What a person says is not necessarily an accurate representation of what she thinks. Implicit attitudes permeate every aspect of our life. The implicit association test (IAT)—the most well-known test of implicit attitudes—is a reaction time measure. So far, the fundamental question about the source of its reaction time differences, i.e., the IAT effect, has remained unanswered. For the first time to our knowledge in IAT research, we applied a sophisticated electrical neuroimaging approach—the microstate approach. Superior to other approaches, the microstate approach allowed us to identify and time the entire chain of mental processes as they unfolded during the IAT. We found that reaction time differences are due to quantitative and not qualitative differences in the underlying mental processes.
Why do people take longer to associate the word “love” with outgroup words (incongruent condition) than with ingroup words (congruent condition)? Despite the widespread use of the implicit association test (IAT), it has remained unclear whether this IAT effect is due to additional mental processes in the incongruent condition, or due to longer duration of the same processes. Here, we addressed this previously insoluble issue by assessing the spatiotemporal evolution of brain electrical activity in 83 participants. From stimulus presentation until response production, we identified seven processes. Crucially, all seven processes occurred in the same temporal sequence in both conditions, but participants needed more time to perform one early occurring process (perceptual processing) and one late occurring process (implementing cognitive control to select the motor response) in the incongruent compared with the congruent condition. We also found that the latter process contributed to individual differences in implicit bias. These results advance understanding of the neural mechanics of response time differences in the IAT: They speak against theories that explain the IAT effect as due to additional processes in the incongruent condition and speak in favor of theories that assume a longer duration of specific processes in the incongruent condition. More broadly, our data analysis approach illustrates the potential of electrical neuroimaging to illuminate the temporal organization of mental processes involved in social cognition.
From the beginning of psychological research, reaction times have been used to probe the nature of mental processes, an approach known as mental chronometry (1, 2). This experimental approach has led to the development of dozens of psychological tests (e.g., Stroop tasks, priming tasks, implicit association tests) that rely on response time differences to assess implicit, unconscious processes (3⇓–5). These response time differences provide unique information that predicts behavior, judgments, physiological responses, and pathology (6, 7). One of the most popular of these tests is the implicit association test (IAT; refs. 8 and 9). The IAT measures implicit attitudes or gut-level evaluations, such as attitudes about different social groups. It is based on the idea that participants are slower at associating incongruent vs. congruent stimuli, a reaction time difference known as the “IAT effect.” For example, participants usually take longer to associate ingroup words with negative attributes (incongruent condition) than with positive attributes (congruent condition), indicating that they hold positive attitudes toward their ingroup.
Despite the widespread use of the IAT, it is still unclear why participants take longer to respond in the incongruent condition. There are two main possible explanations. It could be that additional mental processes are needed in the incongruent condition to solve the task correctly, for instance inhibiting automatic evaluations (e.g., negative evaluation of the outgroup) that interfere with the correct response (e.g., associate outgroup words with positive attributes; ref. 10). Alternatively, the same mental processes may be performed in both conditions, but participants could need more time to perform one or a number of them in the incongruent condition, for instance, selecting a motor response (11).
To illuminate which of these two explanations accounts for the IAT effect, it is necessary to identify and time the entire chain of mental processes involved in executing the IAT. Mental processes are mediated by large-scale neural networks linking groups of neurons in separate cortical areas into functional entities (12⇓–14). Activity in these neural networks can be studied with millisecond resolution by using a spatiotemporal analysis of multichannel electroencephalogram (EEG). By segmenting electrical activity recorded during execution of the IAT into time periods of stable neural network configurations, one can identify functional microstates of the brain that each represent the implementation of a specific mental process (15⇓–17). Capitalizing on such an integrative analysis of space and time information of the EEG data, here, we wished to identify all mental processes involved during the execution of the IAT and to precisely measure the duration of all these processes by determining their onset and offset in time.
