ABSTRACT. 1.0 Introduction: Modeling Broad-Spectrum Mitigation

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1 Broad-Spectrum Mitigation and the Cognitive Neurobiological Interface: considering biological rhythms in Augmented Cognition. By Bradly Alicea Department of Telecommunication, Information Studies, and Media and Cognitive Science Program; Michigan State University ABSTRACT Brain-computer interface design relies upon mitigating human performance. Recent attempts have focused mainly on real-time cognitive processing. This paper will consider biological rhythms as a longer-term and more dynamic factor for determining human performance in such systems. To characterize the contribution of such neurobiological phenomena to human performance, biological rhythms will be proposed as the input for both sub-cognitive state gauges and a predictive model based on complexity theory that modulate the input/output properties of basic cognitive state gauges. The relationship of biological rhythms to cognitive processing exists at multiple temporal scales, and provides an addition-al level of complexity to currently held conceptual models. Most notably, a concept called broad-spectrum mitigation will be considered as a way to improve human performance and more effectively augment cognitive processing. 1.0 Introduction: Modeling Broad-Spectrum Mitigation In the design of direct brain interfaces, an instance of which is the DARPA Augmented Cognition (AC) project, models of mitigating sub-optimal human performance has focused mainly on real-time cognitive processing (see Rapaport et al, 2005). This paper will consider biological rhythms as a longer-term and more dynamic factor for determining human performance in such systems. The relation-ship of biological rhythms to cognitive processing will then be considered as a means of providing an additional level of complexity to the AC conceptual model. Three types of rhythms may be relevant both specifically to attentional states and to human performance in general. Ultradian rhythms may affect daily fluctuations in arousal (Dunlap et al, 2004), while circadian and circannual fluctuations may influence even longer-term trends in human performance and indirectly inform temporally complex mitigation strategies. Specifically, it will be argued that biological rhythms set the underlying context for performance mitigations at different temporal scales. Thus, understanding their effect on arousal and task performance will lead to predictive models that serve as a type of broad spectrum mitigation. Broad spectrum mitigation, as a general concept, uses the complexity of long-term trends in cognitive neurobiological systems to build computational models that fill in gaps inherent in real-time system mitigation. 1.1 Broad-Spectrum Mitigation as a Control System The idea of broad-spectrum mitigation, similar to many current brain interface and Augmented Cognition systems, can be thought of as a semi-closed lo- op control system. Inputs from subcognitive factors such as biological rhythms combine with the more direct contributions from cognitive state gauges to provide performance mitigation to the user that is sensitive to changes in user state at multiple time scales. Figure 1 shows the major contribution of sub-cognitive components is that of an additional input. It is envisioned that inputs from the cognitive and broad-spectrum gauges operate at different time scales, so that while cognitive state gauges are updated continually, broad-spectrum in-puts provide meaningful information to the system on the scale of minutes to hours. Van Dongen et al (1999) have used the Lomb-Scargle periodogram to observe that informative biological rhythms are 1

2 both aperiodic and exist at multiple time scales. This means that not only do sub-cognitive inputs provide secondary and sporadic but critical information to the closed-loop system, they also require novel machine learning models for producing real-time mitigations. Figure 1: the image at the left (A) shows the architecture of a standard Augmented Cognition system. The image at right (B) shows the addition of a sub-cognitive component. 1.2 Iterated Maps as a Predictive Model The other aspect of employing broad-spectrum mitigation is that of developing an adaptive filter that integrates the contributions of both levels of information at specific time steps. Even though the Yerkes-Dodson curve is reliable for predicting when mitigations should be employed, using more ma-thematically rigorous models such as iterated maps (see Strogatz, 2001) may lend insight into events leading up to breakdowns in arousal and self-correct performance shortcomings of real-time statistical classification and pattern recognition methods. An iterated or logistic map can be used to trans-form the closed-loop control system into a dynamical system. The logistic map is sensitive to the behavior of chaotic time series, and can be defined by the following equation xn+1 = λ [xn(1-xn)] [1] where λ is the sum of the cognitive and sub-cognitive gauge readings at a particular time step, and x n is the degree of mitigation applied by the system at a particular time step. The value for lambda is typically positive, while the value of x is generally between 0 and 1. The purpose of this model is to predict the level of mitigation needed in the next moment given current inputs from both the cognitive and subcognitive gauges. Across time, critical values of lambda are reach-ed; during very short intervals, the system undergoes a phase transition and becomes stable at multiple values of xn. This temporary instability should be bursty across time scales; such phenomena imply that broad-spectrum mitigation will be enforced in brief and semi-periodic intervals usually related to some isolated event. Burstiness is a common property of data streaming on the internet and other communication networks (see Di Cairano-Gilfedder and Clegg, 2005), and may also act as a trigger for more complex mitigations. It is predicted that changes in the sub-cognitive state gauge over particular periods of time will drive these bifurcations, which render the system temporarily unstable. The 2

