Testosterone and Its Influence on Emotion Recognition in Young, Healthy Males
2018

Stefanie Rukavina
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Frauke Sachsenweger #Lucia Jerg-Bretzke
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Andreas Ewald Daucher
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Harald C Traue
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Steffen Walter
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Holger Hoffmann
Department of Psychosomatic Medicine and Psychotherapy, Medical Psychology
Ulm University
1814Psychology, UlmGermany

Testosterone and Its Influence on Emotion Recognition in Young, Healthy Males

Psychology
9201810.4236/psych.2018.97106http://www.scirp.org/journal/psych ISSN Online: 2152-7199 ISSN Print: 2152-7180TestosteroneEmotion RecognitionEmotionsSocial StatusDominance
Emotions are an integral part of our everyday lives and our social interactions. An ability to recognize emotions in the faces of others is essential in order to understand these emotional facets of interaction with others. Although Ekman describes the characteristics of basic emotions as universal, discrete and biologically based, there are a number of additional parameters that are assumed to influence the process of emotional recognition and interpretation (e.g. sex hormones). In our study, we investigated the influence of endogenous salivary testosterone on emotion recognition in n = 40 young, healthy males, using the Facially Expressed Emotion Labeling (FEEL) to measure their accuracy in recognizing the six basic emotions. Each emotion was presented at two different intensities (50% and 100%) in a randomized order. A Spearman correlation analysis revealed a negative correlation between testosterone and emotion recognition for disgust shown at an intensity of 50%. A subsequent median comparison (high vs. low testosterone) substantiated this difference and showed an additional trend of a lower ability to recognize fear (50% intensity) and a lower overall recognition rate for all emotions shown at 100% intensity. These subtle differences are discussed below in terms of dominance and status.

Introduction

Recognizing emotions in others is an important aspect of our daily communication and therefore of our emotional competence. Emotions are multidimensional phenomena and, by definition, lead to changes on different levels (Scherer, Psychology 2005;Traue & Kessler, 2003): there is an appraisal process at the cognitive level, accompanied by a subjective feeling. Almost in parallel to these, there are measurable physiological changes within the central and autonomic nervous systems as well as different behavioral responses such as facial expressions.

Most researchers use photographs to measure and understand the ability to decode emotions via nonverbal signals and interpret their results based on the basic emotions defined by Ekman (Ekman, 1992). The definition of these emotions is based on the fundamental idea that emotions are discrete and have evolved biologically (through adaptation to our environments) which in turn makes them universal (i.e. they are expressed and identified in every culture) (Ekman, 1992;Ekman & Cordaro, 2011).

If there is a biological basis for understanding emotions and decoding facial signals, it is worthwhile to look for possible mediators and the responsible regions within the central nervous system, especially the limbic system as the amygdala (Kreibig, 2010;Murphy et al., 2003). These areas of the brain are known to be influenced in particular by sex hormones such as estrogen, progesterone and testosterone (Ter Horst, 2010). A number of studies have been published about testosterone and emotion processing. Many of these were performed using female subjects who received a sublingual application of exogenous testosterone, reporting a lower recognition rate for angry expressions (van Honk & Schutter, 2007) or poorer accuracy rates in the "Reading the Mind in the Eyes" test (Bos et al., 2016). Goetz and colleagues found a positive correlation in men between the endogenous testosterone concentration and the reactivity of the amygdala, the hypothalamus and the periaqueductal grey to angry facial expressions (Goetz et al., 2014). Ackerman and colleagues even report higher amygdala activity in men with higher endogenous testosterone concentration while viewing neutral pictures (Ackermann et al., 2012).

Whether circulating testosterone levels affect sensitivity in terms of emotion recognition remains unknown. Therefore, the aim of our study was to investigate the influence of endogenous testosterone in males on the ability to recognize emotions using the FEEL test (Kessler et al., 2002) with picture material from the JACFEE dataset (Matsumoto & Ekman, 1988) presenting the emotions at intensities of 50% and 100%. Based on the literature reported above, we hypothesized that: 1) Testosterone impacts emotion recognition accuracy leading to a negative correlation between testosterone and emotion recognition accuracy.

2) If testosterone had an impact on emotion recognition, we expected it to be more prominent in the pictures at 50% intensity because the task of emotion recognition is more difficult and the variance higher in these cases.

In order to test these hypotheses the following methods were conducted.

Material and Methods

Sample

A total of 40 right-handed, healthy males aged 20 -30 years (mean = 24.1 years; Psychology SD = 2.6 years; see System (Lang et al., 1997;Lang et al., 2005), which was not part of the analysis for the present study, see Figure 1.

All participants were instructed not to eat or drink for 30 minutes prior to the experiment to prevent any salivary contamination. After a short introduction to the lab, all participants filled out several questionnaires (TAS-20 Scale (Bach et al., 1996), BSRI (Bem, 1974), Empathizing/Systemizing (Samson & Huber, 2010), TIPI (Muck et al., 2007)) for further analyses. Because we used only the TAS-20 scale for this part of the study (see below), the other three personality and gender role questionnaires will not be further explained here. The German paper version of the TAS-20 scale (Toronto Alexithymia Scale-20) was used to check whether any of our "healthy" male sample demonstrated signs of alexithymia, which typically include a deficiency in recognizing and describing emotions, and externally-oriented thinking (Kessler et al., 2006). Based on the TAS Scale, we determined that 4 males from among our sample had alexithymia (see results for further details).

The endogenous testosterone concentration was measured 3 times via salivary samples, see Figure 1. This method of measurement was chosen in order to prevent a "falsely" high or low measurement (directly after or prior to a secretion pulse), since testosterone is secreted in a pulsatile manner. Over time, this collection method enabled us to measure the testosterone level as a mean value for a period of about 2 hours rather than a single instance during the experiment.

