Geschlechterunterschiede im Gehirn und deren Erkennung

Ich hatte schon Artikel, die sich mit der auch sehr frühen Erkennung von Geschlechterunterschieden direkt am Gehirn, etwa durchs Scans, beschäftigen:

Hier einige weitere interessante Studien in die gleiche Richtung:

1. Brain Differences Between Men and Women: Evidence From Deep Learning

Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as specific brain regions. The entropy measure was applied to the lowest-level image features extracted from the first hidden layer to examine the difference of brain structure complexity between men and women. The obtained results compared with the results from using the Support Vector Machine (SVM) and Tract-Based Spatial Statistics (TBSS). The proposed 3D CNN yielded a better classification result (93.3%) than the SVM (78.2%) on the whole-brain FA images, indicating gender-related differences likely exist in the whole-brain range. Moreover, high classification accuracies are also shown in several specific brain regions including the left precuneus, the left postcentral gyrus, the left cingulate gyrus, the right orbital gyrus of frontal lobe, and the left occipital thalamus in the gray matter, and middle cerebellum peduncle, genu of corpus callosum, the right anterior corona radiata, the right superior corona radiata and the left anterior limb of internal capsule in the while matter. This study provides a new insight into the structure difference between men and women, which highlights the importance of considering sex as a biological variable in brain research.

2. Gender Identification of Human Cortical 3-D Morphology Using Hierarchical Sparsity

Difference exists widely in cognition, behavior and psychopathology between males and females, while the underlying neurobiology is still unclear. As brain structure is the fundament of its function, getting insight into structural brain may help us to better understand the functional mechanism of gender difference. Previous structural studies of gender difference in Magnetic Resonance Imaging (MRI) usually focused on gray matter (GM) concentration and structural connectivity (SC), leaving cortical morphology not characterized properly. In this study a large dataset is used to explore whether cortical three-dimensional (3-D) morphology can offer enough discriminative morphological features to effectively identify gender. Data of all available healthy controls (N = 1113) from the Human Connectome Project (HCP) were utilized. We suggested a multivariate pattern analysis method called Hierarchical Sparse Representation Classifier (HSRC) and got an accuracy of 96.77% for gender identification. Permutation tests were used to testify the reliability of gender discrimination (p < 0.001). Cortical 3-D morphological features within the frontal lobe were found the most important contributors to gender difference of human brain morphology. Moreover, we investigated gender discriminative ability of cortical 3-D morphology in predefined Anatomical Automatic Labeling (AAL) and Resting-State Networks (RSN) templates, and found the superior frontal gyrus the most discriminative in AAL and the default mode network the most discriminative in RSN. Gender difference of surface-based morphology was also discussed. The frontal lobe, as well as the default mode network, was widely reported of gender difference in previous structural and functional MRI studies, which suggested that morphology indeed affect human brain function. Our study indicates that gender can be identified on individual level by using cortical 3-D morphology and offers a new approach for structural MRI research, as well as highlights the importance of gender balance in brain imaging studies.

3. Beyond a Binary Classification of Sex: An Examination of Brain Sex Differentiation, Psychopathology, and Genotype

Sex differences in the brain are traditionally treated as binary. We present new evidence that a continuous measure of sex differentiation of the brain can explain sex differences in psychopathology. The degree of sex-differentiated brain features (ie, features that are more common in one sex) may predispose individuals toward sex-biased psychopathology and may also be influenced by the genome. We hypothesized that individuals with a female-biased differentiation score would have greater female-biased psychopathology (internalizing symptoms, such as anxiety and depression), whereas individuals with a male-biased differentiation score would have greater male-biased psychopathology (externalizing symptoms, such as disruptive behaviors).
Using the Philadelphia Neurodevelopmental Cohort database acquired from database of Genotypes and Phenotypes, we calculated the sex differentiation measure, a continuous data-driven calculation of each individual’s degree of sex-differentiating features extracted from multimodal brain imaging data (magnetic resonance imaging [MRI] /diffusion MRI) from the imaged participants (n = 866, 407 female and 459 male).
In male individuals, higher differentiation scores were correlated with higher levels of externalizing symptoms (r = 0.119, p = .016). The differentiation measure reached genome-wide association study significance (p < 5∗10−8) in male individuals with single nucleotide polymorphisms Chromsome5:rs111161632:RASGEF1C and Chromosome19:rs75918199:GEMIN7, and in female individuals with Chromosome2:rs78372132:PARD3B and Chromosome15:rs73442006:HCN4.
The sex differentiation measure provides an initial topography of quantifying male and female brain features. This demonstration that the sex of the human brain can be conceptualized on a continuum has implications for both the presentation of psychopathology and the relation of the brain with genetic variants that may be associated with brain differentiation.

4. Patterns in the human brain mosaic discriminate males from females

In their PNAS article, Joel et al. (1) demonstrate extensive overlap between the distributions of females and males for many brain characteristics, measured across multiple neuroimaging modalities and datasets. They pose two requirements for categorizing brains into distinct male/female classes: (i) gender differences should appear as dimorphic form differences between male and female brains, and (ii) there should be internal consistency in the degree of “maleness–femaleness” of different elements within a single brain. Based on these criteria, the authors convincingly establish that there is little evidence for this strict sexually dimorphic view of human brains, counter to the popular lay conception of a “male” and “female” brain. This finding has broad implications not only for the ontology of gender, but also for the statistical treatment of sex in morphometric analyses.

Critically, however, the conclusion that human brains cannot be categorized into two distinct classes depends largely on the level of analysis. Although the set of properties that distinguish one category from another is rich and flexible, there is rarely a diagnostic form (e.g., what singular physical characteristic reliably distinguishes cats from dogs?) and there is often substantial within-category variability (e.g., breeds of dogs) (2). The failure of the brain to meet these two requirements does not mean that “human brains cannot be categorized into two distinct classes: male brain/female brain.” In fact, an individual’s biological sex can be classified with extremely high accuracy by considering the brain mosaic as a whole.

To demonstrate this, we acquired T1-weighted structural MRI scans for 1,566 individuals, aged 19–35 y (57.7% female), from the freely available Brain Genomics Superstruct Project (3). Cortical thickness and subcortical volume estimates were calculated using the FreeSurfer automatic segmentation algorithm (v5.3; First, 400 subjects were retained as a held-out validation set. Next, penalized logistic regression [elastic net (4, 5)] was used to predict the sex of each individual based on their mosaic, or pattern, of morphometric brain data. Within the training set (n = 1,166), a regression model was built using three repeats of 10-fold cross-validation. The model was then used, without modification, to predict the sex of each individual in the held-out sample. Classification accuracy was extremely high [accuracy: 93%, 95% confidence interval (CI) 89.5–94.9%, P < 10−16] and remained significant if head-size-related measurements were excluded [92% (CI 88.9–94.5%), P < 10−16] or regressed out [70% (CI 65.0–74.2%), P < 10−6]. To borrow the framing of Joel et al. (1), the human brain may be a mosaic, but it is one with predictable patterns.

Despite the absence of dimorphic differences and lack of internal consistency observed by Joel et al. (1), multivariate analyses of whole-brain patterns in brain morphometry can reliably discriminate sex. These two results are not mutually inconsistent. We wholly agree that a strict dichotomy between male/female brains does not exist, but this does not diminish or negate the importance of considering statistical differences between the sexes (e.g., including sex as a covariate in morphometric analyses).

5 Machine learning of brain gray matter differentiates sex in a large forensic sample

Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans.

However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source-based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.

6. Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases).