3 time course of biological rhythms provides a particularly robust means of characterizing subcognitive modulation in real-time; many biological oscillators have characteristic time scales distinct from that of cognitive processes. The advantage of the iterative map is that these points of instability can be predicted given certain inputs and an initial condition. 2.0 Relevance of Biological Rhythms to Augmented Cognition Biological rhythms exist throughout the human body as both standalone pacemakers and complex networks, however some people argue that the suprachiasmatic nucleus (SCN) is a master clock that regulates all other oscillators. Biological rhythms are also modulated both by exogenous and endogenous cues. Biological rhythms can be entrained purely by an external source, created solely by a pacemaker nucleus, or be modulated by a combination of both. Depending on their nature, these clocks m-ay serve as a timing mechanism for cognitive behaviors, sleep-wake cycles, and artificial systems built to predict these processes. 2.1 Types and sources of biological rhythms Oscillators that affect cognitive processes can be both highly itinerant and quite regular. For example, the pathway from the retina to the SCN is modulated by both ambient light levels and Neuropeptide Y (Dunlap et al, 2005). These inputs work together to modulate biological rhythms at several temporal scales related to hourly, daily, and seasonal variation. Light deprivation arising from less exposure to day-light during the winter months is a basic feature of seasonality (Mendlewicz and van Praag, 1983). In a similar manner, this lack of light over weeks and even months can attenuate arousal patterns substantially across an entire photoperiod (Webb, 1982). Such drastic changes in inputs must be taken into ac-count by AC mitigation strategies. Strictly regulated rhythms are built primarily on endogenous mechanisms such as the selective transcription of Per and Clock genes (Reppert and Weaver, 2002). The resulting gene products help to build a clock mechanism in various target tissues and nuclei on a daily basis. For example, the chronon model of circadian periodicity relies on a basic transcriptional cycle that is rate-limiting. The genes governing basic cycles are transcribed in sequence, so a single variable such as the rate of transcription can control the entire period (Dunlap, 1999). A de-tailed understanding of daily variations in the selective production of proteins and their effects on behavior are only beginning to be understood (Koukkari and Sothern, 2005). However, it may be that such molecular-level mechanisms could provide a predictable timing mechanism for cognitive state gauge measurements. 2.2 Biological Rhythms and Behavioral Control A good example of the potential connections between molecular-based timing and cognitive processing comes from the work of Pedemonte and Velluti (2005). Their work has associated hippocampal theta EEG rhythms with autonomic control of heart rate and changes in selective attention. They further argue that the sensory processing characterized by this activity-dependent EEG component requires a timer. This timer regulates the rate and efficiency of information processing and memory consolidataion (Pedemonte and Velluti, 2005). Thus, Theta band activations originating from cellular and molecular-level interactions in the hippocampus, serve as an endogenous temporal organizer for higher-level cognitive processing and arousal states. The 3

4 differential activity of this organizer is also based on states of drowsiness and wakefulness (Pedemonte and Velluti, 2005). In light of these results, one might view endogenous oscillators as a sort of biological mitigation strategy; natural selection's contribution to AC systems. 2.3 Rhythmic Behavior and Interval Timing Mech and Buhusi (2005) have introduced the concept of interval behavioral timing, which includes but is not exclusive to ultradian oscillators. Over the course of several minutes to several hours, a number of important neuron-al and cognitive processes unfold. It is the interaction of these different processes, and specifically the sub-cognitive contribution, that is both of greatest interest and least under-stood. For example, complex mental processes occurring at the interval time scale include decision-making, search behaviors, and conscious time estimation. At the neural and endocrinological levels, corticostriatal long-term potentiation and melatonin secretion modulate the parametric ranges of arousal in relation to each of these cognitive-level behaviors (Mech and Buhusi, 2005). Interestingly, immediate mitigations of arousal may have an effect on later and longer-term manifestations of arousal. Lavie (1989) has found that increased drowsiness enhances ultradian EEG components responsible for one-and-a-half hour long cycles in motor activity and reaction time performance, while at the same time suppressing similar cycles associated with arousal. It stands to reason that a mitigation designed to decrease drowsiness may have the opposite effect of the Lavie finding, and act to influence both reaction time performance and arousal levels in ways not easily predictable by current cognitive state gauges. 2.4 Interval timing across the 24-hour day Changes in arousal during the course of a 24-ho-ur day are common, and are related to homeostatic processes in the sleep-wake cycle. An EEG component in the theta band has been associated with the onset of melatonin secretion, while a high-frequency EEG component in the alpha band tends to exhibit a minimum that maps to fluctuations in body temperature (Aeschbach et al, 1999). Daily fluctuations in body temperature generally follow a curve that reaches a maximum in late afternoon. Similarly, there is a well-studied post-lunch dip in arousal that occurs in early afternoon (Koukari and Sothern, 2005). Comparisons between individuals who work first shift versus third shift or jobs that require long shifts with relatively in-frequent stimulation versus jobs requiring in-tense and constant stimulation may reveal that ultradian trends in arousal differ by setting or individual (Dunlap et al, 2005). Body temperature may also be related to more general mitigation strategies that involve day-long trends in arousal; this may allow a system to better predict in advance which task queuing strategies are optimal given an operator's ultradian profile. 2.5 Caveats to the Broad-Spectrum Approach There are several potential problems to combining biological rhythm and cognitive state data to pr-edict when performance should be mitigated. Under sleep deprivation and forced synchrony, there are significant interactions between homeostatic and circadian factors (Shegal, 2004). In addition, rhythms can be split between oscillators in the left and right suprachiasmatic nucleus. In rats, this is related behaviorally to bursts in locomotor activity that occur at differential points in the 4

