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Detection theory8/10/2023 The goal for the psychophysicist is to estimate D-Prime by having subjects view stimuli and make decisions - the ultimate Text((noiseMean+signalMean)/2.02, 'D-Prime', 'HorizontalAlignment', 'Center') Set to zero (as it is in our example), D-Prime is the signal mean divided by its standard deviation: In the simpleĬase for SDT with normal PDFs and equal variance, the strength of the psychological response, called 'D-Prime', is definedĪs the difference between the means of the signal and noise distributions normalized by the variance. 'Psychophysics' is the study of the relationship between physical stimuli and their psychological response. You can see from the graph that with this criterion, most trials will lead to a 'no' response because the bulk of both PDF's Let's set our criterion to a value of 1.5, and draw it on our graph criterion = 1.5 High and therefore only respond 'yes' when there is a very large internal response. Trying to see flashes of light, on the other hand, might not care so much about misses. For example, a doctor reading an MRI might set a low criterion for detectingĪ tumor because the cost of missing a tumor is high compared to the cost of a false alarm. Nature of the decision can influence this criterion. Is 'yes' if the internal response exceeds this criterion.Ĭriteria can range from being very conservative to avoid making misses, to being liberal to avoid making false alarms. SDT is based on the idea that a subject chooses a 'criterion' level of internal response so that the decision on any trial SignalPDF = NormalPDF(x,signalMean,variance) NoisePDF = NormalPDF(x,noiseMean,variance) The PsychToolbox provides us with a function 'NormalPDF' which is simply a Gaussian function normalized so that its total Our normal distributions will be 'unit normals'. To keep things simple, we'll choose a variance of 1, so that Let's choose some numbers and draw the PDF's for illustration. Will be normally distributed with equal variances but different means. In the simplest case, 'noise' and 'signal' trial distributions Probability distribution function (PDF) along that dimension. (a 'signal trial) will lead to a weak response, causing the subject to respond 'no' - a 'miss' trial.įormally, the internal representation is along a single dimension, and the response to a given stimulus is described by a Or, sometimes a trial containing a stimulus Response that the subject will claim to see a stimulus, causing a false alarm. Sometimes the noise in a trial containing no stimulus (a 'noise' trial) will produce a large enough This means that the internal response to any stimulus will varyįrom trial to trial. Variablity in the neuronal representation of the stimulus. The noiseĬan either be external, caused by variability in the number of photons in the flash, for example or internal, caused by intrinsic The idea behind SDT is that there is 'noise' in the system that interferes with perfect performance on the task. Is a very dim flash of light in the dark. On each trial and the subject must respond 'yes' or 'no' to indicate whether or not anything was seen. The simple 'yes/no' forced choice experimentīefore getting to the 2AFC experiment, we'll start with a simpler version where either no stimulus or a weak stimulus is presented Estimating D-Prime from simple forced-choice.The simple 'yes/no' forced choice experiment.
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