Categorizing and individual as a racial ingroup or outgroup member results in processing and memory differences. However, despite processing differences for racial ingroups and outgroups, very little is known about processing of racial ingroup and outgroup members during intergroup contexts. Thus, the present research investigated attention and memory differences for racial ingroup and outgroup members during competition for attention (i.e., intergroup contexts). In experiment 1, event-related potentials (ERPs) were obtained while participants completed a working memory task that presented 4 faces (2 Black, 2 White) at once then, following a short delay, were probed to indicate the spatial location of one of the faces. Participants showed better location memory for Black than White faces. During encoding, ERP results revealed differences based on the race of the face in P300 amplitudes, such that there was greater motivated processing when attending to Black faces. At probe, the N170 indicated enhanced early processing of Black faces and greater LPCs were associated with better recollection of Black face location. In a follow-up study using the same task, we examined attention and working memory biases for Asian and White faces in Caucasian and Asian participants. Results for both Caucasian and Asian participants indicated better working memory for Asian relative to White faces. Together, results indicate that during intergroup contexts, racial minority faces capture attention, resulting in better memory for those faces. The study underscores that examining racial biases with single stimuli paradigms obscures important aspects of attention and memory biases during intergroup contexts.
Individuals tend to process racial ingroup and outgroup members differently and these differences have important implications for intergroup relations (Marcon et al., 2010; DeGutis et al., 2013; Hayward et al., 2013; Kawakami et al., 2014, 2018). For example, research shows that individuals tend to remember faces of their own race better than those of other races (i.e., own-race bias, cross-race effect) (for a review see Meissner and Brigham, 2001; Walker and Tanaka, 2003; Walker and Hewstone, 2006; Mondloch et al., 2010; Zhao et al., 2014; Wan et al., 2015). While a large body of research has examined memory biases for racial ingroups and outgroups, fewer research has focused on attention biases to these groups. Examining attention biases to racial ingroups and outgroups is important because it has been implicated as a contributing factor in phenomena such as the own-race bias and interaction intentions with outgroup members (Hills and Lewis, 2006; Hugenberg et al., 2010; Hills and Pake, 2013; Kawakami et al., 2014, 2018; Zhou et al., 2015a,b). Additionally, some research suggests that early attention biases to Black relative to White faces may reflect an early vigilance to Black individuals due to stereotypes associating Black people with danger or threat (Trawalter et al., 2008). Despite the important role of attention biases to racial ingroups and outgroups, little is known about how individuals deploy attention when multiple races are present. Contexts in which faces from multiple races are present are more likely to illuminate the way in which race interacts with cognitive processes during intergroup contexts. Therefore, the current research sought to examine whether existing effects associated with own-race biases in attention and memory persist in contexts where faces from multiple races are simultaneously present.
A variety of factors have been proposed to explain processing differences between racial ingroup and outgroup members including perceptual expertise, social categorization, and motivation (Rhodes et al., 1989; Valentine, 1991; Tanaka et al., 2004; Meissner et al., 2005; Walker and Hewstone, 2006; Bernstein et al., 2007; Rhodes et al., 2009; Hugenberg et al., 2010; for a review see Young et al., 2012). For instance, socio-cognitive theories propose that categorization of individuals as members of an ingroup or outgroup results in processing differences and reduced memory for outgroup members (Levin, 2000; Shriver et al., 2008; Hehman et al., 2010; Hugenberg et al., 2010; Shriver and Hugenberg, 2010). In particular, the race-feature hypothesis (Levin, 1996, 2000) proposes that racial outgroup faces are encoded on the basis of race-specifying features, whereas racial ingroup faces are encoded on the basis of individuating features. Thus, attending to race-specifying features of racial outgroup faces is an optimal search strategy because it speeds up detection of racial outgroup members. On the other hand, greater individuation of racial ingroups would result in better memory for racial ingroup members. Altogether, the race-feature hypothesis predicts differences in memory and visual search for ingroup and outgroup members.
Research on the race-feature hypothesis has examined individuals' ability to search for and detect racial ingroup and outgroup faces using visual search tasks (Levin, 1996, 2000; Chiao et al., 2006; Lipp et al., 2009; Sun et al., 2013). In visual search tasks individuals are instructed to indicate the presence or absence of a target face (own- or cross-race) among an array of distractor faces of the opposite race; thereby, incorporating competition for attention between races. However, research has yielded mixed support for a cross-race search asymmetry (Levin, 2000; Sun et al., 2013; Lipp et al., 2009). For example, while Levin (1996) found that White participants were faster at detecting Black faces among an array of White faces, the same effect was not found for African American participants. On the other hand, Chiao et al. (2006) investigated whether a cross-race search asymmetry existed in Black, White, and Black-White biracial individuals. Results indicated that all participants, regardless of race, were faster and more accurate at detecting Black faces among an array of White faces than vice versa, suggesting that during competition between races, Black faces tend to capture attention. It has been proposed that the inconsistent findings on the cross-race search asymmetry could be due to differences in the stimuli used, as well as, cultural differences in processing faces (Lipp et al., 2009; Sun et al., 2013). Nevertheless, research on visual search asymmetries for racial ingroups and outgroups highlights the importance of examining the effects of competition for attention between races on processing of racial ingroup and outgroup faces.
Multiple methodologies, such as event-related potentials (ERPs), have been deployed to further examine the perceptual mechanisms underlying processing differences between racial ingroups and outgroups (Stahl et al., 2008; Brebner et al., 2011; Herzmann et al., 2011; Sessa et al., 2012; Amodio et al., 2014; Cassidy et al., 2014; Wiese et al., 2014). The ERP methodology is advantageous because with good temporal resolution it can be revealed whether race influences earlier, more automatic perceptual processes, or the later, more deliberative processes. In general, research examining processing of racial faces reveal race effects across multiple ERP components such as the N100, N170, N200, N250, and P300 (Ito et al., 2004; Ito and Urland, 2005; Dickter and Bartholow, 2007; Kubota and Ito, 2007; Lucas et al., 2011). For instance, Brebner et al. (2011) used ERPs to determine how encoding categorical (e.g., skin color) versus structural information during face processing affected memory in Caucasian participants. In the study, the skin color and facial structure of faces were modified such that each face had a Black or White skin color and a Black or White facial structure. Participants revealed an own-race bias through greater memory for faces with White than Black skin color, but no accuracy differences by facial structure. Additionally, there were more negative N170s and N250s for faces with Black than White skin color, indicating that skin color effects seem to be the primary driver of early perceptual processing. Greater N170 amplitudes were thought to indicate the influence of greater feature-based processing for faces with Black skin color. Moreover, participants showed greater N200s to faces with White than Black skin color, indicating increased attention and processing for racial ingroup faces. Overall, the authors concluded that early categorization on the basis of skin color increases motivation to attend to ingroup faces, resulting in greater memory for own-race faces.
Other ERP research also indicates that race can quickly capture attention (Ito and Urland, 2003; Dickter and Bartholow, 2007; Ito and Bartholow, 2009). One particular study examined whether individuals would attend to race even when it was irrelevant to task goals by obtaining ERPs during a categorization task that presented competing racial stimuli as flankers (Dickter and Bartholow, 2007). Results for White participants showed an outgroup processing bias; larger P200s and P300s to cross-race than own-race faces revealed increased attention toward the racial outgroup. Conversely, larger N200s to own-race relative to cross-race faces suggested enhanced early processing of ingroup members. Hence, the ERP differences revealed an early race effect in which individuals quickly categorize a person as an ingroup or outgroup member based on race. Importantly, these findings indicated that competing racial stimuli can affect processing of ingroup and outgroup members even when race is irrelevant to task goals. While some research finds relatively automatic effects of race on attention, other research indicates that motivational changes can also influence attention to race (Brosch and Van Bavel, 2012; Correll et al., 2014). Particularly, research indicates that attention to race is flexible and may respond to top-down goals such that attention may be directed to one race (e.g., White) over another race (e.g., Black) depending on the perceiver's goals (Correll et al., 2014). Thus, creating an intergroup context during the presentation of competing racial stimuli may result in different motivational goals that influence early attention to racial ingroup and outgroup faces.
Despite the role of attention in processing of own- and cross-race faces (Zhou et al., 2015a,b), few studies have examined how competition for attention between races during encoding (i.e., intergroup context) may influence memory. Much of the research on the own-race bias in memory uses "old/new" or "remember/know" paradigms that require individuals to encode one face at a time (Rhodes et al., 2009; Stahl et al., 2010; Brebner et al., 2011; Pezdek et al., 2012), which does not allow for the examination of how competition for attention affects recognition of own- and cross-race faces. Although some studies incorporate competition for attention during encoding to assess working memory for own- and cross-race faces, the paradigms used did not incorporate competition between different races (Sessa et al., 2012; Sessa and Dalmaso, 2016). For example, Sessa et al. (2012), obtained ERPs while participants completed a visual working memory task that cued them to memorize one or two faces (encoding presentations that were all-Black or all-White) presented in one visual hemifield. After a short delay, participants were presented with the same number of faces and asked to indicate whether the face(s) had changed. Surprisingly, behavioral results did not show significant differences in memory for White and Black faces. However, amplitude differences in the sustained posterior contralateral negativity (SPCN) between Black and White faces were greater for individuals who showed greater implicit prejudice on the Implicit Association Test, indicating that individuals with higher prejudice encoded Black faces with lower precision than White faces. A follow-up study indicated that eye-gaze direction may also influence working memory maintenance for Black and White faces (Sessa and Dalmaso, 2016). However, given that both studies always presented the same race at encoding, they did not examine what would happen when there is competition for attention between races.
