Towards a physio-cognitive model of the exploration exploitation trade-off.

Managing the exploration vs exploitation trade-off is an important part of our everyday lives. It occurs in minor decisions such as choosing what music to listen to as well as major decisions, such as picking a research direction to pursue. The dilemma is the same despite the context: does one exploit the environment, using current knowledge to acquire a satisfactory solution, or explore other options and potentially find a better answer. An accurate cognitive model must be able to handle this trade-off because of the importance it plays in our lives. We are developing physio-cognitive models to better understand how physiological and cognitive processes interact to mediate decisions to explore or exploit. To accomplish this, we utilize the ACT-R/Φ hybrid architecture (Dancy, 2013; Dancy et al., 2015) and the Project Malmo AI platform (Johnson et al., 2016)

Towards a physio-cognitive model of slow-breathing

How may controlled breathing be beneficial, or detrimental to behavior? Computational process models are useful to specify the potential mechanisms that lead to behavioral adaptation during different breathing exercises. We present a physio-cognitive model of slow breathing implemented within a hybrid cognitive architecture, ACT-R/Φ. Comparisons to data from an experiment indicate that the physiological mechanisms are operating in a manner that is consistent with actual human function. The presented computational model provides predictions of ways that controlled breathing interacts with mechanisms of arousal to mediate cognitive behavior. The increasing use of breathing techniques to counteract effects of stressors makes it more important to have a detailed mechanistic account of how these techniques may affect behavior, both in ways that are beneficial and detrimental. This multi-level understanding is useful for adapting to changes in our physical and social environment, not only for performance, but for physical and mental health

Towards using a physio-cognitive model in tutoring for psychomotor tasks.

We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through a BioHarness, while interacting with GIFT simultaneously. We also uti-lize a computational model to better understand the learner and domain. We focus on a precision-required psychomotor task (i.e., golf putting) and create a series of courses in GIFT that instruct how to do putting with tactical breathing. We report our implementation of a physio-cognitive model that can account for the process of psychomotor skill development, the GIFT extension, and a pilot study that uses the extension. The physio-cognitive model is based on the ACT-R/Φ architecture to model and predict the process of learning, and how it can be used for improving the fundamental understanding of the domain and learner model. Our study contributes to the use of cognitive modeling with physiological con-straints to support adaptive training of psychomotor tasks in ITSs

Bridging ACT-R and Project Malmo, developing models of behavior in complex environments

Cognitive architectures such as ACT-R provide a system for simulating the mind and human behavior. On their own they model decision making of an isolated agent. However, applying a cognitive architecture to a complex environment yields more interesting results about how people make decisions in more realistic scenarios. Furthermore, cognitive architectures enable researchers to study human behavior in dangerous tasks which cannot be tested because they would harm participants. Nonetheless, these architectures aren’t commonly applied to such environments as they don’t come with one. It is left to the researcher to develop a task environment for their model. The difficulty in creating one prevents cognitive architectures from being utilized in more advanced studies. This project aims to address that issue by building a bridge between ACT-R and Project Malmo, an artificial general intelligence test suite. The bridge facilitates easy integration of new missions by allowing researchers to specify how to create the world and update it without worrying about the overhead of Malmo. Furthermore, this study analyses how well ACT-R’s utility learning system will adapt in a complex environment. The Adaptive Gain Theory was implemented to improve how the system adapts by using task engagement, derived from measures of utility, to dynamically modify noise. The system was tested using a modified Symbolic Maze task. Tests revealed the parameters of the Adaptive Gain mechanism need to be refined to have a greater impact on model performance. Nonetheless, the bridge provides an interface for ACT-R to be used to study decision making in a complex environment. Improving the bridge will enable more advanced experiments to be conducted whilst improving the Adaptive Gain Theory implementation will move us one step closer to understanding everyday intelligent behavior

A hybrid cognitive architecture with primal affect and physiology

Though computational cognitive architectures have been used to study several processes associated with human behavior, the study of integration of affect and emotion in these processes has been relatively sparse. Theory from affective science and affective neuroscience can be used to systematically integrate affect into cognitive architectures, particularly in areas where cognitive system behavior is known to be associated with physiological structure and behavior. I introduce a unified theory and model of human behavior that integrates physiology and primal affect with cognitive processes in a cognitive architecture. This new architecture gives a more tractable, mechanistic way to simulate affect-cognition interactions to provide specific, quantitative predictions. It considers affect as a lower-level, functional process that interacts with cognitive processes (e.g., declarative memory) to result in emotional behavior. This formulation makes it more straightforward to connect these affective representations with other related moderating processes that may not specifically be considered as emotional (e.g., thirst or stress). An improved understanding of the architecture that constrains our behavior gives us a better opportunity to comprehend why we behave the way we do and how we can use this knowledge to recognize and construct a more ideal internal and external environment

Incorporating Biologically Realistic Neuron Models into the NEF

Theoretical neuroscience is fundamentally concerned with the relationship between biological mechanisms, information processing, and cognitive abilities, yet current models often lack either biophysical realism or cognitive functionality. This thesis aims to partially fill this gap by incorporating geometrically and electrophisologically accurate models of individual neurons into the Neural Engineering Framework (NEF). After discussing the relationship between biologically complex neurons and the core principles/assumptions of the NEF, a neural model of working memory is introduced to demonstrate the NEF's existing capacity to capture biological and cognitive features. This model successfully performs the delayed response task and provides a medium for simulating mental disorders (ADHD) and its pharmacological treatments. Two methods of integrating more biologically sophisticated NEURON models into the NEF are subsequently explored and their ability to implement networks of varying complexity are assessed: the trained synaptic weights do realize the core NEF principles, though several errors remain unresolved. Returning to the working memory model, it is shown that bioneurons can perform the requisite computations in context, and that simulating the biophysical effects of pharmacological compounds produces results consistent with electrophysiological and behavioral data from monkeys