Does Spontaneous Eye Blink Rate Modulate Distractor
Suppression in the Emotional Attentional Blink?
Julia Beitner 13378131
Internship report Number of credits: 18ec
Supervisor: Dr. Martine van Schouwenburg Research Master’s Psychology
University of Amsterdam
Abstract
Distractor suppression is crucial for goal-directed behavior and can be studied with the attentional blink paradigm, in which awareness of the second of two target stimuli presented in close succession is impaired. A similar effect was found in the emotional attentional blink (EAB), which is characterized by impaired detection of target pictures appearing closely after a task-irrelevant, emotionally arousing picture. Furthermore, an association between dopamine (which is linked to working memory among others) and performance on the attentional blink was found, but the direction of the relationship remains unclear. Given the similarity of attentional blink and EAB, the here proposed EEG study investigated the association of a striatal dopamine marker, spontaneous eye blink rate (sEBR), and the suppression of task-irrelevant distractors behaviorally as well as with the event-related potential P3b, which is thought to reflect consolidation and updating in working memory. While the behavioral and P3b results of the EAB were replicated, neither a relationship of sEBR with EAB performance nor with P3b components were found. Possible explanations for the absence of a relationship with sEBR are discussed, and further research with regard to the P3a component is proposed.
Introduction
In an ever-changing, precisely detailed environment the human being has to deal with a limited amount of awareness. Hence it is crucial to select goal-relevant information and inhibit irrelevant or distracting information. The ability of distractor suppression can be studied with the emotional attentional blink (EAB) paradigm, also known as emotion-induced blindness (see McHugo, Olatunji, & Zald, 2013).
The EAB paradigm is a variation of the attentional blink paradigm (Raymond, Shapiro, & Arnell, 1992) and was first introduced by Most, Chun, Widders, and Zald (2005). Participants had to detect a rotated landscape picture within a rapid serial visual presentation (RSVP) stream of unrotated landscape pictures, each presented for 100 ms. A distractor picture appeared within the RSVP stream either 200 ms (lag 2) or 800 ms (lag 8) prior to the target picture (figure 1), resulting in an impaired performance at the lag 2 condition. This effect is comparable to the pattern found in the attentional blink task, where two targets (e.g., letters) have to be detected in an RSVP stream filled with distractors (e.g., digits) and the detection of the second target is impaired if it follows closely after the first (Dux & Marois, 2009; Shapiro, Raymond, & Arnell, 1997). In contrast to the attentional blink, however, the EAB also has an emotional component, namely the valence of the distractor picture. This crucial manipulation leads to an impaired performance when the distractor valence is negative compared to neutral (Ciesielski, Armstrong, Zald, & Olatunji, 2010; McHugo et al., 2013; Most et al., 2005; Most & Junge, 2008; Olatunji, Ciesielski, & Zald, 2011).
Figure 1. Example of part of an EAB trial. Since the distractor appears 200 ms prior to the target, this figure is showing a lag 2 trial. Reprinted from Kennedy, B. L., Rawding, J., Most, S. B., & Hoffman, J. E. (2014). Emotion-induced blindness reflects competition at early and late processing stages: An ERP study. Cognitive, Affective, & Behavioral Neuroscience, 14(4), 1485-1498.
One influential theory of the attentional blink is the two-stage bottleneck model postulated by Chun and Potter (1995), which states that the attentional blink results from still ongoing consolidation processes of the first target which prevent the consolidation of the second target. This theory is also able to explain the EAB (see McHugo et al., 2013). However, it cannot explain the phenomenon of lag 1 sparing observed in the attentional blink. Lag 1 sparing describes the effect that if the second target appears directly after the first target (i.e., lag 1 and no distractor in between), performance is not impaired and the second target is perceived (Raymond et al., 1992). But according to the two-stage bottleneck model, the performance should be at least equally impaired as in lag 2, since the consolidation process into working memory should be initiated during the first target presentation and outlasts several 100 ms (Chun & Potter, 1995). Recent studies therefore suggest that not the consolidation process into working memory of the first target leads to impairment of the detection of the second target but rather the inability to suppress the succeeding distractor (Di Lollo, Kawahara, Ghorashi, & Enns, 2005; Olivers & Meeter, 2008). In other terms, the detection of the first target opens the gate into working memory while the appearance of the following distractor triggers a gate closing process that impairs the monitoring of the RSVP stream. Consequently, targets in close temporal proximity (but still divided by a distractor in between) do not get access to working memory since their appearance goes undetected due to the ongoing closing process. This is in line with the absence of lag 1 sparing in the EAB, where the detection of the target is even worse in lag 1 compared to lag 2 (Most & Jungé, 2008). Since the distractor in the EAB is task-irrelevant (like distractors in the attentional blink task) but also emotionally salient, it captures involuntarily bottom up attention and might open the gate to working memory. This process would need to be suppressed and attention would then need to be reallocated to the monitoring of RSVP stream. The distractor suppression hypothesis is thus able to explain both attentional blink and EAB.