While competition for attention between races has been incorporated in visual search tasks, the inconsistent findings regarding detection of own- and cross-race faces do not provide a clear picture of how competition for attention affects processing and memory for racial ingroups and outgroups. Examining contexts that present multiple faces of different races is important because competition between races could influence categorization and motivational processes and is also more reflective of intergroup contexts. Apparently, no research has investigated the influence of race on working memory using a paradigm where different racial stimuli compete for attention. Thus, the present research sought to investigate whether working memory for racial ingroups and outgroups is affected when different races compete for attention. In experiment 1, we used ERPs to investigate the mechanisms underlying memory differences for Black and White faces in the presence of competing racial stimuli by employing a visual short-term memory (VSTM) task adapted from Wilken and Ma (2004). The task requires individuals to attend to multiple racial faces during a brief encoding period and following a short delay, participants indicate the spatial location of one of the faces in the former array. We hypothesized that during encoding, where multiple faces of different races are presented, the race that naturally draws the person's attention will result in better accuracy for that group; as well as, clear evidence of this attention bias in the encoding ERP. We also hypothesized that ERPs associated with motivated processing (e.g., P300) would be greater for the racial group that was better remembered later on, allowing us to examine the role of socio-cognitive factors on memory for own- and cross-race faces during competition for attention. Finally, we hypothesized that during the probe, participants would respond more accurately to the stimuli to which their attention was biased during encoding and ERP differences between correctly and incorrectly identified stimuli would be evident. In experiment 2 we sought to generalize the results from experiment 1 to other races by examining location memory for Asian and White faces. Additionally, we aimed to examine own- and cross-race effects on working memory by recruiting Asian and Caucasian participants.
A previous ERP study examining working memory and racial bias (Sessa et al., 2012) obtained 16 participants. However, we used data from a behavioral study that was piloted in our lab to conduct a power analysis for the primary working memory accuracy analysis. The power analysis conducted with G∗Power (Faul et al., 2007) indicated that to achieve 80% power (ηp2 = 0.21) we needed to collect data for 29 participants; thus, we aimed to analyze data for at least 30 participants. 46 students were recruited from The University of Texas at Austin. Four participants were excluded due to psychotropic medications. Additionally, data from seven participants were excluded due to problems with EEG recording (e.g., lack of EEG signal or excessive artifacts). Complete data were examined for the remaining 35 participants (22 females, 13 males, 19.49 ± 1.94 years). 25 participants were White (12 Hispanic/Latino), 7 were Asian, and 3 endorsed "other". Participants were offered course credit or $25 for their participation. Informed consent was obtained from all participants under an IRB approved protocol at the University of Texas, Austin. Data collection was completed prior to data analysis.
A questionnaire was used to obtain health and demographic information. The Symbolic Racism 2000 Scale (SR2KS) was used to assess symbolic racism (Henry and Sears, 2002). In addition, the Color-Blind Racial Attitudes Scale (CoBRAS) was used to assess explicit colorblind racial attitudes (Neville et al., 2000). Stimuli for the VSTM task were selected from the Chicago Face Database (Ma et al., 2015) and altered by applying an oval mask to the face (to remove hair and clothing to the extent possible). Facial stimuli were converted to a black and white color scale to reduce variability across color images. The Chicago Face Database provides ratings for each face on a variety of measures including unusualness on a 1–7 Likert scale (1 = Not at all, 7 = Extremely). Faces rated as least unusual (lower ratings) were chosen (Black M = 1.86, SD = 0.19; White M = 1.76, SD = 0.15) to reduce confounding effects on memory. 80 face images were used including: 40 White faces (20 male and 20 female) and 40 Black faces (20 male and 20 female).
Upon arrival in the lab, participants were consented and explained the procedures and then prepped for EEG recording. EEG recordings were obtained using a Biosemi electrode cap (BioSemi B.V., Amsterdam, Netherlands) with 64 active Ag/AgCl electrodes. Recording sites for the cap conformed to the 10–20 International System. Five additional channels (one below each eye, each outer canthus site, and nasion) were obtained to monitor vertical and horizontal eye movements and blinks. Channels were amplified through a Biosemi Active II amplifier system in a 24-bit DC mode at a sampling rate of 2048 Hz decimated offline to 256 Hz. Participants were seated approximately 60 cm from a 21" LED computer monitor. Participants began with a short EEG resting-state task in which they alternated between 1-min segments of eyes open and closed for eight minutes, with the order counterbalanced across participants.
Following resting EEG measures, participants completed the VSTM paradigm, which was programed using PsychoPy (Peirce, 2007). For each trial, participants viewed a fixation point for 500 ms, followed by an encoding screen. The fixation point, a white cross (height ∼0.1°), was presented in the center of the screen (0° × 0°). During encoding, four pseudo-randomly selected faces (2 Black and 2 White) placed in a clock-like arrangement were presented for 1500 ms. Face stimuli were approximately 6° (width) × 6° (height) and were presented at four equally spaced points, approximately 3.2° from the central fixation point on a gray background (Figure 1). The encoding screen was followed by a delay period containing the fixation point and lasting 1500 ms. Finally, in the probe screen a single face was presented in the center location, which was always one seen in the encoding screen. Participants were asked to indicate the location of the face during encoding by pressing one of four buttons that corresponded to the correct location (1, 2, 3, or 4). Participants were instructed to keep their eyes fixated on the central fixation point at all times. Stimuli were placed at a visual angle that would allow all faces to be visible within a typical window of attention without the need for eye movements. Male and female faces were presented in 6 separate blocks with block order counterbalanced across participants. To familiarize participants with the task, they completed 12 practice trials after which they had the opportunity to ask for further clarification of instructions. Facial stimuli for the practice trials were drawn from the same sample that was used for the experimental trials. Following practice, participants continued the task where a total of 360 experimental trials were presented across the six blocks with a short break after each block. Throughout the experimental trials, each face was presented an average of 18 times (SD = 4.04). Afterward, participants completed five questionnaires in the following order: CES-D, demographic questionnaire, contact questionnaire, SR2KS, and CoBRAS. Participants were debriefed at the end of the experiment.
Figure 1. For each trial, participants fixated on a white cross for 500 ms. At encoding, four pseudo-randomly selected faces (2 Black, 2 White) were displayed in a clock-like arrangement for 1500 ms. The delay screen presented a white cross for 1500 ms. Finally, the probe screen consisted of a single face centrally located. The face probe was always one seen at encoding. Participants indicated the location of the face during encoding by pressing the button corresponding to the correct location (1, 2, 3, or 4).
Behavioral data analysis was performed using R 3.3.2 (R Core Team, 2016). Trials with false starts (reaction times below 300 ms) or with reaction times greater than 2.5 standard deviations above the participant's mean reaction time were identified and excluded. Overall, 2.89% of the data were excluded.
Continuous data were imported off-line and data analysis was conducted using BrainVision Analyzer 2.0 (BrainVision LLC, Morrisville, NC, United States). Data were re-referenced to the average of the left and right mastoids. However, to examine the N170 component, data were re-referenced to the average of all EEG scalp sites based on previous work by Joyce and Rossion (2005) on the best approach for examining the N170 (see also Zhou et al., 2015b). A Butterworth Zero Phase Filter with a low cutoff of 0.1 Hz (12 dB/oct) and a high cutoff of 40 Hz (48 dB/oct) was applied to filter EEG data. Channels to monitor eye-movement artifacts were computed offline. The horizontal EOG channel was created using a bipolar montage of the nasion electrode and each outer canthi electrode (HEOG). For the vertical EOG channel, a bipolar montage of the FP1 and FP2 channels and the electrode placed on the corresponding inferior orbit were used (VEOG). Independent Components Analysis (ICA) was applied to identify and reject ocular artifacts using a meaned slope algorithm for blink detection with the left VEOG as reference. Remaining artifacts were identified using a semiautomatic inspection approach resulting in less than 2% of the data excluded per person. Overall, an average of 0.35% ± 0.003 of the data were excluded. Bad EEG channels were interpolated using a spline interpolation (order: 4, degree: 10, lambda: 1E-05) for three participants, with only one or two channels interpolated for each participant.
Following preprocessing, EEG data were epoched from -200 to 1500 ms post-stimulus onset for two time windows – encoding and probe presentations. For both periods, ERPs were generated by averaging epochs based on the race of the face (Black and White) during the probe period and the accuracy of the location response (correct and incorrect) and were baseline corrected to an average of the -200-0 ms prestimulus interval. Visual inspection and previous work guided examination of several ERP components of interest: the N170, N200, P300, and late positive component (LPC). The scalp locations used to measure each component were visually estimated from projecting the grand-average ERPs over the entire scalp. The time window for the N170, N200, and P300 was obtained by identifying the peak latency ± 25 ms. N170 amplitudes from 145 to 195 and 135 to 185 ms post-stimulus onset for encoding and probe respectively, were averaged across the left (P7, PO7, P9) and right (P8, PO8, P10) parietal scalp sites separately (see Stahl et al., 2008; Cassidy et al., 2014). For the encoding N200, amplitudes from 260 to 310 ms post-stimulus onset were averaged across 3 scalp locations – Fz, Cz, and Pz scalp sites (see Ito et al., 2004; Wiese, 2012). P300 amplitudes from 325 to 375 ms and 350 to 400 ms post-stimulus onset for encoding and probe respectively, were averaged across 3 scalp locations – frontal (Fz, F1, F2), central (Cz, C1, C2), and parietal (Pz, P1, P2) scalp sites. Previous research indicated that the LPC is a separate component from the P300 (Foti et al., 2009); therefore, LPC amplitudes from 500 to 800 ms, a time window that did not overlap with the P300, were averaged across 3 scalp locations - frontal (Fz, F1, F2), central (Cz, C1, C2), and parietal (Pz, P1, P2) scalp sites. Mean amplitudes were generated and entered into a repeated measures ANOVA using R 3.3.2 (R Core Team, 2016). We report all measures, manipulations and exclusions for this study.
A one-way repeated measures ANOVA with race (Black vs. White) as a within-subjects factor was conducted to examine accuracy differences between Black and White probe trials and indicated significantly greater accuracy for the location judgment of Black faces (M = 0.61, SD = ± 0.05), relative to White faces (M = 0.58, SD = ± 0.05), [F(1,34) = 9.16, p = 0.005, ηp2 = 0.21] (Figure 2). Reaction times for correct trials were also examined in a one-way repeated measures ANOVA with race (Black vs. White) as a within-subjects factor and indicated no significant differences in reaction times between Black and White probe trials [F(1,34) = 1.00, p = 0.33, ηp2 = 0.03].
