Download or read book The Redundancy Effect in Human Causal Learning written by Gintare Zaksaite and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Learning written by Alison Gopnik and published by Oxford University Press. This book was released on 2007-03-22 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.
Download or read book Time and Causality written by Marc J. Buehner and published by Frontiers E-books. This book was released on 2014-08-06 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of how humans and other intelligent systems construct causal representations from non-causal perceptual evidence has occupied scholars in cognitive science for many decades. Most contemporary approaches agree with David Hume that patterns of covariation between two events of interest are the critical input to the causal induction engine, irrespective of whether this induction is believed to be grounded in the formation of associations (Shanks & Dickinson, 1987), rule-based evaluation (White, 2004), appraisal of causal powers (Cheng, 1997), or construction of Bayesian Causal Networks (Pearl, 2000). Recent research, however, has repeatedly demonstrated that an exclusive focus on covariation while neglecting contiguity (another of Hume’s cues) results in ecologically invalid models of causal inference. Temporal spacing, order, variability, predictability, and patterning all have profound influence on the type of causal representation that is constructed. The influence of time upon causal representations could be seen as a bottom-up constraint (though current bottom-up models cannot account for the full spectrum of effects). However, causal representations in turn also constrain the perception of time: Put simply, two causally related events appear closer in subjective time than two (equidistant) unrelated events. This reversal of Hume’s conjecture, referred to as Causal Binding (Buehner & Humphreys, 2009) is a top-down constraint, and suggests that our representations of time and causality are mutually influencing one another. At present, the theoretical implications of this phenomenon are not yet fully understood. Some accounts link it exclusively to human motor planning (appealing to mechanisms of cross-modal temporal adaptation, or forward learning models of motor control). However, recent demonstrations of causal binding in the absence of human action, and analogous binding effects in the visual spatial domain, challenge such accounts in favour of Bayesian Evidence Integration. This Research Topic reviews and further explores the nature of the mutual influence between time and causality, how causal knowledge is constructed in the context of time, and how it in turn shapes and alters our perception of time. We draw together literatures from the perception and cognitive science, as well as experimental and theoretical papers. Contributions investigate the neural bases of binding and causal learning/perception, methodological advances, and functional implications of causal learning and perception in real time.
Download or read book Causal Models written by Steven Sloman and published by Oxford University Press. This book was released on 2009-04-17 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation."--Jacket.
Download or read book Causal Cognition in Humans and Machines written by Andrew Tolmie and published by Frontiers Media SA. This book was released on 2022-02-02 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Creating A Memory of Causal Relationships written by Michael J. Pazzani and published by Psychology Press. This book was released on 2014-02-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
Download or read book Models of Human Causal Learning written by Colin S. Beam and published by . This book was released on 2017 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Beyond Contiguity written by William Greville and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Most contemporary theories of causal learning identify three primary cues to causality; temporal order, contingency and contiguity. It is well-established in the literature that a lack of temporal contiguity - a delay between cause and effect - can have an adverse effect on causal induction. However research has tended to focus almost exclusively on the extent of delay while ignoring the potential influence of delay variability. This thesis aimed to address this oversight. Since humans tend to experience causal relations repeatedly over time, we accordingly experience multiple cause-effect intervals. If intervals are constant, it becomes possible to predict when the effect will occur following the cause. Fixed delays thus confer temporal predictability, which may contribute to successful causal inference by creating an impression of a stable underlying mechanism. Five experiments confirmed the facilitatory effect of predictability in instrumental causal learning. Two experiments involving a different aspect of causal judgment found no effects of interval variability, but two further experiments demonstrated that predictability facilitates elemental causal induction from observation. These results directly conflict with findings from studies of animal conditioning, where preference for variable- interval reinforcement is routinely exhibited, and a simple associative account struggles to explain this disparity. However both a temporal coding associative account, and higher-level cognitive perspectives such as Bayesian structural inference, are compatible with these findings. Overall, this thesis indicates that causal learning involves processes above and beyond simple associations.