„Liebe deine Bücher“ vs „2 Seiten Aufsatz mit weirden Thesen und Fakten“

Stokowski schreibt auf Twitter:

Noch mal als Text:

Wenn Frauen mir schreiben, dann häufig so „sorry will nicht stören, wollte nur sagen, liebe deine bücher, danke dafür &sorry nochmal fürs stören“. Männer gerne: 2 Seiten Aufsatz mit weirden Thesen & Fakten aus Biounterricht vor 35 Jahren & 1 Tag später: „na? keine antwort parat?“

Das fasst eigentlich, die Wertungen rausgenommen, weibliche und männliche Eigenschaften im Schnitt ganz gut zusammen.

  • Frauen in vielen Fällen vorsichtiger, weniger auf Diskussionen ausgerichtet, nicht stören wollen, Gefühle transportieren.
  • Männer mit einer inhaltlichen Beschäftigung mit dem Thema, ein Versuch ihre Position darzustellen, auf eine Diskussion aus, versuchen noch mal eine Reaktion zu provozieren, damit die Sachdiskussion losgeht.

Der Tweet wurde in meine Timeline gespült mit einem Tweet darüber (sinngemäß):

Warum Frauen weniger verdienen als Männer:

Und das passt ja auch durchaus. Die klischeehaft dargestellte Frau stellt Agreeablness weit eher dar als der Mann. Und das ist eben etwas, was sich mit vielen Führungspositionen nicht gut verträgt.


Vorhersage des Geschlechts anhand der Gehirnwellen

In der Zeitschrift „Nature“ wurde ein interessanter Artikel veröffentlicht, in dem es darum geht, ob man das Geschlecht einer Person an den Gehirnwellen erkennen kann. Dazu wurde eine AI auf entsprechende Muster angesetzt und ausgewertet, welche Unterschiede diese fand:

Aus dem Abstract:

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10−5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20–25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology.

Quelle: Predicting sex from brain rhythms with deep learning

Vielleicht zunächst etwas zur Methode, um die es da geht, aus der Wikipedia:

Die Elektroenzephalografie (EEG, von altgriechisch ἐγκέφαλος enképhalos, deutsch ‚Gehirn‘, γράφειν gráphein, deutsch ‚schreiben‘) ist eine Methode der medizinischen Diagnostik und der neurologischen Forschung zur Messung der summierten elektrischen Aktivität des Gehirns durch Aufzeichnung der Spannungsschwankungen an der Kopfoberfläche. Das Elektroenzephalogramm (ebenfalls EEG abgekürzt) ist die grafische Darstellung dieser Schwankungen. Das EEG ist neben der Elektroneurografie (ENG) und der Elektromyografie (EMG) eine standardmäßige Untersuchungsmethode in der Neurologie.

Ursache dieser Potentialschwankungen sind physiologische Vorgänge einzelner Gehirnzellen, die durch ihre elektrischen Zustandsänderungen zur Informationsverarbeitung des Gehirns beitragen. Entsprechend ihrer spezifischen räumlichen Anordnung addieren sich die von einzelnen Neuronen erzeugten Potentiale auf, so dass sich über den gesamten Kopf verteilte Potentialänderungen messen lassen.

Zur klinischen Bewertung wird eine Aufzeichnung in mindestens zwölf Kanälen von verschiedenen Elektrodenkombinationen benötigt.

Die Ortsauflösung des üblichen EEGs liegt bei mehreren Zentimetern. Wenn eine höhere Ortsauflösung benötigt wird, so müssen die Elektroden nach neurochirurgischer Eröffnung des Schädels direkt auf die zu untersuchende Hirnrinde aufgelegt werden. Das ist jedoch nur in Sonderfällen z. B. vor epilepsiechirurgischen Eingriffen erforderlich. In diesem Falle spricht man von einem Elektrocorticogramm (ECoG; in deutscher Schreibung Elektrokortikogramm). Das ECoG ermöglicht eine räumliche Auflösung von unter 1 cm und bietet zusätzlich die Möglichkeit, durch selektive elektrische Reizung einer der Elektroden die Funktion der darunterliegenden Hirnrinde zu testen. Dies kann für den Neurochirurgen z. B. bei Eingriffen in der Nähe der Sprachregion von größter Wichtigkeit sein, um zu entscheiden, welche Teile er entfernen darf, ohne eine Funktionseinbuße fürchten zu müssen (vgl. Wachkraniotomie). Eine noch detailliertere Erfassung von Einzelzellaktivität ist nur im Tierexperiment möglich.

Die resultierenden Daten können von geübten Spezialisten auf auffällige Muster untersucht werden. Es gibt aber auch umfangreiche Software-Pakete zur automatischen Signalanalyse. Eine weitverbreitete Methode zur Analyse des EEGs ist die Fouriertransformation der Daten vom Zeitbereich (also der gewohnten Darstellung von Spannungsänderungen im Verlauf der Zeit) in den sogenannten Frequenzbereich. Die so gewonnene Darstellung erlaubt die schnelle Bestimmung von rhythmischer Aktivität.

Bei diesen Mustern treten also Unterschiede auf, die man mittels einer AI ermitteln kann. So etwas sieht dann wohl so aus:



Gehirnwellen Unterschiede Mann Frau

Gehirnwellen Unterschiede Mann Frau

Für einen Laien dürften da viele Bilder recht gleich aussehen, aber die Computer haben dann einige Unterschiede erkannt, die eine Zuordnung ermöglichen:

While not all details of the features used for classification by the deep net have been revealed, our data show that differences in brain rhythms between sexes are mainly in the beta frequency range (cf. Figs 3 and 4). Women are generally better at recognizing emotions and expressing themselves than men34, in part also reflected in differences in responses from the mu-rhythm as a presumed read-out of the mirror neuron system35, and modulations of beta activity during wakefulness have been associated with cognition and emotionally positive or negative tasks36. The discovery from the deep net that information in the beta-range differs between the sexes supports these observations. However, which particular spatiotemporal characteristics of the beta-rhythm differentiate remains enigmatic, and was not further explored.

Die Unterschiede lassen sich demnach auch mal wieder klassischen Geschlechterunterschieden im Schnitt zuordnen.

Es zeigt sich immer wieder, dass erhebliche Unterschiede zwischen den Geschlechtern im Gehirn feststellbar sind. Die Behauptung, dass sich dies nicht auch im Denken niedergeschlagen hat, erscheint damit auch immer absurder.

Ich finde die Anwendung von „Deep Learning“ in dem Bereich sehr interessant. Ich glaube damit werden in Zukunft noch viel mehr Unterschiede aufgezeigt werden, die ansonsten schwer zu bestimmen sind.

Siehe auch:



Geschlechterunterschiede im Gehirn sind bereits im Alter von einem Monat vorhanden

Ein interessante Studie zu Geschlechterunterschieden im Gehirn bei Säuglingen:

The developing brain undergoes systematic changes that occur at successive stages of maturation. Deviations from the typical neurodevelopmental trajectory are hypothesized to underlie many early childhood disorders; thus, characterizing the earliest patterns of normative brain development is essential. Recent neuroimaging research provides insight into brain structure during late childhood and adolescence; however, few studies have examined the infant brain, particularly in infants under 3 months of age. Using high-resolution structural MRI, we measured subcortical gray and white matter brain volumes in a cohort (N = 143) of 1-month infants and examined characteristics of these volumetric measures throughout this early period of neurodevelopment. We show that brain volumes undergo age-related changes during the first month of life, with the corresponding patterns of regional asymmetry and sexual dimorphism. Specifically, males have larger total brain volume and volumes differ by sex in regionally specific brain regions, after correcting for total brain volume. Consistent with findings from studies of later childhood and adolescence, subcortical regions appear more rightward asymmetric. Neither sex differences nor regional asymmetries changed with gestation-corrected age. Our results complement a growing body of work investigating the earliest neurobiological changes associated with development and suggest that asymmetry and sexual dimorphism are present at birth.