Figure 2. Experiment 1 mean accuracy for race of probe along with 95% confidence intervals. Results indicated significantly better location accuracy for Black (M = 0.61, SD = ± 0.05) than White (M = 0.58, SD = ± 0.05) probes.
In the present task, error rates were corrected since chance alone would result in a higher number of between-race than within-race errors. That is, while two responses would result in between-race errors, only one response would result in a within-race error. Thus, to make the number of between-race and within-race errors directly comparable, each subject's between-race error rate was reduced by one-half prior to data analysis, as done in previous research (Taylor et al., 1978). A 2 × 2 repeated measures ANOVA with type of error (within-race vs. between-race) and probe race (Black vs. White) as within-subjects factors was conducted to examine the types of errors made by participants. There was a significant interaction between error type and probe race [F(1,34) = 14.40, p < 0.001, ηp2 = 0.30]. Post hoc Bonferroni comparisons revealed significantly more within-race errors for White (M = 0.18, SD = ± 0.03) than Black faces (M = 0.16, SD = ± 0.03) [t(63.59) = -4.89, p < 0.001, d = -0.38]. However, between-race errors for White (M = 0.03, SD = ± 0.02) and Black faces (M = 0.04, SD = ± 0.02) were not significantly different [t(63.59) = 0.27, p = 0.79, d = 0.07]. There was also a significant main effect of error type, participants made more within-race errors than between-race errors [F(1,34) = 772.82, p < 0.001, ηp2 = 0.96] and a significant main effect of probe race with more errors for White than Black probes [F(1,34) = 9.90, p = 0.003, ηp2 = 0.23] (Figure 3).
Figure 3. Experiment 1 mean corrected error rate per type and probe race along with 95% confidence intervals. Results indicated significantly more within-race errors for White (M = 0.18, SD = ± 0.03) than Black faces (M = 0.16, SD = ± 0.03) but no significant differences in between-race errors for White (M = 0.03, SD = ± 0.02) and Black faces (M = 0.04, SD = ± 0.02).
We also examined the relationship between accuracy and explicit measures of racial attitudes. First, we calculated an accuracy difference score by subtracting accuracy for White faces from accuracy for Black faces (Black accuracy – White accuracy). Subsequently, the difference score was correlated to the overall SR2KS and the CoBRAS scores. Since the Contact Questionnaire, which examined contact with other races, was added to the experiment after data collection had started, responses were not obtained for all participants and therefore data on the Contact Questionnaire was not analyzed. No significant correlations were found between the accuracy difference score and the SR2KS score nor the CoBRAS score. The relationship between error rates and explicit measures of racial prejudice was also examined. An error rate difference score was calculated by subtracting within-race errors from between-race errors (between-race errors – within-race errors). No significant correlations were found between the error rate difference score and the SR2KS nor the CoBRAS.
Tables 1a,b show the average amplitudes for the N170, N200, P300 and LPC during encoding for each condition (defined by probe accuracy) across the scalp locations tested. The N170 at encoding was examined by testing the mean amplitude for 145–195 ms post-stimulus (presentation of four faces) onset across the 2 scalp locations in a 2 × 2 × 2 ANOVA with race (Black vs. White), accuracy (correct vs. incorrect), and location (left vs. right) as within-subjects factors. Results indicated that the three-way interaction between race, accuracy, and location was not significant [F(1,34) = 0.80, p = 0.38, ηp2 = 0.02]. Additionally, there were no significant interactions between race and location [F(1,34) = 0.54, p = 0.47, ηp2 = 0.02], accuracy and location [F(1,34) = 1.00, p = 0.32, ηp2 = 0.03], or race and accuracy [F(1,34) = 2.85, p = 0.10, ηp2 = 0.08]. There were also no significant main effects of race [F(1,34) = 3.36, p = 0.08, ηp2 = 0.09], accuracy [F(1,34) = 3.22, p = 0.08, ηp2 = 0.09], or hemisphere [F(1,34) = 3.86, p = 0.06, ηp2 = 0.10] on N170 amplitudes.
Table 1a. Mean ERP amplitudes (mV) for encoding period.
Table 1b. Mean ERP amplitudes (mV) for encoding period.
The N200 at encoding was examined by testing the mean amplitude for 260–310 ms post-stimulus onset in a 2 × 2 ANOVA for each scalp location (Fz, Cz, and Pz) with race (Black vs. White) and accuracy (correct vs. incorrect) as within-subjects factors. For the Fz location, there was no significant interaction between race and accuracy [F(1,34) = 0.92, p = 0.34, ηp2 = 0.03], and no significant main effects of race [F(1,34) = 1.36, p = 0.25, ηp2 = 0.04], or accuracy [F(1,34) = 2.12, p = 0.06, ηp2 = 0.15]. Secondly, for the Cz location, there was no significant interaction between race and accuracy [F(1,34) = 0.08, p = 0.78, ηp2 = 0.002], and no significant main effects of race [F(1,34) = 2.66, p = 0.11, ηp2 = 0.07], or accuracy [F(1,34) = 0.31, p = 0.58, ηp2 = 0.009]. Finally, for the Fz location, there no significant interaction between race and accuracy [F(1,34) = 3.82, p = 0.06, ηp2 = 0.10]. Moreover, there were no significant main effects of race [F(1,34) = 0.50, p = 0.48, ηp2 = 0.01], or accuracy [F(1,34) = 0.38, p = 0.54, ηp2 = 0.01].
Figure 4. (A) Encoding: P300 at site Pz. More positive P300s were elicited by Black correct and White incorrect trials than Black incorrect and White correct trials. Scalp map shows the P300 differences between correct and incorrect trials for Black (left) and White (right) probes. (B) Probe: N170 at site P10. More negative N170s were elicited for Black than White probes. Scalp map shows the N170 differences between Black and White trials for correct (left) and incorrect (right) trials. (C) Probe: LPC at site Cz. More positive LPC amplitudes were observed for correct than incorrect trials and for Black than White faces. Scalp map shows the LPC differences between Black and White trials for correct (left) and incorrect (right) trials.
A final component examined at encoding was the LPC. The mean amplitude for 500–800 ms post-stimulus onset was tested in a 2 × 2 ANOVA for each scalp location (frontal, central, and parietal) with race (Black vs. White) and accuracy (correct vs. incorrect) as within-subjects factors. Results for the frontal location did not show a significant interaction between race and accuracy [F(1,34) = 0.07, p = 0.79, ηp2 = 0.002], or a significant main effect of race [F(1,34) = 0.10, p = 0.76, ηp2 = 0.003]. However, there was a significant main effect of accuracy [F(1,34) = 5.55, p = 0.02, ηp2 = 0.14] on LPC amplitudes for the frontal location, with more positive amplitudes for incorrect than correct trials. Results for the central location did not show a significant interaction between race and accuracy [F(1,34) = 0.66, p = 0.42, ηp2 = 0.02], or significant main effects of race [F(1,34) = 0.02, p = 0.90, ηp2 < 0.001], or accuracy [F(1,34) = 0.54, p = 0.47, ηp2 = 0.02]. Finally, results for the parietal location revealed a significant interaction between race and accuracy [F(1,34) = 8.28, p = 0.007, ηp2 = 0.20]. Post hoc Bonferroni comparisons revealed a significant difference in LPC amplitudes driven by more positive parietal LPCs at encoding for White than Black probe trials when people responded incorrectly [t(62.63) = -2.08, p = 0.04, d = -0.14]. However, there was no significant difference in parietal LPC amplitudes between White and Black probe trials when people responded correctly, [t(62.63) = 1.34, p = 0.19, d = 0.10].
Table 2a. Mean ERP amplitudes (mV) for probe period.
Table 2b. Mean ERP amplitudes (mV) for probe period.
Next, the P300 at probe was examined (average amplitude for 350–400 ms post-stimulus onset) in a 2 × 2 ANOVA for each scalp location (frontal, central, and parietal) with race (Black vs. White) and accuracy (correct vs. incorrect) as within-subjects factors. Results for the frontal location did not show a significant interaction between race and accuracy [F(1,34) = 0.14, p = 0.71, ηp2 = 0.004] nor a significant main effect of race [F(1,34) = 0.47, p = 0.50, ηp2 = 0.02]. However, results indicated a significant main effect of accuracy [F(1,34) = 18.64, p < 001, ηp2 = 0.35], with greater P300s for correct than incorrect responses in the frontal location. P300 amplitude results for the central location did not show a significant interaction between race and accuracy [F(1,34) = 0.52, p = 0.47, ηp2 = 0.02], or a significant main effect of race [F(1,34) = 0.98, p = 0.33, ηp2 = 0.03]. However, there was a significant main effect of accuracy [F(1,34) = 15.69, p < 0.001, ηp2 = 0.32] with greater P300 amplitudes for correct than incorrect responses in the central location. Finally, for the parietal location there was no significant interaction between race and accuracy [F(1,34) = 0.47, p = 0.50, ηp2 = 0.01], or a significant main effect of race [F(1,34) = 0.51, p = 0.48, ηp2 = 0.01] on P300 amplitudes. However, similar to the frontal and central locations, results for the parietal location showed a significant main effect of accuracy [F(1,34) = 11.56, p = 0.002, ηp2 = 0.25], with more positive P300 amplitudes for correct than incorrect responses.
