Gamma oscillations distinguish mere exposure from other likability effects

Neuropsychologia 54 (2014) 129–138

Contents lists available at ScienceDirect

Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Gamma oscillations distinguish mere exposure from other likability effects Nutchakan Kongthong a, Tetsuto Minami b,n, Shigeki Nakauchi a a

Department of Computer Science and Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka Tempaku, Toyohashi, Aichi 441-8580, Japan Electronics-Inspired Interdisciplinary Research Institute (EIIRIS), Toyohashi University of Technology, 1-1 Hibarigaoka Tempaku, Toyohashi, Aichi 441-8580, Japan

b

art ic l e i nf o

a b s t r a c t

Article history: Received 17 May 2013 Received in revised form 16 December 2013 Accepted 21 December 2013 Available online 31 December 2013

Repeated exposure to neutral stimuli enhances liking for those, which is called mere exposure effect (MEE) (Zajonc, 1968). Its behavioral effects have been extensively investigated. However, the mechanism by which it is generated remains unclear. To elucidate the neural mechanism of the MEE, we recorded electroencephalograms while subjects indicated their preferences for face stimuli with and without MEE induction. According to behavioral data, participants were divided into two groups, one with, and one without MEE tendency. In participants with an MEE tendency, gamma activity (40–60 [Hz]) in the parieto-occipital area was significantly weaker for exposed faces than unexposed ones, indicating a repetition-suppression effect. Gamma activity from sites exhibiting peak repetition-suppression effects was significantly weaker in theoretically genuine MEE trials than non-MEE trials, indicating that emotion processing might influence the MEE. These results suggest that existing theories regarding mechanisms underlying the MEE, namely, fluency misattribution and apprehensiveness reduction might not be mutually exclusive. Moreover, gamma activity might be a potential indicator to distinguish the MEE from other likability effects, at least in the case of human face stimuli. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Mere exposure effect Preferential decision Gamma band oscillation Face stimuli

1. Introduction We make countless decisions every day, from shopping choices to filling out questionnaires. When we choose one item over another, we cannot always know that the decision was made upon considering what we actually value, or if we were just influenced through being exposed to that item, perhaps through advertisements. The mere exposure effect (MEE) in which new stimuli are rated more likable after repeated exposure (Zajonc, 1968) has been reported to exist in several modalities including visual (Bornstein, 1989), auditory (Heingartner & Hall, 1974; Szpunar, Schellenberg, & Pliner, 2004), olfactory (Balogh & Porter, 1986; Prescott, Kim, & Kim, 2008) and somatosensory stimuli (Jakesch & Carbon, 2012; Suzuki & Gyoba, 2005). Robust MEEs have been found for a variety of visual stimuli such as complex polygons, originally unfamiliar faces, and meaningless ideography (Bornstein, 1989). MEEs are also speculated to modulate social attitudes that have been reported in experiments using photographs of faces from different races (Zebrowitz, White, & Wieneke, 2008).

n

Corresponding author. E-mail address: [email protected] (T. Minami).

0028-3932/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.neuropsychologia.2013.12.021

This phenomenon has been experimentally replicated, with the strength of the effect varying according to several factors: stimuli characteristics, stimuli representation, task requirements, and participants0 individual characteristics. Regarding stimuli characteristics, stimuli that are novel before repeated exposure produce a greater effect than those that were originally well-known (for a review, see Bornstein, 1989). Additionally, a number of researches have reported that subliminal exposure generates a stronger MEE than supraliminal exposure does (e.g., Bornstein & Dagostino, 1992; Zebrowitz et al., 2008). However, this result is not universal, and some researches have reported different MEE outcomes from subliminal exposure (e.g., Newell & Shanks, 2007). Regarding task requirements, reliable MEE has been measured through likability ratings or other affective scales such as approachability (e.g., Seamon, McKenna, & Binder, 1998). Although the MEE is considered a robust psychological phenomenon, the strength actually varies among individuals. People who are easily bored tend not to be influenced by mere exposure to stimuli (Bornstein, Kale, & Cornell, 1990), whereas people who do not deeply interpret their environments tend to have stronger MEEs (Hansen & Bartsch, 2001). As for other individual differences, anxiety (Schick, McGlynn & Woolam, 1972a, 1972b) and tolerance ambiguity (Crandall, 1968) have been found to relate to the mere exposure effect. Thus, the mere exposure effect is related to personality and individual differences.

130

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

Although MEEs have been extensively investigated, the mechanism by which they are generated still remains unclear. The most compelling explanations are the misattribution of perceptual fluency: the ease of information processing (Reber, Winkielman, & Schwarz, 1998; Topolinski & Strack, 2009) and the reduction of apprehensiveness towards novel stimuli: increased liking caused by a reduction in stimulus negativity (Zajonc, 2001), both of which have supporting evidence. The former theory is supported by results showing increased likability towards repeated subliminal exposure (e.g., Bornstein & Dagostino, 1992), indicating that perceptual fluency plays an important role in MEE. The latter theory is supported by evidence from a recent functional magnetic resonance imaging (fMRI) study that reported repetition suppression (in which brain activity becomes weaker in response to stimuli previously presented) in lateral orbitofrontal cortex (OFC), a region that responds more to negative stimuli, when subjects were presented with familiarized faces and objects (Zebrowitz & Zhang, 2012). The two theories do not contradict each other but are not precisely equivalent either. However, because most research supports only one of the two theories, each remains dubitable. Aside from MEEs, stimuli features such as attractiveness or esthetic quality also play important roles in human decisionmaking (e.g., Antonakis & Dalgas, 2009; Schacht, Werheid, & Sommer, 2008; Todorov, Mandisodza, Goren, & Hall, 2005). Recent research using electroencephalography (EEG) to measure eventrelated brain potentials (ERPs) has reported that the late positive complex (LPC) of ERPs is modulated by attractiveness when subjects judge facial appearance (Lindsen, Jones, Shimojo, & Bhattacharya, 2010; Schacht et al., 2008; Zhang et al., 2011). This modulation of the late component by attractive or arousing stimuli has been associated with attention and memory encoding (for a review, see Olofsson, Nordin, Sequeira, & Polich, 2008). Additionally, brain oscillatory activity was measured while subjects performed a two-alternative forced-choice paradigm using face stimuli, and authors reported stronger theta band oscillation for preferred second faces, and stronger gamma band oscillations for preferred first faces (Lindsen et al., 2010). Often, distinguishing whether a decision was made upon considering what we actually value, or whether it was influenced by mere exposure, is difficult. The current study employed EEG to investigate the MEE with two aims. First, we wanted to find an indicator that might help distinguish positive attitudes or preferential decisions resulting from the MEE and those resulting from other factors. Second, if such a neural indicator exists, we wanted to know how it could explain the mechanism of the MEE. We investigated brain activity while subjects indicated their preferences for face stimuli with and without MEE induction. In addition, there are individual differences in the effects of MEE and some subject variables were reported to modulate the effect. So, we adopted the approach of dividing participants into MEEtendency and MEE-no-tendency groups based on behavioral data.

