The Science Behind Billion Wicked Thoughts

We are computational neuroscientists, which means we view the mind as software running on the hardware of the brain. We try to figure out how the software works for different cognitive processes. For example, some computational neuroscientists try to figure out how the mind software for face recognition works; this research can even lead to actual computer software that recognizes faces at airports. In our book, we investigate the cognitive processes of sexual desire.

Computational neuroscientists use data from a wide variety of sources to help them figure out how mind software works, such as psychology experiments, brain imaging studies, animal neuroscience, machine learning algorithms, even neurological diseases. For our investigation of the mind software responsible for sexual desire, we drew heavily upon a vast set of behavioral data that has previously been ignored by both computational neuroscientists and sex scientists: Internet data.

Many online data sets reveal very specific and quantifiable cognitive components of sexual desire. Internet searches reveal the popularity of various sexual interests, including the age, weight, race, and preferred anatomy of sexual targets. Individual search histories reveal the frequency, variability, and intensity of sexual activity. They also reveal the correlation between different sexual interests, such as a correlation between interests in women’s feet and submission-themed erotica. They also allow us to make comparisons between the sexual interests and activities of men and women, and between straight men and gay men. We can also compare sexual interests across many different cultures, including non-Western cultures, to identify universal patterns and to identify specific places where there is cultural variation.

The analysis of traffic to erotic web sites is also illuminating, since it also shows the relative popularity of various sexual interests and allows us to compare the sexual interests of different demographic groups. This provides us with convergent evidence to confirm findings based upon search data. Subscriptions to adult web sites provides even more detailed information about the age, gender, and sexual orientation of individuals with various sexual interests, and also allows us to compare the interests and activities of different groups.

An analysis of the content and popularity of erotic videos and stories provides even more convergent evidence, and also allows us to identify with even greater specificity the specific qualities of sexual stimuli that people find arousing, especially the analysis of hundreds of thousands of videos and more than a million stories.

An analysis of the comments that individuals leave on erotic sites, particularly comments on specific videos, images, and stories, also helps us identify specific cognitive components of desire since these comments can include specific qualities of the sexual stimuli that are found arousing and the particular reaction they evoke in the individual. Online sex-seeking personal ads and other online personal ads also provide qualitative and quantitative insight into particular sexual interests and activities.

By drawing upon so many different forms of convergent behavioral data, it becomes possible to quantitatively compare the specific sexual interests and activities of different groups of people. It also allows us to model the specific cognitive processes behind individual sexual desire. But even though this rich online behavioral data is very helpful for providing constraints and insights into the operation of the mind software responsible for sexual desire, it is not enough.

We also drew upon recent brain imaging studies to identify the possible brain architecture responsible for specific cognitive components of desire. We gave primary consideration to brain imaging studies that involved subjects viewing or thinking about erotica. For example, by comparing how the male and female brains react differently to erotica, and then comparing these different reactions to the different sexual interests that men and women seek online, we can make some hypotheses about the underlying mind software.

We also drew upon animal studies. Because scientists have greater freedom in investigating the neural architecture of other creatures, it’s possible to identify the neural systems responsible for sexual behavior with greater specificity in primates, rats, and other animals than in humans. For example, the neural systems responsible for dominant and submissive sexual behavior are much better understood in rats than in humans. But since there are very clear similarities between the brains of humans and rats, it’s possible to make hypotheses about the probable location and operation of analogous domination and submission systems in humans. By combining this knowledge with extensive online data regarding men and women’s interest in domination and submission erotica online, we gain more insight into the underlying mind software.

Evolutionary theory also provides further constraints and intuitions about the mind software responsible for sexual desire, by eliminating certain hypotheses about cognitive systems, and recommending others. All neurocognitive systems with a specific function have been designed by natural selection. For example, evolutionary theory helps us understand why we have specific taste cues, including sweet, sour, savory, salty, and bitter. Likewise, evolutionary theory helps us understand why we have specific sexual cues, and why these cues are different in men and women.

Finally, a growing body of knowledge from the computational neuroscience of other cognitive functions, such as vision, memory, motor function, and various emotions, informs our understanding of the mind software of sexual desire. Since much is known about the mind software responsible for vision and object recognition, these neural algorithms provide further constraints and intuition about the mind software for sexual desire. Clearly, the perception of an erotic video involves neural systems involved in vision and recognition, so this constrains any model of sexual arousal from visual stimuli.

Since the online data reflects people’s natural behaviors that occur when they are hidden behind the veil of anonymity, and since the data involves hundreds of millions of people from around the world, we were able to construct a new model of human sexual desire in women and men, including gay men. The model makes many testable predictions and its various pieces are falsifiable. But at its heart, the model is guided by the answer to a rather simple question that has eluded scientists: what do men and women really like?