To further investigate the involvement of working memory in the attentional blink, several studies recorded the electrophysiological brain activity using EEG (e.g., Fell, Klaver, Elger, & Fernández, 2002; Kranczioch, Debener, & Engel, 2007; Sergent, Baillet, & Dehaene, 2005,
Vogel, Luck, & Shapiro, 1998). The P3b event-related potential (ERP) component is thought to play a role in displaying the updating of context and consolidation of information into working memory (Donchin & Coles, 1988; Kok, 2001; Vogel & Luck, 2002) and has been consistently shown to be suppressed during the attentional blink (Kranczioch et al., 2007; Sergent et al., 2005; Vogel et al., 1998). In particular, the first and second target elicited different amplitudes of the P3b with a larger P3b (reflecting access to working memory) following the first target and a smaller P3b (fewer allocated resources) following the second target. These different P3b amplitudes are also accompanied by the typical behavioral performance attentional blink pattern (Shapiro, Schmitz, Martens, Hommel, & Schnitzler, 2006). Importantly, comparable results were also found when looking at the ERP components of the EAB (Kennedy, Rawding, Most, & Hoffman, 2014). That is, distractor pictures elicited larger P3b amplitudes than target pictures, indicating that distractors are consolidated into working memory as well.
In addition to the P3b ERP component, dopamine has also been suggested to represent a neurophysiological marker of the attentional blink. Dopamine has been found to play a crucial role in several cognitive abilities, such as working memory, attentional selection, and cognitive flexibility (Hazy, Frank, & O’Reilly, 2006; Nieoullon, 2002). Its availability in the prefrontal cortex and the basal ganglia has been shown to modulate the prioritizing of task-relevant information while inhibiting irrelevant stimuli (Baier et al., 2010; Fallon, Williams-Gray, Barker, Owen, & Hampshire, 2012; Roberts et al., 1994). Especially the nigrostriatal pathway is suggested to use feedback and context information in order to support or prevent updating in working memory (Moustafa, Sherman, & Frank, 2008). The association of working memory and dopamine (Sawaguchi & Goldman-Rakic, 1991) was in line with the result of a study performed by Colzato, Spapé, Pannebakker, and Hommel (2007), that showed that participants with better working memory performance (i.e., higher working memory span) showed a smaller attentional blink size (i.e., the difference of performance between lag 2 and lag 8 is smaller). Based on these findings, it has been proposed that dopamine might also modulate attentional blink size, and indeed in line with the attentional blink theory of distractor-initiated inhibition processes, several studies could show that participants with a higher dopaminergic level show a smaller attentional blink size (Colzato, Slagter, Spapé, & Hommel, 2008; Felten et al., 2013; Slagter et al., 2012). However, there are also results that go in the opposite direction, that is participants with a higher dopaminergic level show a larger attentional blink size (Colzato et al., 2011), or found no link between markers of striatal dopamine and attentional blink size at all (Slagter & Georgopoulou, 2013). However, all these studies focused on the relationship of dopamine and task-relevant distractors. It remains unclear if this association also holds true for task-irrelevant distractors, and if so in which direction the effect goes.
Hypotheses
The current study focused on the relationship between dopamine and task-irrelevant, emotionally arousing distractors and investigated the associations of dopaminergic levels and the EAB. Spontaneous eye blink rate (sEBR), which appears to be a marker of striatal dopamine (Jongkees & Colzato, 2016; Karson 1983), was used as a proxy for dopamine levels. Because of the similarities of the EAB and attentional blink paradigms it was assumed that the association between sEBR and performance would also be present here, that is, dopamine modulates the EAB size (i.e., emotional attentional capture):
This effect should also be visible in the ERP components (see Kennedy et al., 2014). Since dopamine is related to working memory, we were especially interested in one of the main components related to working memory, namely P3b.