including the E scale indicating level of extroversion, and the N scale indicating levels of neuroticism.

2.3. Stimuli Two-hundred contrast- and luminance-normalized gray-scaled and emotionally neutral Japanese faces were used as stimuli. Skin texture was adjusted to an equal roughness and hair was removed using 3D face-modeler software (FaceGen Modeller 3.5). The stimuli were selected for likability after pre-study testing. Each face was cropped to be approximately 61 in height and placed on a square gray background. Experiments were carried out in a dark room with magnetic shield. Stimuli were presented on a CRT display (FlexScan T766, Eizo Nanao Corp) with a spatial resolution of 800 pixels  600 pixels and refresh rate of 100 Hz, driven by a ViSaGe visual stimuli generator (Cambridge Research Systems).

2.4. Experimental procedure The stimuli set was divided equally into two fixed groups with the same proportion of male and female faces, which were randomly assigned as either exposure or non-exposure stimuli for each subject. The experiment consisted of 10 sets of an Exposure session (MEE induction) following by an Evaluation session. Fig. 1 illustrates the experimental procedure. During an Exposure session, 10 face stimuli were subliminally presented during a dummy task. In each trial (Fig. 1, top panel), a geometric figure was presented for 100 ms following a 500-ms fixation interval. Next, the face stimulus was presented for 10 ms followed by a white noise mask for 490 ms, and a second geometric figure for 100 ms. Each of the 10 face stimuli were presented 10 times in random order for a total of 100 trials per session. Participants were asked to judge if the first and second geometric figure were the same in color or shape by pressing a button as quickly as possible. If participants responded within 5 s, the task proceeded immediately to the next trial. Otherwise, the next trial began 5 s after the presentation of the second geometric figure.

Exposure session +

500 ms 100 ms 10 ms 490 ms 100 ms 5s

Evaluation Session

2. Material and methods

+

2.1. Participants Twenty-one healthy, right-handed volunteers (mean age, 26.9 75.41 years; 7 female) with normal or corrected to normal vision participated in the experiment. An informed written consent was obtained from participants after procedural details had been explained. Experimental procedures were approved by the Committee for Human Research of Toyohashi University of Technology.

2.2. Questionnaires Basic personality features were assessed using the Maudsley personality inventory (MPI) (Eysenck & Eysenck, 1994), which is composed of two main scales

500 ms 2000 ms 20 s Fig. 1. Experimental procedure. Each participant completed 10 sets, with each set containing an MEE induction session (100 trials) followed by a likability evaluation session (20 trials).

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

131

amplitudes and latencies were averaged over right (P8, P6, P4, PO8, PO4, O2) and left (P7, P5, P3, PO7, PO3, O1) hemispheres and the occipitocentral (POz, Oz) region. LPC mean amplitudes were averaged over posterior parietal area (CPz, Pz, P1, P2, and POz). Averaged amplitudes and latencies for each ERP component were tested with t-tests to examine the difference between MEE and non-MEE trials. MEE trials were defined as those with likability ratings for a particular face higher than average ratings after the Exposure session but which were degraded a month later, and non-MEE trials were defined as trials in which the highly likability for a face was high even after a month. This definition was based on previous results that showed MEEs produced from subliminally viewing 10 randomly presented stimuli should disappear within approximately 1week (Seamon, et al., 1983).

In the Evaluation session following each Exposure session (Fig. 1, bottom panel), a face stimulus was presented for 2000 ms followed by a fixation point for 500 ms. During this session, the 10 faces from the Exposure session and 10 novel face stimuli were presented in random order. After the faces disappeared, participants were asked to rate within 20 s how much they liked the face on a 7-point scale (1 ¼ totally dislike; 4 ¼do not particularly like or dislike; 7 ¼totally like). One set of the two sessions lasted approximately 7 min. Short breaks between sets could be taken upon request. The procedures were repeated 10 times until all 200-face stimuli were rated. EEG was recorded during Evaluation sessions. Each participant was asked to perform an Evaluation session again a month later without EEG recording to measure likability baseline and verify if biased likability measured after exposure sessions truly reflected MEE. We defined highly-rated exposure stimuli that received lower ratings 1 month later as theoretically genuine MEE. A 1-month duration was decided based on a report that MEE induced by subliminally presented stimuli lasts approximately 1 week (Seamon, Brody, & Kauff, 1983). Flow chart of the analysis procedure was shown in Fig. 2.

