Papers

• Abduction

  • Probabilistic models of cognition: Conceptual foundations - X) - ***Trends in Cognitive Sciences***, 2006. [[All Versions](https://scholar.google.com/scholar?oi=bibs&hl=en&cluster=12857321660837478492)]. Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This review outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation.
  • Abduction - ***Plato Stanford***. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.
  • Scientific Explanation - ***Plato Stanford***. A computational philosophy account on Scientific Explanation, a canonical application of Abduction.
  • Scientific Reduction - ***Plato Stanford***. A computational philosophy account on Scientific Reduction, which comes with no explicit boundary with Explanation.
  • Non-monotonic Logic - ***Plato Stanford***. A computational philosophy account on Non-monotonic Logic, a family of formal frameworks devised to capture and represent defeasible inference.
  • Philosophical Writings of Peirce - ***Courier Corporation***, 1955. [[All Versions](https://scholar.google.com/scholar?cluster=3917019015464129592)]. Original writings by C. S. Peirce, the philosopher who first introduces the concept of Abduction.
  • Inference to the Best Explanation - ***Routledge***, 1991. [[All Versions](https://scholar.google.com/scholar?cluster=5097986614430666854)]. Lipton's original paper on Inference to the Best Explanation as a specialized condition of Abduction.
  • Abductive Reasoning and Learning - ***Springer***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=12074269365138058159)]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.
  • Abductive Cognition: The Epistemological and Eco-Cognitive Dimensions of Hypothetical Reasoning - ***Springer***, 2009. [[All Versions](https://scholar.google.com/scholar?cluster=8707351442527595188)]. Most philosophers of science in the twentieth century have concluded that no logic of creative processes exists and, moreover, that a rational model of discovery is impossible. In short, scientific creative inferences are irrational and there is no “reasoning” to hypotheses. On the other hand, some research in the area of artificial intelligence has shown that methods for discovery could be found that are computationally adequate for rediscovering --- or discovering for the first time --- empirical or theoretical laws and theorems.
  • Patterns of abduction - ***Synthese***, 2007. [[All Versions](https://scholar.google.com/scholar?cluster=15230540023076470385&hl=en&as_sdt=0,5)]. A categorization for Abduction in the account of pure philosophy.
  • Abduction: A categorical characterization - ***Journal of Applied Logic***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=17834260152484836885&hl=en&as_sdt=2005&sciodt=0,5)].
  • The Role of Explanatory Considerations in Updating - ***Cognition***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=3089358487428261042)]. This paper investigates experimentally controversy in philosophy about the connection between explanation and inference, of whether judgments of the explanatory goodness of hypotheses do play a role when people revise their degrees of belief in those hypotheses upon the receipt of new evidence.
  • Explanation, updating, and accuracy - ***Journal of Cognitive Psychology***, 2016. [[All Versions](https://scholar.google.com/scholar?cluster=967127146748155733)]. There is evidence that people update their credences partly on the basis of explanatory considerations. Philosophers have recently argued that to minimise the inaccuracy of their credences, people's updates also ought to be partly based on such considerations. However, there are many ways in which explanatory considerations can factor into updating, not all of which minimise inaccuracy. It is an open question whether in their updating, people take explanatory considerations into account in a way that philosophers would deem recommendable. To address this question, the authors re-analyse data from an experiment reported in Douven and Schupbach, “The role of explanatory considerations in updating”.
  • Best, second-best, and good-enough explanations: How they matter to reasoning - ***Journal of Experimental Psychology***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=3067550385175104201)]. There is a wealth of evidence that people’s reasoning is influenced by explanatory considerations. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The main conslusions are that (a) the quality of an explanation is a good predictor of people’s willingness to accept that explanation, and a better predictor than the prior probability of the explanation, and (b) if more than one possible explanation is given, people are the less willing to infer the best explanation the better they deem the second-best explanation.
  • How explanation guides belief change - ***Trends in Cognitive Sciences***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=15240531165875981526)]. Philosophers have argued that people ought to change their graded beliefs via Bayes’ rule. Recent work in psychology indicates that people sometimes violate that rule by attending to explanatory factors. Results from computational modeling suggest that such violations may actually be rational.
