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Books on the topic 'Predictive Reasoning'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

We are all apocalyptic now: On the responsibilities of teaching, preaching, reporting, writing, and speaking out. [S. l.]: R. Jensen, 2013.

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2

Wolsey, Thomas DeVere. Learning to predict and predicting to learn: Cognitive strategies and instructional routines. Boston: Pearson/Allyn & Bacon, 2009.

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3

Bridgeman, Brent. Predictions of freshman grade-point average from the revised and recentered SAT I, Reasoning Test. New York: College Entrance Examination Board, 2000.

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4

Matwijkiw, Bronik. Predictive Reasoning in Legal Theory (Applied Legal Philosophy). Ashgate Pub Ltd, 2003.

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5

Reason and Prediction. Cambridge University Press, 2009.

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6

Gallagher, Shaun. Enactivist Interventions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198794325.001.0001.

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Enactivist Interventions explores central issues in the contemporary debates about embodied cognition, addressing interdisciplinary questions about intentionality, representation, affordances, the role of affect, and the problems of perception and cognitive penetration, action and free will, higher-order cognition, and intersubjectivity. It argues for a rethinking of the concept of mind, drawing on pragmatism, phenomenology, and cognitive science. It interprets enactivism as a philosophy of nature that has significant methodological and theoretical implications for the scientific investigation of the mind. Enactivist Interventions argues that, like the basic phenomena of perception and action, sophisticated cognitive phenomena like reflection, imagining, and mathematical reasoning are best explained in terms of an affordance-based skilled coping. It thus argues for a continuity that runs between basic action, affectivity, and a rationality that in every case remains embodied. It also discusses recent predictive models of brain function and outlines an alternative, enactivist interpretation that emphasizes the close coupling of brain, body, and environment rather than a strong boundary that isolates the brain in its internal processes. The extensive relational dynamics that integrates the brain with the extra-neural body opens into an environment that is physical, social, and cultural and that recycles back into the enactive process. Cognitive processes are in the world, situated in affordance spaces defined across evolutionary, developmental, and individual histories, and are constrained by affective processes and normative dimensions of social and cultural practices.

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7

Learning to Predict and Predicting to Learn: Cognitive Strategies and Instructional Routines. Prentice Hall, 2008.

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8

Superforecasting: The Art and Science of Prediction. Penguin Random House, 2016.

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9

Superforecasting: The Art and Science of Prediction. Penguin Random House, 2015.

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Superforecasting: The Art and Science of Prediction. Penguin Random House, 2015.

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11

Superforecasting: The Art and Science of Prediction. Crown, 2015.

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12

Tetlock, Philip E., and Dan Gardner. Superforecasting: The Art and Science of Prediction. Broadway Books, 2016.

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13

Superforecasting: The Art and Science of Prediction. New York City, New York, USA: Crown, 2015.

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14

Bluhm, Robyn, Gosia Raczek, Matthew Broome, and Matthew B. Wall. Ethical Issues in Brain Imaging in Psychiatry. Edited by John Z. Sadler, K. W. M. Fulford, and Werdie (C W. ). van Staden. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780198732372.013.21.

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With the increasing use of neuroimaging research in psychiatry and the role imaging plays in society more generally in how mental illness is understood, it is important to consider the myriad ethical issues raised by imaging technologies, for example, for medicine, for law, and for patients. This chapter provides an overview of major ethical questions concerning: imaging of ethical reasoning in psychiatric disorder; forensic psychiatry, criminality, and responsibility; mindblindness and empathy in autism; the use of neuroimaging for screening, prediction, and diagnosis; “mind reading” and the right to privacy of thoughts; and the implications of imaging for the ethics and politics of biological psychiatry.

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15

Gerstenberg, Tobias, and Joshua B. Tenenbaum. Intuitive Theories. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.28.

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This chapter first explains what intuitive theories are, how they can be modeled as probabilistic, generative programs, and how intuitive theories support various cognitive functions such as prediction, counterfactual reasoning, and explanation. It focuses on two domains of knowledge: people’s intuitive understanding of physics, and their intuitive understanding of psychology. It shows how causal judgments can be modeled as counterfactual contrasts operating over an intuitive theory of physics, and how explanations of an agent’s behavior are grounded in a rational planning model that is inverted to infer the agent’s beliefs, desires, and abilities. It concludes by highlighting some of the challenges that the intuitive theories framework faces, such as understanding how intuitive theories are learned and developed.

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16

Grush, Rick, and Lisa Damm. Cognition and the Brain. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0012.

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The article explores the relationship between cognition and the brain. Some researches indicate that emotions provide information, anticipate future responses, influence reasoning strategy, index value, and direct attention toward particular objects but few psychologists have attempted to incorporate these results into an integrative general theory of cognition and emotion. Antonio Damasio claims that emotions are primarily representations of somatic states, including visceral and musculoskeletal, at the psychological level. The relationship between the event type and the associated emotional reaction is learned so that when the same type of event is encountered, or the same type of action considered, it can induce the corresponding emotion and the valance of that emotion can influence how the agent behaves in that situation. Damasio argued that somatic markers help facilitate reasoning by providing a rapid processing of potential decision outcomes based on immediate endorsement or rejection, which then helps constrain the decision-making space to a manageable size for which it becomes reasonable to employ more traditional means of evaluation such as cost-benefit analysis on the remaining options. Berthoz argued that the brain is a simulator of action and a generator of hypotheses such that anticipating and predicting the consequences of actions based on the remembered past is one of the basic properties of the brain.

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17

Chen, Min, J. Michael Dunn, Amos Golan, and Aman Ullah, eds. Advances in Info-Metrics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.001.0001.

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Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian methods published in the different proceedings started with the seminal collection of Levine and Tribus (1979) and continuing annually. The objective of this volume is to expand the study of info-metrics, and information processing, across the sciences and to further explore the basis of information-theoretic inference and its mathematical and philosophical foundations. This volume is inherently interdisciplinary and applications oriented. It contains some of the recent developments in the field, as well as many new cross-disciplinary case studies and examples. The emphasis here is on the interrelationship between information and inference where we view the word ‘inference’ in its most general meaning – capturing all types of problem solving. That includes model building, theory creation, estimation, prediction, and decision making. The volume contains nineteen chapters in seven parts. Although chapters in each part are related, each chapter is self-contained; it provides the necessary tools for using the info-metrics framework for solving the problem confronted in that chapter. This volume is designed to be accessible for researchers, graduate students, and practitioners across the disciplines, requiring only some basic quantitative skills. The multidisciplinary nature and applications provide a hands-on experience for the reader.

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