The measurements will be discussed in Section 4.)

All subjects were instructed to fill a small vial three times with a collection sample of approx. 0.5 ml each time, using a small straw to induce the saliva flow.

The vials were stored in a refrigerator after each measurement. All samples were analyzed by SwissHealthMed, Feldkirchen (Germany) using the enzyme-linked immunoassay DEMEDITEC (Testosterone free in Saliva ELISA) and repeated measurements were performed (2 internal controls). The lowest detectable level of testosterone that can be distinguished from zero is 2.2 pg/ml at a confidence interval of 2 SD. (Denoted intra-assay coefficients of variation 5.6% -9.7%, Psychology  Figure 1. The ERECT study procedure: Every 30 min. a salivary sample of 0.5 ml was collected, resulting in a total saliva sample of 1.5 ml. (Since the questionnaires were not part of our analysis, they will not be explained in further detail).

inter-assay variation: 7.0% -8.0%; determined by 20 replicate measurements of 3 saliva samples and duplicate measurements of saliva samples over 10 days). Because testosterone concentration is subject to a diurnal rhythm (high in the morning, low in the evening) (Bribiescas & Hill, 2010), we randomized the participants into 2 groups, inviting them to participate in the experiment at 8:00 AM; 10:00 AM; 2:00 PM or 4:00 PM (20 subjects in the morning and 20 in the evening) to ensure a high variance of the testosterone concentration.

FEEL-Test

After the first salivary collection and the completion of the questionnaires, the subjects were brought to the computer for the FEEL (Facially Expressed Emotion Labeling) test. This computer-based test was first published in 2002 by Kessler and colleagues (Kessler et al., 2002) and measures recognition accuracy Psychology for the six basic emotions defined by Ekman: anger, fear, sadness, disgust, surprise and happiness. In every round, one picture of a facial expression is presented to the subject, who must decide which of the six categories it depicts (forced choice paradigm). The picture material consisted of the JACFEE (Japanese and Caucasian Facial Expressions of Emotions) dataset (Matsumoto et al., 1988) and was presented in a randomized order to prevent sequential effects.

All participants read the instructions given on the screen and clicked "Okay" to proceed. The first six rounds were part of a trial run, giving participants the chance to ask questions about the test and the forced choice screen. Afterwards, the experimenter left the room and the participant started the test. A brief sound was played when each fixation cross (see Figure 2) was displayed to increase the focus of the viewer on the screen. After a short pause, during which a blank screen was displayed for 250 ms, one of the stimuli was presented for 1000 ms. The user was then given 10 seconds to assign the picture to a category. Then, the next fixation cross with sound was presented and so forth. Each FEEL session took about 15 -20 minutes and every subject was shown his individual accuracy rates for each emotion and his overall accuracy rate on the screen (96 stimuli = 96 points = 100%). The FEEL results were exported as. xls files and prepared for statistical analysis using SPSS v.23, see Section 2.3.

Statistical Analysis

We used various statistical methods to analyze our data and our hypotheses, , the calculations for each of which were performed using IBM SPSS Statistics v 23 (IBM, Inc., Armonk, NY, USA). The effect sizes in each case were determined using G*Power 126.96.36.199 software (Faul et al., 2007).

At first, we used a general linear model because of the structure of our data.

The within-subject factor was emotion (anger, surprise, sadness, happiness, disgust, fear), the predictor was intensity and the covariate was testosterone. For the Spearman's correlation, we used the means (SD) of the emotion accuracy scores as well as the testosterone concentration.

Finally, we conducted two Mann-Whitney U tests: One to look for significant differences between both subgroups (median split) for age, emotion regulation and alexithymia scales and one to look for the influence of testosterone on emotion recognition ability.

Because we performed multiple tests (testosterone and emotion intensity as the independent variable), we used Bonferroni corrections to adjust the significance p for the purposes of the specific analysis.

In the case of the correlational analysis we adjusted the significance level to (7 × 2): p = 0.003, which remained the same for the Median Split analysis. In the case of values above the Bonferroni-corrected significance level, we decided to still report the results with the corresponding effect sizes and the statistical power achieved (Nakagawa, 2004).

Results

Although all subjects followed the instructions they were given, the salivary sample of one subject was contaminated and no valid testosterone analysis could be conducted. Accordingly, this person was excluded from further analyses. All results presented here were derived from a sample of n = 39 subjects. The data of height and weight was used to calculate the body mass index, see Table 1. (Unfortunately, the data of two persons are missing).

As we requested a high variance of testosterone concentrations in total, we conducted our experiments both in the morning and in the afternoon. Comparing both median split groups revealed no significant differences between both subgroups for age, personality scales, emotion regulation and alexithymia scales when measured with a Mann-Whitney U test.

The 4 subjects who were found to have alexithymia were not excluded from the final analysis because the TAS scale mean of 45.05 (SD = 9.76) was comparable to those of other studies (Nyklıček & Vingerhoets, 2000). A second analysis Psychology showed that their exclusion from the sample did not change the results in the end.

Emotion Recognition

All participants achieved an overall (for all emotions) accuracy rate of 77.03%.

Accuracy rates differed for each emotion and among intensities. As expected, the accuracy rates for the 50% intensity pictures were significantly lower than those for the full-blown expressions (with the exception of disgust), see Figure 3. We calculated a general linear model with respect to the structure of our data. The analyses revealed that the intensity had a substantial influence on both the rec-

Testosterone and Emotion Recognition

The mean testosterone concentration of all participants was 194.2 pg/ml, which is comparable to those of other studies measuring the free testosterone with salivary samples (Smith et al., 2013). We used the same general linear method described above to look for an influence of testosterone on the recognition rates with different intensities. The model revealed no significant influence of testosterone on emotion recognition accuracy (Waldχ 2 (1, N = 39) = 0.373, p = 0.541).

