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Of Psychopaths, Musical Tastes, Media Relations and Games of Telephone
So I was quite surprised when I was approached by the Guardian to comment on an unpublished abstract. Neuroscientists typically present these as “work in progress” to their colleagues at the annual Meeting of the Society for Neuroscience, which is held in Washington DC in November, this year and at which our lab has 5 such abstracts. Go to this link if you want to read them.
Given these constraints, the Guardian did a good job at explaining this work to a broader audience, emphasizing its preliminary nature (we won’t even attempt to publish this unless we replicate it internally with a larger sample of participants and songs) as well as some ethical concerns inherent to work like this.
What becomes apparent on the basis of our preliminary work is that we can basically rule out the popular stereotype that people with psychopathic tendencies have a preference for classical music and that we *might* be able to predict these tendencies on the basis of combining data from *many* songs – individual songs won’t do, and neither will categories as broad as genre (or gender, race or SES). To confirm these patterns, we need much more data. That’s it.
What happened next is that a lot of outlets – for reasons that I’m still trying to piece together – made this about rap music and a strong link between a preference for rap music and psychopathic traits.
As far as I can tell, there is no such link, I have never asserted there to be one and I am unsure as to the evidentiary basis of such a link at this point.
So all of this is a lesson in media communications. Between scientists and the media, as well as between media and media, media and social media and social media and people (and all other combinations).
So it is basically a game of telephone: What we did. What the (original) media thinks we did. What the media that copies from the original media think we did. What social media thinks we did. What people understand we did. Apparently, all these links are “leaky” or rather unreliable. Worse, the leaks are probably systematic, accumulating systematic error (or bias) based on a cascade of differential filters (presumably, media filters by what they think will gain attention, whereas readers will filter by personal relevance and worldview).
Given that, the reaction of the final recipient (the reader) of this research was basically dominated by their prior beliefs (and who could blame them), dismissing this either as obviously flawed “junk science” or so obvious that it doesn’t even need to be stated, depending on whether the media-rendering of the findings clashed with or confirmed these prior beliefs.
Is publicizing necessarily equal to vulgarizing?
I still think the question of identifying psychopaths based on more than their self-report is important. I also still think that doing so by using metrics without obvious socially desirable answers like music taste is promising, e.g. given their lack of empathy, psychopaths could be taken by particularly lyrics or given their need for stimulation, particular rhythms or beats could resonate with them more than average. But working all that out will take a lot more – and nuanced – work.
And to those who have written me in concern, I can reassure you: No taxpayer money was spent on this – to date.
If you are interested in this, stay tuned.
The author of this article, Pascal Wallisch, is the clinical assistant professor of Psychology at New York University. His research interests are at the intersection of Psychology and Neuroscience, specifically Cognitive and Computational Neuroscience. His current work focuses on motion perception, autism and the appraisal of film.
Dr. Wallisch is the co-author of books including the critically-acclaimed MATLAB for Neuroscientists, the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. Recently he co-authored Neural Data Science: A Primer with MATLAB® and Python, providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.
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