2. Hypothesis: Distractor P3b and target P3b differ between participants with low and high levels of dopamine.
Methods
Participants
To estimate a sufficient sample size (r = –0.53, see Colzato et al., 2008), a power analysis performed with g*power (Faul, Erdfelder, Buchner, & Lang, 2009) suggested that with an alpha error of .05, and power of .95, a sample size of 34 is sufficient. In order to account for possible dropout, 43 participants were recruited through the University of Amsterdam’s online recruitment system. All participants were right-handed and had normal or corrected-to-normal vision. Furthermore, they were not colorblind and had no history of neurological or psychiatric disorders. Participants provided written informed consent and were compensated either at a rate of 10€ per hour or with course credit. The study was approved by the local ethics committee of the University of Amsterdam. Two participants (one female) had to be excluded due to the following reasons. One participant fell asleep during the experiment, and one participant showed task performance below chance. The analyses are based on the remaining 41 participants (35 female, mean age: 21.49 years, age range: 18-26 years).
Experimental Procedure
Participants came for one session that lasted about two hours. Before the start of the experiment, participants were explicitly made aware of upcoming unpleasant pictures. They were shown an example picture from the negative distractor category which was not part of the distractors used in the actual experiment. Then, after the EEG was set up, sEBR was assessed. For the measurement, participants were sitting in a comfortable chair with an approximate distance of 1 meter in front of a black fixation cross hanging on a white wall. They were asked to relax and look (but not stare) at the cross for five minutes, while data was recorded. After the measurement of the sEBR, participants were seated with a distance of approximately 70cm to the screen. They started a practice block of ten trials of the EAB task. They then had the opportunity to ask questions. Afterwards, the actual task started. They completed the practice block and task in a dimly lit room. Every ten minutes, the participants had the opportunity to take a self-timed break and were asked about their mood. EEG was only recorded for the sEBR measurement and the actual task but not for the practice block. Lastly, participants were debriefed.
Materials
A set of photographs depicting landscapes and architectural buildings was used for filler and target pictures. This set has been already used in several EAB studies (e.g., Kennedy et al., 2014; Most, Chun, Widders, & Zald, 2005). 120 distractor pictures were selected from the NAPS database (60 neutral pictures, valence M(SD) = 5.53(0.60), arousal 4.83(0.56); 60 negative pictures, valence 2.76(0.71), arousal 6.73(0.56) and are matched in terms of luminance, contrast, amount of red and green color in CIE L*a*b color space, and entropy (all
Figure 2. 2x3 design of the experiment. The numbers within the cells depict the amount of trials of each condition.
EAB task and design
The EAB task was programmed and presented in Presentation (Version 19.0, Neurobehavioral Systems, Inc., Berkeley, CA, www.neurobs.com). The task had two different distractor valence conditions (neutral, negative) and three different lag conditions (lag 2, lag 4, lag 8), resulting in a 2x3 design. The task consisted of 696 trials, while 96 of them were catch trials (i.e., they contained no target). Each distractor valence at lag 2 appeared 200 times in the task, lag 4 and lag 8 appeared for each distractor valence 50 times (see figure 2).
Within the experiment, conditions were randomly interleaved. One trial consisted of a stream of 17 pictures, each presented for 100 ms. The target picture was a rotated landscape picture. All other positions presented a normal landscape picture, except the distractor picture. The distractor picture either appeared at the fourth, sixth, or eighth position within the stream. 500 ms after the stream ended, the first response screen appeared, where participants had to indicate whether the target was rotated to the left (button arrow left) or to the right (arrow right), or if they did not see a target at all (arrow down). After answering this question, a second response screen appeared, asking about the participants’ certainty of the just given answer, that is, unsure, neutral, or sure (corresponding to the buttons arrow left, down, and right, respectively).
