Modeling fan effects on the time course of associative recognition

We investigated the time course of associative recognition using the response signal procedure, whereby a stimulus is presented and followed after a variable lag by a signal indicating that an immediate response is required. More specifically, we examined the effects of associative fan (the number of associations that an item has with other items in memory) on speed–accuracy tradeoff functions obtained in a previous response signal experiment involving briefly studied materials and in a new experiment involving well-learned materials. High fan lowered asymptotic accuracy or the rate of rise in accuracy across lags, or both. We developed an Adaptive Control of Thought-Rational (ACT-R) model for the response signal procedure to explain these effects. The model assumes that high fan results in weak associative activation that slows memory retrieval, thereby decreasing the probability that retrieval finishes in time and producing a speed–accuracy tradeoff function. The ACT-R model provided an excellent account of the data, yielding quantitative fits that were as good as those of the best descriptive model for response signal data.

► We examine how associative fan affects the time course of recognition.
► High fan lowers asymptotic accuracy or the rate of rise in accuracy, or both.
► We develop an ACT-R model that produces speed–accuracy tradeoff functions.
► The model provides an excellent account of fan effects in response signal data.