In order to look further for a potential influence of testosterone on the accuracy rate, we pursued two additional analyses: a correlation was conducted with testosterone and the emotion recognition and secondly, a Mann-Whitney U test was used to get more information using a median split sample group (low and Figure 3. Emotion recognition rates (mean and SD) for 50% and 100% intensity. *p ≤ 0.05. Psychology high testosterone group (low median group: n = 20; mean concentration = 135.71 pg/ml; high median group = 19; mean concentration = 255.84 pg/ml)), see below.

The Spearman's Rho correlation between the testosterone concentration and all emotions displayed at intensities of 50% and 100% revealed one negative correlation: With increasing testosterone levels, the emotion recognition rate for disgust displayed at 50% intensity decreased (r = −0.395; p = 0.013; d = 0.6), see Figure 4. This difference, however, missed the corrected significance level. Additionally, we found no significant correlation between testosterone and any other emotions or the total score for all emotions.

To substantiate the correlation outlined above, we compared two groups from our sample by means of the Mann-Whitney U-Test using a median split, which gave rise to a subsample of subjects with low testosterone levels and one containing subjects with high testosterone levels (see Table 1). Our results revealed few differences between both groups and also substantiated the correlational association, (see Figure 5(b)) for 50% and Figure 5(a)) for 100% intensity).

Sadness shown at full intensity (Z = −2.11; p = 0.05, d cohen = 0.55) was recognized more poorly by males with high testosterone concentrations as a statistical trend, and the overall recognition rate was also weak in this group (Z = −2.16; p = 0.03; d cohen = 0.35). In addition, we found higher recognition rates for fear at subtle intensities (Z = −1.84; p = 0.07; d cohen = 0.61) and disgust (Z = −2.21; p = 0.028; d cohen = 0.76) in males with lower testosterone levels. Due to the Bonferroni correction, none of the results presented above can be considered significant, however we are reporting them as statistical trends, as the effect sizes are medium.

In conclusion, our results show that with increasing testosterone levels, men tend to show lower emotion recognition rates for full-blown emotions in general  . Emotion recognition rates (mean and SD) for the median split sample with T low = low testosterone and T high = high testosterone for (a) 100% intensity and (b) 50% intensity. ++ p ≤ 0.05; + p ≤ 0.1. and especially for sadness. Furthermore, men with high testosterone concentration show an impaired ability to recognize disgust, albeit on a subtle level.

Discussion

Emotion recognition plays a crucial part in all our communications as it enables us to react adequately to our conversation partners and their emotions. Humans use facial expressions in particular to transmit the so-called basic emotions defined by Ekman (Ekman & Cordaro, 2011). Theoretically, they are presented facially and recognized correctly within practically every culture on this planet. At this point, we would like to emphasize the term "practically" because there are some findings in the literature indicating that specific variables may have an impact on emotion recognition ability (e.g., gender and sex hormones).

The aim of our study was to investigate the influence of the sex hormone testosterone in young males on emotion recognition ability in general, for each ba-Psychology sic emotion and also for emotional facial expressions presented at different intensities (50% vs. 100%). We decided to compare only these two intensities, as 100% is usually used in emotion recognition studies and 50% should enable us to find differences on a subtle level of intensity. Although it would have been possible to present 60% -90% intensity material as well (as it was used in our former study (Hoffmann et al.)), we rejected this option because there was a high risk of a learning effect with our design. For example, one participant rates and identifies 100% anger as displayed the first actor and afterwards (randomly selected), the same actor is portrayed displaying 60% anger. This could lead to improved recognition, not because of the intensity but because the participant remembers the face and the emotion when presented at 100%. There was two possible solutions to this problem: a) expand the range of the picture material to include different intensities presented by different actors or b) present only subsets of pictures to a large sample group as it was realized in (Hoffmann et al., 2010). Due to financial restrictions and our specific interest in hormonal measurements we decided to use only 50% and 100% with the current design.

Our results only partially confirmed our hypothesis that emotion recognition ability decreases with increasing testosterone levels: In particular, the generally decreased correlation we had hypothesized only held true for disgust presented at an intensity of 50%. Although we calculated the correlation analysis with our sample size of n = 39, the correlational factor of r = 0.39 describes a mid-sized effect of d = 0.61 calculated according to Sedlmeier & Renkewitz (Sedlmeier & Renkewitz, 2013). Additional median split comparisons revealed further statistical trends: the total recognition rate for emotions shown at 100% intensity in general, sadness shown at 100% and disgust and fear shown at 50% intensity is lower in males with high testosterone levels but not on a Bonferroni-corrected level. The effect size, however, indicates that testosterone has an observable negative influence on the recognition of specific emotions. We do know that median splits are sometimes criticized (Irwin & McClelland, 2003) but a number of researchers legitimize their use as long as the independent variables do not correlate (Iacobucci et al., 2015). Because this was not the case in our study, we conducted the median split and analyzed our data with respect to this dichotomization and with respect to Bonferroni corrections.

Although our results substantiate the impression given in the literature, namely that testosterone impairs emotion recognition, they do not confirm the thesis that it makes humans or males less empathic and more antisocial. This feeling emerges when reading the literature about testosterone and behavior. For example, females perform more poorly at recognizing angry facial expressions after an exogenous testosterone administration (van Honk & Schutter, 2007). Van Honk and Schutter report a reduced sensitivity to threat signals shown in faces (fear, disgust and anger). However, their study only revealed significant results for anger and not fear or disgust, which had also been expected and defined as threat signals. Interestingly, the latter emotions are the ones for which we found differences in the median comparisons in this study. Psychology How can this be so? It is not yet possible to answer this question adequately. This is because there is one main difficulty when it comes to comparing the two studies, namely that the Van Honk and Schutter study was conducted with females and using a one-off injection of exogenous testosterone. Our results are not comparable with theirs because we used the endogenous testosterone levels of young males instead. Derntl and colleagues (Derntl et al., 2009) report a positive correlation between amygdala reactivity and testosterone during the processing of fearful and angry faces. However, the design of this study (two-alternative responses) also limits a comparison between theirs and ours.