EEG recording
EEG activity was measured using the BioSemi ActiveTwo 64 electrode Ag-AgC1 system (BioSemi, Amsterdam, the Netherlands) at a sampling rate of 512 Hz. Six additional external active electrodes were used: two on the earlobes as reference, and four for electrooculogram (EOG), that is, two vertical (above and below the eye) and two horizontal (next to the outside of the eyes). Individual electrode offset voltages were tried to keep below 30 µV and did never exceed 50 µV, as recommended by BioSemi. Data were referenced online to a common mode sense active electrode which produced a monopolar, non-differential channel. The EEG and EOG data were analyzed and preprocessed in MATLAB R2016a software (Mathworks Inc., 2016) by using functions from the FieldTrip toolbox (Oostenveld, Fries, Maris, & Schoffelen, 2011). Data were re-referenced offline to the average of the earlobe channels, high-pass filtered with a cutoff of 0.5 Hz, and segmented into epochs beginning 1.5 s before the onset of the target and ending 1.5 s after target onset. Epochs were then visually inspected and epochs containing non-stereotypical activity (e.g., muscle activity and electrical noise) were rejected. In addition, if artifacts appeared to be in a single channel and over several epochs (i.e., > 10 %), the channel was marked as bad and entirely interpolated. To remove eye blinks and eye movements from
Position of target after distractor
Lag2 Lag4 Lag8
Distr ac to r valence Neutral 200 50 50 Negative 200 50 50
the data, an independent component analysis (ICA) was performed and components containing the aforementioned were removed. Lastly, data were re-epoched with distractor onset as time zero.
sEBR recording
EOG electrodes were used to measure participants’ spontaneous eye blink rates (sEBR). sEBR is defined by the amount of eye blinks in one minute. Doughty (2016) found that a measurement of five minutes while participants are maintaining a primary gaze in a resting state condition is sufficient to provide reliable results. Since sEBR appears to be confounded by fatigue and is stable during the day (Barbato et al., 2000), measurements always took place between 9am and 17pm. Furthermore, participants were instructed to come with glasses instead of contact-lenses. Participants were naïve to the purpose of the measurement and were not given any instructions about blinking behavior in order to not affect their sEBR.
Recorded EOG activity was re-referenced offline to the average of the earlobe channels. A high-pass filter of 0.5 Hz was applied to remove low-frequency drifts. The second vertical EOG channel was subtracted from its counterpart to create one bipolar channel. An eye blink was defined as a voltage change of at least 200 µV in a time window of 150 ms. Furthermore, 500 ms had to lie in-between two eye blinks. Finally, average sEBR was calculated. Due to one noisy vertical EOG channel, the eye blink detection for one subject was performed solely on the upper vertical EOG channel with an adjusted criterion of a voltage change of at least 100 µV.
Statistical analyses
The behavioral data was analyzed with a 2x3 repeated measures ANOVA with the factors distractor valence (neutral, negative) and lag (2, 4, 8). Based on the results of Kennedy et al. (2014), two main effects of distractor valence and lag and an interaction of both were expected. Specifically, performance for lag 2 was expected to be lower than for lag 8, performance on negative trials was expected to be lower than on neutral trials, and last but not least, the influence of distractor valence to be present on lag 2 trials and absent on lag 8 trials. These planned comparisons were analyzed with one-tailed t-tests.
In order to assess whether there is a relationship between dopamine and EAB size (hypothesis 1) a two-tailed Pearson correlation test examining the association between sEBR and EAB size was planned. sEBR is known as a marker for striatal dopamine and hence used to measure participants’ dopaminergic levels (e.g., Jongkees & Colzato, 2016; Karson, 1983). Since the assumption of bivariate normality of sEBR and EAB size was not met, Spearman’s rank correlation test was performed instead. EAB size was obtained in two steps. First, lag 2 was subtracted from lag 8 for each distractor valence condition, resulting in a negative and a neutral EAB size. Positive values imply worse performance on lag 2 compared to lag 8. Second, neutral EAB size was subtracted from negative EAB size, resulting in one overall EAB size. While positive values imply worse performance on trials with a negative distractor, negative values imply worse performance on trials with a neutral distractor. Hence, the larger the positive value, the larger the emotional blink.