To illustrate the value of our approach, consider the following example: Much like comparing the mental processes in the incongruent and the congruent IAT conditions to reveal why participants take longer to respond in the incongruent condition, you might compare your activities on a Monday with your activities on a Friday to reveal why you went to bed later on Friday. There are two possible reasons for why you went to bed later on Friday: You might have performed a unique activity on Friday (e.g., having a drink with some friends in the evening) that you did not do on Monday and this activity delayed your bed time. Or you might have performed the same activities for a different duration. For instance, in the evening you might have watched television (TV) longer on Friday than on Monday, delaying the time you went to bed. To better understand the origin of such duration differences, it might also be informative to consider whether these differences are driven by differences in the intensity of previous activities. For example, it could be that on Monday you had worked harder than on Friday. As a consequence, you became tired much earlier and watched TV for a shorter duration before you went to bed.
Previous EEG studies have reported differences in event-related potentials (ERPs) between IAT conditions both at early (in the first half of task execution; refs. 18⇓⇓–21) and at late time periods (in the second half of task execution; refs. 18, 19, and 22⇓–24). Thus, differences in both early and late mental processes appear to produce the IAT effect. None of these studies, however, has exploited the full amount of spatiotemporal information available from ERP data. Rather, they have compared ERPs between the two IAT conditions at identical predefined time points, yielding inconclusive evidence about why participants take longer to respond in the incongruent condition. Recall our day-activities example. Imagine that we compare activities between Monday and Friday late in the evening. On Monday, you were watching TV, and on Friday, you were having a drink with some friends. However, is it because you were having a drink that you went to bed later on Friday? Maybe, you had a quick drink on Monday as well, before watching TV. Obviously, you need to consider more than just the late evening. Only a comprehensive analysis of the full range of activities you performed during both days will unambiguously shed light on the reason why you went to bed later on Friday. Similarly, only a comprehensive analysis of the continuous flow of all mental processes occurring between stimulus presentation and response production will unambiguously shed light on the IAT effect.
Therefore, we used a data-driven, spatiotemporal EEG microstate analysis approach that allows identifying and timing all mental processes involved in executing the IAT. We analyzed data from 83 participants (37 soccer fans and 46 political supporters) who performed an ingroup/outgroup IAT. By identifying the series of mental processes during execution of both IAT conditions, we were able to reveal whether the IAT effect is due to additional, unique mental processes or longer, identical mental processes in the incongruent condition. Moreover, we localized the intracranial brain sources that underlie each mental process to ascertain which brain areas are activated when performing the task. Such information allows for speculation about the nature of differences between the two conditions in the stream of mental processing during IAT performance.
On average, participants performed at 95% accuracy in the incongruent condition and at 98% accuracy in the congruent condition [errors incongruent: mean (M) = 14.06, standard deviation (SD) = 10.00; errors congruent: M = 6.84, SD = 6.40; t(82) = 9.16, P < 0.001, η2 = 0.51]. Comparing reaction times in incongruent trials (M = 853.10 ms, SD = 93.56 ms) to those in congruent trials (M = 734.87 ms, SD = 95.83 ms) revealed the classical IAT effect with prolonged reaction times in incongruent trials [t(82) = 14.88, P < 0.001, η2 = 0.73]. The IAT effect was present both in soccer fans [t(36) = 11.95, P < 0.001, η2 = 0.80] and in political supporters [t(45) = 9.66, P < 0.001, η2 = 0.68].
ERPs and Microstate Analysis.
Is the IAT effect explained by additional processes in the incongruent condition?
Based on a silhouette plot, we identified nine clusters that explained 92% of the variance in the data (Fig. S1). The cluster solution with nine clusters was chosen because all clusters had a reasonable structure (silhouette values >0.50). The topographies of the nine clusters are depicted in Fig. 1A (see SI Results for a detailed description of these nine topographies). Fitting the clusters to each grand-mean ERP by means of spatial correlation demonstrated an identical sequence of nine microstates (i.e., mental processes) for both conditions between 0 and 1,000 ms (Fig. 1B). Because two of these microstates occurred after the mean button press, we focus on the seven microstates that occurred between stimulus onset and button press.