2.5.4. Time–frequency analysis Preprocessing for the time–frequency analysis was similar to that of ERP, except that a 0.5–101 Hz band-pass filter was used and trials marked as artifacts during ERP processing were not included. To analyze gamma oscillation, time–frequency representation (TFR) was calculated using the open-source software FieldTrip. TFR was obtained from EEG activity -200 –800 ms relative to stimulus onset and from frequency ranges of 8–100 Hz, in 2-Hz frequency steps by means of a 7-cycle complex Morlet wavelet, and was baseline corrected relative to activity between  200 and 0 ms (active period/baseline) (e.g. Luft, Nolte, & Bhattacharya, 2013). The gamma band was divided into 3 ranges: 26–40 Hz (lower), 40–60 Hz (middle), and 60–80 Hz (higher). Spectral power averaged over the above-mentioned time and frequency ranges was then tested for statistical significance using a non-parametric cluster-based permutation test (p o 0.05) to avoid type-I errors (Maris & Oostenveld, 2007). This permutation test was used to test significant differences between 10 high-likability (higher than the average likability of its set) trials containing exposure stimuli and 10 containing non-exposure stimuli. After identifying the scalp region with a significant exposure effect, trials containing exposure stimuli were divided into those that in which the likability for the face declined after 1 month, and those in which it did not (defined as “MEE trials” and “non-MEE trials” in ERP analysis), and then tested with t-tests (p o 0.05).

2.5. Data acquisition and analysis 2.5.1. EEG recording EEG was recorded with 64 active Ag–AgCl sintered electrodes mounted in an elastic cap according to the extended 10–20 system and amplified by a BioSemi ActiveTwo amplifier (BioSemi, Amsterdam; The Netherlands). The EEG was sampled at 512 Hz. The horizontal electrooculogram (EOG) was recorded from two electrodes at the outer canthi of both eyes and the vertical EOG was monitored from electrodes above and below the right eye. The impedance was kept below 50 kΩ. 2.5.2. Behavioral data analysis MEE is reflected in biased likability: likability for repeatedly viewed (unconsciously) stimuli tends to be higher than that for novel stimuli. To examine MEE behaviorally, participants were divided into MEE-tendency and MEE-no-tendency groups according to behavioral data. An MEE-tendency participant was defined as someone who, on average, rated exposed stimuli higher than novel stimuli. Likability in each group was analyzed separately with a 3-way analysis of variance (ANOVA) with stimulus gender (male/female), exposure condition (Novel/Shown) and observation day (First exposure/1 month later) as factors (p o 0.05).

2.5.5. Source analysis Here, the difference between theoretically genuine MEEs and non-MEEs reflected in EEG signals was observed only in the frequency domain (results for time–frequency analysis were mentioned in ‘time–frequency analysis results’ section). To identify the sources of differential oscillatory activity, a beamforming approach was used (Dynamic Imaging of Coherent Sources; DICS (Gross et al., 2005)). This algorithm has proven to be particularly powerful when localizing oscillatory sources (Liljeström, Kujala, Jensen, & Salmelin, 2005). The DICS spatial filter is constructed from the lead field matrix and the cross-spectral density matrix. The lead field matrix was calculated using a standard BEM model (Oostenveld et al., 2001). The standard brain was divided into a grid with 1 cm resolution. For each grid point, we constructed a common spatial filter (baseline and activation over all conditions; regularization: lambda 5%) from the crossspectral density matrix of the Fourier-transformed data at the frequency of interest (50 710 Hz, multitaper analysis) and the leadfield. We then apply the common spatial filter to the baseline (  150 ms to 50 ms from stimulus onset) and activation data (250 ms to 350 ms from stimulus onset) separately. Finally, source solutions were transformed into relative power changes compared with power

2.5.3. ERP analysis For ERP analysis, after artifacts from eye movement was removed using independent component analysis using the runica algorithm as implemented in EEGLAB, a 0.5–30 Hz digital band-pass filter was applied offline to the continuous EEG data after being recalculated to an average reference with EEGLAB toolbox (version 6.03). The continuous EEG data were epoched into 900-ms segments (  200 toþ700 ms to stimulus onset) and baseline corrected (  100 to 0 ms from stimulus onset). Artifact rejection was carried out with EEGLAB toolbox. Trials defined as artifacts were segments with activity greater than 70 μV or less than  70 μV in amplitude, having abnormal trends (slope greater than 50 μV/epoch or R2 ¼0.3), containing improbable data (single- and all-channel thresholds¼ 5 std), or containing high positive or negative kurtosis values (single- and all-channel thresholds¼ 5 std.) (e.g. Maier, Yeung, & Steinhauser, 2011). N170 (120–200 ms) peak amplitudes and latencies were averaged over right (P10, P8, PO8) and left (P9, P7, PO7) hemispheres. For P2 (150–250 ms), peak

Participants a) Participants were divided base on behavioral data on First day

b) Exposure conditions: NOVEL (not previously shown) and SHOWN (previously shown)

c) Highly-rated exposure stimuli that received lower ratings one month later as theoretically genuine MEE

Group WITH MEE tendency

NOVEL

Group WITHOUT MEE tendency

SHOWN

MEE

NOVEL

SHOWN

non-MEE

Fig. 2. Flow chart of the analysis procedure. (a) The participants were divided into 2 groups (groups with and without MEE tendency) according to their likability ratings on first day. (b) We investigated the differences in EEG signals between NOVEL and SHOWN trials in each group. (c) We defined highly-rated exposure stimuli that received lower ratings one month later as theoretically genuine MEE, and investigated the differences in EEG signals between MEE and non-MEE trials.

132

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

groups (MEE tendency: E scale, 25.1 78.1; N scale, 23.7 77.30; non-MEE tendency: E scale, 31.3 713.4; N scale, 27.5711.2; E-score, P ¼0.38, N-score: P ¼0.22).

estimates in a baseline window and differences between both conditions were tested for statistical significance using a cluster-level randomization in the same manner as with spectral analysis. Thus, source localization for MEE and non-MEE differences was performed for the time window and frequency range in which the difference was found in the group of participants with MEE tendency.