There is, however, one other explanation for these results, according to Mazur and colleagues' interpretation: Testosterone makes humans more dominant (Mazur & Booth, 1998), which is important when attempting to secure your social status. We found a decrease in the emotion detection accuracy rates for sadness and subtle disgust in males with high testosterone concentrations. Both emotions are crucial for specific nuances of communication and especially when one conversation partner wants to be perceived as dominant. For example, showing disgust to a communication partner can indicate disrespect for the words spoken or the person speaking them. This nonverbal communication would not be perceived by the dominant person because his sensitivity to this emotion is decreased. This in turn leads to a feeling of dominance and perhaps also to dominant behavior. The same would apply to the emotion of contempt. Although both emotions (disgust and contempt) are sometimes confused, according to Ekman, they are specific and distinguishable (Ekman, 2003). As we did not measure the emotion category of contempt in our study, it would be interesting to take it into account as well, especially as contempt is an emotion that is based on a feeling of being superior to one's communication partner and is therefore important for the feeling of dominance and social status. The same is true of sadness. Since this emotion is a non-threat signal according to van Honk and Schutter, it might be not necessary for status-seeking males with high testosterone levels to recognize it. A reciprocal association between sadness, tears and testosterone was shown in a 2011 study published in Science (Gelstein et al., 2011), which reported a measurable decrease in testosterone in males after sniffing female tears. However, this decrease was interpreted as being induced by chemosignals. To the best of our knowledge, no other study has reported a decreased emotion recognition rate for sadness connected with testosterone. However, it would be interesting for future studies to try to replicate this finding.

Finally, we discovered a trend indicating a lower recognition rate for subtle fear in males with high testosterone levels. In contrast to disgust and sadness, fear is an emotion that provokes helping behaviors in the observer. This emotion was also defined as a threat stimulus by van Honk and Schutter (van Honk & Schutter, 2007). They also report a declining sensitivity to these signals with increasing testosterone levels, which can be interpreted as indicating that recognition of such signals is not necessary for males wishing to retain their status. Psychology

However, at this point we think it is important not to draw conclusions that are too general. Our results can be interpreted as indicating that the decreased emotion recognition ability is influenced by testosterone, but there must be additional moderating variables (e.g., psychometric variables or other hormones).

Therefore, we would like to encourage future studies to include additional hormonal measurements to reveal the effect of hormones on the human processing of emotions as they play this important role in our everyday lives. There are two additional facts we would like to address here. Of course, we are aware of the advantages and disadvantages of the salivary testosterone measurement method we used for this study. Although we measured testosterone levels three times and believe we have a valid mean value for the testosterone concentration for the duration of the tests, we do not really know the long-term testosterone concentration of our sample. As we believe that the long-term concentration has a more significant effect on emotional skills, we propose that this effect be analyzed over a longer period. As a further comment concerning the measurement method, one could argue that due to a high anticipatory baseline, testosterone levels decrease during an experiment. Nonetheless, we rate our measurement as valid because we obtained relatively stable values. Therefore, we consider this an adequate way of measuring the endogenous testosterone level.

Secondly, it is important to note that in our study, there was no "emotion not identified" option in the FEEL test. This would have given us the opportunity to differentiate better between the recognition accuracies for all emotions.

Conclusion

In conclusion, the results of our study indicate a decreasing ability in emotion recognition with increasing endogenous testosterone level in young healthy males. This was especially true for disgust presented with an intensity of 50% and sadness presented with an intensity of 100%. Further studies should consider this impact of testosterone on emotion recognition and go more into detail to get an idea of how endocrinological parameters can modulate behavior and empathy.

Figure 2 .
2FEEL test: Presentation order (top) and an enlarged view of the decision screen on the (below). Psychology

ognition rate (Waldχ 2 (1, N = 39) = 39.011, p < 0.001 and the perceived intensity of the stimulus material ((Waldχ 2 (3, N = 39) = 26.224, p < 0.001). Post-hoc analyses revealed significant decreases for all emotions besides disgust, seeFigure 3.The means calculated are comparable to those of other studies using the FEEL Test with a healthy sample and utilizing different emotional intensities(Hoffmann et al., 2010).

Figure 4 .
4Spearman correlation between disgust 50% and salivary testosterone.S.Rukavina et al.  Psychology

Figure 5
5Figure 5. Emotion recognition rates (mean and SD) for the median split sample with T low = low testosterone and T high = high tes-

Table 1 )
1were recruited for our study by means of posters distributed throughout the Ulm University campus. At the end of the testing sessions, all participants were reimbursed for their time either with 10 € or 2 participation-hours (necessary for psychology students). The study was designed in accordance with the ethical guidelines set out in the WMA Declaration ofHelsinki and approved (#245/08-UBB/se) by the ethics committee of the Univer-

sity of Ulm (Helmholtzstraße 20, 89081 Ulm, Germany). Each experiment was

undertaken with the understanding and written consent of each participant. The

requirements for participation were that subjects must have no former psychia-

tric diseases, must not be taking hormonal preparations, must not be engaging

in extreme body workouts (such as bodybuilding), show no obesity and must be

heterosexual. The last point was necessary for a second part of our study, an

emotion induction task using pictures from the International Affective Picture

Table 1 .
1Physiological and psychometric data.Age
Height
Weight
BMI
Mean Testosterone
concentration [pg/ml]
Median Group/Testosterone

