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UNIVERSITEIT · STELLENBOSCH · UNIVERSITY jou kennisvennoot ·your knowledge partner
Department of Psychiatry
Faculty of Medicine and Health Sciences
Stellenbosch University
PO Box 19063
Tygerberg
7505
January 2014
The Editor
BMC Medical Education
Dear Editor,
RE: Manuscript ID3086356329350392, entitled "Facial expression recognition and exit examination performance in medical students: a prospective, descriptive study"
Thank you for the opportunity to submit a revision of our manuscript for consideration as an original contribution to BMC Medical Education. We believe that the modifications made in response to the feedback of the peer review process have greatly strengthened the manuscript.
Thank you for this input.
In the annotation below, we indicate where changes have been made in the revised manuscript and key them to the reviewer comments.
We believe we were able to address all of the remaining issues and hope that the manuscript is now suitable for publication.
Kind regards
Authors

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Reviewer I (Philip van Eijndhoven)
Comment 1
There is no clear hypothesis stated in the introduction;
Author response
We have included our hypothesis at the end of the introduction. Thank you for pointing this out.
“We hypothesize that there will be an association between specific facial recognition abilities and academic performance measures and that gender would be an effect modifier in the association.”
Comment 2
There is no clear conclusion in the discussion either …
Author response
In addition to the changes below, the discussion has been redrafted.
“Conclusion
This study suggests an association between facial recognition abilities (i.e. happiness and anger) and academic performance measures in specific subjects (i.e. obstetrics, urology and anesthesiology). Gender is an effect modifier in the association between facial anger recognition abilities and urology examination marks. More extensive work would be required to establish the exact mechanisms underlying the association between specific course components (subjects) and facial expressions. Future research should also include longitudinal studies monitoring other aspects of emotional intelligence in medical students throughout their training.”
Comment 3

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There is no clear argumentation in the introduction for the predictive value of emotional intelligence for examination scores
Author response
We have introduced the following changes to address the comment.
“Whether facial expression recognition plays a role in academic performance of medical students remains unclear as no studies specifically investigated this aspect. What has been investigated and currently intensely debated, are the association between emotional intelligence and academic performance. For example, some studies show weak associations between emotional intelligence and academic performance [11], whereas others show no association [12]. Interestingly, specific entities of emotional intelligence have been shown to predict academic success [13-15]. The reason why these findings are important to consider is that one of the most commonly accepted theoretical models of emotional intelligence relies on emotion perception as a lower order branch upon which higher order branches such as emotion management depends on [16]. Despite the dependence on this lower branch ability, studies assessing the association between the ability to recognize emotions through facial expressions, and academic performance in undergraduate medical students are lacking.”
Comment 4
There is not a clear statistics section for the correlation between the score of emotion recognition and examination scores; what is the level of significance;
Author response

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We have now provided a more detailed statistical description under data analysis for the correlation between emotion recognition and examination scores and included the level of significance.
“Data analysis
Descriptive analyses were done for the academic performance indicators and facial expression recognition measures. Individual facial expression recognition measures of the hexagon task were correlated with those of the animation task using Pearson correlation.
Facial expression recognition measures on both tasks were also individually correlated with the academic performance indicators using Pearson correlation. The significance level for the correlations were adjusted to accommodate the many associations of interest; the threshold pvalue was set as p=0.0002 for the hexagon tasks (n=237) and as p=0.002 for the animation tasks (n=215). For those passing this threshold, mixed model linear regressions was performed to measure the strength of the associations, adjusting for the covariates gender and age. The year effect was incorporated into the model as a random effect, since we were not interested in this effect per se. Loess analysis was first completed to investigate the appearance of the associations, which showed non-linear relationships with the hexagon measures, and were therefor categorized into quartiles for further analysis.”
“Association between facial expression recognition and examination scores
Pearson correlation on the individual facial expression recognition measures between the hexagon and animation tasks was weak (below .2). For the hexagon task, there was a significant association between perceptual discrimination of anger and anesthesia (r = .24; p
= .004) as well as urology examination scores (r = .24; p = .001) (Table 3). For the

