Attention bands in absolute identification

Luce, R. Duncan; Green, David M.; Weber, Daniel L.

doi:10.3758/bf03198705

Cited by 136 publications

(116 citation statements)

References 11 publications

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“…Miller's (1956) classic paper focused on the limited channel capacity available to make absolute magnitude judgments (and explicitly linked signal variance with information transmission). In psychophysical work, Luce, Green, and Weber (1976) proposed that observers are able to strategically control attention bands, selectively monitoring a relatively narrow intensity range. Luce et al suggested that neural variability of the internal representation of intensities will be reduced within the favored attention band, yielding greater sensitivity as measured by signalto-noise ratio.…”

Section: Reference-point Modelsmentioning

confidence: 99%

“…Human (and non-human) observers have limited capacity in working memory to maintain veridical estimates of magnitudes, which therefore vary in their precision (Miller, 1956). To partially compensate, observers focus attention on a favored region, or magnitude band, along the relevant continuum (Luce et al, 1976;Nosofsky, 1983). When making comparisons based on relative concepts, such as ''choose larger'' or ''choose smaller'', attention is guided by a reference point located at or near the end of the continuum cued by the form of the question (Holyoak, 1978;Jamieson & Petrusic, 1975;Marks, 1972).…”

Section: Relational Comparisons Without Explicit Relationsmentioning

confidence: 99%

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The discovery and comparison of symbolic magnitudes

Chen

Holyoak

2014

Cognitive Psychology

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a b s t r a c tHumans and other primates are able to make relative magnitude comparisons, both with perceptual stimuli and with symbolic inputs that convey magnitude information. Although numerous models of magnitude comparison have been proposed, the basic question of how symbolic magnitudes (e.g., size or intelligence of animals) are derived and represented in memory has received little attention. We argue that symbolic magnitudes often will not correspond directly to elementary features of individual concepts. Rather, magnitudes may be formed in working memory based on computations over more basic features stored in long-term memory. We present a model of how magnitudes can be acquired and compared based on BARTlet, a representationally simpler version of Bayesian Analogy with Relational Transformations (BART; Lu, Chen, & Holyoak, 2012). BARTlet operates on distributions of magnitude variables created by applying dimension-specific weights (learned with the aid of empirical priors derived from pre-categorical comparisons) to more primitive features of objects. The resulting magnitude distributions, formed and maintained in working memory, are sensitive to contextual influences such as the range of stimuli and polarity of the question. By incorporating psychological reference points that control the precision of magnitudes in working memory and applying the tools of signal detection theory, BARTlet is able to account for a wide range of empirical phenomena involving magnitude comparisons, including the symbolic distance effect and the semantic congruity effect. We discuss the role of reference points in cognitive and social decision-making, and implications for the evolution of relational representations.

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Section: Reference-point Modelsmentioning

confidence: 99%

Section: Relational Comparisons Without Explicit Relationsmentioning

confidence: 99%

The discovery and comparison of symbolic magnitudes

Chen

Holyoak

2014

Cognitive Psychology

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“…Note, however, that this attentional hypothesis predicts the wrong pattern of same RTs in the same-stimulus conditions. According to most of the theories of attentional focus that address the issue of perceptual variability, placing attention in a given region results in reduced perceptual variability in that region (e.g., Ashby, Prinzmetal, Ivry, & Maddox, 1996;Luce, Green, & Weber, 1976;Tsal & Shalev, 1996). Such an attentional process would be adaptive for better discriminating among the highly similar objects that occur in the dense regions, for example, pair (1, 2) in Condition 1.…”

Section: Red (R) Green (G) and Blue (B) Values And Approximate Colomentioning

confidence: 99%

An exemplar-retrieval model of speeded same–different judgments.

Cohen¹,

Nosofsky²

2000

Journal of Experimental Psychology: Human Perception and Perfor

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R. M. Nosofsky and T. J. Palmeri's (1997) exemplar-based random-walk (EBRW) model of speeded classification is extended to account for speeded same-different judgments among integral-dimension stimuli. According to the model, an important component process of same-different judgments is that people store individual examples of experienced same and different pairs of objects in memory. These exemplar pairs are retrieved from memory on the basis of how similar they are to a currendy presented pair of objects. The retrieved pairs drive a random-walk process for making same-different decisions. The EBRW predicts correctly that same responses are faster for objects lying in isolated than in dense regions of similarity space. The model also predicts correctly effects of same-identity versus samecategory instructions and is sensitive to observers' past experiences with specific same and different pairs of objects.The main tenet of exemplar-based models of cognition is that people store particular instances of events in memory, called exemplars, and that these exemplars are later retrieved to perform a particular task. Exemplar-based models have long been used to model performance in categorization tasks (Medin & Schaffer, 1978). These models assume that people represent categories as a set of exemplars and make categorization decisions by retrieving exemplars from memory. Such models have rendered accurate quantitative accounts of category learning, transfer, and generalization (Hintzman, 1986; Krnschke, 1992;Nosofsky, 1986).Recently, Nosofsky and Palmed (1997) proposed an exemplarbased model of classification, which also accounts for the timecourse of classification. This exemplar-based random-walk (EBRW) model posits that people store category examples along with their category labels in memory. These exemplars are assumed to reside in a multidimensional psychological space. During a classification judgment, exemplars race to be retrieved. How fast an exemplar races is determined by its similarity to the test item. The winning exemplar adds incremental evidence to a random walk process. A response occurs when the random walk counter reaches a criterion. The EBRW correctly predicts effects of withinand between-category similarity, practice, and familiarity on classification response times and accuracies. This model is discussed in more detail below.Exemplar-basad models have also been successful in other domains. For example, Logan's (1988Logan's ( , 1992 instance theory of automaticity construes skilled action as a race between algorithmic and instance-based processes. He suggests that to perform a task

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“…Ennis (1988Ennis ( , 1992 has provided formalizations of exemplar-memory models that incorporate this perceptual variability, and Ennis (1988) and Nosofsky (1988b) discussed the potential benefits of such approaches to understanding a variety of psychological phenomena. Certain types of perceptual selective attention phenomena may be well modeled within such an approach in terms of reduced variances of the perceptual distributions associated with attended stimuli or dimensions (e.g., Luce, Green, & Weber, 1976;Nosofsky, 1983). I believe that the perceptualdistribution approach is potentially most fruitful in designs involving highly confusable stimuli, where, even following protracted training, pairwise discriminations among objects axe poor.…”

Section: Introduction and Issues Regarding Selective Attentionmentioning

confidence: 99%