 |
NBER: Nick Merrill, UC, Berkeley
Nick Merrill, UC, Berkeley
>From: Nick Merrill <ffff_at_berkeley.edu>
>
>
>Hi Mr Healy,
>
>Please find my application for NSF graduate
>student funding for the winter meeting of the NBER digitization group.Â
>
>----------------------------
>
>- Please provide contact information, including email.
>
>Nick Merrill
>UC Berkeley School of Information
><mailto:ffff_at_berkeley.edu>ffff_at_berkeley.edu
>
>- Please provide a short description of the course of study pursued duringÂ
>graduate work, including the type of course work pursued, major and minor
>focus of study, year of advance, and so on.
>
>PhD student
>Entered 2013
>Advance to candidacy Spring 2016
>Major focus: Human-computer interaction, computer-mediated communication
>Minor focus: Distributed systems
>
>Open collaboration and peer production
>User interface design
>Cryptography
>fMRI methods
>Distributed systems
>Machine learning
>
>- Please provide a short description of general
>research interests or other projects pursued. If
>the student has proposed a dissertation, and
>formed a committee, please provide a short
>description, expected date of completion, and general details.
>
>As physiological sensors become smaller and
>cheaper, researchers, tinkerers, and commercial
>service providers are able to build increasingly
>high-resolution models of the human physiology
>as it exists "in the wild," outside of
>laboratories and hospitals. These models allow
>new opportunities, both commercial and
>non-commercial. Already, some insurance
>companies are offering discounts to customers
>who wear a fitness tracker. Meanwhile, the Apple
>Watch allows users to share their heartrate with
>a friend. As our physiologies become a digitized
>good, we must ask: what can data from wearable
>sensors mean? What is their expressive capacity?
>
>I am interested in what meanings physiological
>sensor data can take on, especially when shared
>in non-medical contexts. For example, there are
>correlations between physiological signals and
>emotional response. Even raw physiological
>signals can take on socially contextual
>meanings: raw heartrate data, for instance, can
>signal intimacy, but also fear and anxiety.
>
>1. What meaning do people build around
>physiological signals? How do these meanings
>change in different social contexts?
>
>2. How do interpretations of physiological
>signals affect decision making and behavior?
>
>3. How do these interpretations arise in the
>first place? What mechanisms turn physiological
>signals into social cues? (In other words, is
>there something special about physiological
>sensor data, or would other data from
>non-physiological sources work just as well?)
>
>I am running controlled, lab-based experiments
>on interpretations around heartrate data, and
>how these interpretations affect trusting
>behavior in an iterated prisoners' dilemma game.
>
>I am also running more ecologically valid
>studies on high schoolers in a computer science
>and data literacy class. Students receive a
>wearable sensor that measures breathing, and
>generates algorithmic judgements such about
>stress, focus, and calmness, and are made to
>critically interrogate the sensor, its output,
>and its broader meaning as an artifact.
>
Received on Fri Nov 13 2015 - 13:59:40 EST
|
