Feedback delays can enhance anticipatory synchronization in human-machine interaction.

Research investigating the dynamics of coupled physical systems has demonstrated that small feedback delays can allow a dynamic response system to anticipate chaotic behavior. This counterintuitive phenomenon, termed anticipatory synchronization, has been observed in coupled electrical circuits, laser semi-conductors, and artificial neurons. Recent research indicates that the same process might also support the ability of humans to anticipate the occurrence of chaotic behavior in other individuals. Motivated by this latter work, the current study examined whether the process of feedback delay induced anticipatory synchronization could be employed to develop an interactive artificial agent capable of anticipating chaotic human movement. Results revealed that incorporating such delays within the movement-control dynamics of an artificial agent not only enhances an artificial agent's ability to anticipate chaotic human behavior, but to synchronize with such behavior in a manner similar to natural human-human anticipatory synchronization. The implication of these findings for the development of human-machine interaction systems is discussed.

Anticipation in manual tracking with multiple delays.

Two experiments are reported showing that behavior exhibited in manual tracking is consistent with behavior predicted by a dynamical systems phenomenon known as anticipating synchronization (Voss, 2000). They extend a prior investigation of the effect of delay on anticipatory manual tracking (Stepp, 2009) by also manipulating coupling strength. The coupling scheme in Experiment 1 and that in Experiment 2 go beyond the single delayed feedback coupling used in previous research and articulations of anticipating synchronization. These advanced coupling arrangements are addressed using an extended formulation which allows for multiple feedback delays, a continuous range of delay, or even coupling to real future values. The latter case is specifically investigated in Experiment 2, which utilizes a navigation task that provides a natural way to speak about coupling to future values. (PsycINFO Database Record

A negative group delay model for feedback-delayed manual tracking performance.

We propose that feedback-delayed manual tracking performance is limited by fundamental constraints imposed by the physics of negative group delay. To test this hypothesis, the results of an experiment in which subjects demonstrate both reactive and predictive dynamics are modeled by a linear system with delay-induced negative group delay. Although one of the simplest real-time predictors conceivable, this model explains key components of experimental observations. Most notably, it explains the observation that prediction time linearly increases with feedback delay, up to a certain point when tracking performance deteriorates. It also explains the transition from reactive to predictive behavior with increasing feedback delay. The model contains only one free parameter, the feedback gain, which has been fixed by comparison with one set of experimental observations for the reactive case. Our model provides quantitative predictions that can be tested in further experiments.

Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware.

Neuromorphic hardware are designed by drawing inspiration from biology to overcome limitations of current computer architectures while forging the development of a new class of autonomous systems that can exhibit adaptive behaviors. Several designs in the recent past are capable of emulating large scale networks but avoid complexity in network dynamics by minimizing the number of dynamic variables that are supported and tunable in hardware. We believe that this is due to the lack of a clear understanding of how to design self-tuning complex systems. It has been widely demonstrated that criticality appears to be the default state of the brain and manifests in the form of spontaneous scale-invariant cascades of neural activity. Experiment, theory and recent models have shown that neuronal networks at criticality demonstrate optimal information transfer, learning and information processing capabilities that affect behavior. In this perspective article, we argue that understanding how large scale neuromorphic electronics can be designed to enable emergent adaptive behavior will require an understanding of how networks emulated by such hardware can self-tune local parameters to maintain criticality as a set-point. We believe that such capability will enable the design of truly scalable intelligent systems using neuromorphic hardware that embrace complexity in network dynamics rather than avoiding it.

Synaptic plasticity enables adaptive self-tuning critical networks.

During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.

Whole body lexical decision.

When a person standing upright raises an arm on cue, muscles of the left and right sides of the body exhibit changes prior to and specific to the responding arm. We had standing participants perform a visual lexical decision task ("is this letter string a word?"), responding yes by raising one arm and no by raising the other arm. We recorded onset of the arm movement and onset of electromyographic activity in thigh, trunk, and shoulder muscles. We observed the expected responding arm specificity and found that the onset difference favoring word decisions was evident in similar magnitude at all measurement sites, with the difference at the levels of thigh, trunk and shoulder muscles available 225, 189, and 120 ms, respectively, prior to its manifestation at the level of arm movement. We discuss including (a) whole body reaction time along with event-related potentials in determining the decision-response, brain-body temporal relation and (b) response execution along with response initiation in investigating mental chronometry.

Philosophy for the rest of cognitive science.

Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel's (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically oriented cognitive scientists. As an example of nonrepresentational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp & Turvey, 2009). We then propose a philosophy of science appropriate to nonrepresentational, dynamical cognitive science.

Anticipation in feedback-delayed manual tracking of a chaotic oscillator.

Manual tracking of non-chaotic targets, with and without feedback delay, as well as discrete prediction of chaotic maps have each been demonstrated by humans. Feedback-delayed tracking of chaotic targets, on the other hand, has not been well investigated. To this end, 10 participants were asked to track a chaotically moving target presented on a computer display by means of controlling a similar on-screen object using a pressure sensitive tablet and hand-held stylus. The participants were given delayed visual feedback of their own movements. Task success subsequently required anticipation on the part of the participant. Using 6 values of delay from 20 ms to 1 s, evidence shows that (a) participants are able to synchronize with a chaotic target, even with some amount of applied delay, (b) task performance varies systematically with applied delay, and (c) this same systematic dependence is predicted for systems exhibiting anticipating synchronization.