Erik M. Altmann

Memory for goals: an activation-based model

Goal-directed cognition is often discussed in terms of specialized memory structures like the “goal stack.” The goal-activation model presented here analyzes goal-directed cognition in terms of the general memory constructs of activation and associative priming. The model embodies three predictive constraints: (1) the interference level, which arises from residual memory for old goals; (1) the strengthening constraint, which makes predictions about time to encode a new goal; and (3) the priming constraint, which makes predictions about the role of cues in retrieving pending goals. These constraints are formulated algebraically and tested through simulation of latency and error data from the Tower of Hanoi, a means-ends puzzle that depends heavily on suspension and resumption of goals. Implications of the model for understanding intention superiority, postcompletion error, and effects of task interruption are discussed.

Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal

We examine peoples strategic cognitive responses to being interrupted while performing a task. Based on memory theory, we propose that resumption of a task after interruption is facilitated by preparation during the interruption lag, or the interval between an alert to a pending interruption (e.g. the phone ringing) and the interruption proper (the ensuing conversation). To test this proposal, we conducted an experiment in which participants in a Warning condition received an 8-s interruption lag, and participants in an Immediate condition received no interruption lag. Participants in the Warning condition prepared more than participants in the Immediate condition, as measured by verbal reports, and resumed the interrupted task more quickly. However, Immediate participants resumed faster with practice, suggesting that people adapt to particularly disruptive forms of interruption. The results support our task analysis of interruption and our model of memory for goals, and suggest further means for studying operator performance in dynamic task environments.

An Integrated Model of Cognitive Control in Task Switching.

A model of cognitive control in task switching is developed in which controlled performance depends on the system maintaining access to a code in episodic memory representing the most recently cued task. The main constraint on access to the current task code is proactive interference from old task codes. This interference and the mechanisms that contend with it reproduce a wide range of behavioral phenomena when simulated, including well-known task-switching effects, such as latency and error switch costs, and effects on which other theories are silent, such as with-run slowing and within-run error increase. The model generalizes across multiple task-switching procedures, suggesting that episodic task codes play an important role in keeping the cognitive system focused under a variety of performance constraints.

Forgetting to Remember: The Functional Relationship of Decay and Interference

Functional decay theory proposes that decay and interference, historically viewed as competing accounts of forgetting, are instead functionally related. The theory posits that (a) when an attribute must be updated frequently in memory, its current value decays to prevent interference with later values, and (b) the decay rate adapts to the rate of memory updates. Behavioral predictions of the theory were tested in a task-switching paradigm in which memory for the current task had to be updated every few seconds, hundreds of times. Reaction times and error rates both increased gradually between updates, reflecting decay of memory for the current task. This performance decline was slower when updates were less frequent, reflecting a decrease in the decay rate following a decrease in the update rate. A candidate mechanism for controlled decay is proposed, the data are reconciled with practice effects, and implications for models of executive control are discussed.

Timecourse of Recovery from Task Interruption: Data and a Model

Interruption of a complex cognitive task can entail, for the “interruptee,” a sense of having to recover afterward. We examined this recovery process by measuring the timecourse of responses following an interruption, sampling over 13,000 interruptions to obtain stable data. Response times dropped in a smooth curvilinear pattern for the first 10 responses (15 sec or so) of postinterruption performance. We explain this pattern in terms of the cognitive system retrieving a displaced mental context from memory incrementally, with each retrieved element adding to the set of primes facilitating the next retrieval. The model explains a learning effect in our data in which the timecourse of recovery changes over blocks, and is generally consistent with current representational theories of expertise.

Advance Preparation in Task Switching What Work Is Being Done

The preparation effect in task switching is usually interpreted to mean that a switching process makes use of the interval between task-cue onset and trial-stimulus onset (the cue-stimulus interval, or CSI) to accomplish some of its work ahead of time. This study undermines the empirical basis for this interpretation and suggests that task activation, not task switching, is the functional process in cognitive control. Experiments 1 and 2 used an explicit cuing paradigm, and Experiments 3 and 4 used a variation in which the trial after a task cue was followed by several cueless trials, requiring retention of the cue in memory. Experiments 1 and 3 replicated the preparation effect on switch cost, and Experiments 2 and 4 showed that this effect vanishes when CSI is manipulated between subjects, leaving only a main effect of CSI when the task cue is a memory load.

The preparation effect in task switching: Carryover of SOA

A common finding in task-switching studies isswitch preparation (commonly known as the preparation effect), in which a longer interval between task cue and trial stimulus (i.e., a longer stimulus onset asynchrony, or SOA) reduces the cost of switching to a different task. Three experiments link switch preparation to within-subjects manipulations of SOA. In Experiment 1, SOA was randomized within subjects, producing switch preparation that was more pronounced when the SOA switched from the previous trial than when the SOA repeated. In Experiment 2, SOA was blocked within subjects, producing switch preparation but not on the first block of trials. In Experiment 3, SOA was manipulated between subjects with sufficient statistical power to detect switch preparation, but the effect was absent. The results favor an encoding view of cognitive control, but show that any putative switching mechanism reacts lazily when exposed to only one SOA.

Momentary interruptions can derail the train of thought.

We investigated the effect of short interruptions on performance of a task that required participants to maintain their place in a sequence of steps each with their own performance requirements. Interruptions averaging 4.4 s long tripled the rate of sequence errors on post-interruption trials relative to baseline trials. Interruptions averaging 2.8 s long--about the time to perform a step in the interrupted task--doubled the rate of sequence errors. Nonsequence errors showed no interruption effects, suggesting that global attentional processes were not disrupted. Response latencies showed smaller interruption effects than sequence errors, a difference we interpret in terms of high levels of interference generated by the primary task. The results are consistent with an account in which activation spreading from the focus of attention allows control processes to navigate task-relevant representations and in which momentary interruptions are disruptive because they shift the focus and thereby cut off the flow.

A memory for goals model of sequence errors

A model of routine sequence actions is developed based on the Memory for Goals framework. The model assumes that sequential action is guided by episodic control codes generated for each step, and that these codes decay with time and can be primed by contextual retrieval cues. These control codes serve a place-keeping function that allows the system to infer the correct next action after performance is interrupted. According to the model, perseveration (repeat) errors occur because an older episodic trace intrudes due to noise in the system. Anticipation (skip) errors occur because of failures in reality monitoring, in which the model believes that it has completed a step it has not. The model predicts that perseveration errors should occur more frequently than anticipation errors, and that perseveration errors should occur in a graded fashion away from the current step. Across two different experiments, these predictions were supported at both a qualitative and a quantitative level.

Comparing switch costs: alternating runs and explicit cuing.

The task-switching literature routinely conflates different operational definitions of switch cost, its predominant behavioral measure. This article is an attempt to draw attention to differences between the two most common definitions, alternating-runs switch cost (ARS) and explicit-cuing switch cost (ECS). ARS appears to include both the costs of switching tasks and the switch-independent costs specific to the first trial of a run, with the implication that it should generally be larger than ECS, but worse is that the alternating-runs procedure does not allow these costs to be separated. New data are presented to make these issues concrete, existing data are surveyed for evidence that ARS is larger than ECS, and implications of conflating these measures are examined for existing theoretical constructs.