Hypothesis 2 stated that the relationship of dopamine and EAB size should also be visible in the P3b ERP components. This was assessed with a repeated measures ANOVA for both target and distractor P3b with the average amplitude of the specific P3b component (negative, neutral) as within factor and sEBR as a marker for dopamine level as between factor (low vs. high). The
two groups based on sEBR were formed with a median split. Difference waves were calculated for isolating the specific ERP components. Due to the RSVP, each picture (i.e., filler landscape, distractor, and target pictures) elicited its own sensory components. In order to isolate the ERPs of the critical events (i.e., distractor and target picture), irrelevant background activity can be canceled out by subtracting the ERP in one condition from another (Kennedy et al., 2014; Vogel et al., 1998). For isolating the target P3b component, epochs were centered on the distractor picture and lag 8 trials were subtracted from lag 2 trials. The distractors cancelled each other out, as well as the filler pictures, and the target P3b component of lag 2 remained. Similarly, it was done for the distractor P3b components. Epochs were centered on the target picture and lag 2 trials were subtracted from lag 8 trials, resulting in an isolated distractor P3b of lag 8. This procedure was in both cases (target and distractor) performed for both distractor valences (negative and neutral), eventually resulting in four P3b components. In order to specify time windows of the P3b components, the following procedure was performed. First, data was band-pass filtered from 1 to 30 Hz. Then scalp topographies of the aforementioned difference waves were visually inspected. In order to not bias the selection, data of target and distractor P3b were averaged over both distractor valence conditions. A subset of six electrodes showing the highest peak was selected. The waveform of the selected electrodes was visually inspected and a time window based on width of the peak was specified.
Results
Confirmatory analyses Behavioral results
Targets preceded by a negative distractor 200 ms ago were worse detected than targets preceded by a neutral distractor. This impairment decreases from lag 2 to lag 4, and is absent at lag 8 (figure 3). Behavioral results were analyzed with a 2x3 repeated measures ANOVA with the factors distractor valence (neutral, negative) and lag (2, 4, 8). Since the assumption of sphericity was violated for the lag condition (Mauchly’s W = 0.842, p = .035), a Huyhn-Feldt correction (ε = .90) was applied. The analysis revealed a significant main effect of distractor valence, F(1, 40) = 27.16, p < .001, a significant main effect of lag, F(1.80, 71.91) = 124.87, p < .001, and their significant interaction, F(2, 80) = 8.76, p < .001. A paired t-test showed that performance on lag 2, M = 61.60% (SD = 10.82%) was as expected significantly worse than on lag 8, M = 78.54% (SD = 11.90%), t(40) = −13.49,
p < .001 (one-tailed). Furthermore, performance on negative trials, M = 69.09% (SD = 11.22%),
was significantly lower than on neutral trials, M = 73.52% (SD = 10.72%), t(40) = −5.21, p < .001 (one-tailed). We expected that the influence of the distractor valence is present on lag 2
Figure 3. Mean accuracies of target detection for all conditions. Error bars represent standard error.
and absent on lag 8. A paired t-test showed that target detection on lag 2 was significantly impaired when the target was preceded by a negative distractor, M = 57.51% (SD = 11.23%), than by neutral distractor, M = 65.70% (SD = 11.09%), t(40) = −9.63, p < .001 (one-tailed). Lastly, the performance on lag 8 trials was not influenced by distractor valence, negative: M = 77.66% (SD = 13.01%), neutral: M = 79.41% (SD = 12.30%), t(40) = −1.30, p = .200 (two-tailed). These results are in line with previous research on the EAB (e.g., Kennedy et al., 2014; Most et al., 2005).
sEBR & EAB size
Participants had sEBRs ranging from 3 to 37.80 blinks per minute, with an average of 14.07 (SD = 9.14). The sample represented a wide range of tonic dopaminergic levels and is in line with previous sEBR reports (e.g., Colzato et al., 2008 Colzato, van den Wildenberg, van Wouwe, Pannebakker, & Hommel, 2009; Jongkees & Colzato, 2016; Slagter & Georgopoulou, 2013). Since Colzato et al. (2008) found a correlation between sEBR and attentional blink size, we assumed that sEBR also correlates with the emotional equivalent of attentional blink size, namely EAB size. It was expected that participants with a higher sEBR show either a smaller or a larger blink. However, as it can be seen in figure 4, no significant correlation was found, r(41) = −.09,
p = .559. ERP results
To investigate the possible effects of dopamine on one of the main neural components of the EAB, two groups were formed with a median split based on the sEBRs: a low (20 participants, 3 – 10.40 sEBR score) and a high (21 participants, 11.20 – 37.80 sEBR score) group. Even though no correlation between sEBR and EAB size was found, it is still possible that effects are visible on the neural data.