5 light-dark cycle. While this is clearly observable in the elevated expression of cfos messenger RNA levels, it may not map directly to variations in behavior. The best way to overcome this problem is to measure the most robust parameters that mark changes in the oscillators that control rhythms. For example, high levels of immediate-early gene expression have been observed in the medial lateral habenula and medial preoptic area during active and inactive circadian phases, respectively (Tavakoli-Nezhad et al, 2005). In addition, a neurochemical called Prokineticin 2 has been found to be transmitted by the SCN through ventricular pathways during subjective night when local production of Prokineticin 2 is low (Cheng et al, 2002). 3.0 Discussions and Applications By sampling endocrinological parameters using sweat gland or blood stream sensors or EEG components related to ultradian variation (Hayashi et al, 1994), several novel contributions can be made to the AC theoretical paradigm. First, we can build sub-cognitive state gauges that allow for broad spectrum mitigation. Such sub-cognitive state gauges would operate according to a shifting baseline of arousal, and provide a level of context for the cognitive state gauges. Secondly, a broad spectrum approach allows for better predictive models to be used. Finally, these cognitive neurobiological interfaces could be applied in settings such as long-distance trucking, third-shift work environments, and the human monitoring of nuclear power plants. More sophisticated predictive models might also make for more adaptive systems that more effectively manage both long-term and short-term time-dependent processes simultaneously. 4.0 References Aeschbach, D., Matthews, J.R., Postolache, T.T., Jackson, M.A., Giesen, H.A., Wehr, T.A. (1999). Two circadian rhy-thms in the human electroencephalogram during wakefulness. American Journal of Physiology, 277(6), R1771-R1779. Buhusi, C.V. and Meck, W.H. (2005). What makes us tick? Functional and neural mechanisms of interval timing. Nature Reviews Neuroscience, 6(10), Cheng, M.Y., Bullock, C.M., Li, C., Lee, A.G., Bermack, J.C., Belluzzi, J., Weaver, D.R., Leslie, F.M., Zhou, Q.Y. (2002). Prokineticin 2 transmits the behavioral circadian rhythm of the suprachiasmatic nucleus. Nature, 417 (6887), Di Cairano-Gilfedder, C. and R.G. Clegg (2005). A decade of internet research: advances in models and practices. BT Technology Journal, 23(4), Dunlap, J.C., Loros, J.J., and DeCoursey, P.J. (2004). Chronobiology: biological timekeeping. Sunderland, MA.: Sin-auer. Dunlap, J.C. (1999). Molecular Bases for Circadian Clocks. Cell, 96, Hayashi, M., Sato, K., and Hori, T. (1994). Ultradian rhythms in task performance, self-evaluation, and EEG activity. Per-ceptual Motor Skills, 79(2),

6 Koukari, W.L. and Sothern, R.B. (2005). Introducing Biological Rhythms: A Primer on the Temporal Organization of Life, with Implications for Health, Society, Reproduction, and the Natural Environment. New York: Springer. Lavie, P. (1989). Ultradian rhythms in arousal: the problem of masking. Chronobiology International, 6(1), Mendlewicz, J. and vanpraag, H.M. (1983). Biological Rhythms and Behavior. New York: Karger. Pedemonte, M. and Velluti, R.A. (2005). Sensory processing could be temporally organized by ultradian brain rhythms. Re-vista Neurologica, 40(3), Rapoport, E.D., Downs, J.H., Downs, T.H., and Proffitt, D.R. (2005). First Investigations of an fnir-based Brain-Computer Interface. Proceedings of HCI International 2005, Las Vegas, NV. Reppert, S.M. and Weaver, D.R. (2002). Coordination of circadian timing in mammals. Nature, 418, Seghal, A. (2004). Molecular Biology of Circadian Rhythms. New York: Wiley-Liss. Strogatz, S.H. (2001). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering. New York: Perseus. Tavakoli-Nezhad, M. and Schwartz, W.J. (2005). c-fos expre-ssion in the brains of behaviorally split hamsters in constant light: calling attention to a dorsolateral region of the SCN and medial division of the lateral habenula. Journal of Biological Rhythms, 20(5), VanDorgen, H.P.A., Olofsen, E., VanHartvelt, J.H., and Kruyt, E.W. (1999). Searching for Biological Rhythms: peak detection in the periodogram of unequally spaced data. Journal of Biological Rhythms, 14(6), Webb, W.B. (1982). Biological Rhythms, Sleep, and Performance. New York: Wiley. 6