Finally, the late positive component (LPC) at probe was analyzed (average amplitude for 500–800 ms post-stimulus onset; Figure 4C) in a 2 × 2 ANOVA for each scalp location (frontal, central, and parietal) with race (Black vs. White) and accuracy (correct vs. incorrect) as within-subjects factors. For the frontal location, there was no significant interaction between race and accuracy [F(1,34) = 0.37, p = 0.55, ηp2 = 0.01] on LPC amplitudes. However, there was a significant main effect of race [F(1,34) = 4.72, p = 0.04, ηp2 = 0.12], with greater frontal LPC amplitudes for Black than White probes. There was also a significant main effect of accuracy [F(1,34) = 24.07, p < 0.001, ηp2 = 0.41], with greater frontal LPCs for correct than incorrect responses. LPC amplitude results for the central location did not show a significant interaction between race and accuracy [F(1,34) = 3.62, p = 0.07, ηp2 = 0.10]. However, there was a significant main effect of race [F(1,34) = 6.25, p = 0.02, ηp2 = 0.16], with greater central LPCs for Black than White probes. Additionally, there was a significant main effect of accuracy [F(1,34) = 35.75, p < 0.001, ηp2 = 0.51], with greater central LPCs for correct than incorrect responses. Results for the parietal location did not indicate a significant interaction between race and accuracy [F(1,34) = 1.42, p = 0.24, ηp2 = 0.04] on LPC amplitudes. There was no significant main effect of race [F(1,34) = 4.32, p = 0.05, ηp2 = 0.11] on parietal LPC amplitudes. Lastly, results indicated a significant main effect of accuracy [F(1,34) = 35.51, p < 0.001, ηp2 = 0.51], with greater parietal LPCs for correct than incorrect responses.
To examine the relationship between ERPs and explicit racial bias measures, an amplitude difference score was calculated by subtracting the amplitude for White correct trials from the amplitude for Black correct trials (Black correct amplitude – White correct amplitude). The difference score served as an index of attentional bias. Analyses were conducted only for the ERP components and locations for which there was a significant main effect of race or a race × accuracy interaction for the encoding or probe periods. After calculating the amplitude difference score, the ERP difference score was correlated to the overall scores on the SR2KS and CoBRAS. After correcting for multiple comparisons, there were no significant correlations between SR2KS scores and the encoding P300 (r = -0.19, p > 0.25), and LPC (r = -0.05, p > 0.25) at parietal locations. There were also no significant correlations between CoBRAS scores and the encoding P300 (r = -0.18, p > 0.25), and LPC (r = -0.06, p > 0.25) at parietal locations. For the probe period, there were no significant correlations between SR2KS scores and the left hemisphere N170 (r = -0.16, p > 0.25), the right hemisphere N170 (r = -0.31, p > 0.25), the frontal LPC (r = 0.12, p > 0.25), and the central LPC (r = 0.13, p > 0.25). Finally, for the probe period there were also no significant correlations between CoBRAS scores and the left hemisphere N170 (r = -0.08, p > 0.25), the right hemisphere N170 (r = -0.31, p > 0.25), the frontal LPC (r = -0.05, p > 0.25), and the central LPC (r = -0.05, p > 0.25).
In experiment 1, participants had more accurate working memory for Black than White faces during competition for attention between races; a finding inconsistent with the own-race bias (Meissner and Brigham, 2001; Walker and Hewstone, 2008; Wiese et al., 2014). One explanation for the observed results is that darker faces resulted in greater initial attentional capture and subsequently better memory. Thus, the effects could be attributed to skin color differences rather than race per se. However, this seems unlikely since the early ERP components during encoding (i.e., N170 and N200) did not indicate differences in initial attention to Black and White faces. Nevertheless, a second experiment was conducted to examine whether the prior findings were driven by differences in skin color rather than race.
The primary goal of experiment 2 was to determine whether the greater location accuracy for Black than White faces resulted from differences in skin color. Thus, we examined memory for Asian and White faces since skin color differences between these races tend to be less pronounced. We also examined own- and cross-race effects in Asian and Caucasian participants. We hypothesized that Caucasian participants would have better memory for Asian than White faces whereas Asian participants would have better memory for White than Asian faces.
151 students were recruited from The University of Texas at Austin but data was only examined for participants who indicated they were Caucasian or Asian (n = 105). Of those participants, six were excluded due to data recording problems or below-chance accuracy. Hence, complete data were examined for 61 Caucasian participants (38 females, 22 males, 1 "other", 19.86 ± 6.52 years) and 38 Asian participants (21 females, 16 males, 1 "other", 18.71 ± 0.96 years). Participants were offered course credit for their participation. Informed consent was obtained from all participants under an IRB approved protocol at the University of Texas, Austin. Data collection was completed prior to data analysis.
The questionnaire and materials used for this experiment were the same as Experiment 1, with the exception that the stimuli used included White and Asian faces from the Chicago Face Database (Ma et al., 2015). 80 face images were used including: 40 White faces (20 male and 20 female) and 40 Asian faces (20 male and 20 female).
The procedure for this experiment was the same as Experiment 1 with the exception of the following: EEG recordings were not obtained while participants completed the VSTM paradigm, and during encoding participants viewed two Asian and two White faces (Figure 5).
Figure 5. For each trial, participants fixated on a white cross for 500 ms. At encoding, four pseudo-randomly selected faces (2 Asian, 2 White) were displayed in a clock-like arrangement for 1500 ms. The delay screen presented a white cross for 1500 ms. Finally, the probe screen consisted of a single face centrally located. The face probe was always one seen at encoding. Participants indicated the location of the face during encoding by pressing the button corresponding to the correct location (1, 2, 3, or 4).
Behavioral data analysis was the same as Experiment 1. Overall, 4.09% of the data were excluded for Caucasian participants, and 3.93% of the data were excluded for Asian participants.
A one-way repeated measures ANOVA with race (Asian vs. White) as a within-subjects factor was conducted separately for Caucasian participants and Asian participants to examine accuracy differences between Asian and White probe trials. ANOVA results for Caucasian participants indicated significantly greater accuracy for the location judgment of Asian faces (M = 0.49, SD = ± 0.04), relative to White faces (M = 0.44, SD = ± 0.04), [F(1,60) = 41.88, p < 0.001, ηp2 = 0.41] (Figure 6A). Results for Asian participants also indicated significantly greater accuracy for the location judgment of Asian faces (M = 0.54, SD = ± 0.06), relative to White faces (M = 0.48, SD = ± 0.06), [F(1,37) = 21.37, p < 0.001, ηp2 = 0.73] (Figure 6B). Reaction times for correct trials were also examined for Caucasian and Asian participants in separate one-way repeated measures ANOVAs with race (Black vs. White) as a within-subjects factor. Results for Caucasian participants indicated significantly faster reaction times for the location judgment of Asian faces (M = 0.99, SD = ± 0.03) than White faces (M = 1.00, SD = ± 0.03) [F(1,60) = 7.81, p = 0.007, ηp2 = 0.12]. Additionally, results for Asian participants indicated significantly faster reaction times for the location judgment of Asian faces (M = 0.91, SD = ± 0.03) than White faces (M = 0.92, SD = ± 0.03) [F(1,37) = 4.43, p = 0.04, ηp2 = 0.11]. In contrast with the reaction time results from experiment 1 indicating no differences in reaction times for Black and White faces, the results from experiment 2 suggest an earlier response to Asian faces. It is possible that the earlier response to Asian faces contributed to better working memory accuracy for Asian faces; however, future work will have to examine this possibility since ERPs were not obtained in the present experiment.
Figure 6. Experiment 2 mean accuracy for race of probe along with 95% confidence intervals for Caucasian and Asian participants. (A) Results for Caucasian participants indicated significantly better location accuracy for Asian (M = 0.49, SD = ± 0.04) than White (M = 0.44, SD = ± 0.04) probes. (B) Results for Asian participants indicated significantly better location accuracy for Asian (M = 0.54, SD = ± 0.06) than White (M = 0.48, SD = ± 0.06) probes.
Like in experiment 1, error rates were corrected since chance alone would result in a higher number of between-race than within-race errors. Separate 2 × 2 repeated measures ANOVAs with type of error (within-race vs. between-race) and probe race (Asian vs. White) as within-subjects factors were conducted for Caucasian participants and Asian participants to examine the types of errors made. For Caucasian participants, there was a significant interaction between error type and probe race [F(1,60) = 38.41, p < 0.001, ηp2 = 0.39). Post hoc Bonferroni comparisons revealed significantly more within-race errors for White (M = 0.13, SD = ± 0.03) than Asian faces (M = 0.10, SD = ± 0.03) [t(118.32) = -8.92, p < 0.001, d = -0.64]. However, between-race errors for White (M = 0.08, SD = ± 0.02) and Asian faces (M = 0.08, SD = ± 0.02) were not significantly different [t(118.32) = 0.35, p = 0.73, d = 0.04]. There was also a significant main effect of error type, participants made more within-race errors than between-race errors [F(1,60) = 125.16, p < 0.001, ηp2 = 0.68] and a significant main effect of probe race with more errors for White than Asian probes [F(1,60) = 41.73, p < 0.001, ηp2 = 0.41] (Figure 7A). Results for Asian participants also indicated a significant interaction between error type and probe race [F(1,37) = 30.09, p < 0.001, ηp2 = 0.45]. Post hoc Bonferroni comparisons revealed significantly more within-race errors for White (M = 0.13, SD = ± 0.03) than Asian faces (M = 0.10, SD = ± 0.02) [t(71.93) = -7.00, p < 0.001, d = -1.01]. However, between-race errors for White (M = 0.07, SD = ± 0.02) and Asian faces (M = 0.07, SD = ± 0.02) were not significantly different [t(71.93) = 0.07, p = 0.95, d = 0.01]. There was also a significant main effect of error type, participants made more within-race errors than between-race errors [F(1,37) = 102.20, p < 0.001, ηp2 = 0.73] and a significant main effect of probe race with more errors for White than Asian probes [F(1,37) = 20.57, p < 0.001, ηp2 = 0.36] (Figure 7B).
Figure 7. Experiment 2 mean corrected error rate per type and probe race along with 95% confidence intervals for Caucasian and Asian participants. (A) Caucasian participants made significantly more within-race errors for White (M = 0.13, SD = ± 0.03) than Asian (M = 0.10, SD = ± 0.03) faces but did not differ in between-race errors for White (M = 0.08, SD = ± 0.02) and Asian (M = 0.08, SD = ± 0.02) faces. (B) Asian participants made significantly more within-race errors for White (M = 0.13, SD = ± 0.03) than Asian (M = 0.10, SD = ± 0.02) faces but did not differ in between-race errors for White (M = 0.07, SD = ± 0.02) and Asian (M = 0.07, SD = ± 0.02) faces.
