Friday, September 26, 2014

The Human Dynamic Clamp

In my distant past when I was doing cellular neurophysiology we used a technique called the "voltage clamp", in which the electrophysiology equipment measuring a nerve signal was linked to a computer that could inject current to alter the signal's behavior in a bi-directional interaction, and thus test models for the ion fluxes underlying the signals. Dumas et al. ask if a similar approach could be applied to study human interactions:
For example, were a human to interact with a model constructed to behave like him- or herself, might this tell us something about human beings and how they work together?....scaling the dynamic clamp paradigm from neurons and neural ensembles to human beings and human brains in a principled fashion is nontrivial. A potential starting point is to ground the design of an HDC (Human Dynamic Clamp) in the empirically based theoretical models of coordination dynamics
From their introduction:
...We will describe the HDC for four classes of behavior. Basically, the HDC models the interactions between a human and a virtual partner (VP) in the language of informationally coupled, nonlinear dynamical systems. The movements of the human enter the equations of motion associated with a specific model. This produces the dynamics of the VP that are displayed on a video screen. To complete the reciprocal coupling between the human and VP, the subject sees the motion of the VP. In a first version, the rhythmic movements of the subject enter the equations of motion of the Haken–Kelso–Bunz (HKB) model, considered one of the most extensively tested quantitative models of human motor behavior. Then, we expand the behavioral repertoire of the VP through the excitator model, which describes both rhythmic and discrete movement generation. In a further elaboration, adaptive behavior is introduced through changing parameter dynamics, illustrated here by modifying the intrinsic frequency of the VP. Finally, to study how a VP may adopt a directed behavior and hence play the role of a “teacher,” we use an adaptation of the empirically verified Schöner–Kelso model of behavioral pattern change.
Here is a section of the article abstract (the article appears to be open source, more detailed procedures and equations can be found there):
...the HDC allows a person to interact in real time with a virtual partner itself driven by well-established models of coordination dynamics. People coordinate hand movements with the visually observed movements of a virtual hand, the parameters of which depend on input from the subject’s own movements. We demonstrate that HDC can be extended to cover a broad repertoire of human behavior, including rhythmic and discrete movements, adaptation to changes of pacing, and behavioral skill learning as specified by a virtual “teacher.” We propose HDC as a general paradigm, best implemented when empirically verified theoretical or mathematical models have been developed in a particular scientific field. The HDC paradigm is powerful because it provides an opportunity to explore parameter ranges and perturbations that are not easily accessible in ordinary human interactions. The HDC not only enables to test the veracity of theoretical models, it also illuminates features that are not always apparent in real-time human social interactions and the brain correlates thereof.
Finally, the conclusion sounds very cosmic!
The HDC offers a way to bring mind, brain, and machine together through behavior. Under such a framework, we have shown that it is possible to unify and generalize diverse functions and tasks. The approach is principled: Each new version of the HDC carries the mathematics of all previous versions (Table 1). As long as there is a medium for two-way interaction, a deeper understanding of both the model and what the model is purported to be of become possible. Once coupled bidirectionally to an unconstrained, open dynamical system like a human being, HDC’s behavioral repertoire becomes much richer—in a manner akin, perhaps, to the way human behavior develops and gains depth through social interactions. In experiments, the richness of HDC behavior already led to unsolicited verbal reactions by human subjects, e.g., attribution of agency to the VP. Such spontaneous expressions suggest that the HDC may qualify as a Turing test of humanness, even surpassing its original scope. The Turing test implies only that judges are unable to tell if an agent is a human or a machine, and as such says nothing about the genuineness of the path toward that decision. Here, the HDC is a tool to test hypotheses and gain understanding about how humans interact with each other as well as with machines. In the HDC paradigm, exploration of the machine’s behavior may be viewed as an exploration of us as well.

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