5 animation task, there was a significant negative correlation between perceptual discrimination of happiness and obstetric examination scores (r = -.21, p = .002) (Table 3).
When gender stratified, there was a significant association between perceptual discrimination of anger and anesthesia for males (r = .34; p = .02).
Linear regression analyses
The Loess results indicated that the relationship between anesthesiology scores and hexagon anger scores was possibly not linear and therefore the quartiles for anger (<0.67, 0.67-0.83,
0.83-0.92, >0.92) were used in further linear regression analysis. The global test (p = .003) indicated a significant relationship between anesthesiology performance and the hexagon anger score quartiles (adjusted for gender and age). Results showed that the first quartile differed significantly from the second, third and fourth quartile. There was, on average, an increase of 7 marks from the first to the fourth quartile (Table 4). Gender and age were not significant covariates in the model (p < .05).
The loess results indicated that the relationship between urology scores and hexagon anger scores was not linear and therefore the quartiles for anger were used in further linear regression analysis. The global test (p = .0001) indicated a significant relationship between urology performance and the hexagon anger score quartiles. Gender was an effect modifier in the relationship (p = .03), with a strong positive relationship for males, but a nonsignificant relationship for females (Figure 1). Results for males showed that there is an increase in urology marks from the first to the fourth quartile, with an average increase of .9

6 marks from the first to the fourth quartile (Figure 1). Age was not a significant covariate in the model. “
Comment 5
The statistics are not sound in my opinion: there are 9x11 correlations tested, which needs adjusting of the statistical threshold;
Author response
We have addressed the reviewer’s concern and included a more detailed description of the statistical analysis of data under data analysis. Please see response to comment 4 and changes to tables and figure.
Comment 6
Is there a correlation between mean performance on facial expression recognition and examination performance?
Author response
We have included a column in table 3 with correlation data for mean performance and facial expression recognition. There were no significant correlation between mean performance and any of the facial expression recognition measures.
Comment 7
Of the 502 students recruited, 237 had usable data: describe reasons for dropout?
Author response
FAR testing was only started later in the year (2008) due to technical problems. As the students only rotate once, participants in early 2008 could not be included in this analysis. Students from the latter part of 2010 were not included in the facial affect recognition study as recruitment targets were already met. This is now clarified in the paper:
“Demographic data

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Two hundred and thirty seven (n=237) provided data for the hexagon task and 215 (42%) for the facial affect recognition task. Of the 237 students, 21% were from 2008, 70% from
2009, and 9% from 2010. The study group consisted of 144 (61%) female and 93 (39%) male participants, with a mean (SD) age of 24.1 ± 1.6, range 22-35. Demographic and academic performance data were available for two hundred and fifteen students that rotated through psychiatry in early 2008 prior to the start of the facial affect recognition testing and after the conclusion of facial recognition testing late in 2010. This data was used to assess possible recruitment bias i.e. differences in student profile over time.”
Comment 8
The data are not discussed in the discussion; what do the authors conclude from their data?
Author response
Please see comment 2.
Reviewer II (Anita Laidlaw)
Comment 1
The third paragraph in the discussion, particularly the second and third sentences are lacking in references.
Author response
Thank you pointing this out. References now included
Comment 2
The authors do not define academic performance, this is important when considering exactly what aspects of ‘academic performance’ emotional

8 intelligence and facial expression recognition may be associated with. What the authors mean by academic performance needs clarification and would assist in the discussion of results.
Author response
Academic performance was defined as percentage mark attained across subject or subjects. The content of the measurement within each subject differed i.e. OSCE, OSPE, written components.
“Academic performance was defined as the final mark (%) obtained in each of the clinical subjects”
Comment 3
There is an aim described within the introduction, but no specific research questions are asked.
Author response
We have addressed the reviewer’s concern and included specific objectives in the introduction.
“Specific objectives included 1) to assess associations between blended and dynamic facial expression recognition measures; 2) to assess associations between facial expression recognition measures on both tasks and academic performance measures based on gender; and 3) to measure the strength of significant associations between facial expression recognition and academic performance measures. We hypothesize that there will be an association between specific facial recognition abilities and academic performance measures and that gender would be an effect modifier in the association.”
Comment 4
The final sentence of the introduction should perhaps be part of methods unless it is deemed a vital part of understanding the research questions, and if this is the case this point should be expanded so that the reader understands this link.