24.1 (2.6) 1.79 m 74.6 kg*
23.1
kg/m 2
194.24 (88.38)

low
135.71

high
255.84

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Anatomy of open access publishing: a study of longitudinal development and internal structure

Mikael Laakso
Bo-Christer Björk
Anatomy of open access publishing: a study of longitudinal development and internal structure
http://www.biomedcentral.com/1741-7015/10/124 :124 R E S E A R C H A R T I C L E Open Access
Background: Open access (OA) is a revolutionary way of providing

Background

Open access (OA) has expanded the possibilities for disseminating one's own research and accessing that of others [1,2]. OA, in the context of scholarly publishing, is a term widely used to refer to unrestricted online access to articles published in scholarly journals. There are two distinct ways for scholarly articles to become available OA, either directly provided by the journal publisher (gold OA), or indirectly by being uploaded and made freely available somewhere else on the Web (green OA). Both options increase the potential readership of any article to over a billion individuals with Internet access and indirectly speed up the spread of new research ideas. While the majority of OA journals do not charge authors anything for the services provided, a growing minority of professionally operating journals charge authors fees ranging from 20 to 3800 USD, with an estimated average of 900 USD [3].

OA is closely related to developments in other media content delivery businesses, and its ethos is well aligned with the fundamental openness principle of science itself as well as the ideologies behind Wikipedia and open source software. However, what makes scientific publishing distinct is the influence journal prestige and rankings have on journal selection for authors submitting article manuscripts [4]. There are also vested interests to preserve the status quo of the current subscription market among stakeholders, with dominant publishers seeing OA as a potential threat to the bottom-line. Friction caused by these and other factors can be argued to slow down the process of OA adoption because journals are not direct substitutes for each other and subscriptionbased journal copyright agreements can prohibit parallel distribution of published content. However, following in the footsteps of the National Institutes of Health in the US, public research funders in the UK have recently launched strategies to increase OA to publicly funded research [5]. While the ultimate goal of increasing access to publicly funded research is known and widely accepted it is difficult to reach compromises that balance the longand short-term interests of the stakeholders involved [6].

Important changes in policy facilitating growth of OA happen on many levels, influencing research publishing both upstream and downstream. The examples from the public funders in the US and UK are merely the most ambitious movements so far: public and private research funders large and small, universities, publishers and research institutes all contribute to forming the evolving OA landscape. The problem that has persisted with OA since the start is the lack of readily available data for how this particular subset of journal publishing is developing over time, an aspect which is described in closer detail in the Methods section. Policymakers should have an interest in knowing how common OA is today, how fast the share of OA has increased and what proportion of journal articles are currently OA? The purpose of this study is to provide answers to these types of questions.

Aim of the study

This study focuses on providing measurement of the longitudinal development gold OA publication volume for the years 2000 to 2011 as a whole and by subtype: full immediate journal OA, delayed OA and hybrid OA. As will be described in more detail further on, earlier studies have mostly ignored the subset of delayed OA journals. This is partly because there is no comprehensive index of such journals similar to the service the Directory of Open Access Journals (DOAJ) provides for immediate OA journals, and partly because of the divisive acceptance of delayed OA as a valid form of OA. However, the subset of delayed OA journals is both substantial in volume and is populated with many high-quality journals; five of the 10 most-cited journals within Thomson Reuters Web of Knowledge in the period from 1999 to 2009 are currently delayed OA while the others are subscription-access only [7]. Hybrid OA is the term commonly used for describing individual articles being provided openly within subscription-only journals through an optional author payment; it is only recently that this type of OA has been properly studied [8].

The chosen research aim is related to some existing areas of OA research that warrant mention to clarify the specific contribution of this study. Green OA is not part of the scope of this study as that is a wholly different research problem and one that requires its own set of methods, as different versions of articles are scattered around on the Web. Furthermore, this study does not extensively discuss or evaluate the pros or cons of OA, since there is already a well-developed body of literature focusing on issues such as relationships between OA and readership, citation or impact [9][10][11][12]. In summary, the aim is to provide comprehensive and up-to-date quantitative measurement of gold OA journals and articles. The results and data of this study can then potentially act as a foundation for more targeted research enquiries.

Previous studies

Researchers have applied different methods to cope with the lack of readily available quantitative data to study the OA phenomenon, ranging from labor-intensive manual article-counting [13][14][15] to automated Web-crawling [16,17]. What is known about the early years of OA, both gold and green, is mostly through a series of independent studies providing snapshots for individual years based on sampling various publication indexes. The fact that studies have been based around OA prevalence within different publication indexes and the diverse adopted sampling methods makes comparisons or composition of longitudinal development inexact. Nevertheless, these are the best figures currently available. The earliest comprehensive study suggests the 2003 share for gold OA to have been 2.9% for articles included in the Thomson Reuters Web of Knowledge [18]. The next study was performed for the 2006 publication volume based on data from UlrichsWeb [19] and the DOAJ [20], where a gold OA share of 8.1% and a green OA share of 11.3% resulted in a combined OA share of 19.4% [14]. For 2008 articles, the Thomson Reuters Web of Knowledge gold OA share was measured to be 6.6% and green OA 14%, resulting in a figure of total OA of 20.6% [21]. Also for 2008, a largescale study based on English-language journals listed in the DOAJ calculated that 120,000 articles were published OA either through full immediate OA journals or as individual hybrid OA articles [22]. The first comprehensive longitudinal study on the volume of articles published by full immediate OA journals in the DOAJ resulted in an average annual year-on-year growth rate of 30% from 2000 to 2009, with some 191,000 articles published during 2009 [13]. Another longitudinal study, including both gold and green OA, produced a total OA share of 23.1% for Thomson Reuters Web of Knowledge indexed articles published during 2010 [16]. Outside of this 2010 study of Thomson Reuters Web of Knowledge, there are no comprehensive measurements for OA volume since 2009. This study is designed to provide a longitudinal study implementing a well-documented and easily replicable methodology, producing results applicable to multiple publication indexes, producing results that are easy to follow-up and compare with future measurements.

