A ratio rule from integration theory applied to inference judgments.

Leon, Marcelo; Anderson, Norman H.

doi:10.1037/h0035677

Cited by 61 publications

(34 citation statements)

References 22 publications

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“…The probability of picking an urn, the relative proportion of beads in each urn, the sample size, and the sample makeup can be varied. The typical result is that subjects behave less optimally than predicted by Bayes's theorem (e.g., Leon & Anderson, 1974).…”

Section: Previous Rejections Of Optimal Behaviormentioning

confidence: 99%

“…Consider a test of the Bayesian model in situations in which subjective base rates are assumed to be equal to objective base rates. In these cases, performance falls short of the predictions of Bayes's theorem (Leon & Anderson, 1974). Central to the current theoretical framework, however, is the evaluation stage that transforms the objective source of information into some subjective value.…”

Section: Previous Rejections Of Optimal Behaviormentioning

confidence: 99%

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Models of integration given multiple sources of information.

Massaro¹,

Friedman²

1990

Psychological Review

218

205

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Several models of information integration are developed and analyzed within the context ofa prototypical pattern-recognition task. The central concerns are whether the models prescribe maximally efficient (optimal) integration and to what extent the models are psychologically valid. Evaluation, integration, and decision processes are specified for each model. Important features are whether evaluation is noisy, whether integration follows Bayes's theorem, and whether decision consists of a criterion rule or a relative goodness rule. Simulations of the models and predictions of the results by the same models are carried out to provide a measure of identifiability or the extent to which the models can be distinguished from one another. The models are also contrasted against empirical results from tasks with 2 and 4 response alternatives and with graded responses. Conceptual FrameworkThere is a growing consensus that behavior reflects the influence of multiple sources of information. Auditory and visual perception, reading and speech perception, and decision making and judgment are modulated by a wide variety of influences (Anderson, 1981;Bruno & Cutting, 1988;Falmagne, 1985;Massaro, 1987aMassaro, , 1988aOden, 1981; Perkell & Klatt, 1986;Welch & Warren, 1980). Until only recently, psychological inquiry was aimed at studying the relationship between behavior and a given single source independently of other sources of information. The common strategy was to eliminate or to hold constant all potential sources of information except the source of interest. This research strategy was most apparent in psychophysics but was also pervasive in perception, memory, and learning.The single-factor experiment was the dominant mode of investigation when one-dimensional functional relationships were the primary goal. Trying to understand behavior when multiple sources of information are available poses additional problems. Factorial experiments seem to be the most promising approach, and we have witnessed immense methodological and theoretical progress in this domain. Specifically, the additivefactor method developed by Sternberg (1969) and Anderson's (1970Anderson's ( , 1981 functional measurement are milestones that will not be easily surpassed. Without these methodologies, there would have been a plethora of idle psychologists in the last couple of decades. True, Fisher (1935) bequeathed the statistical tools for factorial designs long before Anderson, Sternberg, andThe writing of this article was supported, in part, by a grant from the National Institutes of Health (NINCDS Grant 20314), a grant from the National Science Foundation (BNS 8812728), and a James McKeen Cattell Fellowship to Dominic W. Massaro; and by a grant from the National Science Foundation (IRI 8812798) to Daniel Friedman.We thank Michael M. Cohen, Yoshihisa Kashima, Roger Shepard, James Townsend, and an anonymous reviewer for their comments.Correspondence concerning this article should be addressed to Dominic W. Massaro, Department of Psychology, University of...

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Section: Previous Rejections Of Optimal Behaviormentioning

confidence: 99%

Section: Previous Rejections Of Optimal Behaviormentioning

confidence: 99%

Models of integration given multiple sources of information.

Massaro¹,

Friedman²

1990

Psychological Review

218

205

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“…However, studies with artificial data are greatly needed to assess minimum design requirements that will allow real deviations from the model to be detected. These studies need to be done for models that are empirically reasonable, and inherently nonadditive, such as the averaging model with unequal weighting, and the ratio model developed in information integration theory (Anderson & Farkas, 1975;Leon & Anderson, 1974;Oden, 1974).5…”

Section: Error Theory For Nonmetric Analysismentioning

confidence: 99%

Note on functional measurement and data analysis

Anderson

1977

Perception & Psychophysics

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The guiding idea of functional measurement is that measurement theory and substantive theory form an organic unity. Psychological scales are inherent in the statement of quantitative psychological laws, and these laws themselves form the base and frame for psychological measurement. Valid scales thus depend on empirically valid laws. But establishing empirical validity of any law requires appropriate data analysis. Several statistical problems are discussed with respect to simple algebraic laws. To illustrate the necessity for proper tests of goodness of fit for algebraic models, five sets of experimental data are reanalyzed. In each case, the factorial plot and the analysis of variance showed that the data were nonadditive. Nevertheless, an additive model was fit to the data. The correlations between the data and the predictions from the additive model were extremely high, ranging from .964 to .9997. The corresponding observed-predicted scatterplots also gave little sign of the deviations from additivity. These correlation-scatterplot analyses conceal and obscure what the factorial plot and the analysis of variance reveal and make clear. Other topics discussed are accepting and rejecting the null hypothesis, the use of nonmetric smoothing for parameter estimation, and problems of stimulus-response-model generality.

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“…The methodological context in which the issues are evaluated. An important methodological point surfaced when Leon and Anderson (1974), using a within-subjects design, found that the judgments of participants in a two-urn task did not reflect base rate neglect; instead, judgments varied appropriately with changes in urn composition (corresponding to base rate) and sample size (corresponding to case cue accuracy). Similar findings have been reported by Ward (1975).…”

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confidence: 99%