Lens Model

Perception

The lens model was originally conceived as a perceptual analogy in which the distal stimulus is perceived by a person in terms of multiple cues that are imperfectly correlated with each other, each providing overlapping (i.e., correlated) information about the stimulus to be perceived. The distal stimulus is portrayed as having rays emanating from it that represent the cues. The lens collects the information and refocuses it so that the perceptual system probabilistically “achieves” the correct percept.

Judgment

Kenneth Hammond brought Brunswik into mainstream psychology by applying his ideas to judgment. In a judgment context, the lens model is best described in terms of an approach called judgment analysis, which requires a substantial number of multi-attribute situations about which a person makes judgments. The purpose of judgment analysis is to understand the relationships between (a) the environment and the attributes, commonly called cues; (b) a person's cognitive system and the cues; and (c) the environment and judgment systems.

The Lens Model

Consider a highly idealized example. Imagine a company that is interested in the validity of the hiring decisions of its personnel department. Fortuitously for the industrial/organizational (I/O) psychologist, for legal reasons, the last 100 applicants have all been hired irrespective of the personnel director's ratings, which were based on five factors, duly recorded. Hence, the judgment data are available for analysis. The company has a criterion for success, and the five factors are conceptualized as cues to both judgment and success. Regression analysis can be used to assess the contribution of the cues to successful prediction and the relationship of the cues to success on the job. The lens model, depicted in Figure 1, provides a rich tool for understanding.

The personnel manager's hiring judgments in the 100 cases are denoted by Ys, and each case is represented by varying values on the seven cues (Xi). The criterion for each case is denoted by Ye. The judgments and criterion values are assumed to be continuous, but in practice they can include binary or categorical cues as well. The 100 predictions of the linear regression models of the criterion and judge are denoted by Y′e and Y′s, respectively.

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