Methods

Sampling

The study is founded on the assumption that the full population of OA journals is listed in the DOAJ. There are OA journals not indexed in this database, but systematically identifying them is not feasible. Because the majority of the 7,372 journals listed in the DOAJ on 1 January 2012 were not included in any indexing service that would reliably keep track of their article output, nor the exact year previously subscription-based journals have converted to OA, gathering data is largely a manual task and one of the major practical challenges for the execution of studies of this type. To strike a balance between feasibility and reliability, stratified random sampling with unequal probabilities was utilized, a sampling method that has proven suitable for similar studies in the past [13]. An argument for adopting this approach in favor of fully random sampling is that the population of OA journals is highly heterogeneous, where a small number of titles output a large proportion of the total article volume [22]. The fact that large journals can be identified with a high degree of certainty through various indexing services also means that reliable, readily available article count information can be used for journals responsible for a major part of the total OA output. A visualization of the sampling is provided in Figure 1A cross-analysis of data available from SCImago [23], Thomson Reuters Web of Knowledge [24] and the DOAJ identified 103 OA journals that had published over 200 articles annually during 2009, 2010 or 2011; these were included in the large journal stratum. The rest of the 7,269 DOAJ journals were represented by a second stratum with a sample of 684 journals selected at random among them, each given an observation weight of 10.62719 (684 × 10.62719 = 7269). The stratum of large journals was only applied an observation weight of 1 since the population of that stratum is exhaustively sampled.

Data collection

Through a previous study using identical sampling and data collection methodology [13], data for 565 journals spanning publication volumes for 2000 to 2009 could be re-used, with only the need to gather publication volumes for two additional years. Since the existing data material lacked coverage for journals added to the DOAJ during 2010 and 2011, an additional randomly selected sample was drawn out of the journals added within the two missing years adhering to the same sampling probability as the pre-existing sample (0.1011), with 222 new journals added to the existing sample of 565 journals.

Where journal publication volumes could be retrieved from either SCImago or Thomson Reuters Web of Knowledge, such data was used. For the majority of journals, the individual journal websites were visited and the annual entries collected manually. It is worthwhile to note that journals often include editorials, news, book reviews, obituaries and other non-research content. Such material was excluded from all measurements in this study. To provide an accurate representation of retrospective OA volume, articles were not collected for subscription-only journals prior to publishing OA. Determining when a journal has initiated OA publishing often requires manual investigation as the information is not always made explicit on the webpages, and the data concerning this is often incorrect in the journal metadata available in the DOAJ. To support the analysis of the sampled journals, additional data from Scopus [25] and Thomson Reuters Web of Knowledge was utilized in addition to the data that is already available through the DOAJ.

Results

The longitudinal development of full immediate OA article volume spanning 2000 to 2011 is presented visually in Figure 2 and numerically in Table 1, where a breakdown of the total volume is provided for articles split into three different categories: online-only journals that require an article-processing charge, online-only journals that do not require an article-processing charge, and journals that still output print versions for subscribers but have all articles available OA online. It is important to point out that journals still producing a print version might also require an article-processing charge in addition to having income from subscriptions. However, such differentiation is not provided here due to the relative rarity of such journals as well as a desire to focus on these three mutually exclusive business models specifically.

Overall there has been growth in the annual output among all three categories since the year 2000, going from a total volume of 20,700 articles in 2000 to 340,000 in 2011. Not depicted in Figure 2 but provided in Table 1 is the number of active OA journals for each respective year (journals with at least one article published during the respective year), which has increased from 744 journals in the year 2000 to 6,713 in 2011. The average number of articles per journal has also seen a constant increase, with an average of 26 articles per journal in 2000, 33 in 2005, and 51 for 2011. However, a reminder about the skewed nature of article distribution among journals is relevant here. There is a handful of journals publishing more than 1,000 articles per year and thousands of journals publishing only a few articles annually.

Inspecting the internal structure of the total article mass reveals some major shifts that have happened over the course of a decade. Journals that also publish a parallel print version, which are often old, established journals that decided to make the online version free when they started putting their content on the Web, provided the majority of the OA content up until the year 2008 where, for the first time, online-only journals took the lead in terms of output volume. Since 2008, the onlineonly journals have sustained a much stronger growth while the OA output provided by journals outputting a print version has plateaued to annual volumes between 100,000 and 110,000 articles. The latter group includes a lot of society journals registered with dedicated portals like SciELO [26], Redalyc [27] and J-Stage [28] providing the technical platform for electronic publishing. Journals with author-processing charges have seen breakout growth during the last three years, going from 80,700 articles in 2009 to 166,700 articles in 2011.

Cross-analysis of the sample with the titles listed in Thomson Reuters Web of Knowledge index and Elsevier's Scopus index was performed, only including the titles present in the respective index to calculate the share of OA articles of all peer-reviewed articles. Table 2 provides the main results of this analysis, presented as longitudinal breakdowns of publisher-provided OA in the two indexing services. Nearly half of all full immediate OA articles published during 2011 were outside of Scopus and two thirds outside of Thomson Reuters Web of Knowledge, meaning that a large portion of article OA article volume lacks coverage in major publication indexes. This issue highlights the importance of using manual data collection methods in OA studies because data available from indexes only provide part of the total picture. In addition to the results concerning full immediate OA journals, Table 2 also contains volume data for two other types of publisher-provided OA in each respective index: delayed OA and hybrid OA.