Results from experiment 2 indicated that both Caucasian and Asian participants had better memory for Asian than White faces, indicating that the effects from experiment 1 were not solely a result of skin color differences between Black and White faces. However, contrary to our initial hypothesis, both Caucasian and Asian participants had better location memory for Asian relative to White faces, we will return to this issue in the general discussion.
The present research investigated the neurocognitive mechanisms associated with racial bias during competition for attention to provide a closer representation of intergroup interactions. In experiment 1, we independently examined distinct processing stages and their brain electrophysiological response by combining the working memory paradigm with electrophysiological recordings. Participants were better able to remember the location for Black than White faces. During the encoding period, the P300 amplitude reflected differential attention to race, with larger amplitudes being associated with Black faces whose location was subsequently correctly remembered. In response to memory probe stimuli, the N170 component indicated enhanced processing of Black relative to White faces. Additionally, correct location responses elicited a larger P300 and LPC. Finally, greater probe LPC amplitudes were associated with better recollection of Black face location. Experiment 2 indicated that the effects from experiment 1 were not solely due to skin color differences since participants had better memory for Asian than White faces. However, both Asian and Caucasian participants had better memory for Asian than White faces, a finding inconsistent with the own-race bias.
By examining ERPs to encoding stimuli and subsequently sorting them by race and accuracy, we identified an electrophysiological signature of attention to the race of face stimuli. The assumption is that those faces whose location was correctly identified at probe were the ones most attended to at encoding. While previous research has found greater N200s for own-race than cross-race faces, indicating greater attention and individuation of own-race faces (Ito and Urland, 2003; Dickter and Bartholow, 2007; Kubota and Ito, 2007; Brebner et al., 2011; Lucas et al., 2011), our results did not indicate significant differences in N200s between White and Black probe trials. The weaker effect on N200 amplitudes during encoding is likely due to the simultaneous presentation of multiple faces. Because four faces were displayed at the same time during encoding, the precise timing of early processing as individuals were mentally moving through the stimuli could not be captured by ERPs, hence the weaker race effects on early visual processing components. Future research should use other methodology, such as eye tracking, to more precisely measure early attentional effects when viewing multiple racial stimuli simultaneously.
The main processing difference captured during encoding was reflected later in processing in the P300, a component associated with arousal and attention to motivationally significant events (Polich and Kok, 1995; Nieuwenhuis et al., 2005). Greater P300 amplitudes for Black correct and White incorrect trials suggest greater attentional allocation to Black faces overall, that likely resulted from increased motivation to attend to Black faces. These results are consistent with the proposal that attention is moderated by motivational factors (Brosch and Van Bavel, 2012; Correll et al., 2014) and that increased motivation to encode cross-race faces will result in better memory (Hugenberg et al., 2010). Thus, the greater P300 amplitudes at encoding for Black faces indicates increased motivation to encode Black faces and likely contributed to greater behavioral accuracy for Black than White face location. The greater motivation to attend to Black faces at encoding may be due to several factors, first participants were instructed to be as accurate as possible, thereby increasing motivation to remember cross-race faces, which naturally would be more difficult. Secondly, in line with previous research indicating greater P300s for emotionally valenced or more arousing stimuli (Eimer et al., 2003; Kubota and Ito, 2007), Black faces may have elicited greater emotional arousal relative to White faces, resulting in greater attention. Finally, it is possible that the intergroup context increased the saliency of race such that participants became aware that the task was targeted at some aspect of race. Perhaps with that framing in mind, egalitarian attitudes toward not appearing prejudiced may have motivated participants to encode and accurately remember Black faces. However, because egalitarian attitudes were not measured in the present study, future research will have to assess this possibility.
To investigate racial bias effects when a single stimulus was presented during retrieval, we examined the ERPs to probes. Experiment 1 indicated that the N170, an early visual processing component associated with perceptual processing of faces, was greater (i.e., more negative deflections) for Black than White faces. While the present N170 results are consistent with previous research (Herrmann et al., 2007; Stahl et al., 2008; Walker et al., 2008; Brebner et al., 2011; Wiese et al., 2014), it is important to note that previous work examining the effects of race on the N170 has produced mixed results. For example, some research has not found effects of race on the N170 (Caldara et al., 2003); while other research has found larger N170s to own-race than cross-race faces (Ito and Urland, 2005). It has been suggested that the mixed findings on the N170 may be due to differences between experimental tasks (Senholzi et al., 2015). In particular, when discrimination at the subordinate level is required (as in the present experiment), it is more likely that the N170 will be greater for cross-race than own-race faces because participants are motivated to attend to cross-race faces, which are more difficult to discriminate than own-race faces (Ito and Bartholow, 2009; Senholzi et al., 2015). Our findings are consistent with this proposal and suggest that the greater N170 amplitudes to Black faces reflect greater attentional resources allocated to Black than White faces. Moreover, motivation to attend to race may have increased since race was made salient through the intergroup context and error rates indicated that when individuals saw two Black and two White faces, racial identity was automatically encoded. Additionally, participants were instructed to be as accurate as possible, thereby increasing their motivation to encode the faces. Together with the accuracy results indicating better memory for Black than White faces, it is likely that greater N170 amplitudes reflect motivational factors during face processing.
In addition to the early probe effects that were sensitive to race, greater P300s were observed for correct relative to incorrect trials regardless of race. As stated previously, increased P300s are associated with greater attention to motivationally significant events (Brosch and Van Bavel, 2012; Correll et al., 2014). Thus, greater accuracy suggests increased information availability reflected through larger P300s. This accuracy effect also extended into the LPC, with greater amplitudes for correct relative to incorrect trials. The LPC is commonly examined in the memory literature and is associated with memory recognition processes (Sanquist et al., 1980; Paller et al., 1987; Van Petten and Senkfor, 1996). Correctly identified old stimuli elicit greater LPCs than new stimuli (Paller et al., 2003), reflecting greater recollection. Likewise, larger LPCs for correct than incorrect trials indexed successful retrieval. Importantly, larger LPCs for Black than White faces also indicated greater recollection and attentional capture for Black faces, which was also evident through better location accuracy for Black probes.
In experiment 2, both Asian and Caucasian participants had better location accuracy for Asian than White faces, a finding inconsistent with the own-race bias in memory. It is possible that these contradictory findings may be due to differences in the processes underlying working memory and long-term memory. In fact, a previous study examining working memory for Black and White faces did not find differences in memory for Black and White faces in White participants (Sessa et al., 2012). The incorporation of competition for attention between races in the present tasks suggests that our results may be better explained by visual search literature examining detection for own- and cross-race faces (Levin, 2000; Chiao et al., 2006; Sun et al., 2013). In fact, our results are consistent with work showing faster and more accurate detection of Black than White faces regardless of perceivers' race (Chiao et al., 2006). Likewise, in the present experiments, it appears that participants were faster at detecting Black and Asian faces relative to White faces. Moreover, the greater attentional capture by Black and Asian faces relative to White faces was sustained throughout encoding. Thus, during intergroup contexts Black and Asian faces are detected more quickly and capture more attention relative to White faces.
In both experiments, error rate patterns were consistent with previous research indicating that perceivers tend to first automatically categorize individuals by race (Taylor et al., 1978; Dickter and Bartholow, 2007). The greater number of within-race than between-race errors indicate that participants encoded the race of the faces even though they were not instructed to attend to race. Moreover, participants made more within-race errors for White relative to Black or Asian faces, providing evidence of greater categorical processing for White faces. It has been proposed that greater categorical processing of faces results in poorer encoding and reduced memory (Levin, 2000; Hugenberg et al., 2010); therefore, greater categorization of White faces likely contributed to the reduced location accuracy for White faces. Altogether, our findings suggest that by simultaneously presenting competing racial stimuli (i.e., intergroup contexts), one can increase the salience of race. Moreover, the saliency of race increased attention to Black and Asian faces, as well as, categorization of White faces, resulting in worse encoding and recognition for White relative to Black and Asian faces.
One limitation of the present experiment is that participants were instructed to fixate on a central fixation cross due to the nature of the ERP experiment and the need to reduce artifacts due to eye movements. By asking participants to fixate on a cross, we may not have captured an individual's natural patterns of visual attention. Although facial stimuli were placed at a visual angle that allowed all faces to be visible in a "window" of attention, forcing participants to fixate on a central location may not be completely ecologically valid. Thus, future studies should examine this issue using other technologies to measure visual attention (e.g., eye tracking). In the present experiments, there was no relationship between location accuracy and explicit racial attitudes, these findings are consistent with a previous meta-analysis indicating no relationship between explicit racial attitudes and own-race biases in memory (Meissner and Brigham, 2001). The present results did not indicate an own-race bias in working memory, nevertheless, these findings contribute to previous literature by showing no direct relationship between racial attitudes and working memory biases to cross-race faces. However, it has been proposed that racial attitudes may play a mediating role on memory for cross-race faces through other factors such as contact with other races (Meissner and Brigham, 2001). Thus, future research should examine whether racial attitudes mediate the relationship between contact with other races and working memory.
The present experiments sought to expand our knowledge of racially biased attention and memory during intergroup contexts by examining what occurs when there is competition for attention between multiple racial stimuli. Overall, our results indicate that when individuals are presented with own- and cross-race faces simultaneously, attention is captured by racial minority faces which results in better working memory for those faces. These findings add in important ways to our understanding of the basic cognitive processes that give rise to and support racially biased attention and memory during competition for attention. First, using paradigms that only present one race may not adequately capture racial biases in attention and memory, thereby limiting the scope of research on racially biased behavior during intergroup contexts. More importantly, presenting faces of multiple races simultaneously appears to have naturally drawn attention to racial minority groups, providing a unique approach for increasing attention to racial minorities without using negative or explicit motivation. Understanding how the saliency of race may affect other types of racially biased behavior at the personal and societal levels will be an important task to complete in future research.
All the data as well as the material is openly accessible at https://osf.io/h7sdf/?view_only=cb1bef04a40c4c23a02cb2317389f890.
This study was carried out in accordance with the recommendations of the Human Subjects Research Training provided by the Office of Research Support and Compliance at The University of Texas at Austin with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Institutional Review Board at The University of Texas at Austin.