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Author response
This sentence is not a vital part of the research questions and we have now moved this sentence to the methods section under facial expression recognition .
Comment 5
How were participants recruited into the facial expression recognition component of the study
(this will assist the reader in determining bias)?
Author response
Changes were made to the description of patient recruitment. Please see comment 7 above.
Comment 6
Was this test carried out in isolation or were participants able to confer?
Author response
Participants could not confer. We adjusted the description of the test procedure.
“Participants (n = 237) completed two computerized tasks assessing facial expression recognition. Students completed the tests in isolation from other students and therefore they could not confer. We used two tasks that have been shown to be sensitive to individual differences in different aspects of emotion processing, including the ability to detect subtle variations in dynamic facial expressions [23] and the ability to label emotions expressed in blended facial expressions [24].”
Comment 7
More information on the content of the assessments for each subject area are required, without this the results are frankly meaningless as the reader does not know what the participants were tested on and therefore what facial expression recognition skills are predicting.
Author response
We introduced a description of the relevant subjects’ assessment methods.

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“The latter consisted of written, oral, and objective structured clinical examination marks combined with the class mark, each with a different structure for the various final year modules for example: Psychiatry utilizes an oral exit examination, Urology utilizes a written examination paper and three clinical case-based orals, Obstetrics and Gynecology utilizes a 8
OSCE and 4 OSPE station design and Anesthesiology uses an OSCE design.”
Comment 8
For the animation test why were only 3 facial expressions tested, compared to the 6 in the hexagon task? The authors need to justify this.
Author response
This study utilized a tried and tested stimulus set with proven sensitivity to individual variations (instead of creating a new set ourselves). The set we used was taken from a previous study (Niedenthal et al., cited in the manuscript) that included only three expressions (validated stimuli for other expressions were not available).
Comment 9
Why was the correlation level of above 0.2 selected for regression analysis, again this needs justification?
Author response
We have now clarified this under data analysis and provided a more detailed section on the statistical analysis of data .
“Data analysis
Descriptive analyses were done for the academic performance indicators and facial expression recognition measures. Individual facial expression recognition measures of the hexagon task were correlated with those of the animation task using Pearson correlation.
Facial expression recognition measures on both tasks were also individually correlated with

11 the academic performance indicators using Pearson correlation. The significance level for the correlations were adjusted to accommodate the many associations of interest; the threshold pvalue was set as p=0.0002 for the hexagon tasks (n=237) and as p=0.002 for the animation tasks (n=215). For those passing this threshold, mixed model linear regressions was performed to measure the strength of the associations, adjusting for the covariates gender and age. The year effect was incorporated into the model as a random effect, since we were not interested in this effect per se. Loess analysis was first completed to investigate the appearance of the associations, which showed non-linear relationships with the hexagon measures, and were therefor categorized into quartiles for further analysis. “
Comment 10
Were year differences examined? The authors need to state whether this was the case (and that there were no differences) to justify merging the data.
Author response
Data analysis did not yield any year differences.
Comment 11
It was not clear why the animation task / happy / Obstetrics association was not examined further in regression analysis, can the authors clarify this?
Author response
We have now added the following:
“ In the mixed model of perceptual discrimination of happiness and obstetric examination scores ( quartiles) the overall test is not significant (p=0.273).”
Comment 12
I find the authors approach to this topic, and therefore their discussion confusing. The authors state that they are examining the association between facial expression recognition and

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‘academic performance’ however, the analysis of their results reflects a distinction between the context of student performance (e.g. obstetrics) rather than the content which you would presume they meant by ‘academic performance’. This confusion afflicts the introduction where no mention is made of any evidence relating to facial expression recognition being associated with varying performance in different contexts, the methods (no mention of the assessment methods), results
(because analysis is by specialty) and the discussion. This conflict needs to be acknowledged (so the results are discussed in this perspective and different literature is introduced) or resolved (by examining the content rather than the context).
Author response
Thank you for highlighting this. We have made a number of changes to the introduction, method and discussion to address this. Please see previous comments 1-3 and 7.
Thank you for reviewing this paper.
Prof. DJH Niehaus