Of the 1.66 million articles indexed by Scopus in 2011, 11% were published in full immediate OA journals, 0.7% as hybrid OA and 5.2% in journals that have a maximum OA delay of 12 months. Together, these account for almost 17% of the total article volume in the whole index. The figures for articles indexed by Thomson Reuters Web of Knowledge are comparable to those of Scopus, with a total publisher-provided OA rate of 16.2% for 2011. Of the 1.29 million articles indexed by Thomson Reuters Web of Knowledge, 7.9% are available in full immediate OA journals, 0.7% as hybrid OA and 6.4% in journals that have a maximum OA delay of 12 months. Overall the results suggest that there has been an increase of about one percentage point annually in relative OA volume in both Scopus and Thomson Reuters Web of Knowledge during 2008 to 2011. Figure 3 presents the longitudinal development of OA publisher output as measured by the number of articles output by publishers based in different regions of the world. This figure, and all that follow, only includes full immediate OA journals, excluding delayed and hybrid OA. Prior to interpretation it needs to be noted that this is a publisher-centric analysis. In some cases, the publisher is not registered within the same country, or even region of the world, as the journal. The results suggest that Latin American countries were early to have substantial OA  output, possibly due to the early availability of the SciELO portal. However, the region has not increased its output at a similar rate as North America, Asia or Europe, who have multiplied their outputs between 2005 and 2011. Figure 4 presents the total OA article volume for 2000, 2005 and 2011 split according to publisher type. The analysis shows that the early years of OA publishing were largely driven by scientific societies, professional associations, universities and their departments as well as individual scientists. Scientific societies and universities have maintained strong growth throughout the decade, while scientist-driven publication has been overshadowed by the article volume produced by the more formally organized publisher types. The most dramatic development since 2005 is the rapid increase in articles published by commercial publishers, jumping from 13,400 articles in 2005 to 119,900 in 2011, resulting in commercial publishers currently being the most common publisher of OA articles. The category of professional non-commercial publishers is a new type of publisher that has rapidly  emerged during the last few years, largely attributed to the journals published by the Public Library of Science. Figure 5 presents the OA article volumes for the years 2000, 2005 and 2011 split across the major scientific disciplines, with an additional category for general science journals. Throughout the decade, articles in journals broadly related to biomedicine have held the lead in terms of article volume, and since 2005 the gap to the other disciplines has been further extended. Biomedical journals published 120,900 articles in 2011, constituting 35.5% of the total OA article output for the year. In second place in terms of volume for 2011 is the social sciences and humanities, almost tied with earth and environmental sciences in third place, publishing 56,000 and 54,900 articles respectively. Coming in fourth place in terms of size is engineering, which is the discipline that has seen the most dramatic relative growth between 2005 and 2011, from publishing only 4,800 articles in 2005 to 37,500 articles in 2011. In fifth place for 2011 is physics and astronomy with 16,000 articles; however, previous studies have shown there to be particularly strong practice and supporting infrastructure for parallel publication within this discipline, potentially lessening the demand for OA journals [21]. Chemistry and chemical engineering is sixth in terms of size with 12,700 articles in 2011, followed by general science journals and mathematics at the tail end with 12,600 and 7,200 articles respectively. The category of general science  journals is a relatively new one with only marginal volume until recently. Journals belonging to this category have little or no limitations with regards to research subject or scope. Though it could be argued that PLOS ONE is a general science journal, the vast majority of actual articles published so far have been within the scope of biomedicine, thus that specific journal was placed within the biomedicine category for this coarse disciplinary breakdown.

Discussion

Over the course of the last decade, OA journal publishing has grown universally across diverse types of journal publishers, geographical regions and scientific disciplines. This has resulted in a continuously growing proportion of journal articles being published OA for each year that has passed, with the most recent measurement from this study being 17% when delayed OA articles with a maximum embargo of 12 months are included. However, despite all the studied dimensions showing increases in annual article output over the decade, the results of the study show that growth has not been uniform across the board. OA publishing seems to be in a very dynamic growth phase, with major shifts in the internal composition happening in a relatively short span of time.

A major strength of the study is associated with the labor-intensive manual approach to data collection, where the annual article volumes for each journal included in the sample was registered for the years 2000 to 2011. This approach reduces the risk of using incorrect, skewed or incomplete source data. The methodological transparency should also enable others to produce comparable numbers to follow-up and compare with the measurements provided here. What can be held as a weakness is the reliance on sampling rather than complete population coverage, however, such an approach is not feasible with the indexing tools currently available and manually collecting the data for over 7,000 journals is a very labor-intensive task.

In comparison with existing studies, this is not only the first study to provide comprehensive gold OA measurement for 2010 and 2011, but the results for the earlier years studied are also more accurate and representative of the actual volumes published at the time. The previous directly comparable study suggested that 191,000 articles were published by full immediate OA journals during 2009 [13], whereas this study suggests the volume for the same year to actually be 225,600. The discrepancy in retrospective annual volumes between these two studies, or any other earlier study using data from the DOAJ, is influenced by the time-lag between the time journals actually start publishing OA and the time they get registered to the DOAJ. In part, this is because journals have to submit a request to the DOAJ to be added, meaning that journals rarely are registered from the first issue they publish, if at all. Another issue is the time the DOAJ takes to process new addition requests; as of September 2012 the backlog of journals currently in queue for evaluation is described as being 'huge' on the DOAJ contact page [20]. Exploring this issue more closely through the sampled journals, it appears that over half of the sampled journals added to the DOAJ during 2010 and 2011 had been publishing OA already prior to 2010, with a handful of cases publishing OA for over a decade prior to DOAJ registration. As was noted in the introduction, most other earlier studies have been limited by only looking at specific OA subsets for specific years, and are thus not directly comparable. However, despite this inability to compare our estimates directly with earlier studies because of methodological incompatibilities, all the results nevertheless speak for the notion of a strong longitudinal growth for OA, particularly so for the biomedical research field.