  • Use of current explanations in multicausal abductive reasoning - ***Cognitive Science***, 2001. [[All Versions](https://scholar.google.com/scholar?cluster=7816050625957759346&hl=en&as_sdt=2005&sciodt=0,5)].
  • Kinematic mental simulations in abduction and deduction - ***Proceedings of the National Academy of Sciences***, 2013. [[All Versions](https://scholar.google.com/scholar?cluster=11864820390497230588)]. This paper presents a theory, and its computer implementation, of how mental simulations underlie the abductions of informal algorithms and deductions from these algorithms. Three experiments tested the theory’s predictions, using an environment of a single railway track and a siding. The results corroborated the use of a kinematic mental model in creating and testing informal algorithms and showed that individuals differ reliably in the ability to carry out these tasks.
  • Abduction − the context of discovery + underdetermination = inference to the best explanation - ***Synthese***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=4261649938116694095&hl=en&as_sdt=0,5)].
  • Towards an Architecture for Cognitive Vision Using Qualitative Spatio-temporal Representations and Abduction - ***Spatial Cognition***, 2002. [[All Versions](https://scholar.google.com/scholar?cluster=8072265283930278310&hl=en&as_sdt=0,5)].
  • Abductive inference within a pragmatic framework - ***Synthese***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=10285954503043361393&hl=en&as_sdt=0,5)].
  • Disjunctive Abduction - ***New Generation Computing***, 2019. [[All Versions](https://scholar.google.com/scholar?cluster=6664745483675209831&hl=en&as_sdt=0,5)].
  • A Probabilistic Theory of Abductive Reasoning - ***ICAART***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=450937566244876051&hl=en&as_sdt=0,5)]. A probabilistic perspective for interpreting Abductive Reasoning.
  • The order effect in human abductive reasoning: an empirical and computational study - ***Journal of Experimental & Theoretical Artificial Intelligence***, 2006. [[All Versions](https://scholar.google.com/scholar?cluster=3803536062463585043&hl=en&as_sdt=0,5)].
  • Abduction, Induction, and Analogy - ***Model-Based Reasoning in Science and Technology***, 2010. [[All Versions](https://scholar.google.com/scholar?cluster=14979764682921693390&hl=en&as_sdt=0,5)]. The distinctions and relations between Abduction, Induction, and Analogy.
  • Remembrance of inferences past: Amortization in human hypothesis generation - ***Cognition***, 2018. [[All Versions](https://scholar.google.com/scholar?cluster=190340622765037472&hl=en&as_sdt=2005&sciodt=0,5)]. A rational account of human hypothesis generation.
  • Defending Abduction - ***Philosophy of Science***, 1999. [[All Versions](https://scholar.google.com/scholar?cluster=13895790050138832555&hl=en&as_sdt=0,5)].
  • On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - ***Synthese***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=7865291004729010145&hl=en&as_sdt=0,5)].
  • Models of Discovery: And Other Topics in the Methods of Science - ***Springer***, 1977. [[All Versions](https://scholar.google.com/scholar?cluster=9932701864897299105&hl=en&as_sdt=0,5)]. The original book on search as scientific thinking.
  • Scientific discovery: Computational explorations of the creative processes - ***MIT Press***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=11327000316248254911)]. The book is divided into four parts. Part I introduces the subject of discovery, defines the scope of our work, and discusses some of the issues that have surrounded and still surround our topic. Parts II and III contain the main body of our results, largely in the form of accounts of the performance of computer programs that simulate human thought processes to make scientific discoveries. Part II is devoted largely to the processes for inducing quantitative theories from data. Part III is devoted mainly to the processes for inducing qualitative descriptive and structural theories from data. In Part IV, on the basis of our experience, we discuss at a lower level of precision how the programs described in the preceding chapters could be combined into a single, more general discovery system, and we describe a wide range of the other component processes that enter into scientific discovery.
