Signal detection theory (SDT) may be applied to any area of psychology in which two different types of stimuli must be discriminated. We describe several of these areas and the advantages that can be realized through the application of SDT. Three of the most popular tasks used to study discriminability are then discussed, together with the measures that SDT prescribes for quantifying performance in these tasks. Mathematical formulae for the measures are presented, as are methods for calculating the measures with lookup tables, computer software specifically developed for SDT applications, and general purpose computer software (including spreadsheets and statistical analysis software).

/pdf/calculation-of-signal-detection-theory-measures-4xcday6n3y.pdf

Calculation of signal detection theory measures

The effect of outliers on reaction time analyses is evaluated. The first section assesses the power of different methods of minimizing the effect of outliers on analysis of variance (ANOVA) and makes recommendations about the use of transformations and cutoffs. The second section examines the effect of outliers and cutoffs on different measures of location, spread, and shape and concludes using quantitative examples that robust measures are much less affected by outliers and cutoffs than measures based on moments. The third section examines fitting explicit distribution functions as a way of recovering means and standard deviations and concludes that unless fitting the distribution function is used as a model of distribution shape, the method is probably not worth routine use.

Methods for dealing with reaction time outliers.

Reasoning ability is (little more than) working-memory capacity?!

A model of memory retrieval is described. The model embodies 4 main claims: (a) temporal memory-traces of items are represented in memory partly in terms of their temporal distance from the present; (b) scale-similarity-similar mechanisms govern retrieval from memory over many different timescales; (c) local distinctiveness-performance, on a range of memory tasks is determined by interference from near psychological neighbors; and (d) interference-based forgetting-all memory loss is due to interference and not trace decay. The model is applied to data on free recall and serial recall. The account emphasizes qualitative similarity in the retrieval principles involved in memory performance at all timescales, contrary to models that emphasize distinctions between short-term and long-term memory.

/pdf/a-temporal-ratio-model-of-memory-5fvweswtk5.pdf

A Temporal Ratio Model of Memory

A computational model of human memory for serial order is described (OSCillator-based Associative Recall [OSCAR]). In the model, successive list items become associated to successive states of a dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive states of which cue successive recalls. The model provides an integrated account of both item memory and order memory and allows the hierarchical representation of temporal order information. The model accounts for a wide range of serial order memory data, including differential item and order memory, transposition gradients, item similarity effects, the effects of item lag and separation in judgments of relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various temporal resolutions, longer term memory for serial order, list length effects, and the effects of vocabulary size on serial recall.

Oscillator-based memory for serial order

/pdf/a-revised-table-of-d-for-m-alternative-forced-choice-4jnp447s15.pdf

A revised table of d’ for M-alternative forced choice

Speed and accuracy of recency judgments for events in short-term memory.

Models of the visual perceptual matching process are evaluated in two experiments in which the relative speeds of “same” and “different” responses were manipulated. In the first experiment, subjects were tested under two bias conditions: they were instructed to respond “same” only when sure or to respond “different” only when sure. Such bias is found to have a very large effect on the speed and accuracy of “same” and “different” responses, changing “same” responses from being faster than “different” responses (the usual result) in the sure “different” condition to being slower in the sure “same” condition. In the second experiment, the relative speed of “same” responses was slowed significantly by the addition of difficult “different” judgments. These results are used to argue against models that contain an identity matcher and against models that have separate components to account for errors and reaction time. We also point out that, although it is well known that the relative speeds of positive and negative responses are subject to bias manipulations, this fact has been ignored by many researchers in developing models in which the reaction time difference between positive and negative responses is used as a measure of a stage of processing.

/pdf/speed-and-accuracy-of-same-and-different-responses-in-1tx2yfipef.pdf

Speed and accuracy of same and different responses in perceptual matching

/pdf/a-basic-package-for-n-way-anova-with-repeated-measures-trend-3egyje3rf2.pdf

A BASIC package for N-way ANOVA with repeated measures, trend analysis, and user-defined contrasts

/pdf/on-the-misguided-use-of-reaction-time-differences-a-reply-to-46fqsno5kf.pdf

On the misguided use of reaction-time differences: A reply to Proctor and Rao (1982)

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