Microstate analysis of the IAT-evoked ERPs. (A) Topographies of the nine microstate clusters in the sequence of occurrence. Head seen from above: Red indicates positive values and blue negative values, referred to average reference. The colored background corresponds to the assignment shown in B and C. (B) Microstates across time for the congruent (Upper) and incongruent condition (Lower) plotted over the Global Field Power (GFP). Colors refer to the microstate topographies shown in A. The hand symbols indicate mean response times. The vertical axis indicates GFP (in microvolts); the horizontal axis indicates time (in milliseconds). Black horizontal arrows indicate microstates in which significant, Bonferroni-corrected duration differences were observed between IAT conditions (P < 0.001). Note that we only considered microstates that lasted for at least 10 ms (shorter microstates are shown in white). (C) Localization of the intracortical sources as estimated with sLORETA for the full sequence of microstates during the IAT. Bonferroni-corrected, significant voxels are colored, with increasing t values from red to yellow. Note that, for each microstate, the grading of colors was adapted to accentuate the main activation clusters, such that identical colors in different microstates do not represent identical t values. We labeled the main activation clusters and framed the localization with the same color code as the corresponding microstates in A and B. Please note that intracortical sources are only shown for microstates 2–8 due to the chosen source localization strategy (for details, see SI Materials and Methods and SI Results). PCC, posterior cingulate cortex; SMA, supplementary motor area; TPJ, temporo-parietal junction.
Silhouette plot for the optimal cluster solution. Plot of silhouette values for the spatial K-means clustering of 1,000 ERP maps (500 grand-mean ERP maps of the congruent condition and 500 grand-mean ERP maps of the incongruent condition). On the y axis, all ERP maps belonging to a particular cluster are ordered by decreasing silhouette value. The x axis represents the silhouette value. The silhouette value for each ERP map is a measure of how similar that ERP map is to ERP maps in its own cluster, compared with ERP maps in other clusters. The silhouette value can range between −1 and 1. A high silhouette value indicates that the ERP map is well-matched to its own cluster, and poorly matched to neighboring clusters. The average silhouette value and the number of ERP maps for each cluster is given at the left. The empirical interpretation of the average silhouette value for a cluster is shown at the bottom. The colored background corresponds to the cluster assignment shown in Fig. 1A. Following the method of Rousseeuw (50), the optimal number of clusters is selected such that a maximum number is obtained while all clusters retain a reasonable structure.
Importantly, there were no additional microstates in the incongruent condition, indicating that additional mental processes in the incongruent condition were not causing the longer reaction times in this condition. To assess the robustness of these findings, we compared results between soccer fans and political supporters by rerunning the topographic fitting procedure separately for each group. The sequence of microstates was identical for both groups, and there were no additional microstates in the incongruent condition for either soccer fans or political supporters, indicating that our results apply beyond a specific population performing a specific IAT version (Fig. S2). Also, the same sequence of microstates was found when separately rerunning the cluster and topographic fitting procedures in participants with large and small implicit bias (based on a median split, see Fig. S3).
Microstate analysis of the IAT-evoked ERPs. (A) Topographies of the nine microstate clusters in the sequence of occurrence. Head seen from above: Red indicates positive values and blue indicates negative values, referred to average reference. The colored background corresponds to the assignment shown in B. (B) Results of the spatiotemporal ERP analysis, separately for each condition (rows) and group (columns). The microstates are marked in color on the superimposed grand mean ERP waveforms of all 64 channels for the Soccer-IAT congruent condition (Upper Left), Soccer-IAT incongruent condition (Lower Left), Politics-IAT congruent condition (Upper Right), and Politics-IAT incongruent condition (Lower Right). The vertical axis indicates amplitude (in microvolts), and the horizontal axis indicates time (in milliseconds). Please note that the sequence of microstates was identical for both groups and an additional microstate in the incongruent condition did not occur in soccer fans or in political supporters.
Microstate analysis of the IAT-evoked ERPs in the groups: High IAT bias (n = 41) versus Low IAT bias (n = 42). Topographies of the nine microstate clusters in the High IAT bias group (A) and in the Low IAT bias group (B) in the sequence of occurrence. Head seen from above: Red indicates positive values and blue indicates negative values, referred to average reference. The colored background corresponds to the assignment shown in C. (C) Results of the spatiotemporal ERP analysis. The microstates are marked in color on the superimposed grand mean ERP waveforms of all 64 channels for the congruent condition of the High IAT bias group (Upper Left), for the incongruent condition of the High IAT bias group (Lower Left), for the congruent condition of the Low IAT bias (Upper Right), and for the incongruent condition of the Low IAT bias group (Lower Right). The vertical axis indicates amplitude (in microvolts); the horizontal axis indicates time (in milliseconds).
