DyadxMachina

dyad x machina Emotions represent complex neural processes that lie at the helm of humanity's best and worst. We study affective neuroscience, do basic research in psychophysiology, and use deep learning with the goal of creating "the affective layer" which we believe will fuel tomorrow's technologies. The affective layer is what we use to guide ourselves in the world. When we make a decision, we not only analyse the potential pros and cons using cognition (think prefrontal cortex), we also feel through possible futures to come to a final decision (think limbic system to VMPFC). We all know how friends can influence our purchases, but what isn't as obvious is the complex array of negative and positive emotions that move us to act and finally press the "buy" button. MOVING FROM LAB > FIELD > MACHINE LEARNING MODEL Tomorrow's applications should adapt to the user's affective state in real-time rather than act as if the user is a monolithic persona that rarely changes. This is typically quantified in the lab using measures like EDA, EEG, HRV, and fMRI technology (or even transcranial magnetic stimulation), but it can also be quantified out in the field and in applications through physiological wristbands like the E4 or a fitbit. Deep learning and TensorFlow has allowed us to use computer vision (i.e. faces), audio, and complex physiological signal processing to better classify affective states and model their neural substrates. Research has also been done using clickstream data and in-app feature usage to create "affective" features to feed into models without the need for physiological sensors, we are interested in this work as well. FROM LAB > FIELD > MACHINE LEARNING MODEL We'd love to collaborate to help bring the affective layer to life.