From cognitive science to cognitive neuroscience to neuroeconomics

As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction in neuroeconomics. Rather than following a top-down strategy, neuroeconomists should be pragmatic in the use of available data from animal models, information regarding neural pathways and projections, computational models of neural function, functional imaging and behavioural data. By providing convergent evidence across multiple levels of organization, neuroeconomics will have its most promising prospects of success

Session 5: Development, Neuroscience and Evolutionary Psychology

Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog

Coming to Terms Will Do It: Students Engaging With Climate Change Through Sensemaking and Collective Efficacy Perceptions

Within climate change instruction, effective instructional crisis communication is necessary to attain cognitive, affective, and behavioral learning outcomes so students comprehensively learn the reality and implications of this planetary crisis. I locate this learning as coming to terms with climate change. This study explores how students affectively and cognitively learned to come to terms with the immense threat of the climate crisis outside their initial exposure to climate change fear appeals communicated in their classrooms. Drawing from interviews and focus groups with college students, I found students came to terms with climate change outside their classrooms by coping with the immense threat while enacting sensemaking with their peers. These findings suggest coping and sensemaking are crucial for students to come to terms with climate change after instructor-delivered fear appeals to access the efficacy needed to face this planetary threat. Ultimately, this study advances instructional crisis communication by providing insight into student to student out-of-classroom communication and how it affects cognitive and affective learning outcomes concerning climate change

The climate change sublime: Leveraging the immense awe of the planetary threat of climate change

Environmental communication scholarship has not significantly advanced the fundamental theories of sublime discourse since their introduction with John Muir and his advocacy for Yosemite National Park. Such a lacuna is problematic, as humanity is entering the age of the Anthropocene where vast ecological destruction is becoming increasingly relevant, and audience engagement is essential if we are to mitigate the worst to come. This essay seeks to remedy the lack of inquiry into how sublime discourse is used to engage audiences with elements of the Anthropocene, in particular, climate change. Based on the analysis of two documentaries, Chasing Ice and Chasing Coral, I find that the sublime response to the Anthropocene and the films’ modifications to the sublime rhetoric pattern are novel and uniquely engage audiences with climate change, to varying degrees of success. Ultimately, I argue that such sublime rhetoric is capable of overcoming the constraints associated with communicating the diffuse and overwhelming threat of climate change, demonstrating its viability in this instance of the Anthropocene

The Right and the Good: Distributive Justice and Neural Encoding of Equity and Efficiency

Distributive justice concerns how individuals and societies distribute benefits and burdens in a just or moral manner. We combined distribution choices with functional magnetic resonance imaging to investigate the central problem of distributive justice: the trade-off between equity and efficiency. We found that the putamen responds to efficiency, whereas the insula encodes inequity, and the caudate/septal subgenual region encodes a unified measure of efficiency and inequity (utility). Notably, individual differences in inequity aversion correlate with activity in inequity and utility regions. Against utilitarianism, our results support the deontological intuition that a sense of fairness is fundamental to distributive justice but, as suggested by moral sentimentalists, is rooted in emotional processing. More generally, emotional responses related to norm violations may underlie individual differences in equity considerations and adherence to ethical rules

Bayesian priors are encoded independently from likelihoods in human multisensory perception

It has been shown that human combination of crossmodal information is highly consistent with an optimal Bayesian model performing causal inference. These findings have shed light on the computational principles governing crossmodal integration/segregation. Intuitively, in a Bayesian framework priors represent a priori information about the environment, i.e., information available prior to encountering the given stimuli, and are thus not dependent on the current stimuli. While this interpretation is considered as a defining characteristic of Bayesian computation by many, the Bayes rule per se does not require that priors remain constant despite significant changes in the stimulus, and therefore, the demonstration of Bayes-optimality of a task does not imply the invariance of priors to varying likelihoods. This issue has not been addressed before, but here we empirically investigated the independence of the priors from the likelihoods by strongly manipulating the presumed likelihoods (by using two drastically different sets of stimuli) and examining whether the estimated priors change or remain the same. The results suggest that the estimated prior probabilities are indeed independent of the immediate input and hence, likelihood

Data-Driven Methods for System Identification and Lyapunov Stability

This thesis focuses on data-driven methods applied to system identification and stability analysis of dynamical systems. In the first major contribution of the theorem we propose a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system. The algorithmic structure consists of two neural networks and a satisfiability modulo theories (SMT) solver. The first neural network is responsible for learning the unknown dynamics. The second neural network aims to identify a valid Lyapunov function and a provably stabilizing nonlinear controller. The SMT solver then verifies that the candidate Lyapunov function indeed satisfies the Lyapunov conditions. We provide theoretical guarantees of the proposed learning framework in terms of the closed-loop stability for the unknown nonlinear system. We illustrate the effectiveness of the approach with a set of numerical experiments. We then examine another popular data driven method for system identification involving the Koopman operator. Methods based on the Koopman operator aim to approximate advancements of the state under the flow operator by a high-dimensional linear operator. This is accomplished by the extended mode decomposition (eDMD) algorithm which takes non-linear measurements of the state. Under the suitable conditions we have a result on the weak convergence of the eigenvalues and eigenfunctions of the eDMD operator that can serve as components of Lyapunov functions. Finally, we review methods for finding the region of attraction of an asymptotically stable fixed point and compare this method to the two methods mentioned above