GG performed the testing, data collection, and analysis, performed data interpretation under the supervision of DS, and drafted the manuscript. DS critically revised the manuscript. All authors contributed to the study concept and design, and approved the final version of the manuscript for submission.
This work was supported by a Ford Foundation Predoctoral Fellowship to GG.
We thank Bianca Chavez, Marissa Hansen, Fariya Sahadat, Jiazhou Chen, Italia Lopez-Valenzuela, Annmarie Calderon, and Allie Vasak for assistance in data collection, and Drs. Stephanie Sherman and Logan Trujillo for statistical and ERP analysis advice.
^ Repeated measures ANOVAs were also conducted for only White participants (n = 25) to ensure that the effects that were found were still evident even after participants from other races (i.e., Asian and "Other") were excluded, here we present results for the encoding P300 at the parietal location. Results for the parietal location did not indicate a significant main effect of race (p > 0.05) nor accuracy (p > 0.05) on P300 amplitudes. While the interaction between race and accuracy was not significant [F(1,24) = 2.66, p = 0.12, ηp2 = 0.10] P300 amplitudes followed the same pattern as the overall sample (n = 35).
^ Results for White participants (n = 25) for the probe N170 followed the same pattern as the overall sample (n = 35). There was a significant main effect of hemisphere [F(1,24) = 5.64, p = 0.03, ηp2 = 0.19] with more negative N170 amplitudes in the left than right hemisphere. Additionally, there was a significant main effect of race [F(1,24) = 11.28, p = 0.003, ηp2 = 0.32] with more negative N170 amplitudes for Black than White probes. There were no other significant main effects (p > 0.05) nor interactions (p > 0.05) on N170 amplitudes for White participants.
Faul, F., Erdfelder, E., Lang, A. G. and Buchner, A. (2007). G∗Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191.
Copyright © 2019 Gonzalez and Schnyer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


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The Challenges and Achievements of a Female in Clinical Research
Karina Reid March 7, 2019
Every year on March 8th, we celebrate International Women's Day, a global day to express the successes and pitfalls women are subject to in social, economic, cultural and political environments around the world. The Canadian Liver Foundation (CLF) is fortunate to not only be partially led by incredibly intelligent and diligent female leaders, but to interact with tremendous female role-models; be it healthcare professionals, volunteers, researchers, caregivers and more.
This leads us to our conversation with Dr. Amanda Ricciuto, a clinical research fellow at the Hospital for Sick Children (SickKids) and the CLF's 2016 Designated "Taking Action Against PSC" Award recipient. Dr. Ricciuto spoke to us about the innovative work her CLF-funded research project produced, her experience as a female pediatrician and the extraordinary role models she credits to her growth as a professional.
Dr. Amanada Ricciuto of the Hospital for Sick Children (SickKids) in Toronto.
Where did your interest in hepatology/gastroenterology begin?
I would say during my teenage years when my then seven-year-old brother was diagnosed with Crohn's disease, a type of inflammatory bowel disease (IBD). I witnessed firsthand the impact that this chronic disease (including multiple medication trials, three hospitalizations and two surgeries for bowel resection) has on a patient and their family. This experience played a big part in my decision to pursue medicine in the first place.
I then fell in love with pediatrics during my time at the Montreal Children's Hospital as a medical student (training at McGill University, in Montreal). I spent a few months doing pediatric gastroenterology as a pediatrics resident, which I very much enjoyed. Overall, it was this experience that solidified my decision to pursue a career in this field.
I think GI is unique, it encompasses multiple facets of medicine (including chronic diseases) and offers the opportunity for rewarding, meaningful, long-term relationships with patients; acute medical issues; complex medical problems; procedures; and, of course, the opportunity for all kinds of research. This versatility is one of the elements I love most about GI/hepatology.
Tell us about the "Pediatric PSC-IBD: Optimizing colitis monitoring strategies to enhance liver disease outcomes" research project
Primary sclerosing cholangitis (or PSC) is a chronic, progressive liver disease for which there is currently no effective treatment. About 20% of children with PSC progress to end-stage liver disease (cirrhosis), for which they will need a new liver. There is also a very strong association between PSC and IBD (75% of people suffering from PSC also have IBD). The reasons for this link are not completely understood, but it is thought to relate to interactions, or "cross-talk," between the liver and gut. Therefore, the current thinking is that it is beneficial to heal the bowel of patients with IBD and PSC. To do so, we first need a reliable way to monitor IBD activity (i.e. to determine whether the gut is healed or inflamed). The clinical experience led us to suspect that patients with PSC and IBD often have minimal symptoms despite having active intestinal inflammation shown on colonoscopy.
Research, and specifically, research in gastroenterology and hepatology, tend to be male-dominated fields. The CLF feels the importance of having equal opportunities for women in liver research.
The research project in question set out to test this theory. We also wanted to determine whether a stool marker of inflammation called fecal calprotectin (FC), which is a break-down product of white blood cells, performs well as a monitoring tool in children with IBD and PSC (PSC-IBD).
We recruited 87 children with IBD at SickKids, including a mix of patients with and without PSC. All of our children had a colonoscopy conducted and stool collected for FC measurement. We found that children with PSC who reported no symptoms had active intestinal inflammation on the colonoscopy more often compared to children without PSC who reported no symptoms. We also confirmed that FC is a useful monitoring tool in this group with levels <100 reliably indicating the healing of the gut. This research implies that children with PSC-IBD who report feeling well remain at risk of silently-active IBD (IBD with no symptoms).
Additionally, we confirmed that monitoring strategies, like FC examination or colonoscopies, should be used to determine whether healing of the bowel has been achieved.
What was your experience as a woman studying gastroenterology/hepatology? Were you challenged or supported in a positive way?
Pediatrics tends to be a female-predominant medical specialty, and the group at SickKids in Toronto was no exception. I was fortunate to train alongside several exceptional female colleagues. While I do not have children, several of my co-fellows did, and it was inspiring to see how they masterfully balanced a gruelling clinical workload with their family obligations. I am also fortunate to be able to honestly say that I felt my gender was a non-issue during my GI fellowship. It simply was not relevant. It was an environment where individuals were recognized and rewarded for their productivity and accomplishments and supported in every way to achieve their maximum potential. I experienced one major life event during my training, and that was getting married. My requests for time off to be in Montreal (where I got married) to make final wedding arrangements were entirely respected.
Amanda holding a sample for her PSC-IBD research project.
Are there any female colleagues you look up to or inspired by?
I could not have asked for better mentors and role models during my GI/hepatology training at SickKids—both of which happen to be women! My PSC research and Ph.D. thesis in clinical epidemiology at the University of Toronto are co-supervised by Dr. Binita Kamath, a hepatologist, and Dr. Anne Griffiths, an IBD specialist, both leaders in their respective disciplines.
They are superb clinicians and have contributed to advancing their fields through important research endeavours. These are two passionate, driven and incredibly successful women, whose career trajectories reflect this. I deeply admire them both and often jokingly tell my fellow trainees that "I want to be like them when I grow up."
Above and beyond their professional accomplishments, both Dr. Kamath and Dr. Griffiths have been tremendous mentors to me. My definition of a good mentor is someone who puts the needs of the mentee ahead of his/her own and who creates opportunities for the mentee. Both my co-supervisors embody this conception to its fullest. Their support and guidance has repeatedly elevated me to a higher level of success than I would have been able to attain on my own, and they have done this silently, in the background, seeking nothing further than the satisfaction of knowing they have contributed to my advancement.
Both of these women are mothers with incredibly busy lives. They have been very open to discussing the challenges of being a junior faculty while starting a family, a position in which in the near future, I may find myself in. While I am very driven on a professional level, I certainly do not want my work-related ambitions to stifle my personal experiences. My female role models have helped me to appreciate that "life happens, and the rest somehow sorts itself out." Watching these women do what they do on a daily basis over the past three years has been nothing short of inspirational, has motivated me to be a better clinician and researcher and has helped me to realize that I too can do it all.
What is the one thing you wish the general public knew about liver disease or liver health?
Simply, the importance of organ donation. Unfortunately, we have no current effective medical therapies for a number of liver diseases, such as PSC. In such cases, liver transplantation represents the only possibility for survival. There is a shortage of organs for both children and adults. Patients die while on wait lists because of this. The general public holds tremendous power to improve outcomes for these patients by consenting to donate organs and tissue.
Amanda posing inside SickKids Hospital (photo credit: SickKids)
Dr. Ricciuto is a clinical research fellow at the Hospital for Sick Children, and a student at the University of Toronto pursuing a Ph.D. in Clinical Epidemiology.
Learn more about current and past CLF funded research projects.
Posted in News, Research
Healthy Slow Cooker Recipes

redpajama_set_name: RedPajamaCommonCrawl


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Is dairy bad for climate change?
Is dairy bad for the climate?
Is dairy milk bad for the environment?
How much does dairy contribute to climate change?
Why is dairy bad for you?
How does eating less dairy help the environment?
What milk is worse for the environment?
How does dairy farming affect global warming?
Is vegan milk environmentally friendly?
Is dairy or beef worse for the environment?
Can you live off dairy?
Is drinking milk bad for you?
Should you avoid dairy?
Is cow milk bad for health?
Why You Should Go dairy Free?
Dairy cows and their manure produce greenhouse gas emissions which contribute to climate change. Poor handling of manure and fertilizers can degrade local water resources. And unsustainable dairy farming and feed production can lead to the loss of ecologically important areas, such as prairies, wetlands, and forests.
Cows and other dairy animals emit huge amounts of methane, which is about 25 times more damaging to the climate than carbon dioxide. Producing animal feed also contributes to global warming, and animal waste can impact water and air quality.
Cow's milk is inarguably the most popular and accessible kind of milk. Unfortunately, it has by far the greatest impact on the environment. Cow's milk requires nine times as much land and produces three times as much carbon emissions than any non-dairy milk alternative.
Milk production only contributes approximately 1.3% of all U.S. GHG emissions, the researchers say. (By comparison, transportation as an industry in the U.S. accounts for 28% of GHG emissions.)
