Scaling Behavior in the Temporal Context Model

The Temporal Context Model (TCM) postulates a distributed representation of temporal context that provides the cue for episodic recall tasks. TCM, coupled with the Luce Choice Rule for determining probability of recall, a conjunction referred to as TCMFR, is able to explain the existence of the long-term recency effect, as well as predicting the persistence of associative effects even with the inclusion of a delay between items. Here, quantitative predictions of TCMFR such as the magnitude of the delay interval is increased in continuous-distractor free recall are developed. The magnitude of the recency effect is operationally defined as the ratio of the probability of first recall (PFR) of the last list item to the PFR of the next-to-last item. Properties of associative effects are operationalized by using analogous measures derived from conditional response probability (CRP) curves. TCMFR predicts a decrease in recency with increasing delay. The rate of this decay and the qualitative pattern of change with increasing delay depend on the rate of contextual drift. For a range of values of the rate of contextual drift, TCMFR also predicts a transient increase in the recency effect as the length of the delay increases from zero. The model predicts that contiguity effects in free recall should follow a similar pattern, but that associative asymmetry, ubiquitously observed in free recall, should decay monotonically with increases in the delay interval.