Theory for Psychophysical Learning

Schoeffler, Michael

doi:10.1121/1.1909534

Cited by 27 publications

(37 citation statements)

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“…We use this model not as a replacement for SDT (and do not create new measures of sensitivity and bias based on it), but as an extension of classic SDT that can illustrate how different sources of noise may affect measurable statistics. This model incorporates confidence ratings and encapsulates aspects of decision uncertainty present in numerous previous models (see, e.g., Busemeyer & Myung, 1992;Erev, 1998;Kac, 1969;Schoeffler, 1965;Treisman & Williams, 1984), but does so at a level that does not incorporate learning and other trial-by-trial dynamics present in many of these previous models. This simplification allows us to evaluate the role of decision noise in general, independent of the specific assumptions of these theories (i.e., learning scheme, response mapping, criterion sampling/drift, etc.).…”

Section: The Decision Noise Model (Dnm) a Signal Detection Model Withmentioning

confidence: 99%

“…Some theorists have suggested that the decision criterion drifts along a sensory continuum from trial to trial, perhaps in response to error feedback (see, e.g., Kac, 1969). Others have suggested that decision criteria are sampled from a distribution on each trial (e.g., Erev, 1998), and still others have suggested that the observer learns a probabilistic function mapping sensory evidence onto the response (e.g., Schoeffler, 1965). Exactly how noise enters into the decision process is not important for our argument; thus, we aspresent substantial challenges for SDT and are not just complications caused by degenerate criterion placement, as was suggested by Treisman.…”

Section: Mapping From Percept To Responsementioning

confidence: 99%

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Decision noise: An explanation for observed violations of signal detection theory

Mueller

Weidemann

2008

Psychonomic Bulletin & Review

142

206

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Signal detection theory (SDT) has become a prominent and useful tool for analyzing performance across a wide spectrum of psychological tasks, from single-cell recordings and perceptual discrimination to high-level categorization, medical decision making, and memory tasks. The utility of SDT comes from its clear and simple account of how detection or classification performance can be translated into psychological quantities, such as sensitivity and bias. Whether its use is appropriate for a specific application depends on a number of underlying assumptions, and even though these assumptions are rarely tested, SDT has proved useful enough that it is considered one of the great successes of cognitive psychology. Yet, SDT has also undergone criticism, which began to emerge when this theory was relatively young. Criticisms of SDTSDT assumes that percepts are noisy and give rise to overlapping perceptual distributions for signal and noise trials. In order to distinguish between signal and noise trials, the observer uses a decision criterion to classify the percepts. Signal responses are "hits" when they are correct and "false alarms" when they are incorrect; similarly, noise responses can be classified as "correct rejections" and "misses." Many criticisms of SDT have centered on how the observer places a decision criterion during a detection or classification task, and whether a deterministic criterion is used at all (see, e.g., Dorfman & Biderman, 1971;Dorfman, Saslow, & Simpson, 1975;Kac, 1969;Kubovy & Healy, 1977;Larkin, 1971).Clearly, when initially performing a signal detection task, 1 an observer may be unable to estimate stimulus distributions and payoff values accurately; thus, one might expect the placement of a decision criterion to improve with experience, approaching a static optimal criterion. Yet, some results suggest that even with extensive practice, responses can be suboptimal: There are numerous demonstrations of human probability micromatching in signal detection tasks (see, e.g., Dusoir, 1974;Lee, 1963;Thomas, 1973Thomas, , 1975 and other demonstrations that static decision criteria are not typically used (e.g., Healy & Kubovy, 1981;Lee & Janke, 1964;Lee & Zentall, 1966;Treisman & Williams, 1984). Despite the fact that models accounting for these dynamics are based on a fairly reasonable assumption (i.e., that the decision criterion should improve with experience), they have not enjoyed the success of classic SDT-probably because they add layers of complexity to the theory that are not easily accommodated or validated. Given that even the basic assumptions required by SDT are rarely tested, it is perhaps not surprising that tests of these additional factors happen even less frequently. 465Copyright 2008 Psychonomic Society, Inc. THEORETICAL AND REVIEW ARTICLESDecision noise: An explanation for observed violations of signal detection theory SHANE T. MUELLERIndiana University, Bloomington, Indiana AND CHRISTOPH T. WEIDEMANN University of Pennsylvania, Philadelphia, PennsylvaniaIn signal detection the...

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Section: The Decision Noise Model (Dnm) a Signal Detection Model Withmentioning

confidence: 99%

Section: Mapping From Percept To Responsementioning

confidence: 99%

Decision noise: An explanation for observed violations of signal detection theory

Mueller

Weidemann

2008

Psychonomic Bulletin & Review

142

206

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“…These models, and various special cases of them, can usually be rejected on the basis of subjects' reports of where they move their criteria. Likewise, various probabilistic models, which have no criteria at all, such as Lee's (1971) rnicromatching model and Schoeffler's (1965) directional stimulus generalization model, can be rejected, at least for numer-…”

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confidence: 99%

On probabilistic categorization: The Markovian observer

Ward

Livingston²,

1988

Perception & Psychophysics

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A normative model (the Markovian observer) is described for a numerical decision task (an analogue of signal detection) in which the sequence of stimuli instantiates a two-state Markov chain. The expected-value observer of classical signal detection theory is a special case of the Markovian observer. An experiment is also described in which subjects performed the numerical detection task for different Markov chains of stimuli. Neither the ordinary expected-value observer nor the Markovian observer adequately described the performance of actual subjects, particularly when the stimulus sequence was random. A modification of the Markovian observer (the Markovian observer "ignorant" of the transition probabilities governing the stimulus sequence), similar to Kubovy and Healy's ideal-learner model, was simulated on a computer and provided a good qualitative fit to the data, especially to the distribution of violations of signal detection theory's single-criterion rule.

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“…A number of previous studies have considered the manner in which S categorizes a sample point that came from one of two possible normal probability distributions (Lee, 1963;Lee & Janke, 1964;1965). Such a distribution was called an externally distributed stimulus, since it is analogous to a stimulus such as a tone or attitude statement which is presumed in detection theory and Thurstone theory to give rise internally on each presentation to a sample point from a probability distribution.…”

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confidence: 99%