Quelle: Investigation of brain structure in the 1-month infant (Scihub Volltext Link)

Aus der Studie:

Unterschiede Gehirn Mann Frau 1 Monat

Unterschiede Gehirn Mann Frau 1 Monat

Da geht es um die

  1. Größe des Gehirns von männlichen und weiblichen Babies nach Geburt.
  2. Das Volumen der weißen Substanz im Gehirn
  3. Das Volumen der grauen Substanz im Gehirn

Wie man sieht ist das Gehirn der männlichen Babies im Durchschnitt zB größer, und zwar über die hier erfassten Alter hinweg, auch wenn es einzelne männliche Babies mit relativ kleinen und einige Mädchen mit relativ großen Gehirnvolumen gibt. Der Trend ist aber recht deutlich.

Auch die Daten zu den verschiedenen Bereichen zeigen deutliche Unterschiede:

Unterschiede Gehirn Mann Frau 1 Monat

Unterschiede Gehirn Mann Frau 1 Monat

Es wird schwer das mit einer unterschiedlichen Sozialisiation zu erklären. Sie müsste dann wohl bereits im Mutterleib ansetzen. Was allerdings pränatale Hormone in der Tat machen, wie man beispielsweise an dem Testosteronspiegel sieht:

Testosteron Maenner Frauen

Testosteron Maenner Frauen

Aus einer Besprechung der Studie:

Dean’s team found that the boys’ brains were 8.3 per cent bigger, in line with the sex difference in brain volume found in adults and the few other available infant studies. Also as seen in adults, male brains had relatively more white matter (connecting tissue) and female brains more grey matter, relative to total brain size.

A number of specific neural areas were larger in males, such as parts of the limbic system involved in emotions, including the amygdala, insula, thalamus and putamen. The researchers also found evidence for relatively larger hippocampi, an area involved in memory, which has more commonly been found to be larger in females, although not universally so. Meanwhile female brains were relatively larger in other limbic areas such as parts of the cingulate gyrus, caudate and parahippocampal gyrus, and they had a few white-matter structures that were relatively larger.

These sex differences were smaller than has been observed in adults, which suggests that maturation continues this differentiation, likely through the high volume of sex steroid receptors in these brain areas. The alternative suggestion is that the subsequent differentiation is due to socialisation, but for the forces of socialisation to work along the same lines as pre-existing biological forces would suggest that socialisation is at most a feedback loop between biology and society.

There were a lot of brain areas that differed structurally between the sexes, but it would be irresponsible to draw any firm conclusions about what they might mean for function and behaviour. For instance,  what could differences in overall insula size possibly mean psychologically when the area is associated with “compassion and empathy, perception, motor control, self-awareness, cognitive functioning”, “interpersonal experience” and “psychopathology”?

Insofern liegt noch viel Arbeit vor den Forschern, bis sie die Unterschiede wirklich verstehen. Aber dennoch entzieht diese Studie vielen, die auf einen Blank Slate abstellen und annehmen, dass Geschlechterunterschiede nur auf Sozialisiation zurück gehen können einiges an Boden bzw. erfordert, dass diese ihre Thesen kritisch hinterfragen.

Vgl auch:

Unterschiede in Empathisieren /Systematisieren bei Männern und Frauen und Geisteswissenschaftlern und Naturwissenschaftlern


„Wie ich als Frau im Technikbereich merkte, dass dort einfach nicht meine Art von Leuten arbeiten“

Eine interessante Schilderung macht aus meiner Sicht deutlich, wie fremd Nerds oder Menschen, die sehr stark im „Dinge“ statt im Personenbereich denken, auf Leute wirken, die eher im „Personen“ Bereich zu verorten sind:

No, the reason I left is that I came into work one Monday morning and joined the guys at our work table, and one of them said “What did you do this weekend?”

I was in the throes of a brief, doomed romance. I had attended a concert that Saturday night. I answered the question with an account of both. The guys stared blankly. Then silence. Then one of them said: “I built a fiber-channel network in my basement,” and our co-workers fell all over themselves asking him to describe every step in loving detail.

At that moment I realized that fundamentally, these are not my people. I liked the work. But I was never going to like it enough to blow a weekend doing more of it for free. Which meant that I was never going to be as good at that job as the guys around me.

Natürlich sind das schon recht extreme Sachen. Aber ich kenne den Typ auch, passenderweise durch eine Gruppe von Informatikern, zu denen ein Freund von mir gehörte.

Sie redeten auch gern über diverse Projekte, die sie hatten und das erstaunliche war aus meiner Sicht, dass sie keinerlei Gespür dafür hatten, ob der andere gelangweilt war oder das Thema merkwürdig fand. Sie erzählten die Einzelheiten ihrer letzten Warhammer Campagne und gingen dabei in alle technischen Details oder andere ähnliche Sachen, auch wenn man merkte, dass der Gesprächspartner nach einer Gelegenheit suchte, sich höflich aus dem Gespräch zu verabschieden oder es irgendwie zu wechseln.

Natürlich gibt es auch ganz normale, weltgewandte Informatiker. Aber eben auch die relativ großen Nerds, denen die Personenebene nicht so einfach fällt.

Ich kann mir vorstellen, dass eine solche Atmosphäre für Personen, die solche Themen langweilig finden, langweilig ist und sie sich eher einen anderen Kollegenkreis wünschen oder jedenfalls das Gefühl hat da nicht reinzupassen.

Google Manifesto #GoogleManifesto

Bei Google soll intern ein „Manifesto“ zur dortigen Diversitypolitik umhergehen, dass von einem anonymen Mitarbeiter erstellt worden ist.

Es handelt sich um diesen Text:

Reply to public response and misrepresentation

I value diversity and inclusion, am not denying that sexism exists, and don’t endorse using stereotypes. When addressing the gap in representation in the population, we need to look at population level differences in distributions. If we can’t have an honest discussion about this, then we can never truly solve the problem. Psychological safety is built on mutual respect and acceptance, but unfortunately our culture of shaming and misrepresentation is disrespectful and unaccepting of anyone outside its echo chamber. Despite what the public response seems to have been, I’ve gotten many personal messages from fellow Googlers expressing their gratitude for bringing up these very important issues which they agree with but would never have the courage to say or defend because of our shaming culture and the possibility of being fired. This needs to change.


  • Google’s political bias has equated the freedom from offense with psychological safety, but shaming into silence is the antithesis of psychological safety.
  • This silencing has created an ideological echo chamber where some ideas are too sacred to be honestly discussed.
  • The lack of discussion fosters the most extreme and authoritarian elements of this ideology.
  • Extreme: all disparities in representation are due to oppression
  • Authoritarian: we should discriminate to correct for this oppression
  • Differences in distributions of traits between men and women may in part explain why we don’t have 50% representation of women in tech and leadership. Discrimination to reach equal representation is unfair, divisive, and bad for business.

Background [1]

People generally have good intentions, but we all have biases which are invisible to us. Thankfully, open and honest discussion with those who disagree can highlight our blind spots and help us grow, which is why I wrote this document.[2] Google has several biases and honest discussion about these biases is being silenced by the dominant ideology. What follows is by no means the complete story, but it’s a perspective that desperately needs to be told at Google.

Google’s biases

At Google, we talk so much about unconscious bias as it applies to race and gender, but we rarely discuss our moral biases. Political orientation is actually a result of deep moral preferences and thus biases. Considering that the overwhelming majority of the social sciences, media, and Google lean left, we should critically examine these prejudices.

Left Biases

  • Compassion for the weak
  • Disparities are due to injustices
  • Humans are inherently cooperative
  • Change is good (unstable)
  • Open
  • Idealist

Right Biases

  • Respect for the strong/authority
  • Disparities are natural and just
  • Humans are inherently competitive
  • Change is dangerous (stable)
  • Closed
  • Pragmatic

Neither side is 100% correct and both viewpoints are necessary for a functioning society or, in this case, company. A company too far to the right may be slow to react, overly hierarchical, and untrusting of others. In contrast, a company too far to the left will constantly be changing (deprecating much loved services), over diversify its interests (ignoring or being ashamed of its core business), and overly trust its employees and competitors.