  • Exploring science: The cognition and development of discovery processes - ***MIT Press***, 2000. [[All Versions](https://scholar.google.com/scholar?cluster=13091264356550286420)]. In this book, D. Klahr sets out to describe the cognitive and developmental processes that have enabled scientists to make the discoveries that comprise the body of information we call "scientific knowledge." Over the past decade, Klahr and his colleagues have conducted laboratory experiments in which they create discovery contexts, computer-based environments, to evoke the kind of thinking characteristic of scientific discovery in the "real world." In attempting to solve the problems posed by the discovery tasks, experiment participants (from preschoolers to university students) use many of the same higher-order cognitive processes used by practicing scientists. Through his work, Klahr integrates two disparate approaches–the content-based approach and the process-based approach– to present a comprehensive model of the psychology of scientific discovery.
  • Dual Space Search During Scientific Reasoning - ***Cognitive Science***, 1988. [[All Versions](https://scholar.google.com/scholar?cluster=17542852673494089523&hl=en&as_sdt=2005&sciodt=0,5)]. The original paper on the dual space search as scientific thinking theory.
  • The AHA! Experience: Creativity Through Emergent Binding in Neural Networks - ***Cognitive Science***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=10006889101167052798&hl=en&as_sdt=0,5)].
  • Explanation-seeking curiosity in childhood - ***Current Opinion in Behavioral Sciences***, 2020. [[All Versions](https://scholar.google.com/scholar?cluster=4167956555501133663&hl=en&as_sdt=2005)]. A piece of developmental pshchological evidence for Abduction in young children.
  • Scientific Discovery - ***Plato Stanford***. A computational philosophy account on Scientific Discovery, the process or product of successful scientific inquiry, sometimes an Abduction-like (Explanation) thinking pattern.
  • Complexity Management in a Discovery Task - ***CogSci'92***, 1992. [[All Versions](https://scholar.google.com/scholar?cluster=18138712608977258974&hl=en&as_sdt=2005&sciodt=0,5)]. Advanced experiments on dual space search.
  • Hypothesis generation, sparse categories, and the positive test strategy - ***Psychological Review***, 2011. [[All Versions](https://scholar.google.com/scholar?cluster=4329636480235863472&hl=en&as_sdt=2005&sciodt=0,5)].
  • Children and adults as intuitive scientists - ***Psychological Review***, 1989. [[All Versions](https://scholar.google.com/scholar?cluster=9577945454476127070&hl=en&as_sdt=2005&sciodt=0,5)]. A perspective against search as scientific thinking.
  • Abduction and styles of scientific thinking - ***Synthese***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=9336871656706514469&hl=en&as_sdt=0,5)]. A computational philosophy account connecting Abduction and scientific thinking.
  • Imagination and the generation of new ideas - ***Cognitive Development***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=16920774374067505248&hl=en&as_sdt=2005&sciodt=0,5)]. A piece of evidence for rationalization in childhood.
  • A dual-space model of iteratively deepening exploratory learning - ***International Journal of Human-Computer Studies***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=17337189265334825678)]. This paper describes a cognitive model of exploratory learning, which covers both trial-and-error and instruction-taking activities. The model, implemented in Soar, is grounded in empirical data of subjects in a task-oriented, trial-and-error exploratory learning situation. A key empirical finding reflected in the model is the repeated scanning of a subset of the available menu items, with increased attention to items on each successive scan. This is explained in terms of dual search spaces, the external interface and the user's internal knowledge, both of which must be tentatively explored with attention to changing costs and benefits.
  • Heuristics for Scientific Experimentation: A Developmental Study - ***Cognitive Psychology***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=2469515962071844494&hl=en&as_sdt=2005&sciodt=0,5)]. A piece of evidence on children have basic scientific thinking skills.
  • A 4-Space Model of Scientific Discovery - ***CogSci'95***, 1995. [[All Versions](https://scholar.google.com/scholar?cluster=1063157789682040473&hl=en&as_sdt=2005&sciodt=0,5)]. Extending the dual space search.
  • When to trust the data: Further investigations of system error in a scientific reasoning task - ***Memory & Cognition***, 1996. [[All Versions](https://scholar.google.com/scholar?cluster=3131191372086488656&hl=en&as_sdt=2005&sciodt=0,5)]. A behavioral account on the shift between bottom-up observation and top-down reasoning.
  • Confirmation, disconfirmation, and information in hypothesis testing - ***Psychological Review***, 1987. [[All Versions](https://scholar.google.com/scholar?cluster=1954141597807453515&hl=en&as_sdt=0,5)]. A psychological account on hypothesis testing.