3.3. ERP results 3. Results

Data from one participant that contained artifacts in 50% of trials was excluded from ERP and TFR analysis. This left 10 participants in both the MEE and non-MEE tendency groups. A t-test was performed separately for each component and hemisphere. For ERP amplitude, no significant effect was found for the LPC [P ¼0.8207], N170 [left: P ¼0.7198; right: P¼ 0.7075] or P2 [left: P ¼0.899; middle: P ¼0.8591; right: P ¼0.7229]. Similarly, no significant effect was found for latency in either in N170 [left: P¼ 0.5693; right: P¼ 0.959] or P2 [left: P¼ 0.7327; middle: P¼ 0.7469; right: P¼ 0.8782].

3.1. Behavioral results Averaged likability ratings are shown in Fig. 2. To capture brain activity that reflects bias in preference caused by the MEE, the 21 participants were divided into 2 groups according to their behavioral results. Participants who rated previously presented faces higher than novel faces were grouped as participants with an MEE tendency (MEE-tendency group). Subjects who did not show an ideal bias according to MEE literature were classified as without an MEE tendency (non-MEE tendency group). Initially, 11 participants were assigned to this group, but one was excluded from EEG analysis due to excessive artifacts. Therefore, we reanalyzed the behavioral data from the remaining 10 participants who survived artifact rejection for use as a reliable verification index. To make sure that the grouping was proper, a three-way analysis of variance (ANOVA) was performed separately for the two groups. A main effect of exposure condition (F[1, 9]¼ 20.285, P¼ 0.0015) and an interaction between exposure conditions and observation day (F[1, 9]¼6.785, P¼0.0285) were found in the MEE-tendency group. A simple main effect of exposure condition was significant only on “First exposure” (F[1, 9]¼ 25.527, P¼0.0001). This can be interpreted to mean that bias from repetitive exposure was significant, but attenuated by “1 month later”. In contrast, although a main effect of exposure condition was also found in the non-MEE tendency group (N¼10; F[1, 9]¼5.712, P¼0.0406), it was opposite in direction, with lower ratings for the exposure-stimuli set on both days.

3.4. TFR results Grand averaged TFR showed gamma bursts during the 150– 350 ms following stimulus onset (Fig. 3). For MEE tendency group, the permutation test revealed a significant effect of repeated exposure during high likability trials during the 250–350 ms following stimulus onset in 40–60 Hz Gamma activity at posterior parietal sites (P ¼0.004; channels: P1, PO3, POz, O1, Oz, O2, and Iz) (Fig. 4). The main effect of exposure in the occipito-parietal cluster peaked in channel PO3, POz, and Oz. To separate high likability related to the MEE from high likability related to other effects, trials of Shown were divided into MEE or non-MEE using the likability measure from each day. The average power of gamma activity over 3 electrode sites in the occipito-parietal area (PO3, POz, and Oz) for MEE trials was significantly weaker than that in non-MEE trials (P o0.05) (Fig. 5). We also tested the difference between Novel and Shown trials for non-MEE tendency group, and no effect of exposure condition was found.

3.2. Questionnaire results 3.5. Associations of gamma power and MPI scores Mean MPI scores for all subjects were 27.8 711 (E scale) and 25.1 79.46 (N scale). No significant difference was found between mean MPI scores for the MEE-tendency and non-MEE tendency

To find additional evidence supporting the relationship between personality, emotion (anxiety as negative emotion), and NOVEL SHOWN

First exposure **

*

3.5

3

Group with MEE tendency (N = 10)

Group without MEE tendency (N = 11)

N.S.

*

Group with MEE tendency (N = 10)

Group without MEE tendency (N = 11)

4

average likability

average likability

4

1 month later

3.5

3

Fig. 3. Likability rating results: Left panel shows average likability ratings measured in the Evaluation sessions. Right panel shows average likability ratings measured 1 month after the initial test day. The significant difference between ratings for Novel and Shown trials in the MEE-tendency group vanished after 1 month, while that of the non-MEE tendency group did not.

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

NOVEL

80 60 40 20

80 60

0

0.2 0.4 Latency [s]

0.6

0.8

-0.2

100

100

80

80

60 40 20 -0.2

1.4

40

1.2

20

Frequency [Hz]

Frequency [Hz]

Group WITHOUT MEE tendency

-0.2

SHOWN

100

Frequency [Hz]

Frequency [Hz]

Group WITH MEE tendency

100

133

0

0.2 0.4 Latency [s]

0.6

0.8

1

0.8

0.6

60 0.4 (n.u.)

40 20

0

0.2 0.4 Latency [s]

0.6

0.8

-0.2

0

0.2 0.4 Latency [s]

0.6

0.8

Fig. 4. Grand-average (across subjects and all EEG sensors) time–frequency representation of relative power change for oscillatory activity. Color indicates power change relative to baseline from  200 to 0 ms relative to stimulus onset. The abbreviation n.u. refers to normalized units. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

N-scores in all participants was marginally significant (MPI-N [r ¼ 0.430, P ¼0.059], MPI-E[r ¼ 0.180 P ¼0.447]).

p=0.004

4

0

t-values

3.6. Source-localization results To identify the sources accounting for the gamma power difference between MEE and non-MEE trials, we applied a beamforming approach (DICS [Gross et al., 2005]). As shown in Figs. 6– 8, the source–activity difference between MEE and non-MEE trials for the MEE-tendency group was localized over the right superior frontal gyrus, the right insula, and the right putamen (Po 0.01).