Only facts and reason can shed light on these biases, but when it comes to diversity and inclusion, Google’s left bias has created a politically correct monoculture that maintains its hold by shaming dissenters into silence. This silence removes any checks against encroaching extremist and authoritarian policies. For the rest of this document, I’ll concentrate on the extreme stance that all differences in outcome are due to differential treatment and the authoritarian element that’s required to actually discriminate to create equal representation.

Possible non-bias causes of the gender gap in tech [3]

At Google, we’re regularly told that implicit (unconscious) and explicit biases are holding women back in tech and leadership. Of course, men and women experience bias, tech, and the workplace differently and we should be cognizant of this, but it’s far from the whole story.

On average, men and women biologically differ in many ways. These differences aren’t just socially constructed because:

  • They’re universal across human cultures
  • They often have clear biological causes and links to prenatal testosterone
  • Biological males that were castrated at birth and raised as females often still identify and act like males
  • The underlying traits are highly heritable
  • They’re exactly what we would predict from an evolutionary psychology perspective

Note, I’m not saying that all men differ from women in the following ways or that these differences are “just.” I’m simply stating that the distribution of preferences and abilities of men and women differ in part due to biological causes and that these differences may explain why we don’t see equal representation of women in tech and leadership. Many of these differences are small and there’s significant overlap between men and women, so you can’t say anything about an individual given these population level distributions.

Personality differences

Women, on average, have more:

  • Openness directed towards feelings and aesthetics rather than ideas. Women generally also have a stronger interest in people rather than things, relative to men (also interpreted as empathizing vs. systemizing).
  • These two differences in part explain why women relatively prefer jobs in social or artistic areas. More men may like coding because it requires systemizing and even within SWEs, comparatively more women work on front end, which deals with both people and aesthetics.
  • Extraversion expressed as gregariousness rather than assertiveness. Also, higher agreeableness.
  • This leads to women generally having a harder time negotiating salary, asking for raises, speaking up, and leading. Note that these are just average differences and there’s overlap between men and women, but this is seen solely as a women’s issue. This leads to exclusory programs like Stretch and swaths of men without support.
  • Neuroticism (higher anxiety, lower stress tolerance).This may contribute to the higher levels of anxiety women report on Googlegeist and to the lower number of women in high stress jobs.

Note that contrary to what a social constructionist would argue, research suggests that “greater nation-level gender equality leads to psychological dissimilarity in men’s and women’s personality traits.” Because as “society becomes more prosperous and more egalitarian, innate dispositional differences between men and women have more space to develop and the gap that exists between men and women in their personality becomes wider.” We need to stop assuming that gender gaps imply sexism.

Men’s higher drive for status

We always ask why we don’t see women in top leadership positions, but we never ask why we see so many men in these jobs. These positions often require long, stressful hours that may not be worth it if you want a balanced and fulfilling life.

Status is the primary metric that men are judged on[4], pushing many men into these higher paying, less satisfying jobs for the status that they entail. Note, the same forces that lead men into high pay/high stress jobs in tech and leadership cause men to take undesirable and dangerous jobs like coal mining, garbage collection, and firefighting, and suffer 93% of work-related deaths.

Non-discriminatory ways to reduce the gender gap

Below I’ll go over some of the differences in distribution of traits between men and women that I outlined in the previous section and suggest ways to address them to increase women’s representation in tech and without resorting to discrimination. Google is already making strides in many of these areas, but I think it’s still instructive to list them:

  • Women on average show a higher interest in people and men in things
  • We can make software engineering more people-oriented with pair programming and more collaboration. Unfortunately, there may be limits to how people-oriented certain roles and Google can be and we shouldn’t deceive ourselves or students into thinking otherwise (some of our programs to get female students into coding might be doing this).
  • Women on average are more cooperative
  • Allow those exhibiting cooperative behavior to thrive. Recent updates to Perf may be doing this to an extent, but maybe there’s more we can do. This doesn’t mean that we should remove all competitiveness from Google. Competitiveness and self reliance can be valuable traits and we shouldn’t necessarily disadvantage those that have them, like what’s been done in education. Women on average are more prone to anxiety. Make tech and leadership less stressful. Google already partly does this with its many stress reduction courses and benefits.
  • Women on average look for more work-life balance while men have a higher drive for status on average
  • Unfortunately, as long as tech and leadership remain high status, lucrative careers, men may disproportionately want to be in them. Allowing and truly endorsing (as part of our culture) part time work though can keep more women in tech.
  • The male gender role is currently inflexible
  • Feminism has made great progress in freeing women from the female gender role, but men are still very much tied to the male gender role. If we, as a society, allow men to be more “feminine,” then the gender gap will shrink, although probably because men will leave tech and leadership for traditionally feminine roles.

Philosophically, I don’t think we should do arbitrary social engineering of tech just to make it appealing to equal portions of both men and women. For each of these changes, we need principles reasons for why it helps Google; that is, we should be optimizing for Google—with Google’s diversity being a component of that. For example currently those trying to work extra hours or take extra stress will inevitably get ahead and if we try to change that too much, it may have disastrous consequences. Also, when considering the costs and benefits, we should keep in mind that Google’s funding is finite so its allocation is more zero-sum than is generally acknowledged.

The Harm of Google’s biases

I strongly believe in gender and racial diversity, and I think we should strive for more. However, to achieve a more equal gender and race representation, Google has created several discriminatory practices:

  • Programs, mentoring, and classes only for people with a certain gender or race [5]
  • A high priority queue and special treatment for “diversity” candidates
  • Hiring practices which can effectively lower the bar for “diversity” candidates by decreasing the false negative rate
  • Reconsidering any set of people if it’s not “diverse” enough, but not showing that same scrutiny in the reverse direction (clear confirmation bias)
  • Setting org level OKRs for increased representation which can incentivize illegal discrimination [6]

These practices are based on false assumptions generated by our biases and can actually increase race and gender tensions. We’re told by senior leadership that what we’re doing is both the morally and economically correct thing to do, but without evidence this is just veiled left ideology[7] that can irreparably harm Google.

Why we’re blind

We all have biases and use motivated reasoning to dismiss ideas that run counter to our internal values. Just as some on the Right deny science that runs counter to the “God > humans > environment” hierarchy (e.g., evolution and climate change) the Left tends to deny science concerning biological differences between people (e.g., IQ[8] and sex differences). Thankfully, climate scientists and evolutionary biologists generally aren’t on the right. Unfortunately, the overwhelming majority of humanities and social scientists learn left (about 95%), which creates enormous confirmation bias, changes what’s being studied, and maintains myths like social constructionism and the gender wage gap[9]. Google’s left leaning makes us blind to this bias and uncritical of its results, which we’re using to justify highly politicized programs.

In addition to the Left’s affinity for those it sees as weak, humans are generally biased towards protecting females. As mentioned before, this likely evolved because males are biologically disposable and because women are generally more cooperative and areeable than men. We have extensive government and Google programs, fields of study, and legal and social norms to protect women, but when a man complains about a gender issue issue [sic] affecting men, he’s labelled as a misogynist and whiner[10]. Nearly every difference between men and women is interpreted as a form of women’s oppression. As with many things in life, gender differences are often a case of “grass being greener on the other side”; unfortunately, taxpayer and Google money is spent to water only one side of the lawn.

