Detecting and predicting changes

Brown, Scott; Steyvers, Mark

doi:10.1016/j.cogpsych.2008.09.002

Cited by 109 publications

(135 citation statements)

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“…Many studies have avoided any explicit discussion of whether this sensitivity should be called normative (e.g., Stewart et al, 2002), others have argued that it reflects the cognitive limitations of the human learner (Sakamoto et al, 2008;Sanborn et al, 2010), and still others have suggested that order-sensitive learning rules are necessary if the learner is to be able to adapt to a changing world (Elliott & Anderson, 1995;Nosofsky et al, 1992). The latter perspective is mirrored rather explicitly in the literature on sequential effects (Yu & Cohen, 2009) and change detection (Brown & Steyvers, 2009). …”

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confidence: 93%

“…boundary. This model is closely related to the tracking model used by Brown and Steyvers (2009), but it has links to other models, too. For instance, in the extreme case in which w = 1, this heuristic corresponds to a relative judgment strategy in which each stimulus is compared only to the last stimulus in the experiment, and it becomes a slight simplification of the model used by Stewart et al (2002).…”

Section: Methodsmentioning

confidence: 99%

“…In most models, the weight assigned to a particular observation tends to decay approximately exponentially as a function of age (Brown & Steyvers, 2009;Nosofsky et al, 1992;Yu & Cohen, 2009), although in some cases the decay has been proposed to be a power function (Elliott & Anderson, 1995), which would be more in keeping with the literature on memory (Rubin, Hinton, & Wenzel, 1999;Wixted & Ebbesen, 1997). A similar outcome exists in the judgmental forecasting literature, which examines how people perceive and extrapolate time series data: Once again, an exponential weighting rule appears to account for human performance (see Wright, 1994, andLawrence, Goodwin, O'Connor, &Onkal, 2006, for overviews).…”

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confidence: 99%

“…When the pattern of change is systematic rather than arbitrary, it is not sufficient merely to detect changes (e.g., Brown & Steyvers, 2009): An ideal learner is able to anticipate them by forming sensible expectations about when the world changes and how that change occurs. Although our experiments are very simple examples of this, involving regular linear change in one dimension, they provide evidence that people do form these sorts of expectations during category learning.…”

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confidence: 99%

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Learning time-varying categories

2013

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Many kinds of objects and events in our world have a strongly time-dependent quality. However, most theories about concepts and categories either are insensitive to variation over time or treat it as a nuisance factor that produces irrational order effects during learning. In this article, we present two category learning experiments in which we explored peoples' ability to learn categories whose structure is strongly time-dependent. We suggest that order effects in categorization may in part reflect a sensitivity to changing environments, and that understanding dynamically changing concepts is an important part of developing a full account of human categorization.

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confidence: 93%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Learning time-varying categories

2013

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“…The computationally-justified message passing scheme we developed, FBL, uses the same class of approximations as Daw et al (2008) as categorization Shi et al, 2010), sentence parsing (Levy et al, 2009), prediction (Brown & Steyvers, 2009), perceptual bistability (Gershman et al, 2012, and even human and animal learning (Lu et al, 2008;Rojas, 2010) to explain trial order effects. Sampling algorithms tend to come with asymptotic guarantees: with enough samples any computation done with these algorithms will be indistinguishable from computation done with the full probability distribution.…”

Section: Kinds Of Approximationsmentioning

confidence: 99%

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Sanborn

Silva

2013

Journal of Mathematical Psychology

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Different levels of analysis provide different insights into behavior: computationallevel analyses determine the problem an organism must solve and algorithmiclevel analyses determine the mechanisms that drive behavior. However, many attempts to model behavior are pitched at a single level of analysis. Research into human and animal learning provides a prime example, with some researchers using computational-level models to understand the sensitivity organisms display to environmental statistics but other researchers using algorithmic-level models to understand organisms' trial order effects, including effects of primacy and recency. Recently, attempts have been made to bridge these two levels of analysis.Locally Bayesian Learning (LBL) creates a bridge by taking a view inspired by evolutionary psychology: Our minds are composed of modules that are each individually Bayesian but communicate with restricted messages. A different inspiration comes from computer science and statistics: Our brains are implementing the algorithms developed for approximating complex probability distributions. We show that these different inspirations for how to bridge levels of analysis are not necessarily in conflict by developing a computational justification for LBL. We Preprint submitted to ElsevierApril 26, 2013 demonstrate that a scheme that maximizes computational fidelity while using a restricted factorized representation produces the trial order effects that motivated the development of LBL. This scheme uses the same modular motivation as LBL, passing messages about the attended cues between modules, but does not use the rapid shifts of attention considered key for the LBL approximation. This work illustrates a new way of tying together psychological and computational constraints.Keywords: rational approximations; locally Bayesian learning; trial order effectsOur goal when we model behavior depends on the level of analysis. If we analyze behavior at Marr (1982)'s computational level, then we aim to determine the problem that people are attempting to solve. Or, as more often found in psychology, we might be interested in the mechanism that drives behavior, placing us at Marr (1982)'s algorithmic level. In human and animal learning, both computational-level (Courville et al., 2005;Danks et al., 2003;Dayan et al., 2000) and algorithmic-level models (Rescorla & Wagner, 1972;Mackintosh, 1975;Pearce & Hall, 1980) have been developed. Models developed at different levels of analysis have different strengths and this can be seen in how these models of human and animal learning are applied: computational-level approaches are used to explain how organisms are sensitive to complex statistics of the environment (De Houwer & Beckers, 2002;Mitchell et al., 2005;Shanks & Darby, 1998) and algorithmiclevel models are used to explain how organisms are sensitive to the presentation order of trials (Chapman, 1991;Hershberger, 1986;Medin & Edelson, 1988).The computational and algorithmic levels provide different perspectives on model d...

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Complex Probabilistic Inference

Gershman

Beck

2017

Computational Models of Brain and Behavior

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Detecting and predicting changes

Cited by 109 publications

References 35 publications

Learning time-varying categories

Learning time-varying categories

Constraining bridges between levels of analysis: A computational justification for locally Bayesian learning

Complex Probabilistic Inference

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