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WIREs Cogn Sci
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Judgment: a cognitive processing perspective

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Historically, judgment research has been mainly concerned with identifying regularities in sensation (e.g., discriminability laws) and assessing judgment accuracy. More recently, the focus has shifted toward specifying the information processing mechanisms underlying judgment and modeling them, for example, as cognitive strategies. We contrast this strategy approach with previous prominent research programs on judgment and provide an overview of various process‐level accounts that have been proposed in terms of computational models (e.g., compensatory and noncompensatory cue‐abstraction strategies, evidence accumulation, exemplar processing, and parallel constraint satisfaction). Importantly, empirical investigations show that the cognitive processes underlying judgment differ considerably as a function of the individual's cognitive capacity and characteristics of the task environment (e.g., information cost, cognitive capacity, cue inter‐correlations, relationship between cues and the to‐be‐judged criterion). We argue that these systematic contingencies in strategy use can be understood as adaptive responses to costs in learning, information acquisition, and strategy execution. WIREs Cogn Sci 2013, 4:665–681. doi: 10.1002/wcs.1259 This article is categorized under: Psychology > Reasoning and Decision Making
The Weber–Fechner Law in psychophysics states that the magnitude of a sensation is a logarithmic function of the physical stimulus intensity. Just‐noticeable differences (JNDs) are measured indirectly based on paired‐comparison judgments and express the minimum proportional difference between the magnitude of two sensations required to distinguish them.
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The two types of learning tasks used in Ref . The feedback that participants received is indicated by the dotted rectangles.
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Representation of cue information in Ref .
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Representation of a parallel constraint satisfaction network for a comparative judgment task involving two objects (top nodes) and four cues (middle nodes). Bidirectional links between nodes have different weights for spreading the activation. After activation is fed into the network through the general validity node, it spreads through the network until a stable and consistent solution is reached, in which one object is clearly favored over the other.
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Cue environments with a skewed (a) and an equal (b) distribution of cue weights.
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Example of a MouseLab task in which participants have to predict the success of investment funds from different market segments (columns) based on recommendations of various brokers (rows). The information is shown by clicking on the boxes marked with a “?”. The number, type, and sequence of pieces of information acquired can be tracked.
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Schematic representation of Brunswik's lens model. The environment is depicted on the left, with the criterion YE being predicted by a set of k interrelated cues X. The judge is represented on the right, using the same cues for his judgment YS. The correlation between the criterion YE and the judgment YS is a function of accuracy, typically referred to as achievement. The correlation between the predicted judgment and the predicted criterion value (based on the regression models of the environment and the judge) is called matching (G).
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