Do participants take longer to perform certain processes in the incongruent condition?
Because the IAT effect could not be explained by additional microstates in the incongruent condition, we next examined microstate durations in the incongruent compared with the congruent condition. We found that two microstates, microstate 4 (starting around 220 ms) and microstate 6 (starting around 450 ms) were prolonged in the incongruent condition (microstate 4: Minc = 34 ms, Mcon = 28 ms, P = 0.0014; microstate 6: Minc = 218 ms, Mcon = 170 ms, P < 0.001; Fig. 1B and Table 1). Thus, duration differences in microstates 4 and 6 contribute to the IAT effect.
Descriptive onsets and offsets of microstates in milliseconds
Do differences in the intensity of previous processes contribute to the duration differences?
Recall from our example in the Introduction that differences in the duration of microstates might be driven by differences in the intensity of previous microstates. We tested this possibility with regard to all microstates that preceded the two microstates in which duration differences were found, i.e., microstates 4 and 6. We found that differences in the intensity of microstate 3 (Minc = 1.67 µV, Mcon = 1.55 µV, P < 0.001) modulated differences in the duration of the subsequent microstate 4: The more intense microstate 3 was, the longer microstate 4 took in the incongruent than the congruent condition (r = 0.29, P < 0.01). No other microstate intensity difference modulated the durations of microstate 4 or microstate 6 (P > 0.13).
Do individual differences in the duration of processes help explain individual differences in implicit bias?
Correlational analyses revealed that the duration of microstate 6 contributed to individual differences in implicit bias: Increased time spent in microstate 6 in the incongruent compared with the congruent condition was associated with an increased implicit bias [r(81) = 0.25, P = 0.021]. Individual differences in the duration of all other microstates did not correlate with implicit bias (all r values <|0.09|, P values >0.20).
In a final step, we source localized microstates 3, 4, and 6 to get an idea about the nature of each mental process that accounts for the IAT effect (Fig. 1C; for a detailed description of source localization results of all microstates, see SI Results, Table S1, and Fig. S4). Microstate 3 was characterized by activity in the left temporal pole (BA22/38), the left insula (BA13), and the dorsal anterior cingulate cortex (dACC; BA24). Microstate 4 was characterized by activity in the lingual gyrus (BA18) and other occipito-temporal areas. Finally, microstate 6 was characterized by activity in the middle cingulate cortex (MCC; BA23/24/31) and the posterior parietal cortex (PPC; BA40).
Time plots main activation clusters of microstates 2–8. Shown in this figure are subject-wise normalized and log-transformed current source density for regions of interest (ROIs; 10-mm sphere around the maximum t value) comprising the main activation clusters of microstate 2 [(A) Right extrastriate cortex, BA19; x = 50, y = −69, z = 7; MNI], microstate 3 [(B) Left insula, BA13, x = −39, y = 5, z = −5; (C) Left temporal pole, BA38, x = −51, y = 11, z = −21; (D) Dorsal anterior cingulate cortex, BA32, x = −5, y = −5, z = 35], microstate 4 [(E) Right lingual gyrus, BA18; x = 15, y = −67, z = −3], microstate 5 [(F) Left temporo-parietal junction, BA39; x = −36, y = −59, z = 26; (G) Posterior cingulate cortex, BA23; x = 5, y = −52, z = 23], microstate 6 [(H) Middle cingulate cortex, BA23; x = 0, y = −25, z = 33; (I) Posterior parietal cortex, BA40; x = −42, y = −33, z = 44], microstate 7 [(J) Supplementary motor area, BA6; x = 6, y = −2, z = 67; (K) Premotor cortex, BA6; x = 30, y = −5, z = 65], and microstate 8 [(L) Premotor cortex, BA6; x = 46, y = 1, z = 51]. In black, SEs are shown. Further, the time periods of microstate 2–8 (average of the incongruent and congruent condition) are colored with transparent planes. The colored backgrounds correspond to the cluster assignment shown in Fig. 1A.
Detailed overview of activated voxels in microstate 2–8
Behavioral Results: D scores.
