The Adaptive Character of Thought

By John R. Anderson. 1990

Subfields: Cognitive Science & Psychology

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In Cognitive Science & Psychology

Citations in the corpus

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018, background): “This last point aligns with the “rational analysis” explanation of visual cognition [4], suggesting that the idiosyncrasies of biological perception arise as a consequence of a rational agent attempting to solve natural tasks.”
Automatically Composing Representation Transformations as a Means for Generalization (2018, background): “Reformulation and cognitive development: Reformula-tion , or re-representing a problem to make it easier to solve, dates back at least to the 1960s [4, 43, 76], but is relatively unexplored in deep learning.”
SNIF-ACT: A Cognitive Model of User Navigation on the World Wide Web (2007, unknown): “with the rational analysis methodology initiated by Anderson and his colleagues ( Anderson, 1990; Oaksford & Chater, 1994, 1996).”
Rationality for Mortals: How People Cope with Uncertainty (2008, methodology): “Optimizing models are based on Abraham Wald’s (1947) statistical theory, which has a stopping rule that is optimal relative to given probabilities of Type I and Type II errors (e.g., Anderson, 1990; Busemeyer & Rapoport, 1988).”
Learning Causal Laws (2003, background): “Most laboratory studies of learning abstract causal knowledge (e.g., Anderson, 1990) have not addressed the problem of simultaneously learning causal laws and the concepts over which those laws are defined, yet much causal knowledge can only be learned in this “chicken-andegg” fashion.”
Decision by sampling. (2006, background): “Dougherty, Gettys, and Ogden (1999) decision making model MINERVA-DM (based on Hintzman’s, 1984, 1988; memory model) gives a mechanism by which the support for hypotheses depends on the similarity to traces stored in memory, providing an account of many heuristics and biases (see also Juslin & Perss”
Developing Managers as Learners and Researchers: Using Action Learning and Action Research (2006, background): “As a result, they may not be able to bring this knowledge into public view for teaching or learning purposes (Pleasants, 1966; Polanyi, 1966; Anderson, 1990; Reber, 1993).”
An integrated theory of the mind. (2004, methodology): “Subsequent research on this task (Byrne, Anderson, Douglass, & Matessa, 1999), collecting eye movements and relating them to search times, has shown that the control process is more complex than described in the simple model above. Byrne (2001) presents a more sophisticated learningdriven ACT-R mode”
Goal Inference as Inverse Planning (2007, methodology): “Like much work in computer vision, the inverse planning framework provides a rational analysis (Anderson, 1990) of goal inference.”
Bayesian learning theory applied to human cognition. (2011, unknown): “p({data}|Mi) = ∫ p({data}|Mi, θ)p(θ |Mi)dθ (7)”
Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. (2010, methodology): “Rather we must use and understand both process and accuracy (and more generally judges’ goals) in tandem to explain choice and judgment (cf. Anderson, 1990).”
Evolutionary versus instrumental goals: How evolutionary psychology misconceives human rationality (2003, methodology): “Anderson (1990) accepts Stich's (1990; see also Skyrms, 1996) argument that evolutionary adaptation (hereafter termed evolutionary rationality) does not guarantee perfect human rationality in the instrumental sense which is focused on goals of the whole organism.”
Uncertainty and Learning (2003, background): “Further, since learning about the correlational structure of the world is exactly the purview of theoretical statistics, it is possible to build statistical computational theories [5], in which probabilistic learning models form the computational level description of learning, and thus provide ratio”
Shortlist B: a Bayesian model of continuous speech recognition. (2008, background): “The starting point for this work is an analysis of the computational problems that must be solved in order to complete the task of speech recognition successfully (Marr, 1982)—in Anderson’s (1990) terms, a Rational Analysis.”
Theory-based causal induction. (2009, background): “Finally, Anderson (1990) provided a computational analysis of data involving the interaction between spatial separation and temporal contiguity in causal induction.”
A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. (1997, unknown): “such as Anderson and Freebody (1981), Anglin (1993), Nagy and An-”