The same compassion for those seen as weak creates political correctness[11], which constrains discourse and is complacent to the extremely sensitive PC-authoritarians that use violence and shaming to advance their cause. While Google hasn’t harbored the violent leftists protests that we’re seeing at universities, the frequent shaming in TGIF and in our culture has created the same silence, psychologically unsafe environment.


I hope it’s clear that I’m not saying that diversity is bad, that Google or society is 100% fair, that we shouldn’t try to correct for existing biases, or that minorities have the same experience of those in the majority. My larger point is that we have an intolerance for ideas and evidence that don’t fit a certain ideology. I’m also not saying that we should restrict people to certain gender roles; I’m advocating for quite the opposite: treat people as individuals, not as just another member of their group (tribalism).

My concrete suggestions are to:

De-moralize diversity.

  • As soon as we start to moralize an issue, we stop thinking about it in terms of costs and benefits, dismiss anyone that disagrees as immoral, and harshly punish those we see as villains to protect the “victims.”

Stop alienating conservatives.

  • Viewpoint diversity is arguably the most important type of diversity and political orientation is one of the most fundamental and significant ways in which people view things differently.
  • In highly progressive environments, conservatives are a minority that feel like they need to stay in the closet to avoid open hostility. We should empower those with different ideologies to be able to express themselves.
  • Alienating conservatives is both non-inclusive and generally bad business because conservatives tend to be higher in conscientiousness, which is require for much of the drudgery and maintenance work characteristic of a mature company.

Confront Google’s biases.

  • I’ve mostly concentrated on how our biases cloud our thinking about diversity and inclusion, but our moral biases are farther reaching than that.
  • I would start by breaking down Googlegeist scores by political orientation and personality to give a fuller picture into how our biases are affecting our culture.

Stop restricting programs and classes to certain genders or races.

  • These discriminatory practices are both unfair and divisive. Instead focus on some of the non-discriminatory practices I outlined.

Have an open and honest discussion about the costs and benefits of our diversity programs.

  • Discriminating just to increase the representation of women in tech is as misguided and biased as mandating increases for women’s representation in the homeless, work-related and violent deaths, prisons, and school dropouts.
  • There’s currently very little transparency into the extend of our diversity programs which keeps it immune to criticism from those outside its ideological echo chamber.
  • These programs are highly politicized which further alienates non-progressives.
  • I realize that some of our programs may be precautions against government accusations of discrimination, but that can easily backfire since they incentivize illegal discrimination.

Focus on psychological safety, not just race/gender diversity.

  • We should focus on psychological safety, which has shown positive effects and should (hopefully) not lead to unfair discrimination.
  • We need psychological safety and shared values to gain the benefits of diversity
  • Having representative viewpoints is important for those designing and testing our products, but the benefits are less clear for those more removed from UX.

De-emphasize empathy.

  • I’ve heard several calls for increased empathy on diversity issues. While I strongly support trying to understand how and why people think the way they do, relying on affective empathy—feeling another’s pain—causes us to focus on anecdotes, favor individuals similar to us, and harbor other irrational and dangerous biases. Being emotionally unengaged helps us better reason about the facts.

Prioritize intention.

  • Our focus on microaggressions and other unintentional transgressions increases our sensitivity, which is not universally positive: sensitivity increases both our tendency to take offense and our self censorship, leading to authoritarian policies. Speaking up without the fear of being harshly judged is central to psychological safety, but these practices can remove that safety by judging unintentional transgressions.
  • Microaggression training incorrectly and dangerously equates speech with violence and isn’t backed by evidence.

Be open about the science of human nature.

  • Once we acknowledge that not all differences are socially constructed or due to discrimination, we open our eyes to a more accurate view of the human condition which is necessary if we actually want to solve problems.

Reconsider making Unconscious Bias training mandatory for promo committees.

  • We haven’t been able to measure any effect of our Unconscious Bias training and it has the potential for overcorrecting or backlash, especially if made mandatory.
  • Some of the suggested methods of the current training (v2.3) are likely useful, but the political bias of the presentation is clear from the factual inaccuracies and the examples shown.
  • Spend more time on the many other types of biases besides stereotypes. Stereotypes are much more accurate and responsive to new information than the training suggests (I’m not advocating for using stereotypes, I [sic] just pointing out the factual inaccuracy of what’s said in the training).

[1] This document is mostly written from the perspective of Google’s Mountain View campus, I can’t speak about other offices or countries.

[2] Of course, I may be biased and only see evidence that supports my viewpoint. In terms of political biases, I consider myself a classical liberal and strongly value individualism and reason. I’d be very happy to discuss any of the document further and provide more citations.

[3] Throughout the document, by “tech”, I mostly mean software engineering.

[4] For heterosexual romantic relationships, men are more strongly judged by status and women by beauty. Again, this has biological origins and is culturally universal.

[5] Stretch, BOLD, CSSI, Engineering Practicum (to an extent), and several other Google funded internal and external programs are for people with a certain gender or race.

[6] Instead set Googlegeist OKRs, potentially for certain demographics. We can increase representation at an org level by either making it a better environment for certain groups (which would be seen in survey scores) or discriminating based on a protected status (which is illegal and I’ve seen it done). Increased representation OKRs can incentivize the latter and create zero-sum struggles between orgs.

[7] Communism promised to be both morally and economically superior to capitalism, but every attempt became morally corrupt and an economic failure. As it became clear that the working class of the liberal democracies wasn’t going to overthrow their “capitalist oppressors,” the Marxist intellectuals transitioned from class warfare to gender and race politics. The core oppressor-oppressed dynamics remained, but now the oppressor is the “white, straight, cis-gendered patriarchy.”

[8] Ironically, IQ tests were initially championed by the Left when meritocracy meant helping the victims of the aristocracy.

[9] Yes, in a national aggregate, women have lower salaries than men for a variety of reasons. For the same work though, women get paid just as much as men. Considering women spend more money than men and that salary represents how much the employees sacrifices (e.g. more hours, stress, and danger), we really need to rethink our stereotypes around power.

[10] “The traditionalist system of gender does not deal well with the idea of men needing support. Men are expected to be strong, to not complain, and to deal with problems on their own. Men’s problems are more often seen as personal failings rather than victimhood,, due to our gendered idea of agency. This discourages men from bringing attention to their issues (whether individual or group-wide issues), for fear of being seen as whiners, complainers, or weak.”

[11] Political correctness is defined as “the avoidance of forms of expression or action that are perceived to exclude, marginalize, or insult groups of people who are socially disadvantaged or discriminated against,” which makes it clear why it’s a phenomenon of the Left and a tool of authoritarians.

Update 7:25pm ET: Google’s new Vice President of Diversity, Integrity & Governance, Danielle Brown, issued the following statement in response to the internal employee memo:


I’m Danielle, Google’s brand new VP of Diversity, Integrity & Governance. I started just a couple of weeks ago, and I had hoped to take another week or so to get the lay of the land before introducing myself to you all. But given the heated debate we’ve seen over the past few days, I feel compelled to say a few words.

Many of you have read an internal document shared by someone in our engineering organization, expressing views on the natural abilities and characteristics of different genders, as well as whether one can speak freely of these things at Google. And like many of you, I found that it advanced incorrect assumptions about gender. I’m not going to link to it here as it’s not a viewpoint that I or this company endorses, promotes or encourages.

Diversity and inclusion are a fundamental part of our values and the culture we continue to cultivate. We are unequivocal in our belief that diversity and inclusion are critical to our success as a company, and we’ll continue to stand for that and be committed to it for the long haul. As Ari Balogh said in his internal G+ post, “Building an open, inclusive environment is core to who we are, and the right thing to do. ‘Nuff said. “

Google has taken a strong stand on this issue, by releasing its demographic data and creating a company wide OKR on diversity and inclusion. Strong stands elicit strong reactions. Changing a culture is hard, and it’s often uncomfortable. But I firmly believe Google is doing the right thing, and that’s why I took this job.