Target P3b
To isolate the P3b component elicited by the target at lag 2 for both distractor valences, lag 8 trials were subtracted from lag 2 trials separately for both distractor valences. This results eventually in two target P3b components of trials with a neutral and a negative distractor. Visual inspection of amplitudes averaged over both distractor valences revealed maximal activity in the electrodes C1, Cz, C2, CP1, CPz, and CP2 (represented in the topography in white in figure 5 and 6), and a positive peak in the time window 460 – 640 ms after target onset (660 – 840 ms after distractor onset; represented as two black lines in figure 5 and 6). This is consistent with other studies investigating the P3b component (Kennedy et al., 2014; Polich, 2007; Sergent et
Figure 4. Relationship between EAB size and individual sEBR in minutes.
al., 2005). The difference waves of neutral and negative distractor trials for both sEBR groups can be seen in figure 5 and 6, respectively. In order to analyze the P3b components, a one-way repeated measures ANOVA on the amplitudes with distractor valence as within factor and sEBR group as between factor was performed. The ANOVA revealed a significant main effect of distractor valence, F(1,39) = 6.00, p = .019, indicating that the target P3b elicited following a neutral distractor, M = 1.09 µV (SD = 1.19), was significantly larger than the target P3b elicited following a negative distractor, M = 0.70 µV (SD = 0.94), which is in line with Kennedy
Figure 5. Difference curves (lag 2–lag 8) of the target P3b for the neutral condition for both low and high sEBR group. The topographic map shows the neutral target P3b averaged over both sEBR groups at a latency of 535 ms post-target onset (i.e., 735 ms post-distractor onset). Waveforms were averaged over electrodes represented in white. Area between the two black lines indicates the measurement window for the P3b component (660 – 840 ms post-distractor onset). Neutral Target P3b T arget on s et Di s tr ac tor ons e t - 1.5 µV 1.5 µV 735 ms 1.5 µV - 1.5 µV Di s tr ac tor ons e t T arget on s et Negative Target P3b
Figure 6. Difference curves (lag 2–lag 8) of the target P3b for the negative condition for both low and high sEBR group. The topographic map shows the negative target P3b averaged over both sEBR groups at a latency of 560 ms post-target onset (i.e., 760 ms post-distractor onset). Waveforms were averaged over electrodes represented in white. Area between the two black lines indicates the measurement window for the P3b component (660 – 840 ms post-distractor onset).
et al. (2014). However, contrary to our assumptions, no significant group effect, F(1,39) = 1.92,
p = .174, nor an interaction of both, F(1,39) = .32, p = .573, were found. In other words, sEBR
had no influence on the amplitudes of the target P3b.
Taken together, we replicated the finding of Kennedy et al. (2014) of a larger target P3b following a neutral distractor than a negative distractor. However, contrary to our hypothesis, sEBR played no role in modulating target P3b amplitude.
Distractor P3b
In order to determine whether the distractor pictures also elicited a P3b component, lag 2 was subtracted from lag 8, resulting in an isolated distractor component of the lag 8 trials. The same procedure as for the target P3b was also performed for the distractor P3b. That is, the waveform averaged over neutral and negative distractor trials was visually inspected in order to choose time window and electrodes, capturing the maximal activity. This procedure resulted in a time window of 400 – 540 ms after distractor onset and the electrodes FC1, FCz, FC2, C1, Cz, and C2. Again, this is in line with previous research on the P3b component (Kennedy et al., 2014; Polich, 2007; Sergent et al., 2005). However, note that these characteristics also show similarities with the P3a component (Bledowski, Prvulovic, Hoechstetter, et al., 2014; Bledowski, Prvulovic, Goebel, et al., 2014; Polich, 2007; Polich & Criado, 2006). The difference waves for neutral and negative distractors and both sEBR groups can be seen in figure 7 and 8, respectively. A one-way repeated measures ANOVA on the P3b amplitudes with distractor valence as within factor and sEBR group as between factor revealed a significant main effect of distractor valence, F(1,39) = 5.33, p = .026. As it has been already found by Kennedy et al. (2014), negative distractor P3b amplitude, M = 1.67 µV (SD = 1.23), was significantly larger than neutral distractor P3b amplitude, M = 1.22 µV (SD = 1.09). However, contrary to our assumption, sEBR played again no role in the modulation of the amplitudes, that is, neither a significant main effect of sEBR group, F(1,39) = 0.36, p = .553, nor an interaction of both, F(1,39) = 0.08, p = .784. 3 µV -3 µV 450 ms 0 0.2 0.4 0.6 0.8 D is tra c to r on s et T a rg e t o n s e t Neutral Distractor P3b
Figure 7. Difference curves (lag 8–lag 2) of the distractor P3b for the neutral condition for both low and high sEBR group. The topographic map shows the neutral distractor P3b averaged over both sEBR groups at a latency of 450 ms post-distractor onset. Waveforms were averaged over electrodes represented in white. Area between the two black lines indicates the measurement window for the P3b component (400 – 540 ms post-distractor onset).
