The Cognitive Status of Risk: A Response to Thompson

Discussing the role that probability theory should play in Risk analysis and management, Dr. Valverde argues that Thompson\u27s approach puts too much emphasis on the distinction between Risk subjectivism and Risk objectivism in addressing the question, When are Risks real

Consciousness as Integrated Information: A Provisional Philosophical Critique

Giulio Tononi (2008) has offered his integrated information theory of consciousness (IITC) as a ‘provisional manifesto’. I critically examine how the approach fares. I point out some (relatively) internal concerns with the theory and then more broadly philosophical ones; finally I assess the prospects for IITC as a fundamental theory of consciousness. I argue that the IITC’s scientific promise does carry over to a significant extent to broader philosophical theorizing about qualia and consciousness, though not as directly as Tononi suggests, since the account is much more focused on the qualitative character of experience rather than on consciousness itself. I propose understanding it as ‘integrated information theory of qualia’(IITQ), rather than of consciousness

Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness

Recent work in human cognitive neuroscience has linked self-consciousness to the processing of multisensory bodily signals (bodily self-consciousness [BSC]) in fronto-parietal cortex and more posterior temporo-parietal regions. We highlight the behavioral, neurophysiological, neuroimaging, and computational laws that subtend BSC in humans and non-human primates. We propose that BSC includes body-centered perception (hand, face, and trunk), based on the integration of proprioceptive, vestibular, and visual bodily inputs, and involves spatio-temporal mechanisms integrating multisensory bodily stimuli within peripersonal space (PPS). We develop four major constraints of BSC (proprioception, body-related visual information, PPS, and embodiment) and argue that the fronto-parietal and temporo-parietal processing of trunk-centered multisensory signals in PPS is of particular relevance for theoretical models and simulations of BSC and eventually of self-consciousness

Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling

Copyright by the Ecological Society of America.Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises

Prolegomena to a Theory and Model of Spoken Persuasion: A Subjective-Probabilistic Interactive Model of Persuasion (SPIMP)

Various disciplines such as rhetoric, marketing, and psychology have explored persuasion as a social and argumentative phenomenon. The present thesis is predominantly based in cognitive psychology and investigates the psychological processes the persuadee undergoes when faced with a persuasive attempt. The exploration concludes with the development of a concrete model for describing persuasion processing, namely The Subjective-Probabilistic Interactive Model of Persuasion (SPIMP). In addition to cognitive psychology, the thesis relies on conceptual developments and empirical data from disciplines such as rhetoric, economics, and philosophy. The core model of the SPIMP relies on two central persuasive elements: content strength and source credibility. These elements are approached from a subjective perspective in which the persuadee estimates the probabilistic likelihood of how strong the content and how credible the source is. The elements, however, are embedded in a larger psychological framework such that the subjective estimations are contextual and social rather than solipsistic. The psychological framework relies on internal and external influences, the scope of cognition, and the framework for cognition. The SPIMP departs significantly from previous models of persuasion in a number of ways. For instance, the latter are dual-processing models whereas the SPIMP is an integrated single-process approach. Further, the normative stances differ since the previous models seemingly rely on a logicist framework whereas SPIMP relies on a probabilistic. The development of a new core model of persuasion processing constitutes a novel contribution. Further, the theoretical and psychological framework surrounding the elements of the model provides a novel framework for conceptualising persuasion processing from the perspective of the persuadee. Finally, given the multitude of disciplines connected to persuasion, the thesis provides a definition for use in future studies, which differentiates persuasion from argumentation, communicated information updating, and influence

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences