Events appear to be represented distinctly in memory in large numbers at a fine grain, even in tasks in which memory retention is not a primary performance measure. In Experiment 1, participants classified character strings in sequences governed by randomly-alternating instructions. Response times were fastest near the start of a sequence, slowed gradually throughout the sequence, then sped up again near the start of the next sequence. This speedup and gradual slowdown were modeled in the ACT-R architecture as a combination of priming and interference effects in episodic memory. The model correctly predicts the absence of these effects in Experiment 2. in which the instruction must be inferred from the trial stimulus and hence is not a source of priming. These findings suggest (a) that episodic encoding is a pervasive side effect of cognitive performance; (b) that elements of episodic memory interact through priming and interference—effects traditionally associated with semantic memory; and (c) that brief interruptions of task performance have more complex effects than previously documented.

The notion that memory for goals is organized as a stack is central in cognitive theory in that stacks are core constructs leading cognitive architectures. However, the stack over-predicts the strength of goal memory and the precision of goal selection order, while under- predicting the maintenance cost of both. A better way to study memory for goals is to treat them like any other kind of memory element. This approach makes accurate and well-constrained predictions and reveals the nature of goal encoding and retrieval processes. The approach is demonstrated in an ACT-R model of human performance on a canonical goal-based task, the Tower of Hanoi. The model and other considerations suggest that cognitive architectures should enforce a two-element limit on the depth of the stack to deter its use for storing task goals while preserving its use for attention and learning.

Serial attention is the process of focussing mentally on one item at a time. This process has two phases: attention switching and attention maintenance. Attention switching involves rapidly building up the activation of a new item to dominate old items. Attention maintenance involves letting the current item decay while in use to prevent it from intruding on the next item later on. SASM , a model based on this analysis, suggests that this balance of high initial activation followed by gradual decay reflects a strategic adaptation to ask demands on one hand and principles of memory on the other. The model makes novel and accurate predictions about response times and error rates, integrates past use and current context as memory activation sources, and integrates attention switching and attention maintenance into one unified account.