To sum it up, we found no evidence that sEBR stays in any relationship with distractor P3b amplitudes. However, we replicated the finding by Kennedy et al. (2014) that the negative distractor P3b is significantly larger than the neutral distractor P3b.
Exploratory analyses
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Discussion
The present study used the EAB task to investigate the possible role of dopamine in emotional distractor suppression. Using a new set of distractor pictures, selected from the NAPS database, we replicated the behavioral EAB performance results, such that target detection is impaired if the target follows more closely after the distractor picture, and this is especially true if the distractor depicts a negative picture compared to a neutral picture (e.g., Most et al., 2005). Since our dataset was also matched in terms of low-level visual features, it cancels out other possible explanations causing the EAB such as, for example, a salience effect due to an increased amount of red color in negative pictures, which show injured skin. This strengthens the evidence that the EAB is induced by the emotional content of the distractor pictures. Moreover, we did also replicate the P3b findings by Kennedy et al. (2014). Targets following a neutral distractor elicited a larger P3b than targets following a negative distractor. This pattern was reversed for the distractors: negative distractors elicited a larger P3b than neutral distractors.
Furthermore, we were specifically interested in whether dopamine (measured through the striatal dopamine marker sEBR) modulates performance and P3b components of the EAB task. Due to the heterogeneity of previous reported studies, we assumed that there is an association between sEBR and performance on the task, and P3b components. However, contrary to our
0.8 0.6 0.4 0.2 0 Di s trac to r o n s e t T a rg e t o n s e t Negative Distractor P3b 452 ms
Figure 8. Difference curves (lag 8–lag 2) of the distractor P3b for the negative condition for both low and high sEBR group. The topographic map shows the negative distractor P3b averaged over both sEBR groups at a latency of 452 ms post-distractor onset. Waveforms were averaged over electrodes represented in white. Area between the two black lines indicates the measurement window for the P3b component (400 – 540 ms post-distractor onset).
hypotheses, no evidence was found that sEBR relates to EAB size (i.e., the emotional attentional capture) nor to the neural measures (i.e., distractor and target P3b).
Slagter and Georgopoulou (2013), who failed to replicate the association of sEBR and attentional blink size, discussed two possible explanations. First, they point out several differences in their attentional blink design compared to Colzato et al.’s (2008) design (e.g., duration of stimuli), which could have effected target selection processes and thereby caused the observed discrepancies. This explanation is, albeit, difficult to transfer to our paradigm because it is naturally completely different in its appearance compared to attentional blink task designs. In fact, it is not even clear if there is an association of sEBR and EAB size at all, considering that this is the first study to look at this relationship with the EAB paradigm. However, Slagter and Georgopoulou’s (2013) second explanation is about the task of the attentional blink design. In their study, the task was to look for a red and then a green letter, whereas Colzato et al. (2008) implemented a categorical task where participants had to detect two black digits. It has been found that a set switch as in Slagter and Georgopoulou’s (2013) study impairs attentional blink performance especially at shorter lag conditions (Potter, Chun, Banks, & Muckenhoupt, 1998). Additionally, Kelly and Dux (2011) have found that attentional blink sizes of different attentional blink tasks are not correlated within the same individuals. Kelly and Dux (2011) therefore suggest that the different tasks rely at least partially on distinct cognitive processes. Slagter and Georgopoulou (2013) argue that this might have masked individual differences in attentional blink size. Considering the EAB paradigm, it becomes obvious that it resembles more a set switch attentional blink task than a categorical attentional blink task. The process of first suppressing an irrelevant yet attention-capturing distractor and then searching for a target picture might also rely on different cognitive abilities as compared to the cognitive requirements for a categorical task as in Colzato et al.’s (2008) study. It is therefore possible that the association of sEBR and attentional blink size found in attentional blink paradigms using categorical tasks is not visible or even present in our study. Since we were looking at the effect of emotion on the attentional blink and not at the attentional blink per se, it might be a different process altogether. The possible role of dopamine in this process remains open. Lastly, the previously mentioned heterogeneity of the results hints at the idea that dopamine might not follow a linear but rather an inverted-U-shaped function (Cools & D’Esposito, 2011). This inverted-U-shaped function indicates that depending on the dopaminergic level, dopamine can enhance as well as impair different cognitive abilities. Since this is more complex to study than a linear relationship, it could not only explain the heterogeneity of studies, but also the absence of a relationship in the current study.
Additionally, it might be worthwhile to further investigate the temporal dynamics of the EAB. For example, Sergent et al. (2005) performed a detailed ERP study. They investigated the electrophysiological differences of several ERPs between trials containing a blink (i.e., the second target was not perceived) and trials containing no blink (i.e., first and second target were perceived). This revealed significant differences between the trials. More specifically, P3a and P3b were absent in trials affected by the blink. In our analyses, ERPs were averaged irrespective of response (i.e., correct vs. incorrect). However, to regard the distinction of these two cases in the analyses seems to be necessary, since otherwise valuable information might be averaged out. This might explain why we did not find differences between the sEBR groups. It could be possible that differences caused by dopamine might become visible in the differentiation of blink and no-blink trials. More importantly, in regard to the distractor, the P3a might be worth to consider for future analyses as well. In context of paradigms using distractors and targets,
the P3b is said to reflect context updating and consolidation into working memory (Donchin & Cole, 1988; Kok, 2001; Vogel & Luck, 2002) and has a parietal distribution over the scalp, whereas the slightly earlier P3a has been mainly associated with an orienting response and reallocation of attention to salient but irrelevant stimuli (Bledowski, Prvulovic, Goebel, et al., 2004; Friedman, Cycowicz, & Gaeta, 2001; Polich & Comerchero, 2003) and has a more fronto-central distribution over the scalp. Moreover, it has been found, that both P3a and P3b are sensitive to emotional arousal and valence of stimuli (Delplanque, Silvert, Hot, Rigoulot, & Sequeira, 2006; Delplanque, Silvert, Hot, & Sequeira, 2005). Amplitudes for both components were larger for high arousing than low arousing stimuli, which is in line with the results reported by Kennedy et al. (2014) and the here presented results: Amplitudes for negative distractors were larger than for the neutral distractors. Furthermore, P3a and P3b can occur closely together and overlap each other (Bledowski, Prvulovic, Goebel, et al., 2004; Halgren & Marinkovic, 1995). Regarding this information, our here presented distractor P3b resembles more a P3a and it is very likely that it is actually comprised of a large P3a and a somewhat smaller P3b: it peaks on average ca. 100 ms earlier than the P3b elicited by the target, it is more frontocentrally distributed than the parietal P3b, its amplitude is on average twice as high as the P3b, and lastly, since the distractors are salient but task-irrelevant it would be expected that they capture involuntarily attention, which would be in line with behavioral performance and should be reflected by a P3a. However, note, that some of these characteristics, such as latency and amplitude, are also possibly influenced by other factors like task difficulty (Polich, 1987), target detection (Sergent et al., 2005), and valence and arousal of the distractor picture (Delplanque et al., 2005, 2006). Concerning the associations of dopamine with working memory and attentional selection and its role in prioritizing task-relevant information while inhibiting irrelevant stimuli (Baier et al., 2010; Fallon et al., 2012; Hazy, et al., 2006; Moustafa et al., 2008; Nieoullon, 2002; Roberts et al., 1994), it might be possible that the influence of dopamine becomes visible on the P3a but not on the P3b. However, the here presented distractor P3b should not be mistaken as the P3a. Since both components occur closely together, it is recommended to perform a temporal principle component analysis in order to identify components accurately (Kayser & Tenke, 2003; Polich, 2007).
In summary, we could replicate the behavioral and P3b-related findings that have been previously reported in studies using the EAB paradigm (Ciesielski et al., 2010; Kennedy et al., 2014; McHugo et al., 2013; Most et al., 2005; Most & Junge, 2008; Olatunji et al., 2011). Since we used another set of distractor pictures than previous studies, this provides further evidence that the blink observed in the EAB paradigm is caused by emotional content. However, contrary to our hypotheses, we found neither a relationship of the striatal dopamine marker sEBR with EAB size nor with the P3b components elicited by the distractor and target. The effect of dopamine on (emotional) distractor-suppression still remains unclear and further research is needed to illuminate the influence of dopamine in tasks as the EAB.
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