IMPORTANT: Where can magazines be recycled?
Milk and other dairy products are the top source of saturated fat in the American diet, contributing to heart disease, type 2 diabetes, and Alzheimer's disease. Studies have also linked dairy to an increased risk of breast, ovarian, and prostate cancers.
According to scientists, "without meat and dairy consumption, global farmland use could be reduced by more than 75% — an area equivalent to the United States, China, the European Union, and Australia combined — and still feed the world," The Guardian asserts in a deep-dive article that avoiding meat and dairy is the " …
Cows milk. A 2018 study by researchers at Oxford University concluded that producing a glass of cow's milk has at least three times more environmental impact than producing a glass of any non-dairy milk. This is due to a combination of: Greenhouse gas emissions: around 0.6kg per 200 ml glass.
These gases – mainly methane, nitrous oxide and carbon dioxide – are produced at different stages of dairy farming. Methane, the most potent of these greenhouse gases, is first produced as the cow digests its food. … These gases all contribute to global warming.
Bottom Line: Plant-based milks are more environmentally sustainable than dairy. … Both cashew milk and macadamia milk are significantly better than dairy milk and use less water than almond milk but it would lower their environmental impact to try and get non-GMO or organic versions where pesticides are not used.
IMPORTANT: How do companies make recycled paper?
When it comes to environmental impact, beef is by far the worst offender compared to other proteins. But, that doesn't mean that cheese—or any dairy product—is in the clear. According to the Oxford study, "tenth-percentile GHG emissions and land use of dairy beef are then 36 and 6 times greater than those of peas."
"The only food that provides all the nutrients that humans need is human milk," Hattner said. "Mother's milk is a complete food. … Technically, adults could survive on human milk, too, she said; the sticking point would be finding a woman who is willing to provide it (and enough of it).
It's packed with important nutrients like calcium, phosphorus, B vitamins, potassium and vitamin D. Plus, it's an excellent source of protein. Drinking milk and dairy products may prevent osteoporosis and bone fractures and even help you maintain a healthy weight.
"Dairy isn't necessary in the diet for optimal health, but for many people, it is the easiest way to get the calcium, vitamin D, and protein they need to keep their heart, muscles, and bones healthy and functioning properly," says Vasanti Malik, nutrition research scientist with the Harvard T.H. Chan School of Public …
But they acknowledge that cow's milk does raise health concerns. It can carry harmful pathogens, including salmonella and E. coli, and many infants and children are allergic to it, though some outgrow their allergy.
IMPORTANT: Are foil envelopes recyclable?
One of the top benefits of cutting out dairy is the removal of excess saturated fats, sugar and salt from your diet, thus lessening your calorie intake and promoting a healthy weight. Dairy is also renowned as an acidic food, disrupting your body's acid/alkaline balance.


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If you are interested in a career with Nest Egg Solutions, please contact us for more information and a confidential chat.
Tony Pearson is Director and founder of Nest Egg Solutions in 2003, after a decade working in the retail finance industry.
Tony commenced his career in finance in 1993 when he joined AVCO Finance (now GE Money) in Central Queensland. This was a "baptism of fire" introduction to the world of finance and he thoroughly enjoyed the fast paced world of consumer lending and insurance.
In 1996 he was transferred to the Sunshine Coast as an Assistant Manager to expand his financial knowledge from one of AVCO's most respected Branch Managers. While at AVCO he was able to learn how to deal with client's highs and lows when they discussed their financial concerns. This enabled Tony to develop effective solutions to client's requirements.
In 1999 he left AVCO and started as the Sunshine Coast Area Loans Manager for Wide Bay Australia and was tasked with rebuilding their lending portfolio on the Sunshine Coast. In the first year, he doubled the Building Societies lending book on the Sunshine Coast.
In 2001 Wide Bay Australia appointed Tony to their new Financial Planning arm. In this field he excelled and in 2003, he was approached by Charter Financial Planning to start his own Financial Planning Firm. From this offer Nest Egg Solutions was born.
Since 2003 Tony, along with his team, has built a business which is focused on empowering people to ensure their own current and future financial freedom through effective strategies.
He is committed to ensuring the Financial Planning Industry moves to becoming a profession upon which all clients can depend, He is a Certified Financial Planner (CFP) member of the Financial Planning Association of Australia (FPA) and is a SMSF Specialist Advisor (SSA) with the Self managed Super Fund Professional Association of Australia (SPAA). He has previously served for two years as SPAA's Sunshine Coast Chapter Chair and as a member on SPAA's Queensland Committee.
He is also currently Chairman of the Queensland branch of Authorised Representative Association (ARA) a body which aims to improve the professionalism of Financial Advice Networks representative's.
Alison has worked in the Finance industry for the past 15 years, spending the last 4 years specialising in Tax Return Preparation with Nest Egg Tax Solutions. Alison joined the team in October 2004; she started as the assistant to the Financial Planner and Mortgage Broker where she gained a wealth of experience to be able to create the back office procedural environment to help the business run smoothly.
Rae-Lee
Rae-Lee comes from a long banking background, starting as a junior in the ANZ Bank in Bundaberg in 1982 and finishing as Area Loans Consultant at AusWide Bank on the Sunshine Coast in 2014.
Before joining Nest Egg Solutions in July 2015 Rae-Lee & her husband completed a certificate III in TESOL and taught English in a remote village called Gunge in Tanzania, Africa for 3 months.
Rae-Lee brings a wealth of experience in customer service, loves travelling and meeting people of different cultures and daily walks along the beach front at sunrise.
Quote for ^IXIC
© Nest Egg Solutions 2008


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Irish-trained physician finding opportunity on PEI
Dr. Padraig Casey
Dr. Padraig Casey is in the early stages of a medical career and raising a family life. He's building them both on Prince Edward Island.
Casey grew up in Ireland's County Louth and received his medical training in Dublin. When the time came to enter into medical practice, he saw both personal and professional opportunities in Prince Edward Island.
"We decided to emigrate to Canada in 2016 after I'd been qualified for a couple of years. We were looking for places and a friend of ours who was in Newfoundland told us about Prince Edward Island," he said.
"Honestly, up to that point I hadn't heard of PEI, but when I looked into it, PEI seemed like a place that would suit us. I emailed the recruiter and they pretty much called me back within 24 hours and arranged a site visit. We moved here within months."
Casey says PEI fit the bill because it provided a good job, access to good schools, a nice mix of town and country lifestyle, and a relatively low cost of living.
"We had three children under five and wanted to have a more balanced life," he said. Some important factors eased the transition. Casey's wife, Amy, was able to find meaningful work at the University of Prince Edward Island, and the local medical community welcomed Casey.
"There are some other Irish doctors practicing here which made a nice sort of welcoming committee, but on top of that, all the doctors have been good about helping me as a newcomer to the country ," he said.
"There's a lot of good support from specialists in the medical community here; that's not necessarily the case in every community. There's scope here to broaden my skills and work up to my training. I find that a hugely rewarding part of the job."
The Recruitment and Retention Secretariat, in collaboration with Health PEI, worked with the Casey family to help them secure a practice and to navigate the paperwork around immigration and work permits. The Secretariat also provided a financial incentive in the form of a return-in-service grant for a three year commitment to practice in PEI.
"The first two weeks were pretty hectic, getting drivers' licenses, buying two cars and that sort of thing, but the transition after that has been pretty easy, really," Casey said.
"There are direct flights back to Dublin from Halifax which makes it quite convenient. I know that made the transition easier for my family here and at home. There's kind of long tradition of Irish people leaving the country to find work."
Casey said his experience with Prince Edward Island so far suggests this province is the right choice for his family and his professional life.
"With what we've found here and the support that has been offered, we're delighted with our decision to come here."
Learn more about financial incentives for physicians on Prince Edward Island.
Tweets by AgInfoPEI
Department of Health and Wellness
Recruitment and Retention Secretariat
3rd Floor, Sullivan Building
16 Fitzroy Street, PO Box 2000
Charlottetown, PE C1A 7N8
healthrecruiter@gov.pe.ca


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Tag Archives: Milestone
Episode 56: Completely Fabulous!
Welcome to Completely Comics Episode 56: Completely Fabulous!
Byte Girl's Senior Prom/Graduation
Netflix'n it up
Recorder problems
DC has been teasing the aftermath of "The Culling" mini-event between Superboy, Teen Titans and Legion Lost. 1)Superboy's life is completely changed following the event. He had been controlled by N.O.W.H.E.R.E. all of his life, and now that he's free, what will he do now? 2) Kid Flash was recognized by Timber Wolf and T-Wolf attacked him. Kid Flash doesn't seem to know much about his past. Does Timber Wolf know more about his past then he does? 3) The Titans themselves are thrown into a mysterious place with no visible means of returning home. The Legionnaires are to home to the the 31st Century and discover the worst thing that could possibly happen. 4)The Ravagers series starts with some of the teens who escaped from N.O.W.H.E.R.E. led by Caitlyn Fairchild.
JLI Cancelled? Rumors around the internet all point to Justice League International is ending after issue #12 and the Annual. However, there is speculation that it may go on under a different title since the writer Dan Jurgens has a project he cannot announce yet.
DC Changes LGBT policy: Dan Didio had previously stated that DC would not change the sexual orientation of existing DC characters, but instead would create new ones, such as the Teen Titan's character Bunker. However, DC's policy on this has 'evolved' and revealed that one of DC's most prominent characters will in fact be gay in the New 52. The word is that will be an iconic male characters who has yet to appear in the New 52. It will be revealed sometime in June.
Astonishing X-Men #51: First gay wedding in comics. Northstar and Kyle.
DCUO Download Content 4 – "The Last Laugh" : Mark Hamill and Adam Baldwin return to their classic roles of the Joker and Superman. More expanded Pvp play, Shields as weapons.
Sin City: A Dame to Kill For, is set to premiere on October 4th, 2013. So far, it has been reported that Mickey Rourke and Jessica Alba have signed onto the film.