4. Discussion

-4 Fig. 5. Topography for the significant effect of exposure condition: in the MEEtendency group, the cluster-based permutation test revealed a significant effect of exposure condition. A big cluster in posterior sites contains the peak effect of exposure (PO3, POz, and Oz).

neural MEE found in the current study, we tested the correlation between MPI scores and the size of the repetition-suppression effect (difference between Novel and Shown gamma averaged over 7 posterior sites that showed a significant difference between exposure conditions). Results showed a significant negative correlation between repetition-suppression effect size of gamma activity and E-scores (r ¼ 0.795, P o0.01) for the MEE-tendency group (not significant for MPI-N [r ¼0.339, P ¼0.34]). For non-MEE tendency group, both E and N scores significant positively correlated with difference of gamma activity (MPI-E [r ¼ 0.677, P o0.05], MPI-N [r ¼ 0.657, Po 0.05]), though there was no significant repetition-suppression effect (Fig. 7). In addition, the correlation between repetition-suppression effect size of gamma activity and

The current study focused on the mechanism underlying the mere exposure effect (MEE), which has a tremendous impact on decision making without our awareness. By combining EEG analysis with a psychological approach, we examined the difference in the brain activity during preferential judgments that could be attributed to the MEE and during those that could not. Employing a typical method for studying MEE, an induction session was conducted to artificially create the effect. Subliminal repetitive presentation was used to maximize the effect and eliminate any bias that might occur if participants could guess the purpose of the experiment. To bridge the neural and behavioral effects, participants were grouped according to their tendency for behavioral MEE. Neural evidence for the MEE was found only in the group of participants with an MEE tendency. Posterior gamma oscillation (40–60 Hz) in response to previously presented faces was weaker than for novel faces only for faces that were judged more attractive than average. This is interpreted as a repetitionsuppression effect in which processing became more efficient for repeated stimuli and required less neural activity. We further compared brain activity for highly-rated exposure stimuli that

134

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

MEE

100

Average gamma power (relative)

1.05

80

Frequency[Hz]

Frequency[Hz]

n.u.

non-MEE

100

60 40

1.4

80

1.2

60

1 0.8

40

0.6 20

20 0.4

1

-0.2

0

0.2

0.4

Latency[s]

0.6

0.8

-0.2

0

0.2

0.4

Latency[s]

0.6

0.8

n.u.

1.2

0.95

1

0.9

0.8

0.85

MEE

non-MEE

n.u.

Fig. 6. (A) Comparison of gamma strength between non-MEE and MEE trials: in the MEE-tendency group, a t-test for three channels exhibiting a peak effect of exposure revealed that gamma activity for genuine MEE trials was significantly weaker than for non-MEE trials. (B) Time–frequency representations (average over three channels) of MEE and non-MEE trials, and topographies of the gamma band power in the interval 250–350 ms for MEE and non-MEE trials. The abbreviation n.u. refers to normalized units.

received lower ratings 1 month later (theoretically genuine MEE) with those receiving similarly high ratings even 1 month after induction (other likability effects/non-MEE). This comparison revealed that gamma activity for MEE trials was significantly weaker than that for non-MEE trials. Combined with correlations between participants0 social anxiety levels and neural MEE with source-localization results, weaker gamma activity for genuine MEE trials suggests the possibility that emotion processing affects the MEE. The repetition-suppression effect we observed supports the idea that perceptual fluency is the key factor underlying MEE. At the same time, the differences in repetition-suppression effects between MEE and non-MEE trials can be interpreted as interference by emotion processing, supporting the theory that suggests reduction of apprehensiveness drives the MEE. However, there is still no palpable direct evidence that emotion processes are influential on subliminally induced MEE. Only one study has reported MEE strength to be modulated by facial expression (Kawakami & Yoshida, 2011), with repetition of emotional faces inducing a stronger MEE. In that study, the effect of facial expression was considered “information in the face”. This stronger MEE for emotional faces was interpreted to be consistent with the perceptual fluency theory, stating that stimuli with more information (complex stimuli) induce stronger MEE than simple stimuli due to greater changes in fluency. Note that this study used a unique behavioral measurement that has never been used in any other MEE study, so its results might not be directly comparable. To investigate the neural activity of MEE reflected in human behavior, we used the average likability to separate participants0 data before the analysis. Therefore, we found a main effect of exposure condition, in which likability towards Shown trials was higher than Novel trials in participants with an MEE tendency. The attenuation of this strong main effect after 1 month verified that the biased likability seen after the MEE induction session did not result from a natural preference bias towards those particular stimuli. A main effect of exposure condition was also found in

participants with no MEE tendency. However, this bias was opposite to the usual MEE in which likability toward exposed stimuli was lower than novel stimuli. Moreover, this bias was preserved even after 1 month after attempting to induce MEE. Therefore, this bias likely resulted from biased preferences towards stimuli despite the stimuli-likability screening done in a pre-study test. This behavioral result, to some extent, ensured that we succeeded in MEE induction and that the brain activity measured from participants with an MEE tendency would contain neural activity that reflects MEE. We focused on the neural activity that might help differentiate high preference influenced by repeated exposure from high preference influenced by other factors such as personal preference. For this reason, trials with low-likability faces (not higher than the average likability for its set) were excluded from EEG analysis. A permutation test in TF analysis shows that, in the MEE-tendency group, gamma activity (40–60 Hz) for Shown-trials was significantly weaker than Novel-trials in occipito-parietal sites during the 250–350 ms after stimuli onset. The weakened gamma-band activity for repeatedly presented stimuli found in our study is consistent with that found in a previous repetition-priming study (Gruber & Müller, 2002). Thus, our result reflects repetition suppression caused by the sharpening of stimulus representation. Repetition suppression has also been reported in several other MEE and priming studies (e.g. De Gardelle, Waszczuk, Egner, & Summerfield, 2012; Senkowski & Herrmann, 2002). However, it is still possible that personal preference drove high-likability for some faces, and that the repetition-suppression effect does not reflect MEE alone. This question was clarified by the t-test result that revealed averaged gamma activity (40–60 Hz, 250–350 ms after stimuli onset) over sites with peak repetition-suppression effects was significantly weaker in Shown MEE trials than Shown non-MEE trials. A previous study suggested that gamma activity in the 25–50 Hz range reflects encoding of face configurations (Anaki, Zion-Golumbic, & Bentin, 2007). Higher gamma activity

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

Group with MEE tendency

Group without MEE tendency

r = 0.34, p = 0.33

35

135

50

30

r = 0.68, p = 0.031

40

MPI N

MPI N

25 20

30 20

15 10

10 5

0

0.05

0.1

0.15

0 -0.4

0.2

Gamma power difference (n.u.)

