Computational Cognitive Neuroscience

This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” (or reverse-engineering) strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell recording, and electroencephalography. What sets computational cognitive neuroscience apart, however, is the explanatory role of analytic techniques from disciplines as varied as computer science, statistics, machine learning, and mathematical physics. These techniques serve to describe neural mechanisms computationally, but also to drive the process of scientific discovery by influencing which kinds of mechanisms are most likely to be identified. For this reason, understanding the nature and unique appeal of computational cognitive neuroscience requires not just an understanding of the basic research strategies that are involved, but also of the formal methods and tools that are being deployed, including those of probability theory, dynamical systems theory, and graph theory

The effect of a "none of the above" ballot paper option on voting behavior and election outcomes

We investigate how voter and political candidate behavior and election results are affected by an explicit blank vote option “None of the above” (NOTA) on the ballot paper. We report evidence from two online survey experiments conducted in the weeks preceding the 2016 U.S. Presidential Election and the 2016 Austrian run-off election for President. We subjected participants either to the original ballot paper or to a manipulated ballot paper where we added a NOTA option. We find that introducing a NOTA option on the ballot increases participation and reduces the vote shares of non-establishment candidates. NOTA is chosen more frequently by voters with a protest motive, who are either unhappy with the candidate set or with the political establishment in general. Using a laboratory experiment we further explore the reaction of political candidates to the existence of a NOTA option. We replicate our field evidence that NOTA diverts votes from a protest option (e.g. an inferior candidate or policy), thus decreasing the likelihood that the protest option actually wins. However, (establishment) candidates anticipate this shift and become more likely to make unfair policy proposals when NOTA is present. As a result, a NOTA option on the ballot in our laboratory setting improves efficiency but increases inequality.Series: Department of Strategy and Innovation Working Paper Serie

Models and Mechanisms in Network Neuroscience

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are non-explanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general

Reply to “Comment on: ‘Flexural Strength by Fractography in Modern Brittle Materials’”

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108044/1/jace13133.pd

Measuring the Economic and Cultural Values of Historic Heritage Places

This research project on the valuation of cultural heritage is developing a methodology for a quantitative valuation study of the use and non-use values of historic heritage places. The methodology developed in this project will provide a template for subsequent empirical applications to generate data of use in the formulation of heritage policies and programs. The initial stages in reaching this larger objective have involved determining how historic heritage places are to be categorised for measurement, establishing the process by which heritage attributes will be determined for valuation, and developing and trialling an effective measurement framework. The principal techniques being developed for valuation assessment in the project are derived from choice-modelling (CM) and contingent-valuation methods (CVM). A particular focus of the project is on the systematic integration of cultural and economic value assessments within a consistent theoretical framework, reflecting the need for an economic perspective on the significance criteria currently in use in heritage policy in Australia.Community/Rural/Urban Development, Environmental Economics and Policy,

Flexural Strength by Fractography in Modern Brittle Materials

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102133/1/jace12627.pd

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project

Mechanisms in Cognitive Science

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework

Models and Mechanisms in Network Neuroscience

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are non-explanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general