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This chapter describes identification experiments where an observer is asked to identify which of several nonblank stimuli has been presented. Only identification experiments using stimulus intensities so low that the stimuli themselves are imperfectly discriminable from a blank stimulus are considered, because these near-threshold experiments are particularly suited for studying multiple visual pattern analyzers as discussed in Chapter 1. This chapter presents classification experiments (identification experiments in which there is a one-to-one correspondence between the responses and stimuli) and three different kinds of discrimination experiments (identification with only two stimuli). The appropriate multidimensional signal detection models for interpreting their results are presented as developments of the models in preceding chapters.

This chapter continues Chapter 9's description of identification experiments by describing three more paradigms: simple detection and identification; the 2x2 paradigm; and concurrent identification. The models of Chapter 9 are used with new decision rules added for two of the additional paradigms. It discusses the effects of relaxing the assumptions (e.g., allowing correlation among analyzer outputs) and how one can measure these effects in the identification paradigm. It also discusses transducer functions and imperfect attention as they affect results of identification experiments. This and the preceding chapter are illustrated with results of experiments on the spatial-frequency dimension.

This chapter considers the uncertainty arising from sources extrinsic to the observer, in particular from the observer's ignorance on any given trial as to which of several alternative stimuli will be presented. Explanations based on multiple independent analyzers are presented, and their predictions for uncertainty experiments and summation experiments are calculated. These models differ in their assumptions about the probability distribution characterizing each analyzer's output and in their assumptions about how the multiple analyzer's outputs are combined to form the observer's decision rule. Several generalization are presented, e.g., probability distributions predicting shallower ROC slopes predict larger blocked-summation effects but smaller uncertainty effects. Further, these models provide some insight into and justification for the quick nonlinear pooling model presented in Chapter 4.

Intrinsic uncertainty arises from sources intrinsic to the observer, in particular from the observer's inability to attend to only the relevant information (attention limitations) and inability to remember the set of possible stimuli (memory limitations). This intrinsic uncertainty can be incorporated into the models based on multiple independent analyzers that were presented in the previous chapter for cases of extrinsic uncertainty. This chapter considers the interaction of uncertainty assumptions with the transducer function (output versus input function) of individual analyzers; the overall transducer function of all the analyzers used by the decision rule; the psychometric function for the observer; and the ROC curves. The control of attention and individual differences is discussed.

Summation experiments using patterns that are far apart along any dimension (e.g., two lines of very different orientations) can answer the question of whether there are multiple analyzers along that dimension. Two classes of models are presented: probabilistic models in which variability in analyzers' outputs causes increased performance (probability summation); and deterministic models that predict increased performance without assuming variability by incorporating nonlinear pooling (Minkowski, Quick) into decision rules. This chapter presents the results on the spatial-frequency dimension (and their possible dependence on retinal inhomogeneity). An appendix derives convenient formulas in terms of observable quantities to allow easy use of the models in many situations. This chapter together with Chapters 7 through 10 cover what is sometimes called multidimensional signal detection theory.