Recent computational models and physiological studies suggest that simple, two-alternative forced-choice decision making can be conceptualized as the gradual accumulation of sensory evidence. Accordingly, information is sampled over time from a sensory stimulus, giving rise to an activation function. A response is emitted when this function reaches a criterion level of activity. Critically, the phenomenon known as speed-accuracy tradeoff (SAT) is modeled as a shift in the response boundaries (criterion). As speed stress increases and criterion is lowered, the information function travels less distance before reaching threshold. This leads to faster overall responses, but also an increase in error rate, given that less information is accumulated. Psychophysiological data using EEG and single-unit recordings from monkey cortex suggest that these accumulator models are biologically plausible. The present work is an effort to strengthen this position. Specifically, it seeks to demonstrate a neural correlate of criterion and demonstrate its relationship to behavior. To do so, subjects performed a letter discrimination paradigm under three levels of speed stress. At the same time, electroencephalogram (EEG) was used to derive a measure known as the lateralized readiness potential, which is known to reflect ongoing motor preparation in motor cortex. In Experiment 1, the amplitude of the LRP was related to speed stress: as subjects were forced to respond more quickly, less information was accumulated before making a response. In other words, criterion lowered. These data are complicated by Experiment 2, which found that there are boundary conditions for this effect to obtain.

Speed-accuracy tradeoffs, velocity profiles, and physiological tremor are fundamental characteristics of human movement. The principles underlying these phenomena have long attracted major interest and controversy. Each is well established experimentally but as yet they have no common theoretical basis. It is proposed that these three phenomena occur as the direct consequence of a movement response planning system that acts as an intermittent optimal controller operating at discrete intervals of ~100 ms. The BUMP model of response planning describes such a system. It forms the kernel of adaptive model theory which defines, in computational terms, a basic unit of motor production or BUMP. Each BUMP consists of three processes: (i) analysing sensory information, (ii) planning a desired optimal response, and (iii) executing that response. These processes operate in parallel across successive sequential BUMPs. The response planning process requires a discrete time interval in which to generate a minimum acceleration trajectory of variable duration, or horizon, to connect the actual response with the predicted future state of the target and compensate for executional error. BUMP model simulation studies show that intermittent adaptive optimal control employing two extremes of variable horizon predictive control reproduces almost exactly findings from several authoritative human experiments. On the one extreme, simulating spatially-constrained movements, a receding horizon strategy results in a logarithmic speed-accuracy tradeoff and accompanying asymmetrical velocity profiles. On the other extreme, simulating temporally-constrained movements, a fixed horizon strategy results in a linear speed-accuracy tradeoff and accompanying symmetrical velocity profiles. Furthermore, simulating ramp movements, a receding horizon strategy closely reproduces experimental observations of 10 Hz physiological tremor. A 100 ms planning interval yields waveforms and power spectra equivalent to those of joint-angle, angular velocity and electromyogram signals recorded for several speeds, directions, and skill levels of finger movement. While other models of response planning account for one or other set of experimentally observed features of speed-accuracy tradeoffs, velocity profiles, and physiological tremor, none accounts for all three. The BUMP model succeeds in explaining these disparate movement phenomena within a single framework, strengthening this approach as the foundation for a unified theory of motor control and planning.

Empirical studies show that similar patterns emerge from a large number of different biological systems. For example, the group size distributions of several fish species and house sparrows all follow power law distributions with an exponential truncation. Networks built by ant colonies, slime mold and those are designed by engineers resemble each other in terms of structure and transportation efficiency. Based on the investigation of experimental data, we propose a variety of simple stochastic models to unravel the underlying mechanisms which lead to the collective phenomena in different systems. All the mechanisms employed in these models are rooted in the concept of selective reinforcement. In some systems the reinforcement can build optimal solutions for biological problem solving. This thesis consists of five papers. In the first three papers, I collaborate with biologists to look into group formation in house sparrows  and the movement decisions of damsel fish.  In the last two articles, I look at how shortest paths and networks are  constructed by slime molds and pheromone laying ants, as well as studying  speed-accuracy tradeoffs in slime molds' decision making. The general goal of the study is to better understand how macro level patterns and behaviors emerges from micro level interactions in both spatial and non-spatial biological systems. With the combination of mathematical modeling and experimentation, we are able to reproduce the macro level patterns in the studied biological systems and predict behaviors of the systems using minimum number of parameters.

Speed-accuracy tradeoffs, velocity profiles, and physiological tremor are fundamental characteristics of human movement. The principles underlying these phenomena have long attracted major interest and controversy. Each is well established experimentally but as yet they have no common theoretical basis. It is proposed that these three phenomena occur as the direct consequence of a movement response planning system that acts as an intermittent optimal controller operating at discrete intervals of ~100 ms. The BUMP model of response planning describes such a system. It forms the kernel of adaptive model theory which defines, in computational terms, a basic unit of motor production or BUMP. Each BUMP consists of three processes: (i) analysing sensory information, (ii) planning a desired optimal response, and (iii) executing that response. These processes operate in parallel across successive sequential BUMPs. The response planning process requires a discrete time interval in which to generate a minimum acceleration trajectory of variable duration, or horizon, to connect the actual response with the predicted future state of the target and compensate for executional error. BUMP model simulation studies show that intermittent adaptive optimal control employing two extremes of variable horizon predictive control reproduces almost exactly findings from several authoritative human experiments. On the one extreme, simulating spatially-constrained movements, a receding horizon strategy results in a logarithmic speed-accuracy tradeoff and accompanying asymmetrical velocity profiles. On the other extreme, simulating temporally-constrained movements, a fixed horizon strategy results in a linear speed-accuracy tradeoff and accompanying symmetrical velocity profiles. Furthermore, simulating ramp movements, a receding horizon strategy closely reproduces experimental observations of 10 Hz physiological tremor. A 100 ms planning interval yields waveforms and power spectra equivalent to those of joint-angle, angular velocity and electromyogram signals recorded for several speeds, directions, and skill levels of finger movement. While other models of response planning account for one or other set of experimentally observed features of speed-accuracy tradeoffs, velocity profiles, and physiological tremor, none accounts for all three. The BUMP model succeeds in explaining these disparate movement phenomena within a single framework, strengthening this approach as the foundation for a unified theory of motor control and planning.

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