Download or read book Causal Learning written by and published by Academic Press. This book was released on 1996-09-26 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Psychology of Learning and Motivation publishes empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditions to complex learning and problem solving. This guest-edited special volume is devoted to current research and discussion on associative versus cognitive accounts of learning. Written by major investigators in the field, topics include all aspects of causal learning in an open forum in which different approaches are brought together. Up-to-date review of the literature Discusses recent controversies Presents major advances in understanding causal learning Synthesizes contrasting approaches Includes important empirical contributions Written by leading researchers in the field
Download or read book The Oxford Handbook of Causal Reasoning written by Michael Waldmann and published by Oxford University Press. This book was released on 2017-03-30 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Although causal reasoning is a component of most of our cognitive functions, it has been neglected in cognitive psychology for many decades. The Oxford Handbook of Causal Reasoning offers a state-of-the-art review of the growing field, and its contribution to the world of cognitive science. The Handbook begins with an introduction of competing theories of causal learning and reasoning. In the next section, it presents research about basic cognitive functions involved in causal cognition, such as perception, categorization, argumentation, decision-making, and induction. The following section examines research on domains that embody causal relations, including intuitive physics, legal and moral reasoning, psychopathology, language, social cognition, and the roles of space and time. The final section presents research from neighboring fields that study developmental, phylogenetic, and cultural differences in causal cognition. The chapters, each written by renowned researchers in their field, fill in the gaps of many cognitive psychology textbooks, emphasizing the crucial role of causal structures in our everyday lives. This Handbook is an essential read for students and researchers of the cognitive sciences, including cognitive, developmental, social, comparative, and cross-cultural psychology; philosophy; methodology; statistics; artificial intelligence; and machine learning.
Download or read book Explaining Human Causal Learning Using a Dynamic Probabilistic Model written by Randall Rojas Rojas and published by . This book was released on 2010 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Interaction of Learning and Memory in Human Causal Behaviour written by Michael Edward Le Pelley and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Effects of Self explanation as Causal Mechanism Elicitation Methodology on Causal Reasoning Task Performance written by Ioan Gelu Ionas and published by . This book was released on 2010 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: People have always tried to find explanations for how and why things happen to and around them. While understanding causality is fundamental for both science and everyday life, researchers found that people have a multitude of problems when dealing with causality. This research studies the effects of using a domain independent cognitive strategy, self-explanation (the explanation given to self as opposed to the explanation provided by others), on learners' performance on causal reasoning tasks. The strategy is used to encourage learners to think about and explain the mechanism(s) behind the causal relation(s) they are observing. For this purpose, a completely randomized two-group between subjects experiment was designed and conducted online. Using a nonintrusive intervention based on brief practice of self-explanation this study shows that learners reporting higher levels of prior knowledge benefit from the use of this strategy, while the learners reporting lower levels do not. This suggests the existence of a threshold value/range of prior knowledge in the relevant domain(s) that needs to be reached before similar cognitive strategies based on self-explanation could become effective. The strategy can be implemented in both face-to-face and in online contexts, with almost no time cost for the learner. In addition, online instruction and learning environments as well as of other software applications could be easily designed or redesigned to include causal mechanism elicitation components or tools with significant improvement in learner performance on causal reasoning tasks.
Download or read book A Study of the Independent Effect of Use Redundancy Upon Human Inference Process in Tasks of Varying Predictability written by Edward Allen Schenk and published by . This book was released on 1968 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Study of the Independent Effect of Cue Redundancy Upon the Human Inference Process in Tasks of Varying Predictablility written by Edward Allen Schenck and published by . This book was released on 1968 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Causal Inference and Large Language Models from the Causal Invariance Framework written by Emily Frances Wong and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics serves as the grammar of all science, and central to the goal of science is understanding cause-effect relationships. Scientists rely on research methodology and statistical tools to uncover causal relationships, and engineers rely on statistical methods to create artificial assistants to aid daily life. Neither statistical learning nor next-word-prediction (used to train artificial general intelligence) are consistent with rational causal learning and reasoning in humans. The present thesis examines the fundamental goals and assumptions made in dominant statistical methods and discusses their implications for statistical inference and commonsense reasoning in artificial general intelligence (AGI). The first section introduces and evaluates a causal alternative to logistic regression, which estimates the causal power (from the causal invariance framework) of treatments among covariates. Causal invariance is defined as the influence of a candidate cause (elemental or conjunctive) that is independent of background causes, with the aspiration of acquiring knowledge that's useable, in the minimalist sense being able to generalize from a learning context to an application context. The second and final section investigates current benchmark tasks used to evaluate causal reasoning in large language models (e.g., GPT-3, GPT-4), and introduces a stricter test informed by psychological literature on human causal cognition under the causal invariance framework.
Download or read book Encyclopedia of the Sciences of Learning written by Norbert M. Seel and published by Springer Science & Business Media. This book was released on 2011-10-05 with total page 3643 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naïve theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.
