Dissertations / Theses on the topic 'Cognitive engineering'

This research evaluates the usefulness of transformative experience (TE) in engineering education. With TE, students 1) apply ideas from coursework to everyday experiences without prompting (motivated use); 2) see everyday situations through the lens of course content (expanded perception); and 3) value course content in new ways because it enriches everyday affective experience (affective value). In a three-part study, we examine how engineering educators can promote student progress toward TE and reliably measure that progress.

For the first study, we select a mechanical engineering technical elective, Flow Visualization, that had evidence of promoting expanded perception of fluid physics. Through student surveys and interviews, we compare this elective to the required Fluid Mechanics course. We found student interest in fluids fell into four categories: complexity, application, ubiquity, and aesthetics. Fluid Mechanics promotes interest from application, while Flow Visualization promotes interest based in ubiquity and aesthetics. Coding for expanded perception, we found it associated with students’ engineering identity, rather than a specific course. In our second study, we replicate atypical teaching methods from Flow Visualization in a new design course: Aesthetics of Design. Coding of surveys and interviews reveals that open-ended assignments and supportive teams lead to increased ownership of projects, which fuels risk-taking, and produces increased confidence as an engineer.

The third study seeks to establish parallels between expanded perception and measurable perceptual expertise. Our visual expertise experiment uses fluid flow images with both novices and experts (students who had passed fluid mechanics). After training, subjects sort images into laminar and turbulent categories. The results demonstrate that novices learned to sort the flow stimuli in ways similar to subjects in prior perceptual expertise studies. In contrast, the experts’ significantly better results suggest they are accessing conceptual fluids knowledge to perform this new, visual task. The ability to map concepts onto visual information is likely a necessary step toward expanded perception.

Our findings suggest that open-ended aesthetic experiences with engineering content unexpectedly support engineering identity development, and that visual tasks could be developed to measure conceptual understanding, promoting expanded perception. Overall, we find TE a productive theoretical framework for engineering education research.

This paper presents the integration of an intelligent decision support model (IDSM) with a cognitive architecture that controls an autonomous non-deterministic safety-critical system. The IDSM will integrate multi-criteria, decision-making tools via intelligent technologies such as expert systems, fuzzy logic, machine learning, and genetic algorithms.

Cognitive technology is currently simulated within safety-critical systems to highlight variables of interest, interface with intelligent technologies, and provide an environment that improves the system’s cognitive performance. In this study, the IDSM is being applied to an actual safety-critical system, an unmanned surface vehicle (USV) with embedded artificial intelligence (AI) software. The USV’s safety performance is being researched in a simulated and a real-world, maritime based environment. The objective is to build a dynamically changing model to evaluate a cognitive architecture’s ability to ensure safe performance of an intelligent safety-critical system. The IDSM does this by finding a set of key safety performance parameters that can be critiqued via safety measurements, mechanisms, and methodologies. The uniqueness of this research lies in bounding the decision-making associated with the cognitive architecture’s key safety parameters (KSPs). Other real-time applications (RTAs) that would benefit from advancing cognitive science associated with safety are unmanned platforms, transportation technologies, and service robotics. Results will provide cognitive science researchers with a reference for the safety engineering of artificially intelligent safety-critical systems.

The effectiveness of a cognitive radio (CR) system depends mainly on involved spectrum sensing techniques. The main aim of CR is for effective utilization of the spectrum opportunistically by sharing it with secondary users (SUs), when the primary user (PU) is absent. In this project, cooperative spectrum sensing using weights based on the distance measures from the PU and Multitaper Method (MTM) method is briefly explained. The results show that MTM method provides more accurate threshold value compared to other methods for low signal to noise ratios (SNRs), hence improving the spectrum sensing technique. The results also show that MTM method requires a lesser number of Orthogonal Frequency Division Multiplexing (OFDM) sub-blocks compared to Periodogram (PE) for the same performance.