0.4

40

25

MPI E

MPI E

0.2

50

30

20

30

20

15 10

0

r = 0.66, p = 0.039

r = -0.79, p = 0.006

35

-0.2

Gamma power difference (n.u.)

0

0.05

0.1

0.15

0.2

Gamma power difference (n.u.)

10 -0.4

-0.2

0

0.2

0.4

Gamma power difference (n.u.)

Fig. 7. The correlation between the size of the repetition-suppression effect and MPI E and N scores in groups with and without MEE tendency. The x-axis indicates the size of the repetition-suppression effect (difference between Novel and Shown gamma averaged over seven posterior sites that showed a significant difference between exposure conditions) and the y-axis MPI E and N scores. R indicates the correlation coefficients, and P represents the statistical values for the relationship. The abbreviation n.u. refers to normalized units.

was suggested to reflect semantic processing related to memory retrieval (Anaki et al., 2007; Zion-Golumbic & Bentin, 2006). Because stimuli in both groups were consciously perceived only once, and preexisting information for all faces should therefore not have differed, the mechanism for weaker gamma activity in MEE trials relative to non-MEE trials found here is unlikely to be related to differences in semantic processing or memory retrieval. Although, the possibility that face-configuration encoding processes exist to varying extents cannot be completely ruled out, it is difficult to explain why the process for faces in MEE trials was weaker, as exposure to all stimuli was equal. Additionally, the weakened process cannot be explained by sharpening or expertise theory. Gamma-activity modulation due to attention (Jensen, Kaiser, & Lachaux, 2007) is also unlikely to be the cause, since P2 and LPC, which are components known to be modulated by attention, did not show any significant effect. A possible explanation might lie in the relationship of gamma activity and emotional processing. Gamma activity has been reported to be stronger for aversive or negative stimuli than positive or neutral stimuli (Luo, Holroyd, Jones, Hendler, & Blair, 2007; Oya, Kawasaki, Howard, & Adolphs, 2002). The possible association of gamma activity modulated by MEE condition and participant-anxiety level is supported by a previous study of familiarity and anxiety, which reported that a high-anxiety participant group rated familiar cartoons higher and unfamiliar cartoons lower than a low-anxiety group (Schick et al., 1972a, 1972b). Unrestricted to gamma oscillation, modulation of the repetition-suppression effect by emotional face stimuli has also been reported in a number of fMRI studies (e.g. Bentley,

Vuilleumier, Thiel, Driver, & Dolan, 2003; Ishai, Pessoa, Bikle, & Ungerleider, 2004). Previous studies of gamma oscillation and repetition suppression mentioned above support the possibility that differences in the repetition-suppression effect between MEE and non-MEE trials might result from emotion processing. NonMEE trials, despite generating high likability ratings, might still produce relatively higher negative emotions. Note that “high likability” mentioned in this study is based on a relative scale, and the average likability for Shown-stimuli was still below ‘4’ which should be the medium rating on a 7-point scale. We found a significant negative correlation in the MEEtendency group between the amount of gamma activity suppressed by repetition and the MPI-test E-scores. This implies that people who are more extroverted tend to have smaller repetition induced suppression of gamma activity. Extroversion has been reported to be negatively correlated to anxiety level (Jylha & Isometsa, 2006; Terasawa, Shibata, Moriguchi, & Umeda, 2013). In addition, both E and N scores significantly positively correlated with the amount in the no-MEE tendency group. Neuroticism is positively correlated strongly with anxiety (e.g. Jylha & Isometsa, 2006). Although the absence of a statistically significant repetition-suppression effect in the non-MEE tendency group precludes the claim that people who are not influenced by mere exposure do not exhibit repetition suppression, it is highly possible that MEE is associated with repetition suppression (the perceptual fluency theory of MEE) at least in people who have the tendency. Therefore, it can be inferred that people who have lower levels of social anxiety exhibit smaller neural MEE. This is in line

136

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

MEE

non-MEE

Fig. 8. (A) Source-localization results performed on data from the MEE-tendency group reveal areas responsible for differences in MEE and non-MEE oscillation plotted onto axial slices (z¼  18 toþ66) of a standard T1-weighted MR image. (B) Source-localization results performed on data from the MEE-tendency group reveal areas responsible for differences in MEE and non-MEE oscillation. Color indicates statistical significance, with positive t values representing stronger activity for non-MEE. The abbreviation n.u. refers to normalized units. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