We calculated D scores according to the improved scoring algorithm (46). This score represents the difference between mean response latencies in congruent and incongruent trials, divided by the pooled SD across trials. D scores were significantly different from 0 in soccer fans [M = 0.69, SD = 0.32, t(36) = 13.15, P < 0.001, η2 = 0.83] and in political supporters [M = 0.54, SD = 0.35, t(45) = 10.71, P < 0.001, η2 = 0.72]. Pooling all subjects together, we also obtained a D score that was significantly different from 0 [M = 0.61, SD = 0.34, t(82) = 16.34, P < 0.001, η2 = 0.77].
Detailed Description of Cluster Topographies.
The topographies of the nine clusters in their sequence of occurrence are shown in Fig. 1A. Cluster 1 had a contingent negative variation-like topography, characterized by a central negativity. The topography of cluster 2 showed the characteristic P1 distribution with a bilateral positivity over occipital electrodes. Cluster 3 reflected a bilateral posterior negativity and a frontal positivity. Cluster 4 showed a less pronounced posterior negativity and a developing posterior positivity. A P3-like posterior positivity was definitive in cluster 5. This posterior positivity moved slightly in an anterior direction in clusters 6, 7, and 8. Within the same time range, an anterior negativity developed that reached its maximum in cluster 8. This anterior negativity was still present in cluster 9 together with a right-lateralized positivity. The sequence of early clusters 2–4 strongly resembled early ERP components (also called, P1-N1-P2) related to visual word processing (70), and the overall sequence of the nine clusters was in good agreement with the sequence of scalp topographies shown during another study that used a different version of the IAT (23).
Detailed Description of Source Localization Results.
In a final step, we localized the brain sources that characterized the distinct microstates across conditions (Fig. 1C; for a detailed overview of activated voxels, please see Table S1, and for time plots of the reported activation clusters, please see Fig. S4). No significant voxels appeared in microstate 1 (all P > 0.14). P1-like microstate 2 was characterized by activation in the extrastriate cortex (BA18/19), which is consistent with previous source localizations of the P1 and indicates early visual processing (71, 72). In the following microstate 3 that occurred around 200 ms, a network consisting of the left temporal pole (BA22/38), the left insula (BA13), and the dorsal anterior cingulate cortex (dACC; BA24) was active. The coactivation of these areas (31) and its time of appearance (32) might indicate arousal-related processing. Microstate 4 occurred around 220 ms and was characterized by activity in the lingual gyrus (BA18) and other vision-related occipito-temporal areas. Activity in these regions suggests that lexical perceptual processing of the presented words takes place during this microstate (29, 30). The following, P3-like microstate 5 was characterized by two main activation areas, one in the left temporo-parietal junction (TPJ; BA7/39/40) and another in the precuneus and the posterior cingulate cortex (PCC; BA7/31/39). These areas and the P3 component in general are considered to be involved in attention and memory processes (73, 74). Possibly, after lexical processing during microstate 4, the word is categorized as “ingroup,” “outgroup,” “good,” or “bad” during microstate 5 by accessing previously stored knowledge from memory. The following microstate 6 that occurred in temporal proximity to the button press was characterized by activity in the middle cingulate cortex (MCC; BA23/24/31) and the posterior parietal cortex (PCC; BA40). Activity in these regions suggests that participants implement cognitive control to select the motor response during this microstate (37⇓–39). This finding implies that after participants have mentally assigned the word to a specific category during microstate 5, they now select the motor response that correctly classifies the word. The last microstate before button press, microstate 7, was characterized by activity in motor-related frontal regions, encompassing the premotor cortex, the supplementary motor area (both BA6), and the primary motor cortex (BA4). We postulate that the motor response (i.e., the button press) is prepared and executed during this microstate (75, 76
Digital Image Design IAT 100 (3)
This is a project-based course that introduces the theory and hands-on practice of art and design in digital media. As the introductory course in IAT, this course teaches the core fundamental principles in 2D visual design, sequential and animation design. Students learn the fundamentals of digital photography and vector image creation. The theory is contextualized in contemporary new media design practice and is broadly applicable across disciplines. Breadth-Humanities.
|D100|| Susan Clements-Vivian|| We 10:30 AM – 11:20 AM|| SUR 2600, Surrey|
|D101|| We 2:30 PM – 4:20 PM|| SUR 3130, Surrey|
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