The results, in particular the finding that approximately 17% of scholarly journal articles are already now made openly available on the Web within a year by the publishers, should be an important input for the policy discussions on OA in venues like the US Congress, the European Commission and the UK Finch Committee that recently published its report with OA-guidelines for British research funders [6]. This study also sheds new light on the relative contributions of the two complementary routes for achieving OA, the publisher-provided gold route and the author-provided green route, indicating that the contribution of gold (both immediate and articles withheld for short embargo periods) is much larger than many earlier estimates. The results should also be considered together with two other recent studies [3,9]. These studies suggest that the level of article-processing charges paid is on average around 900 USD, which is lower than generally believed, and that the scientific impact of OA journals founded in the last decade, and in particular in biomedicine, is on par with similar subscription journals, as measured by average number of citations.

It no longer seems to be a question whether OA is a viable alternative to the traditional subscription model for scholarly journal publishing; the question is rather when OA publishing will become the mainstream model. What remains to be seen is whether the growth will continue at a similar rate as measured during last few years, or if it will accelerate to an even steeper part of the S-shaped adoption pattern typical of many innovations [29]. As in many other markets where the Internet has thoroughly rewritten the rules of the game, an interesting question is if new entrants, like Public Library of Science and BioMed Central, will take over the market or if the old established actors, commercial and society publishers with subscription-based revenue models, will be able to adapt their business models and regain the ground they have so far lost. Future studies on the internal structure of OA publishing are likely to witness the anatomy transforming yet again. Most of the major internal shifts in OA journal publishing have only happened fairly recently during the last few years and, judging by the momentum at which things are moving, it is hard to imagine the internal dynamics settling down any time soon.

Authors' contributions ML and B-CB conceived, designed and coordinated the study. ML handled most of the data collection and analysis. Both authors participated equally to interpretation of the results and writing of the manuscript. Both authors have read and approved the final manuscript.

Figure 1
1Visualization of the sampling.

Figure 2
2Annual volumes of articles in full immediate open access journals, split by type of open access journal.

Figure 3
3Open access publisher output across geographic regions.

Figure 4
4Open access publisher type analysis.

Figure 5
5Open access across major scientific disciplines.

Table 1
1Estimated annual article and journal counts in full immediate open access journalsYear
2000 2001 2002 2003 2004
2005
2006
2007
2008
2009
2010
2011

Online-only OA journals (APC) Articles
795 2332 4,936 6,247 7,532 12,143 17,256 25,949 40,689 54,296 90,932 136,264

Journals
53
120
167
189
256
344
425
630
950
1,239
1,494
1,824

Online-only OA journals (no
APC)

Articles
5,445 10,690 13,844 17,238 20,106 26,626 33,067 38,991 46,362 62,521 81,421 93,513

Journals
334
484
613
804 1,006
1,272
1,538
1,793
2,048
2,399
2,548
2,495
Subscription-based print
journals with OA content
online

Articles 14,461 23,095 27,234 39,814 51,614 66,494 75,486 86,691 100,393 108,793 116,003 110,353

Journals
357
550
630
847 1,106
1,375
1,539
1,819
2,011
2,149
2,170
2,395
All OA journals
Articles 20,702 36,117 46,013 63,299 79,253 105,262 125,809 151,630 187,444 225,610 288,357 340,130

Journals
744 1,154 1,410 1,841 2,368
2,991
3,502
4,243
5,010
5,788
6,213
6,713

APC: article-processing charge; OA: open access.

Table 2
2Proportion of publisher-provided (gold) open access in major indexesArticles indexed in Scopus
2008
2009
2010
2011

Total a
1,469,286
1,550,413
1,588,636
1,658,643

In full immediate OA journals b
118,751
133,817
163,670
181,706

Share of articles published in full immediate OA journals
8.1%
8.6%
10.3%
11%

Hybrid OA c
4,718
8,095
10,135
12,089

Delayed OA d
78,054
82,271
81,404
85,714

Total share OA
13.7%
14.5%
16.1%
16.9%

Articles indexed in Web of Knowledge
2008
2009
2010
2011

Total e
1,154,803
1,203,692
1,235,202
1,294,051
In full immediate OA journals b
76,537
85,852
103,514
116,192

Share of articles published in full immediate OA journals
6.6%
7.1%
8.4%
9.0%

Hybrid OA c
3774
6476
8108
9671

Delayed OA d
76,076
80,338
79,058
83,420

Total share OA
13.5%
14.3%
15.4%
16.2%

a

All articles + reviews, retrieved 22 May 2012 through scopus.com; b results of this study; c results of previous research [8]; d results of an ongoing study by the
authors, based on absolute volume calculations for articles in 462 unique journals available through HighWire Press, PubMed Central, Elsevier and so on with an
open access delay of 12 months or less; e all articles + reviews included in the Science Citation Index Expanded, Social Sciences Citation Index, Arts & Humanities
Citation Index, retrieved 22 May 2012 through apps.webofknowledge.com. OA: open access.

http://www.biomedcentral.com/1741-7015/10/124 :124
Authors' information ML is a doctoral student in Information Systems Science at the Hanken School of Economics, Helsinki, Finland. B-CB is professor of Information Systems Science at the Hanken School of Economics, Helsinki, Finland.Competing interestsThe authors declare that they have no competing financial interests. B-CB founded an OA journal in the 1990s and is emeritus Editor-in-Chief. B-CB is a current board member of the Open Access Scholarly Publishers Association.
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Cite this article as: Laakso and Björk: Anatomy of open access publishing: a study of longitudinal development and internal structure. E Rogers, Diffusion of Innovations. New YorkFree Press10Rogers E: Diffusion of Innovations New York: Free Press; 1995. Cite this article as: Laakso and Björk: Anatomy of open access publishing: a study of longitudinal development and internal structure. BMC Medicine 2012 10

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