Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning

By Mike Oaksford, N. Chater. Behavioral and Brain Sciences, 2009

915 citations recorded by the source corpus.

Subfields: Artificial Intelligence, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/348d4d282ba8b55cfae5d94de2392f8934a0b7c7

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Citations in the corpus

Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, background): “Recent publications show that human reasoning and learning can also be cast into the form of probabilistic inference problems [27–29].”
Rationality for Mortals: How People Cope with Uncertainty (2008, background): “That is, many psychologists also build as-if models of behavior, as illustrated by various Bayesian approaches that model behavior but not cognitive processes (e.g., Oaksford & Chater, 2007).”
Dynamic inference and everyday conditional reasoning in the new paradigm (2013, methodology): “We then turn to possible reactions to these problems, looking at Oaksford and Chater’s (2007) learning approach and causal Bayes nets.”
Memory, reasoning, and categorization: parallels and common mechanisms (2014, methodology): “In contrast, single-process models (e.g., JohnsonLaird, 1994; Oaksford and Chater, 2007) assume that both types of reasoning may be the result of a single common process but may differ in their decision criteria (with a more conservative criterion used in deduction; see Rips, 2001).”
On the distinction between rationality and intelligence: Implications for understanding individual differences in reasoning. (2012, background): “Contributing to this alternative interpretation have been evolutionary psychologists, adaptationist modelers, and ecological theorists (Anderson, 1990; Cosmides & Tooby, 1996; Gigerenzer, 2007; Marewski, Gaissmaier, & Gigerenzer, 2010; Oaksford & Chater, 2007).”
The conditional in mental probability logic (2010, methodology): “In the early nineties of the last century, Chater and Oaksford introduced probabilistic models to the field of deductive reasoning [40, 41, 11, 43, 42].”
New paradigm psychology of reasoning (2009, background): “O&C’s book has already been extensively reviewed in Behavioral and Brain Sciences, and they have extended andmodified their proposals in replies to the commentaries (Oaksford & Chater, 2009a, 2009b).”
Because Hitler did it! Quantitative tests of Bayesian argumentation using ad hominem (2012, background): “That is, propositions do not have truth values of 0 or 1, but rather an individual’s degree of belief in a particular proposition can be understood as a probability between 0 and 1 (see also e.g., Evans & Over, 2004; Howson & Urbach, 1996; Oaksford & Chater, 1998, 2007).”
What Does It Mean to be Biased: Motivated Reasoning and Rationality (2014, background): “…have also been arguments over normative evaluations of the tasks (e.g., Gigerenzer, 1991; Koehler, 1996), both in judgment and decision-making and in the context of investigations of human rationality in adjacent fields such as logical reasoning (e.g., Oaksford & Chater, 1994, 2007; Wason, 1968).”
Against Logical Form (2010, background): “…on at least one account, both mental models and formal rules are wrong, because reasoning is not deductive but intrinsically probabilistic (e.g., oaksford & Chater, 2007). one difficulty with this view, as i mentioned at the outset , is that it offers no obvious explanation of how human beings…”
Herbert Simon's Decision-Making Approach: Investigation of Cognitive Processes in Experts (2010, background): “This approach is becoming increasingly popular (e.g., Oaksford & Chater, 2009).”
Bayesian just-so stories in psychology and neuroscience. (2012, background): “For example, Oaksford and Chater (2009) wrote: A good deal of empirical data about human reasoning (and indeed, human cognition more generally) can be understood as arising from the structure of the problem itself—that is, the nature of the problem drives any reasonable algorithmic solution to have…”
Noise as a Resource for Computation and Learning in Networks of Spiking Neurons (2014, background): “A look at the current state of the art in artificial intelligence shows that these approaches have been largely abandoned, and replaced by more flexible probabilistic data structures and inference methods (see [35] and [36] for an account of this ‘‘probabilistic turn’’).”
Betting on conditionals (2010, methodology): “…paradigm in the psychology of reasoning, with new probability theories of the natural language indicative conditional (Chater & Oaksford, 2009; Evans, 2007; Evans & Over, 2004; Manktelow, Over, & Elqayam, in press; Oaksford & Chater, 2001, 2007, 2009; Over, 2009; Pfeifer & Kleiter, 2006, 2010).”