with a report that people with lower anxiety levels are affected less by repetitive exposure (Schick et al., 1972a, 1972b). Results from source localization also support the possibility that emotion processing influences MEEs. The insula, putamen, and superior frontal gyrus (SFG) have been indicated as cortices responsible for MEE and non-MEE activity differences. The insula has been speculated to be involved in cognitive-emotional processes in great apes, such as empathy and self-awareness of emotional feelings (Critchley, Wiens, Rotshtein, Ohman, & Dolan, 2004; Martino, 2006; Xue, Lu, Levin, & Bechara, 2010). . Activity in the putamen, part of reward circuit, has been reported to be more activated during processing of negative emotions such as sadness and fear (Badgaiyan, 2010; Grabowski et al., 2000). Despite its involvement in several cognitive processes that make it difficult to clarify its role in the current experiment, there is a rather clear explanation for recruitment of the SFG when focusing on emotionprocessing literature. The SFG has been reported to be more activated by faces in negative self-reference context conditions (presenting negative dialog such as “She thinks you are incompetent” before presenting face stimuli) (Schwarz, Wieser, Gerdes, Muhlberger, & Pauli, 2013). It has also been reported to respond more strongly to negative evaluation in social phobics (Blair et al., 2008). We would like to point out some limitations in our study. One limitation is that small sample size may have reduced statistical power because 21 subjects were divided into two groups, so that negative findings in our study should be treated with caution. Another limitation concerns the localization accuracy of the sources. We used the standard head model with standard electrode locations. The localization accuracy of the sources can be improved by building head models with the individual MRI and electrode location from the subjects. Our results suggest that posterior gamma activity might be a potential indicator that distinguishes MEE and other likability effects, at least with regard to human-face stimuli. The repetition-suppression effect found here is consistent with that found in most priming and repetition studies, supporting the idea that perceptual fluency influences MEEs. At the same time, differences in repetition-suppression effects between MEE and non-MEE trials might result from emotion processing, supporting the idea that MEE results from reduced apprehensiveness towards novel stimuli. Thus, our results suggest that perceptual fluency and emotion processing both play important roles in MEE.

Acknowledgments This work was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (Grant numbers 22300076 and 23700311) from the Ministry of Education, Culture, Sports, Science, and Technology, and SCOPE, from the Ministry of Internal Affairs and Communications, Japan.

References Anaki, D., Zion-Golumbic, E., & Bentin, S. (2007). Electrophysiological neural mechanisms for detection, configural analysis and recognition of faces. NeuroImage, 37, 1407–1416. Antonakis, J., & Dalgas, O. (2009). Predicting elections: Child0 s play! Science, 323, 1183. Badgaiyan, R. D. (2010). Dopamine is released in the striatum during human emotional processing. Neuroreport, 21, 1172–1176. Balogh, R., & Porter, R. H. (1986). Olfactory preferences resulting from mere exposure in human neonates, Vol. 9. Amsterdam: PAYS-BAS: Elsevier. Bentley, P., Vuilleumier, P., Thiel, C. M., Driver, J., & Dolan, R. J. (2003). Effects of attention and emotion on repetition priming and their modulation by cholinergic enhancement. Journal of Neurophysiology, 90, 1171–1181.

137

Blair, K., Geraci, M., Devido, J., McCaffrey, D., Chen, G., Vythilingam, M., et al. (2008). Neural response to self- and other referential praise and criticism in generalized social phobia. Archives of General Psychiatry, 65, 1176–1184. Bornstein, R. F. (1989). Exposure and affect: Overview and meta-analysis of research, 1968–1987. Psychological Bulletin, 106, 265–289. Bornstein, R. F., & Dagostino, P. R. (1992). Stimulus recognition and the mere exposure effect. Journal of Personality and Social Psychology, 63, 545–552. Bornstein, R. F., Kale, A. R., & Cornell, K. R. (1990). Boredom as a limiting condition on the mere exposure effect. Journal of Personality and Social Psychology, 58, 791–800. Crandall, J. E. (1968). Effects of need for approval and intolerance of ambiguity upon stimulus preference. Journal of Personality, 36, 67–83. Critchley, H. D., Wiens, S., Rotshtein, P., Ohman, A., & Dolan, R. J. (2004). Neural systems supporting interoceptive awareness. Nat Neurosci, 7, 189–195. De Gardelle, V., Waszczuk, M., Egner, T., & Summerfield, C. (2012). Concurrent repetition enhancement and suppression responses in extrastriate visual cortex. Cerebral Cortex Eysenck, H. J., & Eysenck, S. B. G. (1994). Manual of the eysenck personality questionnaire: EPQ-R adult. Edits, Hodder & Stoughton. Grabowski, T. J., Bechara, A., Damasio, H., Ponto, L. L. B., Parvizi, J., Hichwa, R. D., et al. (2000). Subcortical and cortical brain activity during the feeling of selfgenerated emotions. Nature Neuroscience, 3, 1049. Gross, J., Pollok, B., Dirks, M., Timmermann, L., Butz, M., & Schnitzler, A. (2005). Task-dependent oscillations during unimanual and bimanual movements in the human primary motor cortex and SMA studied with magnetoencephalography. NeuroImage, 26, 91–98. Gruber, T., & Müller, M. M. (2002). Effects of picture repetition on induced gamma band responses, evoked potentials, and phase synchrony in the human EEG. Brain Research – Cognitive Brain Research, 13, 377–392. Hansen, T., & Bartsch, R. A. (2001). The positive correlation between PErsonal need for structure and the mere exposure effect. Social Behavior and Personality, 29, 271–276. Heingartner, A., & Hall, J. V. (1974). Affective consequences in adults and children of repeated exposure to auditory stimuli. Journal of Personality and Social Psychology, 29, 719–723. Ishai, A., Pessoa, L., Bikle, P. C., & Ungerleider, L. G. (2004). Repetition suppression of faces is modulated by emotion. Proceedings of the National Academy of Sciences, 101, 9827–9832. Jakesch, M., & Carbon, C. C. (2012). The mere exposure effect in the domain of haptics. PloS One, 7(2), e31215. Jensen, O., Kaiser, J., & Lachaux, J.-P. (2007). Human gamma-frequency oscillations associated with attention and memory. Trends in Neurosciences, 30, 317–324. Jylha, P., & Isometsa, E. (2006). The relationship of neuroticism and extraversion to symptoms of anxiety and depression in the general population. Depression and Anxiety, 23, 281–289. Kawakami, N., & Yoshida, F. (2011). Multiple mere exposure effect: category evaluation measured in the Go/No-go association task (GNAT). Shinrigaku Kenkyu, 82, 424–432. Liljeström, M., Kujala, J., Jensen, O., & Salmelin, R. (2005). Neuromagnetic localization of rhythmic activity in the human brain: A comparison of three methods. NeuroImage, 25, 734–745. Lindsen, J. P., Jones, R., Shimojo, S., & Bhattacharya, J. (2010). Neural components underlying subjective preferential decision making. NeuroImage, 50, 1626–1632. Luft, C. D., Nolte, G., & Bhattacharya, J. (2013). High-learners present larger midfrontal theta power and connectivity in response to incorrect performance feedback. The Journal of neuroscience, 33, 2029–2038. Luo, Q., Holroyd, T., Jones, M., Hendler, T., & Blair, J. (2007). Neural dynamics for facial threat processing as revealed by gamma band synchronization using MEG. NeuroImage, 34, 839–847. Maier, M. E., Yeung, N., & Steinhauser, M. (2011). Error-related brain activity and adjustments of selective attention following errors. NeuroImage, 56, 2339–2347. Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods, 164, 177–190. Martino, B. D. (2006). Frames, biases, and rational decision-making in the human brain. Science, 313, 684. Newell, B. R., & Shanks, D. R. (2007). Recognising what you like: Examining the relation between the mere-exposure effect and recognition. European Journal of Cognitive Psychology, 19, 103–118. Olofsson, J. K., Nordin, S., Sequeira, H., & Polich, J. (2008). Affective picture processing: An integrative review of ERP findings. Biological Psychology, 77, 247–265. Oya, H., Kawasaki, H., Howard, M. A., & Adolphs, R. (2002). Electrophysiological responses in the human amygdala discriminate emotion categories of complex visual stimuli. The Journal of Neuroscience, 22, 9502–9512. Prescott, J., Kim, H., & Kim, K. O. (2008). Cognitive mediation of hedonic changes to odors following exposure. Chemosensory Perception, 1, 2–8. Reber, R., Winkielman, P., & Schwarz, N. (1998). Effects of perceptual fluency on affective judgments. Psychological Science, 9, 45–48. Schacht, A., Werheid, K., & Sommer, W. (2008). The appraisal of facial beauty is rapid but not mandatory. Cognitive, Affective, & Behavioral Neuroscience, 8, 132–142. Schick, C., McGlynn, R. P., & Woolam, D. (1972a). Perception of cartoon humor as a function of familiarity and anxiety level. Journal of Personality and Social Psychology, 24, 22–25. Schick, C., McGlynn, R. P., & Woolam, D. (1972b). Perception of cartoon humor as a function of familiarity and anxiety level. Journal of Personality and Social Psychology, 24, 22–25.