Comic Artist Ernie Chan passes away: A week after renowned Filipino comic book artist Tony DeZuniga passed away, the industry lost another pillar on May 16 in the United States when Ernesto "Ernie" Chan, 71, of Marvel and DC comics fame died after almost a year long battle with cancer. A relative, who asked not to be named prior to seeking permission from the artist's immediate family, confirmed via Facebook that Chan died in California last May 16, or just two-and-a-half-months shy of his 72nd birthday. Chan is best known in the comics world for his work on Conan the Barbarian and Detective Comics.
Thor 2: Bleeding Cool's rumor mill is saying that the Enchantress, a long time Thor villain will be in the upcoming Thor 2.
Lobo: Bleeding Cool also shared a rumor that Dwayne 'the Rock' Johnson will be playing Lobo in an upcoming DC movie.
3) Resource Review: The World's Finest http://www.worldsfinestonline.com/main.php
Also, we talk about how difficult it is to sign up for hte the Avengers Alliance game on Facebook.
4) Wiki of the Week: Rocket from Young Justice: Invasion
Taken from Comicvine. http://www.comicvine.com/rocket/29-16695/
Raquel Ervin was born in Paris Island, the poorest, most crime-ridden neighborhood in Dakota. Although she is only the sidekick of the title character, Icon, she is the actual protagonist of the series. She yearned to become a writer,, but lacked the motivation. That is, until she met Augustus Freeman IV, a corporate lawyer who was secretly a stranded alien with superhuman powers. This occurred while she and her friends were robbing Freeman's home. Raquel convinced Augustus to become the superhero Icon, and to take her on as his sidekick, Rocket. While in costume, she wore a belt Icon fashioned out of his escape pod's inertia winder, which allowed her to manipulate kinetic energy.
Shortly after she began adventuring with Icon, Raquel discovered that she was pregnant by her ex-boyfriend, Noble. She decided to keep the baby, becoming the first single teenage mother in comics. She gave birth to a son, whom she named Amistad Augustus Ervin, named "Amistad" after famous ship that brought slaves over from Africa to America, and "Augustus" after Augustus Freeman IV, the Human alias of Dakota City superhero Icon. During her pregnancy, Raquel trained her best friend Darnice to temporarily assume duties as Rocket. Raquel eventually resumed her role as Rocket.
Rocket was more liberal than Icon, which caused them to clash on a number of occasions. She befriended Static, another teenage superhero from Dakota City. While it has been hinted that someday they might become more than friends, their relationship remained platonic throughout the run of their respective titles.
In the revised Milestone continuity (which merged with the main DC Universe), Rocket is still partnered with Icon.
All of Rocket's superhuman powers derive from the inertia winder installed in her belt buckle. This device is one of two from Icon's escape pod, the other is installed in Hardware's current suit of armor. When activated, the inertia winder surrounds Rocket in an "inertia field," which absorbs, stores, and redirects any kinetic energy used against it. This field protects her from anything invested with kinetic energy: physical blows, bullets, falls, falling debris, etc. The inertia field normally extends a few inches from Rocket's body, but she can expand the field to much greater sizes. She once enclosed a crowd of about 50 people within her field without it losing its durability.
Rocket is able to project stored energy in blasts usually from her hands over a distance, causing great damage depending on the amount of energy she uses. She can also fly by releasing the stored kinetic energy beneath her, launching her skyward. She is particularly skilled in this ability, able to perform complex aerial maneuvers and hover by controlling the amount of energy released. Rocket has also learned to how to use her inertia winder to surround her opponents in an "inertialess field" which negates any kinetic energy within it down to the molecular level, effectively immobilizing them. Some opponents even fall unconscious because the field can prevent oxygen molecules from reaching their lungs.
Other uses of his field includes imbuing objects with large amounts of kinetic energy when thrown increasing their speed, effectively allowing her to throw rocks at the speed of bullets. Alternatively she can release this energy when she throws a punch delivering enough shock force to stagger beings as powerful as Icon if she uses a high enough amount.
Rocket's inertia field has certain weaknesses due to its very nature. The field will not activate if a person or object exerts little kinetic energy against it. This makes Rocket vulnerable to stealth attacks, which rely on slower movements and minimal force. Also, the inertia field offers little protection against weapons based on non-kinetic energy like thermal (e.g., flamethrowers) or electrical (e.g., tasers).
Raquel Ervin is a voracious reader and a gifted writer. A former high school gymnast, she is very agile and has quick reflexes. Rocket is also a good hand-to-hand combatant due to her crime-fighting experience as Icon's sidekick.
Rocket appears in the animated series Young Justice, starting off in several brief cameos before joining the main cast at the end of the first season. She first appears in "Revelation," where she battles one of Poison Ivy's plant creatures during the Injustice League's attack on several major cities. She later reappears in "Failsafe" as one of the Earth's heroes who repels an alien invasion, although this was only an illusion created by Miss Martian as a training exercise.
She officially becomes a member of the team upon Icon's induction into the Justice League. She first joins the group during a mission to locate Cheshire, which leads to an ambush orchestrated by the Riddler. She is shown to harbor an attraction to Aqualad, who helps her after she is attacked by Apokoliptian weaponry.
Rocket appears in the sequel series Young Justice: Invasion, now five years older. A legal adult, Rocket is nown shown as a member of the Justice League alongside Zatanna.
TEEN TITANS #9
"THE CULLING" finale!• THE TEEN TITANS and THE LEGION LOST duke it out with N.O.W.H.E.R.E. and the madman known as Harvest to stop the slaughter of innocent children.• This issue leads directly into this month's RAVAGERS #1!• Continued from TEEN TITANS ANNUAL #1 SUPERBOY #9 and LEGION LOST #9! Written by: Scott Lobdell
Tagged as 31st Century, A Dame to Kill For, Adam Baldwin, Astonishing X-Men, Bi, Booster Gold, Byte Girl, Caitlyn Fairchild, Conan the Barbarian, Dan Jurgens, DC Animation, DC Comics, DC Universe Online, DCUO, DLC 4, Dwayne Johnson, Enhantress, Ernie Chan, Executioner, Gay, Hardware, Harvest, Icon, Jessica Alba, JLI, JLI #12, Joker, Kid Flash, Kyle, Legion Lost, Legion of Super-Heroes, Lesbian, LGBT, Lobo, Mark Hamill, Mickey Rourke, Milestone, N.O.W.H.E.R.E., Netflix, Northstar, PvP, Rocket, Shield, Sin City, Superboy, Superman, Teen Titans, The Culling, The Last Laugh, The Ravagers, The Rock, Thor 2, Timber Wolf, Transgender, Troy Price, Will Mitchell, World's Finest Online, Young Justice
February 25, 2011 · 9:43 am
Episode 9 – Completely Infectious!
1) Welcome to Completely Comics Episode 9: Completely Infectious! This week's show features the usual lovable hosts sharing the past week in their lives. They share comic news and review www.comicbulletin.com. Then Will shares a tribute to the work of Dwayne McDuffie and Will's Muy Buenos.
Troy news including Dr. Horrible over at http://www.drhorrible.com and The Cape Podcast over at www.thecapepodcast.com
Will news Upper Respiratory Infections and Bytegirl
Multi-Media comic related news
4) Review of http://www.comicsbulletin.com/
5) Wiki of the Week – Dwayne McDuffie information from ComicVine at http://www.comicvine.com/dwayne-mcduffie/26-42450/
7) Contact Information: Email us at completelycomics (at) gmail.com or call in and leave a voicemail at 641.715.3900 and enter extension 694617#
Tagged as All-Star Superman, Anthony LaPaglia, Bruce Timm, Byte Girl, Christiina Hendricks, Diamond Gem Awards, Dr. Horrible, Dr. Horrible's Sing-a-long Blog, Dwayne McDuffie, Frank Quitely, Grant Morrison, Jimmy Olsen, Lex Luthor, Lois Lane, Matthew Gray Gubler, Milestone, Milestone Comics, Neil Patrick Harris, Netflix, Sam Liu, Static Shock, The Cape, The Cape Podcast, Upper Respirtory Infection


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Tuesday Keynote 2017
Dr. Peter Bauer
Deputy Director Research Department, European Centre for Medium-Range Weather Forecasts
See keynote in Agenda Planner
FORECASTING THE FUTURE ROLE OF HPC IN WEATHER AND CLIMATE PREDICTION
Weather and climate prediction are high-performance computing applications with significant societal and economic impact, ranging from disaster response and climate change adaptation strategies to agricultural production and energy policy. Forecasts are based on millions of observations made every day around the globe, which are then input to numerical models. The models represent complex processes that take place on scales from hundreds of metres to thousands of kilometres in the atmosphere, the ocean, the land surface, the cryosphere and the biosphere. Forecast production and dissemination to users is always time critical, and output data volumes already reach petabytes per week.
Meeting the future requirements for forecast reliability and timeliness needs 100-1000 times bigger HPC resources than today – and towards exascale. To meet these requirements, the weather and climate prediction community is undergoing one of its biggest revolutions since its foundation in the early 20th century. The revolution encompasses a fundamental redesign of mathematical algorithms and numerical methods, the ingestion of new programming models, the implementation of dynamic and resilient workflows and the efficient post-processing and handling of big data.
The talk will introduce the history of weather science and share examples of its skill and ambition. The science challenge will be translated into computing and data challenges, as well as the current avenues the weather and climate prediction community is taking in preparing for the new computing age.
Dr Peter Bauer is the Deputy Director of the Research Department at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, UK. He obtained a masters degree from the University of Cologne and a PhD degree from the University in Hamburg, Germany, both in meteorology. During his career, he was awarded post-doctoral and research fellowships by UCAR and NASA. He worked for the German Aerospace Center leading a research team on satellite meteorology before joining ECMWF in 2000. He is the author and co-author of over 100 peer-reviewed scientific journal papers and is a member of several advisory committees for national weather services, the World Meteorological Organization and European space agencies. He is coordinating the FET-HPC project ESCAPE and co-coordinates the ESiWACE centre of excellence. At ECMWF, his current duties also include the management of the Scalability Programme that will prepare ECMWF for the upcoming computing era.