Part of building an open, inclusive environment means fostering a culture in which those with alternative views, including different political views, feel safe sharing their opinions. But that discourse needs to work alongside the principles of equal employment found in our Code of Conduct, policies, and anti-discrimination laws.

I’ve been in the industry for a long time, and I can tell you that I’ve never worked at a company that has so many platforms for employees to express themselves—TGIF, Memegen, internal G+, thousands of discussion groups. I know this conversation doesn’t end with my email today. I look forward to continuing to hear your thoughts as I settle in and meet with Googlers across the company.



Bei Gizmodo, siehe den Link oben, heißt es:

The text of the post is reproduced in full below, with some minor formatting modifications. Two charts and several hyperlinks are also omitted.

Gerade die Charts und die Links, vermutlich auf Studien, wären natürlich interessant gewesen.

Das Video dazu habe ich gefunden:

ich ergänze mal auf die Schnelle ein paar Links von mir:

Women, on average, have more:


Warum brillante Frauen eher Karrieren außerhalb der STEM-Fächer wählen

Ein interessanter Artikel führt Gründe an, die eher dazu führen, dass Frauen sich nicht für Berufe in den „STEM-Fächern“ (Naturwissenschaften, Technik, Ingenieurwissenschaften und Mathematik) entscheiden.

Der erste liegt in „Sachen vs Personen“

Things versus people.Su et al (2009) performed a meta-analysis of studies including a total of over 500,000 people examining gender differences in interests.  Despite claims that gender differences are typically “small” (Hyde, 2005), Su et al found a gigantic gender difference in interests.  Women preferred working with people, whereas men preferred working with things, a preference that is detectable within the first two days of birth and among our close species relatives, rhesus monkeys!  To be sure, these differences were not absolute.  Not every man prefers working with things, and not every woman prefers working with people.  But the effect size was d= .93, and even if you are not familiar with effect sizes, this would make it one of the largest effects in social psychology; it is gigantic.

Dieser grundlegende Unterschied war hier auch schon häufiger angesprochen worden. Der Effekt scheint mir sehr robust immer wieder bestätigt zu werden und erklärt, warum Frauen ehemals rein männliche Fächer wie Medizin oder Jura recht problemlos eroberten, während die STEM-Fächer insgesamt einen geringeren Anteil an Frauen haben.

JUST math skills versus math and verbal skills.  This same issue of differing interests was approached in a different way by Wang, Eccles, and Kenny (2013). Disclosure: Eccles was my dissertation advisor and longterm collaborator; I am pretty sure she identifies as a feminist, has long been committed to combating barriers to women, and is one of the most objective, balanced social scientists I have ever had the pleasure to know.

In a national study of over 1,000 high school students, they found that:

1. 70 percent more girls than boys had strong math and verbal skills;

2. Boys were more than twice as likely as girls to have strong math skills but not strong verbal skills;

3. People (regardless of whether they were male or female) who had only strong math skills as students were more likely to be working in STEM fields at age 33 than were other students;

4. People (regardless of whether they were male or female) with strong math and verbal skills as students were less likely to be working in STEM fields at age 33 than were those with only strong math skills.

Here are their conclusions, in their own words (p. 5):

“Results revealed that mathematically capable individuals who also had high verbal skills were less likely to pursue STEM careers than were individuals who had high math skills but moderate verbal skills. One notable finding was that the group with high math and high verbal ability included more females than males…

Our study provides evidence that it is not lack of ability that causes females to pursue non-STEM careers, but rather the greater likelihood that females with high math ability also have high verbal ability and thus can consider a wider range of occupations than their male peers with high math ability, who are more likely to have moderate verbal ability.”

Ich vermute, dass Leute mit guten sprachlichen Eigenschaften auch lieber mit Leuten zusammenarbeiten und Leute mit entsprechenden schlechten Eigenschaften auch eher darauf verzichten. Interessant ist, dass sich dies bei Männern ebenso zeigt. Auch die, die dort beide Fähigkeiten haben wählen eher andere Fächer. Das macht eine reine Beeinflussung durch Geschlechterrollen unwahrscheinlicher.

Es wäre demnach eine Unterscheidung zwischen verschiedenen Stärken vs eine Entscheidung in einem Bereich zu arbeiten, in dem man besonders gut ist. Es passt im übrigen auch zu dem Klischee des Nerds und Geeks, dessen Kompetenzen eher nicht im sozialen Bereich liegen.

Ein weiterer Faktor wären die reinen Zahlen:

The Numbers

The Council of Graduate Schools puts out regular reports, such as this one, that include the gender distribution in various fields.

Council of Graduate Schools
Source: Council of Graduate Schools

Lo and behold, there is not “pervasive evidence of” a gender gap in graduate enrollments, though there is a gap in some STEM fields. Completely consistent with the work by Su et al and by Wang et al, in nearly all fields that are about people, not only is there no gap disadvantaging women, there are actually more women than men! (Health, education, social and behavioral sciences, public administration, arts and humanities, and even biological sciences).  The same report found that, overall, across all fields, the „gap“ is in the „wrong“ direction: 57 percent of enrollees in graduate programs are women.

Even if there is discrimination against women in these fields, it is not preventing women from entering those fields in droves. (Indeed, the logic of “gap = discrimination”—a logic I have repeatedly rejected but which runs rampant throughout the social sciences and general public—would have us believe there is widespread discrimination against men in most fields now).

Furthermore, this pattern is completely consistent with the idea that girls and women have different interests (Su et al) and skills (Wang et al) that lead them to prefer non-STEM careers.

Frauen studieren, nur eben lieber andere Fächer. Wenn es mit Leuten und Leben zu tun hat, dann sind sie dort weitaus eher zu finden als in Fächern, die sich damit weniger beschäftigen.

Auch interessant ist der folgende Absatz:

Surely girls and women have, historically, been discriminated against in such fields.  But discrimination in 1950 or 1970 does not constitute evidence of ongoing discrimination.  Furthermore, the evidence that girls and women prefer non-STEM fields is not an argument to avoid combating sexist discrimination where it can still be found.  Nonetheless, the list of social science victim2 groups is so long, that, most likely, almost all of us have been the target of discrimination or hostility at some point in our lives, rendering the question of whether some groups are more victimized than others muddier than it seems.

However equivocal the evidence for “bias” in the present may be as an explanation for the gender gap in STEM fields, there is ample evidence of bias. Scientific bias! Social scientists clearly „prefer“ bias explanations over other, deeply important, scientifically rigorous, social developmental evidence, such as that offered by Su et al and Wang et al.  This table reveals just how extreme this bias is:

Lee Jussim
Source: Lee Jussim

The key entry here is the citation counts in the far right.  The Moss-Racusin study is, by conventional standards, the weakest of the studies.  Its sample size is a fraction of that of the others.  It studies a relatively minor situation (hiring lab managers).  It was a single study (Su et al is a meta-analysis of scores of studies; Williams and Ceci reported five separate studies).  In contrast to Wang et al, it only studied an event at a single time point; it did not follow people’s career trajectories.

This does not make Moss-Racusin et al a “bad” study; it is merely weaker on virtually all important scientific grounds than the others.  This is not to argue that the other studies are “perfect,” either; all studies have imperfections.  But by conventional scientific standards, Su et al’s meta-analysis, the replications in Williams and Ceci, the longitudinal Wang et al study, and the far larger sample sizes in all three mean that, on most scientific methodological standards, they are superior to the Moss-Racusin et al study.

And yet, look at the citation counts.  Others are citing the Moss-Racusin et al study out the wazoo. Now, Wang et al and Williams and Ceci came out later, so probably the most useful column is the last.  Since 2015, the weaker Moss-Racusin study has been cited 50% more often than the other three combined!  That means there are probably more papers citing the Moss-Racusin et al study and completely ignoring the other three, than there are papers citing even one of the other three! What kind of „science“ are we, that so many „scientists“ can get away with so systematically ignoring relevant data in our scientific journals?

