research

The division between subject and object, agent and non-agent, has consistently been dubious philosophically, with physical manifestations of automatons the exception rather than the rule. Now we are increasingly faced with computational objects and relational artifacts that put into question cherished notions of human agency and intentionality. syngva is a creature that develops through evolutionary processes idiosyncratic movements in response to singing. syngva serves two parallel roles. For the user, syngva enables a form of non-linguistic reflection, serving as a catalyst for novel vocal behaviors provoked by the motions of the object. For myself, syngva acts as a sociological probe, allowing me to study in-situ relationship formation, agency, and control in response to an “intelligent” creature. The evaluation approach draws heavily from actor-network theory (ANT), a methodology that in part places objects on the same ontological level as human agents. This re-centering of agency intimates a different way of looking at the person-object dyad that focuses on the interactions themselves without reference to pre-existing theories.

A note on naming: The word “syngva” comes from Old Norse meaning simply “to sing”. I see this project taking a number of forms as it develops. Rather than enumerating each new revision with the suffixes “Version 1.0″, “Version 2.0″, and so on, I have decided to add letters to the end of the word instead, moving to the next letter of the alphabet with each new revision. Thus the first version is “syngvaa”, the second is “syngvab”, and so on.

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amia is the first device within a framework that we call “amiable media”, or technologies that aim at integrating the physical and digital worlds to give rise to new forms of interpersonal communication.

We did a simple user survey beforehand to get an idea of how people communicate with loved ones, what that form of communication misses from face-to-face communication, and whether or not people would be interested in a device like amia.

amia is a device for helping two people keep in touch: think academic couples separated by long distances, a spouse who travels frequently, and so on. The device has two main modes: passive and active. In passive mode, microphones on one device pick up the ambient noise level and transmit this noise level to the companion device. A band the encircles amia glows in response to the ambient noise level of the other device. In this way, we have a means of indicating the presence of the other person without being too intrusive.

The second mode is the active mode, made up of a number of components. First, heat sensors on device will activate when a person handles the device; this information is transferred to the companion device and translated into a more “reddish” color as the temperature on the other device increases. Secondly, capacitive sensors pick up hand motions across the outside of the device, which are converted into pulses or sequences of light; the user can then “compose” sequences of light patterns to send to the companion device, thus allowing for abstract interpersonal communication. Finally, actuators on the surface of the devices can be pushed in or popped out; pushing in on one device leads to popping out of the corresponding actuator on the companion device. Thus the users can, perhaps, send messages or play games using these push-in, pop-out components.

The goal with this project was to try and find a way for two people separated by distance to keep in contact non-verbally, since cell phones and IM are so prevalent, but yet miss out on a lot of the nuances of real-life interpersonal communication.

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That people have emotional responses to music is a truism. However, we have little understanding of the ways in which music brings about these emotions. Indeed, we lack decent ways to measure these responses in a quantitative way. As an early step in this area, we devised a listening experiment with a novel response paradigm. Listeners chose from a set of around twenty emotional descriptors, selecting a strength value for each chosen word. Importantly, we did not prevent the listener from selecting conflicting words, or limit her to only one choice. We then used unsupervised machine learning techniques to explore the space of responses. Early results show good agreement with prior studies, but with the potential for more nuanced understanding. We plan to extend this work into considering a broader space of influencing factors on emotional response.

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