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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 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 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.

The chapter ranges broadly over a number of topics, and summarizes the empirical findings on D. B. of 1986. It discusses issues that arose when blindsight research first emerged. These include stray light and other artefacts, with the appropriate controls that have been applied. The question of whether blindsight is degraded normal vision, together with other relevant research, is considered. The question of response criteria in signal detection terms is aired. Neural pathways and levels are reviewed together with a discussion of the differential physiological and anatomical properties of striate vs. non-striate pathways. A definition of blindsight is advanced, followed by a discussion of the importance of addressed issues of awareness in biology and for practical aspects of empirical research. The chapter ends with a discussion of the importance of ‘commentary keys’ for neuropsychological research and the underlying ‘commentary system’.

This chapter introduces the basic detection task: an observer receives a stimulus (in any sensory domain) that may or may not contain a weak signal and must decide whether the signal is present or not. The key elements of a mathematical model for this situation based on statistical decision theory are described.

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.

The recognition heuristic uses a recognition decision to make an inference about an unknown variable in the world. Theories of recognition memory typically use a signal detection framework to predict this binary recognition decision. This chapter integrates the recognition heuristic with signal detection theory to formally investigate how judges use their recognition memory to make inferences. The analysis reveals that false alarms and misses systematically influence the performance of the recognition heuristic. Furthermore, judges should adjust their recognition response criterion according to their experience with the environment to exploit the structure of information in it. Finally, the less-is-more effect is found to depend on the distribution of cue knowledge and judges' sensitivity to the difference between experienced and novel items. Theoretical implications of this bridge between the recognition heuristic and models of recognition memory are 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.

The psychological notion of a “goal” can take various forms, but at the bottom all goal-directed activity seems to require that the correct control signal be detectable by the cognitive system against a background of old or alternative signals. A simple signal detection model based on this premise explains a variety of empirical phenomena from the domain of task switching that might otherwise seem unrelated and that have no obvious explanation in terms of standard, but somewhat naïve, “reconfiguration” accounts of cognitive control. The model can be used to frame discussion of a variety of memory- and attention-related processes, including encoding, retrieval, priming, and inhibition. This chapter suggests that interference among control signals is the basic constraint on goal-directed behavior and that decay of such signals is an architectural housecleaning process that prevents this interference from becoming catastrophic. This analysis provides a reasonably direct account of within-run effects and first-trial effects in task switching.

This chapter treats the measurement of visual sensitivity, or how we detect threshold or near-threshold stimuli. It begins with a brief review of the classical methods of measuring thresholds. It then presents the signal-detection theory framework and an analysis of several classical and modern procedures, including method of limits, method of adjustment, method of constant stimuli, and alternative forced choice. The chapter ends with the signal-detection approach to more complex situations involving inputs from multiple detectors and tasks that involve stimuli in multidimensional space, decision uncertainty and the general recognition theory.

Many decisions that people are called on to make can be thought of as involving thresholds for action. In each case, we can understand the decision maker to be answering two questions: (1) How strong are the arguments in favor of taking this action? (2) How strong must the arguments be in order for me to take the action? Decision makers in court cases, whether judges or jurors, are commonly required to make this kind of decision. The aim of this chapter is to set out a framework for analyzing decisions to take action in a judicial context. We begin by outlining a general model, continue with a description of several studies of mock-juror decision making, and conclude with implications for studying judges.

The examination of recognition memory confidence judgements indicates that there are two separate components or processes underlying episodic memory. Recollection is assumed to reflect a threshold process whereby qualitative information about the study event is retrieved, whereas familiarity reflects a classical signal-detection process whereby items exceeding a familiarity response criterion are accepted as having been studied. Evidence from cognitive, neuropsychological, and neuroimaging studies indicate that the model is in agreement with the existing recognition results, and indicate that recollection and familiarity are behaviourally, neurally, and phenomenologically distinct memory retrieval processes.

The aim of statistical decision theories is to understand how evidence, prior knowledge, and values lead an organism to commit to one of a number of alternatives. Two main statistical decision theories, signal-detection theory and sequential analysis, assert that decision makers obtain evidence—often from the senses—that is corrupted by noise and weigh this evidence alongside bias and value to select the best choice. Signal-detection theory has been the dominant conceptual framework for perceptual decisions near threshold. Sequential analysis extends this framework by incorporating time and introducing a rule for terminating the decision process. This extension allows the trade-off between decision speed and accuracy to be studied, and invites us to consider decision rules as policies on a stream of evidence acquired in time. In light of these theories, simple perceptual decisions, which can be studied in the neurophysiology laboratory, allow principles that apply to more complex decisions to be exposed. The goal of this chapter is to “go beyond the data” to postulate a number of unifying principles of complex decisions based on our findings with simple decisions. We make speculative points and argue positions that should be viewed as controversial and provocative. In many places, a viewpoint will merely be sketched without going into much detail and without ample consideration of alternatives, except by way of contrast when necessary to make a point. The aim is not to convince but to pique interest. The chapter is divided into two main sections. The first suggests that an intentionbased framework for decision making extends beyond simple perceptual decisions to a broad variety of more complex situations. The second, which is a logical extension of the first, poses a challenge to Bayesian inference as the dominant mathematical foundation of decision making.

This chapter presents an overview of cognitive research on visual attention from the pioneering studies in the 1950s to the mature psychological field of today. Classical empirical phenomena and findings are described alongside theories that have shaped the field. The long-standing debate between serial and parallel models of visual attention is given special emphasis. The chapter also describes the signal detection and biased choice theories, which represent early attempts at describing attention and categorization in mathematical terms.

This chapter is all about changes and developments related to the Weber's Law during the past fifty years. Two ideas have transformed our understanding of sensory discrimination and of sensation since 1958. These are the ideas that sensory discrimination is differentially coupled to the physical world and that there is no absolute judgement. During the 1960s, many researchers, prompted by signal detection theory, proposed models for Weber's Law. Most of these models were based on particular sensory modality and aimed to explain the law without recourse to a logarithmic transform.

Metacognition, or “knowing that you know,” is a core component of consciousness. Insight into a perceptual or conceptual decision permits us to infer perceptual or conscious knowledge underlying that decision. However, when assessing metacognitive performance care must be taken to avoid confounds from decisional and/or confidence biases. There has recently been substantial progress in this area and there now exist promising approaches. This chapter introduces type 1 and 2 signal detection theory (SDT), and describes and evaluates signal detection theoretic measures of metacognition. It discusses practicalities for empirical research with these measures, for example, alternative methods of transforming extreme data scores and of collecting confidence ratings, with the aim of encouraging the use of SDT in research on metacognition. It concludes by discussing metacognition in the context of consciousness.

This chapter examimes changes and developments in the study of human performance in psychology. It discusses major findings concerning signal detection, the relation between choice reaction time (RT) and amount of stimulus information, the notion of limited capacity, and linear stage theory. This chapter suggests that psychologists should not become too fascinated by the new intellectual climate of cognitive neuroscience so as to continue neglecting human factors.