(Again, this does not make the Moss-Racusin study “bad.” The bias here reflects a far broader field problem, it does not constitute a weakness in the paper itself).

And that, gentle reader, is a gigantic scientific bias.  It might even be beyond bias. Some might call it an “obsession” with discrimination and bias so severe that it is blinding many in our field to major findings regarding gender differences that contribute to preferences for different types of fields.   

Wer eine Benachteiligung von Frauen behauptet, der wird also wesentlich häufiger zitiert als jemand, der eine Bevorzugung von Frauen behauptet. Die Benachteiligung von Frauen ist die „gewünschte Geschichte“, an rationalen Erklärungen dafür ist man nicht interessiert. Das Opfernarrativ muss eben auf jeden Fall erhalten bleiben. Ob es Frauen zusätzlich abschreckt und damit zu weniger Frauen in dem Bereich führt ist dabei wohl eher egal.

Das Schlußwort ist auch interessant:

If this analysis has any validity, the societal push to equalize gender distributions may be deeply dysfunctional, because it can succeed only by having the perverse effect of pushing people into fields they do not prefer. Of course, on moral grounds, we want to insure that all people have equal opportunities to enter any particular career.  But if there are bona fide gender differences in preferences and interests, equal opportunities may never translate into equal outcomes.

Was auch der Grund dafür ist, dass eine reine Gleichstellungspolitik eher Ungerechtigkeiten produziert, weil Unterschiede ausgeblendet werden.

Metastudie zu Geschlechterunterschieden in der Gehirnstruktur

Eine interessante Metastudie hat verschiedene Studien zu Geschlechterunterschieden in der Gehirnstruktur ausgewertet:


• This is the first meta-analysis of sex differences in the typical human brain.
• Regional sex differences overlap with areas implicated in psychiatric conditions.
• The amygdala, hippocampus, planum temporale and insula display sex differences.
• On average, males have larger brain volumes than females.
• Most articles providing sex differences in volume are in the ‘mature’ category.

The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18–59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions.

Quelle:  A meta-analysis of sex differences in human brain structure

Wie man an den Werten sieht, gibt es durchaus deutliche Unterschiede:

Unterschiede Gehirn Mann Frau

Unterschiede Gehirn Mann Frau

Aus der Besprechung:

3.3.1. Regional volume meta-analysis
All 16 studies included in the volume voxel-based meta-analysis included a between-group comparison of GM volume, leading to a total of 264 reported GM foci. Only 4 studies performed a WM volume comparison, with a total of 30 WM foci. Since 30 data points are insufficiently spatially dense to perform a meta-analysis, only a coordinate-based meta-analysis on GM volume is currently possible. The 16 studies provided a total of 2186 brains (49% female) aged between 7 and 80 years old. Because an FDR-correction at voxel-level q = 0.05 gave diffuse spatial results, we opted for a more stringent correction to capture the most reliable group differences. The (FDR q = 0.01) thresholded Z-value was 3.428 for the male > female contrast and 3.616 for the female > male contrast, and results are reported in Table 4 using an extent threshold of 60 continuous voxels.

On average, males have larger grey matter volume in bilateral amygdalae, hippocampi, anterior parahippocampal gyri, posterior cingulate gyri, precuneus, putamen and temporal poles, areas in the left posterior and anterior cingulate gyri, and areas in the cerebellum bilateral VIIb, VIIIa and Crus I lobes, left VI and right Crus II lobes. Females on average have larger volume at the right frontal pole, inferior and middle frontal gyri, pars triangularis, planum temporale/parietal operculum, anterior cingulate gyrus, insular cortex, and Heschl’s gyrus; bilateral thalami and precuneus; the left parahippocampal gyrus and lateral occipital cortex (superior division).

3.3.2. Regional tissue density meta-analysis
Eight of the nine studies (eight of the ten age-matched groups) investigating voxel-based sex differences in brain tissue density performed a GM analysis, with a total of 86 reported foci. Only three performed a WM density analysis with a total of 13 foci again discouraging a meta-analysis. The eight studies provided a total number of 741 brains (53% female), aged between 10 and 81 years. Results are reported (with FDR q = 0.05). Z-values were 3.247 for the male > female contrast and 3.445 for the female > male contrast, reported in Table 4 with an extent threshold of 60 continuous voxels. Areas of higher GM density in males compared to females included the left amygdala, hippocampus, insular cortex, pallidum, putamen, claustrum, and an area in the right VI lobe of the cerebellum. The left frontal pole has significantly higher GM tissue density in females compared to males.

Also eine Vielzahl von Regionen, bei denen Unterschiede erkennbar sind.

vgl. auch:

„Was hat mansplaining mit Frauen zu tun? Gar nichts“

In einer Diskussion bei Onyx fand ich zwei Kommentare zum Thema „Mansplaining“

Zunächst schreibt Leszek bei Onyx:

Hier wird eine bestimmte negativ beurteilte Verhaltensweise in geschlechter-essentialistischer Weise einem bestimmten Geschlecht zugeordnet anstatt auf Menschen als Individuen zu fokussieren.
Findest du sowas nicht einseitig, sexistisch und kritikwürdig?

Und selbst wenn es stimmen würde, dass Männer solches Verhalten IM SCHNITT häufiger an den Tag legen als Frauen, was erstmal durch ernsthaft wissenschaftliche Untersuchungen – also solche jenseits des Mainstream-Feminismus – zu belegen wäre, dann würde das 1. nichts daran ändern, dass es sich trotzdem um eine Minderheit handelt und 2. wären in dieser Hinsicht auch die Ursachen aus einer um Objektivität bemühten Haltung heraus zu analysieren.

Wäre es nicht möglich, dass in solchen Situationen manche Männer einfach nicht wissen, dass sich eine Frau mit einem bestimmten Thema auch gut auskennt?

Und wäre es nicht möglich, dass die – leider sozialwissenschaftlich und sozialpsychologisch gut belegte – durchschnittliche Partnerwahlpräferenz von Frauen für Selbstbewusstsein und Status bei Männern bei manchen Männern dazu führen könnte, zu versuchen ihren Partnerwert zu betonen, indem sie gegenüber Frauen Wissen und Kompetenz signalisieren?

Und wäre es nicht möglich, dass es Frauen gibt, die auf die Signalisierung von Wissen und Kompetenz positiv reagieren?

Dann wäre hier nämlich zu berücksichtigen, dass spezifische Partnerwahlpräferenzen und Verhaltensweisen von Frauen ihren Beitrag zu diesem Phänomen leisten.

Und selbst wenn ein Mann genau wüsste, dass sich die Frau mit dem entsprechenden Thema auch gut auskennt, wäre es nicht möglich, dass ein Mann sich so verhält einfach um zum Ausdruck zu bringen, dass er ihre Interessen teilt und auf diesem Gebiet kompetent ist?

Führt der geschlechter-essentialistische Vorwurf des „Mansplaining“ nicht dazu die gleichen Verhaltensweisen, wenn sie von Frauen ausgehen, großzügig zu übersehen oder gar als positives Zeichen weiblicher Emanzipation zu interpretieren, während bei Männern umstandslos eine negative Motivation unterstellt wird, obwohl auch eine ganz andere Motivation dahinter stehen kann?

Berechtigte Hinweise, dass da viel Hass drin ist und der Vorwurf gern schlicht zur Abwertung von Männern genutzt wird.

