Deep learning in gender-specific brain psychology and anatomy research
And how Convolutional Neural Network Architecture help make conclusions on cognitive-behavioral differences in the sexes
Do hormones play a role in our cognitive reasoning abilities? Are the male and female human brains structured differently? The answer is yes. Structural differences in nerve-cell clusters between the male and female brains give us enough reason for considering gender as a biological variable in brain research. The differences in the average Intelligence Quotient between men and women are small in magnitude and inconsistent, this information is not enough to conclude differences in brain anatomy. This is where artificial aid kicks in.
Modern research
Machine learning has witnessed enormous attention over the last few years. The modern rush started when so-called deep neural networks began outperforming other existing models. These improvements show the potential for medical imaging technology, data analysis, and diagnostics. Layered MRIs and image processing have made it possible to group various functions of the human brain, specific to a particular sex, and determine the subtle differences in personality traits.
Brain anatomy research, aided by neural network frameworks, specifically a Convolutional Neural Network architecture, helped conclude that these biological differences are a consequence of a gender-specific evolutionary process.
The basics
Why Machine learning? What are deep neural networks? Machine learning is a way to build an intuitive understanding of data by training systems to recognize patterns. After training, they should be able to make decisions, even if it is a set of circumstances they have never encountered before.
Deep neural networks are such systems. Deep learning algorithms visualize data as layers. They run data through several “layers” of algorithms, each giving a simplified representation of the data to the next layer. A layered neural network is designed to interpret visual inputs and perform tasks such as image classification and object detection.
The main goal is to generalize the learned patterns from previous data and deliver correct predictions for new, unseen data.
What is a Convolutional Neural Network? A Convolutional Neural Network is a deep learning algorithm that can identify and analyze features in images. CNNs can also be used for text identification, more widely known as natural language processing; let’s save that for another discussion. CNNs are also used for deep learning applications in healthcare, and in this case, medical imaging.
- The first purpose of a convolution tool is to split the various features of the image for interpretation, and (here) the nerve-cell structures are analyzed. A CNN architecture itself is inspired by the connectivity patterns of neurons within the human brain.
- The other purpose is to identify a fully connected layer that uses the convolution layer’s output to predict the best classification for the network.
The process
Stimulating electrical signals in the brain cause corresponding emotional responses, and these responses correlate to a specific nerve cluster in the brain. The respective MRIs corresponding to the stimulus is then parsed using a CNN. The structures of the clusters are compared. Furthermore, the stimuli can be reversed and reported if the necessary response is observed. The inputs (stimuli) are paired with the outputs (responses), and hence we develop a generalized pattern. This can then be used to draw conclusions and predict responses.
Both male and female MRIs were given to the algorithm, and it was capable of predicting gender within machine precision. This proved that observable anatomical differences existed in the human brain.
Other conclusions drawn from the study were that women respond more strongly to negative emotional stimuli than men. This leads to an increased risk of depression and anxiety disorders in women. Similarly, other regions of the brain were compared and mapped, including the left thalamus, and hypothalamus, and medial prefrontal cortex. For detailed inferences refer to Brain Differences Between Men and Women: Evidence From Deep Learning.
Early research
The psychology behind these findings supports these differences better. Social psychologists during the 1990s ridiculed the notion of any fundamental cognitive differences between male and female minds.
“At the time (when Diane Halpern continued her research in 1991), it seemed clear that any gender differences in thinking abilities were due to socialization practices, artifacts and mistakes in the research, also bias and prejudice,” Halpern wrote in her preface to the first edition, Sex Differences in Cognitive Abilities. However, after critical studies in the 2000s, it stood clear that behavioral differences among the sexes existed.
“Behavioral differences in mating, parenting, and aggression are essential for survival and propagation,” says Shah, a Stanford professor of psychiatry, behavioral sciences, and neurobiology. Psychological behaviors and differences in both males and females occur, and they are inherent rather than learned. The difference in their neural circuits means the nerve cell clusters are evolutionarily wired in a certain way into our brains.
Image processing is just one of many applications of Neural networks. Every day new sub-fields of machine learning spring up, and there is more to learn and discover. Let us know what you liked most about this article, and leave a comment if you understood these Deep learning concepts better!