Bad decisions can have devastating consequences, and there is a vast body of literature suggesting that human judgment and decision-making are riddled with numerous systematic violations of the rules of logic, probability theory, and expected utility theory. The discovery of these cognitive biases in the 1970s challenged the concept of Homo sapiens as the rational animal and has profoundly shaken the foundations of economics and rational models in the cognitive, neural, and social sciences. Four decades later, these disciplines still lack a rigorous theoretical foundation that can account for people’s cognitive biases. Furthermore, designing effective interventions to remedy cognitive biases and improve human judgment and decision-making is still an art rather than a science. I address these two fundamental problems in the first and the second part of my thesis respectively.

To develop a theoretical framework that can account for cognitive biases, I start from the assumption that human cognition is fundamentally constrained by limited time and the human brain’s finite computational resources. Based on this assumption, I redefine human rationality as reasoning and deciding according to cognitive strategies that make the best possible use of the mind’s limited resources. I draw on the bounded optimality framework developed in the artificial intelligence literature to translate this definition into a mathematically precise theory of bounded rationality called resource-rationality and a new paradigm for cognitive modeling called resource-rational analysis. Applying this methodology allowed me to derive resource-rational models of judgment and decisionmaking that accurately capture a wide range of cognitive biases, including the anchoring bias and the numerous availability biases in memory recall, judgment, and decision-making. By showing that these phenomena and the heuristics that generate them are consistent with the rational use of limited resources, my analysis provides a rational reinterpretation of cognitive biases that were once interpreted as hallmarks of human irrationality. This suggests that it is time to revisit the debate about human rationality with the more realistic normative standard of resource-rationality. To enable a systematic assessment of the extent to which human cognition is resource- rational, I present an automatic method for deriving resource-rational heuristics from a mathematical specification of their function and the mind’s computational constraints. Applying this method to multi-alternative risky-choice led to the discovery of a previously unknown heuristic that people appear to use very frequently. Evaluating human decision-making against resource-rational heuristics suggested that, on average, human decision-making is at most 88% as resource-rational as it could be.

Since people are equipped with multiple heuristics, a complete normative theory of bounded rationality also has to answer the question of when each of these heuristics should be used. I address this question with a rational theory of strategy selection. According to this theory, people gradually learn to select the heuristic with the best possible speed-accuracy trade-off by building a predictive model of its performance. Experiments testing this model confirmed that people gradually learn to make increasingly more rational use of their finite time and bounded cognitive resources through a metacognitive reinforcement learning mechanism.

Overall, these findings suggest that—contrary to the bleak picture painted by previous research on heuristics and biases—human cognition is not fundamentally irrational, and can be understood as making rational use of bounded cognitive resources. By reconciling rationality with cognitive biases and bounded resources, this line of research addresses fundamental problems of previous rational modeling frameworks, such as expected utility theory, logic, and probability theory. Resource-rationality might thus come to replace classical notions of rationality as a theoretical foundation for modeling human judgment and decision-making in economics, psychology, neuroscience, and other cognitive and social sciences.

In the second part of my dissertation, I apply the principle of resource-rationality to develop tools and interventions for improving the human mind. Early interventions educated people about cognitive biases and taught them the normative principles of logic, probability theory, and expected utility theory. The practical benefits of such interventions are limited because the computational demands of applying them to the complex problems people face in everyday life far exceed individuals’ cognitive capacities. Instead, the principle of resource-rationality suggests that people should rely on simple, computationally efficient heuristics that are well adapted to the structure of their environments. Building on this idea, I leverage the automatic strategy discovery method and insights into metacognitive learning from the first part of my dissertation to develop intelligent systems that teach people resource-rational cognitive strategies. I illustrate this approach by developing and evaluating a cognitive tutor that trains people to plan resource-rationally. My results show that practicing with the cognitive tutor improves people’s planning strategies significantly more than does practicing without feedback. (Abstract shortened by ProQuest.)