, the smaller was the electro-cortical activation by the experimental stimuli. This fourth majorfinding is in agreement with decreases in the extent and intensityof brain activation related with practice and experience observedin many other domains (Kelly and Garavan, 2005) and was alsoreported in an fMRI study of drawing portraits (Solso, 2001) andan EEG study of preparation and execution of the right movements(Percio et al., 2010). In all of these studies, experts showed lowerdegrees of activation when performing tasks they are specialised in.

This apparently well replicable observation can be subsumedunder the broader topic of “neural efficiency” due to intensivepractice (see Haier et al., 1988). As Kelly and Garavan (2005) haveelaborated in their review, task practice leads to decreases in extentor intensity of brain activation through increases in neural effi-ciency, defined as a sharpening of neural responses in task-relevantprocessing networks (see also Percio et al., 2010; Petersen et al.,1988; Poldrack, 2000).

Our results thus add to the research on the effects of exper-tise on the cognitive processes involved in the appreciation of art(Augustin and Leder, 2006; Kay, 1991, 2000; Kozbelt, 2001; Kozbeltand Seeley, 2007; Locher et al., 2008; Nodine and Krupinski, 1998;Pihko et al., 2011; Silvia, 2007; Vogt and Magnussen, 2007) andtheir neural underpinnings (Altenmüller et al., 2000; Berkowitzand Ansari, 2010; Bhattacharya and Petsche, 2005; Brattico et al.,2008; Calvo-Merino et al., 2005, 2010b; Cross et al., 2006; Kirk et al.,2009b, 2011; Orgs et al., 2008; Tervaniemi, 2009). Understandingthe effects of expertise on behaviour and its neural foundationsis not only important for research on learning, training, and skilldevelopment. In the domain of art appreciation, moreover, it con-stitutes a key argument against the notion of the stability andobjectivity of the art appreciation, and a fundamental tool in

understanding how the art experience is generated. The studiesquoted here have shown that such experience is modulated by con-textual cues, by the information provided, and by the developmentof expertise.

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Despite the plausibility of the interpretation given before, aotential caveat of all expertise studies is the quasi-experimentalature of the comparison between high-scorers and low-scorers,hich opens the possibility of confounds between expert status and

ther known or unknown variables (such as differences in recogni-ion of artistic style, age of production of the artwork, etc.; Augustint al., 2011) associated with it that may be relevant for the pro-esses under scrutiny. (The same applies to correlative studies likehe present one, in which art expertise is investigated as a contin-ous variable.) Therefore, the present results are preliminary in apecific sense: They do show that art expertise is negatively cor-elated with the amplitude of the electro-cortical potentials thatccompany “higher-order” art processing (cognition, evaluation,ffect rather than low-level perception), but they do not show whyhis is the case. Future studies that put less emphasis on the rela-ive ecological validity of “non-instructed” free viewing conditions,

ay be guided by current models of aesthetic experience such aseder et al. (2004) model in designing experimental task modifica-ions that have more specific bearings for processing componentspecified in this model.

Ad (5). That the negative relationship between art expertise andRP amplitude held not only for paintings, but also for their filteredersions and even plain colour fields, is another finding that readsounter-intuitive only at first glance. Indeed, although expertises by definition domain-specific, experts in certain domains haven various studies been shown to excel non-experts in taskshat are not related to their domain of expertise. Chi (1978), fornstance, found that young chess experts outperformed childrenon-experienced with chess in memory and learning tasks throughetter use of grouping and rehearsal strategies. Similar effectsave been found repeatedly in relation to musicians’ performance

n general attention (Posner et al., 2008), the recognizing of emo-ions in speech prosody (Lima and Castro, 2011), spatial abilitiesRauscher et al., 1993; Rauscher and Zupan, 2000), or verbal mem-ry (Chan et al., 1998; Franklin et al., 2008; Ho et al., 2003; Jakobsont al., 2003) as well as visual artists’ performance in detectingnd resolving figural problems (Kay, 1991, 2000) or in generalerception tests (Kozbelt, 2001; Kozbelt and Seeley, 2007). Thesetudies can be conjointly summarised by hypothesising that artxperts “export” their art viewing strategies to non-artistic stimuliuch that relationships between art expertise and the processingf artistic stimuli are mirrored in the processing of control stimuli.

To summarise, the present study contributed to the exist-ng art expertise literature in demonstrating that art expertises associated with reduced “higher-order” event-related potentialmplitudes under free viewing conditions of visual art and non-rtistic visual stimuli that can be considered to reflect increasedeural efficiency due to extensive practice.

cknowledgements

This research was funded by a research grant of the Deutscheorschungsgemeinschaft (DFG) given to Raphael Rosenberg andhristoph Klein (RO 2281/3-1). The authors would like to thankr. Christopher Saville for his help with the figures.

ppendix A. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.biopsycho.012.10.013.

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