Len Dalgleish

A multiple-observations model for response latency and the latencies of correct and incorrect responses in recognition memory.

A model for response latency in recognition memory is described which is a strength model incorporating the notion of multiple observations and with the additional assumptions that the variance of the strength distributions increase with set size and that the observer attempts to keep his error rate at a constant level over set size. It is shown that the model can, without recourse to particular parameter values, predict a near linear RT set-size function and, since it is a (TSD) model in its decision aspects, can account for errors and hence error latencies in the recognition task. After the model is described, two experiments are performed which test the prediction that correct mean latency is generally shorter than incorrect mean latency. The prediction is confirmed and this feature is discussed in general, the model being compared with that of Juola, Fischler, Wood, and Atkinson (1971) in this respect. Some possible modifications to the latter model are also considered.

What is the signal in signal detection

This study concerns the nature of the stimulus represented along the decision axis in the yes/no auditory detection task. Two contrasting interpretations, absolute and difference representation of the stimulus, are tested by raising the carrier tone embedded within the background “noise” to the level of the signal, on occasional “catch” trials. Results indicate that difference detection may be the preferred mode of operation when a carrier tone is present and the task is a difficult one. Implications for the TSD model are discussed in terms of the relative efficiency of the two detection mechanisms.

Order and serial position effects in response time with multiple-item probe recognition

We examined the order effect in item-recognition response time, that is, differences in response time for multiple-item probes containing items in the same or in the reverse order as those in the memory set. Experiment 1 used the response condition in which only one item must be positive for a positive response, Experiment 2 used homogeneous probes in which all the items are either positive or negative, and Experiment 3 used the condition in which all the items must be positive. Of particular interest were the serial position variations in order effects for probes containing items that were adjacent in the memory set. We previously found that such effects are an indication of subjective grouping of the memory set and the matching of the probe with these subgroups. The order effect in the one-positive condition was only weak in most cases, but it was strong with homogeneous probes when the memory set was objectively grouped or was ungrouped but with a constant set size. There were also strong order effects in the all-positive condition for probes with items that were nonadjacent in the memory set. Our results are interpreted in terms of a parallel match process based on a distribution over position of items in subjective or objective groups. We account for the origin of the distribution-over-position process in terms of multiple representations of the grouped memory sets. The model assumes that each subgroup is represented in memory several, and perhaps very many, times and that considerable error in item positioning can occur over the multiple representations of any group.

The components of latency of response in two models for auditory detection with deadlines

Two models are described for the case of auditory detection with response time deadlines. These are the “timing” model, modified from Luce and Green (1972), and the “interval of uncertainty” (IU) model, modified from the “counting” model of McGill (1967). The two models are expressed in the form of additive component latencies, including a component which varies inversely with probability of response and a component for a longer response on the “nondeadline” response condition. An experiment is described, incorporating deadlines on both s and n trials separately, and the additive component latencies of the models are calculated. The “interval of uncertainty” model is seen to represent the data better than the “timing” model, even though different versions of the latter model are considered. Parameters are then fitted to give a representation of the IU model. The data from one subject of another study not employing deadlines can also be fitted to the model, and an interesting problem then arises concerning the exact locus of the speed-accuracy tradeoff. It is concluded that the IU model needs extending if it is to describe the data without the use of a component of latency which varies inversely with response probability.