138

N. Kongthong et al. / Neuropsychologia 54 (2014) 129–138

Schwarz, K. A., Wieser, M. J., Gerdes, A. B., Muhlberger, A., & Pauli, P. (2013). Why are you looking like that? How the context influences evaluation and processing of human faces. Social Cognitive and Affective Neuroscience, 8, 438–445. Seamon, J. G., Brody, N., & Kauff, D. M. (1983). Affective discrimination of stimuli that are not recognized: Effects of shadowing, masking, and cerebral laterality. Journal of Experimental Psychology – Learning Memory and Cognition, 9, 544–555. Seamon, J. G., McKenna, P. A., & Binder, N. (1998). The mere exposure effect is differentially sensitive to different judgment tasks. Consciousness and Cognition, 7, 85–102. Senkowski, D., & Herrmann, C. S. (2002). Effects of task difficulty on evoked gamma activity and ERPs in a visual discrimination task. Clinical Neurophysiology, 113, 1742–1753. Suzuki, M., & Gyoba, J. (2005). Cross-modal mere exposure effects between visual and tactile modalities. Perception, 34 ECVP Abstract Supplement. Szpunar, K. K., Schellenberg, E. G., & Pliner, P. (2004). Liking and memory for musical stimuli as a function of exposure. Journal of Experimental Psychology – Learning, Memory and Cognition, 30, 370–381. Terasawa, Y., Shibata, M., Moriguchi, Y., & Umeda, S. (2013). Anterior insular cortex mediates bodily sensibility and social anxiety. Social Cognitive and Affective Neuroscience, 8, 259–266. Todorov, A., Mandisodza, A. N., Goren, A., & Hall, C. C. (2005). Inferences of competence from faces predict election outcomes. Science, 308, 1623–1626.

Topolinski, S., & Strack, F. (2009). Motormouth: mere exposure depends on stimulus-specific motor simulations. Journal of Experimental Psychology – Learning, Memory and Cognition, 35, 423–433. Xue, G., Lu, Z., Levin, I. P., & Bechara, A. (2010). The impact of prior risk experiences on subsequent risky decision-making: The role of the insula. NeuroImage, 50, 709. Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology Monograph Supplement, 9, 1–27. Zajonc, R. B. (2001). Mere exposure: A gateway to the subliminal. Current Directions in Psychological Science, 10, 224–228. Zebrowitz, L. A., White, B., & Wieneke, K. (2008). Mere exposure and racial prejudice: exposure to other-race faces increases liking for strangers of that race. Social Cognitive, 26, 259–275. Zebrowitz, L. a., & Zhang, Y. (2012). Neural evidence for reduced apprehensiveness of familiarized stimuli in a mere exposure paradigm. Social Neuroscience, 7, 347–358. Zhang, Y., Kong, F., Chen, H., Jackson, T., Han, L., Meng, J., et al. (2011). Identifying cognitive preferences for attractive female faces: an event-related potential experiment using a study-test paradigm. Journal of Neuroscience Research, 89, 1887–1893. Zion-Golumbic, E., & Bentin, S. (2006). Dissociated neural mechanisms for face detection and configural encoding: Evidence from N170 and induced gammaband oscillation effects. Cerebral Cortex, 17, 1741–1749.