Auch interessant fand ich den folgenden Kommentar von Jochen Schmidt bei Onyx:

Als Ergänzung würde ich vielleicht den Akzent ein wenig verschieben. Ausgehend von Deiner Kritik „… eine bestimmte negativ beurteilte Verhaltensweise in geschlechter-essentialistischer Weise einem bestimmten Geschlecht zugeordnet anstatt auf Menschen als Individuen zu fokussieren“, würde ich mal vom Geschlecht des jeweiligen Zuhörers abstrahieren.

Das im Video-Clip beschriebene Verhalten erlebe ich als Berater täglich: wenn die Ingenieure mit den Monteuren streiten, wenn der Kunde mit dem Lieferanten streitet, wenn die Laborleiter mit den Abteilungsleitern streiten, wenn die Projektleiter mit den Leuten vom Controlling streiten, ich erlebe es in jedem Meeting, ich erlebe es sogar auf der Weihnachtsfeier.

Was hat mansplaining mit Frauen zu tun? Gar nichts. Männer erklären anderen Männern ständig irgendwelche Sachen, und zwar ungefragt (ähnlich „Mittelwert“ unten). Genauer: sie versuchen, es dem jeweils anderen Mann zu erklären – wobei der andere Mann sich seinerseits bemüht, dem ersteren Mann etwas zu ganz anderes zu erklären, was diesen anderen Mann jedoch zu weiteren hartnäckigen Erklärungs-Versuchen anstachelt …

Auf diesen Weise halten sich die Männer und ihre Erklärungsversuche gegenseitig in Schach. Wenn jeder dem anderen etwas erklären will, dann muß jeder mal ab und zu die Luft anhalten, sofern das Gespräch überhaupt noch aufrecht erhalten werden soll. (Nebenbei: in einem früheren Projekt hatte ich mal einen griechischen Kollegen, den habe ich aus gegebenem Anlaß gefragt: Warum sind die Griechen so unverschämt? Seine trockene Antwort: „Das kommt Dir nur so vor; die Griechen sind *alle* unverschämt, da hebt die Unverschämtheit des Einen die Unverschämtheit des Anderen auf; wenn alle unverschämt zueinander sind, dann müssen sie sich letztlich zusammenraufen, um doch noch was gebacken zu kriegen … Du bist *nicht* unverschämt – *das* ist das Problem.“)

So, was passiert nun, wenn Männern Frauen gegenüber das tun, was sie anderen Männern gegenüber ständig tun? Wenn sie also wieder irgendwelche weit hergeholten Erklärungen vom Stapel lassen? Nun, offenbar tun viele Frauen dann nicht das, was Männer in so einer Situation reflexartig tun: dagegen halten mit noch weiter hergeholten Erklärungen. Viele Frauen hören stattdessen brav zu und denken sich ihren Teil. Hinterher gehen sie dann zur Gleichstellungsbeauftragten und präsentieren ihr Opfer-Abo.

Natürlich kann man kritisieren, daß Männer offenbar ein unbezähmbares Verlangen danach haben, anderen Leuten – ungefragt – irgendwelche Sachen zu erklären. (Bei dieser Kritik sollte man allerdings jene Faktoren berücksichtigen, die Du oben genannt hast: z. B. daß es für Männer wichtig ist, in der Kommunikation ihren Status abzusichern, oder gar einen höheren Status zu erwerben). Aber eines darf man nicht: den Erklärungs-Drang von Männern gendern, d.h. darstellen als ein Verhalten, das Männer typischerweise *nur* Frauen gegenüber an den Tag legen. Wenn ich *allen* Leuten auf die Füße trete, dann soll man gefälligst nicht so tun, als würde ich immer nur den Frauen auf die Füße treten. In einem solchen Gendern offenbart sich ein doppelter Standard, der nicht zulässig ist („Mansplaining von Mann zu Mann ist OK – mansplaining von Mann zu Frau ist übel.“)

Soweit also mein kleiner Versuch, einen „geschlechter-essentialistischen“ Ansatz zu vermeiden …

Wie Du vielleicht bemerkt hast, habe ich Dir die ganze Zeit über etwas erklärt – obwohl Du mich gar nicht danach gefragt hast. Wenn Du jetzt nicht schleunigst reagierst wie ein richtiger Mann, dann würde ich einfach noch ein Dutzend weiterer Erklärungen aus dem Ärmel schütteln, die ich Dir allesamt ins Trommelfell puste😉

Die Theorie, dass kein Gesprächsstil ist, der der Herabwürdigung von Frauen dient, sondern einer, bei dem es darum geht, dass Männer gerne Fakten austauschen und erklären, würde ich auch teilen. Ich habe schon häufig Männern etwas erkärt, was ich faszinierend fand oder sie haben mir etwas erklärt, bis sie oder ich mit dem Hinweis unterbrochen worden sind, dass wir die gleiche Doku gesehen haben.

Einen weitern interessanten Beitrag gab es bei Voice for Men:

Feminists are upset—they are angry and bitter. When are they not? They say that students give higher teaching evaluations to male professors. Reviews of male professors, they say, are more likely to include the words “brilliant,” “intelligent” or “smart,” and far more likely to contain the word “genius.” Meanwhile, women are more likely to be described as “mean,” “harsh,” “unfair,” or “strict,” and a lot more likely to be called “annoying.”

Is it possible that men like to repeat what they have learned, but reconstructed in their mind to ensure clarity?

Is it possible that our ability to recapitulate—to teach—is sharpened by our constant desire to explain and re-explain?

Yes, men refuse to ask for directions. We want to figure it out ourselves; and when we do, we explain it. Approach a group of men on the street and ask for directions. Brace yourself: they will all explain at once and gesticulate in all directions, offering all sorts of improvements and considerations: rejoice in that masculine moment.(…)

It is important to ask questions, to try to answer, to try to explain to oneself, to try to state the answer in newer ways, to try to question and re-explain; and to do it aloud. If someone objects, incorporate their objection—make it your own. If they are angry and accuse you of patronizing, smile; and try to incorporate some humor next time to soften your explanation.(…)

As I go about my day-to-day business, I explain things. I explain things to myself and to imagined listeners. Even if I don’t know the final answer, I like talking about the question. Sometimes I cannot distinguish if I am talking aloud or to myself. (Yeah, alright: I talk to myself.) Sometimes, I make a fool out of myself when I talk. I mean, who wants to hear this endless splatter of questions and possible answers? So when someone tells me to stop, I learn where the line is.(…)

Why do men get such good evaluations?

When men explain things repetitively (even to people who already understand), we are organizing our thoughts, shifting them around, reformulating causality, searching for flaws in our comprehension, fixing our understanding, fleshing out connections, trying to dominate the subject matter (that’s a good thing—we’re not doing it to women; we’re doing it to the subject matter), looking for ways to rise above it.

Yes, men are mansplainers. Yes, men do get better teaching evaluations. Do you think feminists will ever show enough modesty to get off the pedestal and realize that sexism does not explain this link? Do you think feminists might ever see a connection here? (…)

Der Nutzen von „Mansplaining“ liegt aus meiner Sicht in der Tat auf der Hand. Man teilt Wissen, man kann in einer Diskussion neue Wissen erlangen, man kann darstellen, dass man Ahnung hat. Zudem kann es auch schlicht aufs einer Begeisterung für ein bestimmtes Thema aus einem heraussprudeln und auch dieses starke Interesse, Wissen zu einem Thema zu sammeln kann sehr hilfreich sein.

Wenn man dann bedenkt, dass bei den Unterschieden von Männern und Frauen gerade auch der Unterschied zwischen „systematischen und emotionalen Gehirn“ immer wieder vorgefunden wird wäre zu vermuten, dass der Ansatz zu diesem Verhalten durchaus biologisch sein könnte. Das passt auch dazu, dass Männer sich eher für Sachthemen interessieren und auch besser in Tests zum Allgemeinwissen abschneiden.