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Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
 Psychological Review
, 2006
"... A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probab ..."
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Cited by 26 (7 self)
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A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trialbytrial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The backpropagation of target values to attention allows the model to show trialorder effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.
Experimental Practices in Economics: A Challenge . . .
"... This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script ..."
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Cited by 17 (4 self)
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This article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performancebased monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined ìscriptî to experiments for
Locally Bayesian Learning
"... This article is concerned with trialbytrial, online learning of cueoutcome mappings. In models structured as successions of component functions, an external target can be backpropagated such that the lower layer’s target is the input to the higher layer that maximizes the probability of the highe ..."
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Cited by 7 (6 self)
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This article is concerned with trialbytrial, online learning of cueoutcome mappings. In models structured as successions of component functions, an external target can be backpropagated such that the lower layer’s target is the input to the higher layer that maximizes the probability of the higher layer’s target. Each layer then does locally Bayesian learning. The resulting parameter updating is not globally Bayesian, but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs, and then maps attentionally filtered inputs to outputs. The model is applied to the humanlearning phenomenon called highlighting, which is challenging to other extant Bayesian models, including the rational model of Anderson, the Kalman filter model of Dayan and
Semirational Models of Conditioning: The Case of Trial Order
, 2007
"... Bayesian treatments of animal conditioning start from a generative model that specifies precisely a set of assumptions about the structure of the learning task. Optimal rules for learning are direct mathematical consequences of these assumptions. In terms of Marr’s (1982) levels of analyses, the mai ..."
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Cited by 4 (2 self)
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Bayesian treatments of animal conditioning start from a generative model that specifies precisely a set of assumptions about the structure of the learning task. Optimal rules for learning are direct mathematical consequences of these assumptions. In terms of Marr’s (1982) levels of analyses, the main task at the computational level
The Role of Base Rates in Category Learning
"... : Previous researchers have discovered perplexing inconsistencies in how human subjects appear to utilize knowledge of category base rates when making category judgments. In particular, Medin and Edelson (1988) found an "inverse base rate effect" in which subjects tended to select a rare category wh ..."
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: Previous researchers have discovered perplexing inconsistencies in how human subjects appear to utilize knowledge of category base rates when making category judgments. In particular, Medin and Edelson (1988) found an "inverse base rate effect" in which subjects tended to select a rare category when tested with a combination of conflicting cues, and Gluck and Bower (1988) reported apparent "base rate neglect" in which subjects tended to select a rare category when tested with a single symptom whose objective diagnosticity was equal for all categories. In this article I suggest that two principles underlie those effects: First, base rate information is learned and consistently deployed during all training and testing cases. Second, the dominant effect of base rates is to cause frequent categories to be learned before rare categories, so that the common categories are encoded in terms of their typical features, and the rare categories are encoded by whichever features distinguish them ...
A Developmental Perspective on Order and Learning: Temporal Effects on Cued Attention
"... The two experiments in this paper provide evidence for order effects obtained from adult and child populations. Experiment 1 compares different versions of baserate and canonical highlighting tasks investigating the differences between visual processing of cues and inference based knowledge. Compar ..."
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The two experiments in this paper provide evidence for order effects obtained from adult and child populations. Experiment 1 compares different versions of baserate and canonical highlighting tasks investigating the differences between visual processing of cues and inference based knowledge. Comparisons based on adults ’ individual performance are also addressed. Experiment 2 implements designs from Experiment 1 to investigate the nature of order effects on children ages 45yearsold.
Perception, Action and Utility: The Tangled Skein
, 2011
"... Normative theories of learning and decisionmaking are motivated by a computationallevel analysis of the task facing an animal: what should the animal do to maximize future reward? However, much of the recent excitement in this field originates in how the animal arrives at its decisions and reward ..."
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Normative theories of learning and decisionmaking are motivated by a computationallevel analysis of the task facing an animal: what should the animal do to maximize future reward? However, much of the recent excitement in this field originates in how the animal arrives at its decisions and reward predictions—algorithmic questions about which the computationallevel analysis is silent.
13 Perception, Action, and Utility: The Tangled Skein
"... Statistical decision theory seems to offer a clear framework for the integration of perception and action. In particular, it defines the problem of maximizing the utility of one’s decisions in terms of two subtasks: inferring the likely state of the world, and tracking the utility that would result ..."
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Statistical decision theory seems to offer a clear framework for the integration of perception and action. In particular, it defines the problem of maximizing the utility of one’s decisions in terms of two subtasks: inferring the likely state of the world, and tracking the utility that would result from different candidate actions in different states. This computationallevel description underpins more processlevel research in neuroscience about the brain’s dynamic mechanisms for, on the one hand, inferring states and, on the other hand, learning action values. However, a number of different strands of recent work on this more algorithmic level have cast doubt on the basic shape of the decisiontheoretic formulation, specifically the clean separation between states ’ probabilities and utilities. We consider the complex interrelationship between perception, action, and utility implied by these accounts. Normative theories of learning and decision making are motivated by a computationallevel analysis of the task facing an organism: What should
Comments welcome. Cue competition in function learning: Blocking and highlighting
, 2001
"... In function learning, people learn to predict a continuous outcome from continuous cues. In category learning, people learn to predict a nominal outcome. The present research demonstrates that two complementary forms of cue competition, previously found in category learning, also occur in function l ..."
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In function learning, people learn to predict a continuous outcome from continuous cues. In category learning, people learn to predict a nominal outcome. The present research demonstrates that two complementary forms of cue competition, previously found in category learning, also occur in function learning. One form of cue competition is blocking of learning about a redundant cue (Kamin, 1968). A second form of cue competition is highlighting of a diagnostic cue (a.k.a. the inverse base rate effect; Medin & Edelson, 1988). For tests with conflicting cues, the results show bimodality of responses, as opposed to averaging, which implies exclusive selectivity that cannot be discerned from category learning paradigms. It is argued that these effects are caused, in both category and function learning, by attentional shifts. No previously published model of function learning can account for these effects, but a model by Kalish, Lewandowsky, and Kruschke (2001) is promising. This article reports evidence of two types of strong cue competition in function learning. One effect is “blocking ” of learning about a redundant relevant cue (Kamin, 1968). The other effect is what I call “highlighting, ” previously referred