Niels Taatgen

Threaded cognition: An integrated theory of concurrent multitasking

The authors propose the idea of threaded cognition, an integrated theory of concurrent multitasking--that is, performing 2 or more tasks at once. Threaded cognition posits that streams of thought can be represented as threads of processing coordinated by a serial procedural resource and executed across other available resources (e.g., perceptual and motor resources). The theory specifies a parsimonious mechanism that allows for concurrent execution, resource acquisition, and resolution of resource conflicts, without the need for specialized executive processes. By instantiating this mechanism as a computational model, threaded cognition provides explicit predictions of how multitasking behavior can result in interference, or lack thereof, for a given set of tasks. The authors illustrate the theory in model simulations of several representative domains ranging from simple laboratory tasks such as dual-choice tasks to complex real-world domains such as driving and driver distraction.

An integrated theory of prospective time interval estimation: The role of cognition, attention, and learning.

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary tasks on attention). The authors subsequently used the module as a part of the adaptive control of thought--rational (ACT-R) architecture to model a new experiment that combines attention, learning, dual tasking, and time perception. Finally, the model predicts time estimation, learning, and attention in a new experiment. The model predictions and fits demonstrate that the proposed integrated theory of prospective time interval estimation explains detailed effects of attention and learning during time interval estimation.

Too much control can hurt: A threaded cognition model of the attentional blink

Explanations for the attentional blink (AB; a deficit in identifying the second of two targets when presented 200-500 ms after the first) have recently shifted from limitations in memory consolidation to disruptions in cognitive control. With a new model based on the threaded cognition theory of multi-tasking we propose a different explanation: the AB is produced by an overexertion of control. This overexertion is produced by a production rule that blocks target detection during memory consolidation. In addition to fitting many known effects in the literature, the model predicts that adding certain secondary tasks will decrease the AB. In Experiment 1, a secondary task is added to the AB task in which participants have to respond to a moving dot. As predicted, AB decreases. Experiment 2 expands this result by controlling for learning, and adds a second variation, rotating the first target. For this variation the model predicts an increase in AB, which is indeed what we found.

Why do children learn to say “Broke”? A model of learning the past tense without feedback

Learning the English past tense is characterized by a U-shaped learning function for the irregular verbs. Existing cognitive models often rely on a sudden increase in vocabulary, a high token-frequency of regular verbs, and complicated schemes of feedback in order to model this phenomenon. All these assumptions are at odds with empirical data. In this paper a hybrid ACT-R model is presented that shows U-shaped learning without direct feedback, changes in vocabulary, or unrealistically high rates of regular verbs. The model is capable of learning the default rule, even if regular forms are infrequent. It can also help explore the question of why there is a distinction between regular and irregular verbs in the first place, by examining the costs and benefits of both types of verbs.

Production compilation: a simple mechanism to model complex skill acquisition.

In this article we describe production compilation, a mechanism for modeling skill acquisition. Production compilation has been developed within the ACT-Rational (ACT-R; J. R. Anderson, D. Bothell, M. D. Byrne, & C. Lebiere, 2002) cognitive architecture and consists of combining and specializing task-independent procedures into task-specific procedures. The benefit of production compilation for researchers in human factors is that it enables them to test the strengths and weaknesses of their task analyses and user models by allowing them to model the learning trajectory from the main task level and the unit task level down to the keystroke level. We provide an example of this process by developing and describing a model learning a simulated air traffic controller task. Actual or potential applications of this research include the evaluation of user interfaces, the design of systems that support learning, and the building of user models.

Toward a unified theory of the multitasking continuum: from concurrent performance to task switching, interruption, and resumption

Multitasking in user behavior can be represented along a continuum in terms of the time spent on one task before switching to another. In this paper, we present a theory of behavior along the multitasking continuum, from concurrent tasks with rapid switching to sequential tasks with longer time between switching. Our theory unifies several theoretical effects - the ACT-R cognitive architecture, the threaded cognition theory of concurrent multitasking, and the memory-for-goals theory of interruption and resumption - to better understand and predict multitasking behavior. We outline the theory and discuss how it accounts for numerous phenomena in the recent empirical literature.

Pupil dilation deconvolution reveals the dynamics of attention at high temporal resolution

The size of the human pupil increases as a function of mental effort. However, this response is slow, and therefore its use is thought to be limited to measurements of slow tasks or tasks in which meaningful events are temporally well separated. Here we show that high-temporal-resolution tracking of attention and cognitive processes can be obtained from the slow pupillary response. Using automated dilation deconvolution, we isolated and tracked the dynamics of attention in a fast-paced temporal attention task, allowing us to uncover the amount of mental activity that is critical for conscious perception of relevant stimuli. We thus found evidence for specific temporal expectancy effects in attention that have eluded detection using neuroimaging methods such as EEG. Combining this approach with other neuroimaging techniques can open many research opportunities to study the temporal dynamics of the mind’s inner eye in great detail.

The Problem State: A Cognitive Bottleneck in Multitasking

The main challenge for theories of multitasking is to predict when and how tasks interfere. Here, we focus on interference related to the problem state, a directly accessible intermediate representation of the current state of a task. On the basis of Salvucci and Taatgens (2008) threaded cognition theory, we predict interference if 2 or more tasks require a problem state but not when only one task requires one. This prediction was tested in a series of 3 experiments. In Experiment 1, a subtraction task and a text entry task had to be carried out concurrently. Both tasks were presented in 2 versions: one that required maintaining a problem state and one that did not. A significant overadditive interaction effect was observed, showing that the interference between tasks was maximal when both tasks required a problem state. The other 2 experiments tested whether the interference was indeed due to a problem state bottleneck, instead of cognitive load (Experiment 2: an alternative subtraction and text entry experiment) or a phonological loop bottleneck (Experiment 3: a triple-task experiment that added phonological processing). Both experiments supported the problem state hypothesis. To account for the observed behavior, computational cognitive models were developed using threaded cognition within the context of the cognitive architecture ACT-R (Anderson, 2007). The models confirm that a problem state bottleneck can explain the observed interference.

The Nature and Transfer of Cognitive Skills

This article presents the primitive elements theory of cognitive skills. The central idea is that skills are broken down into primitive information processing elements that move and compare single pieces of information regardless of the specific content of this information. Several of these primitive elements are necessary for even a single step in a task. A learning process therefore combines the elements in increasingly larger, but still context-independent, units. If there is overlap between tasks, this means the larger units learned for 1 task can be reused for the other task, producing transfer. The theory makes it possible to construct detailed process models of 2 classic transfer studies in the literature: a study of transfer in text editors and 1 in arithmetic. I show that the approach produces better fits of the amount of transfer than Singley and Andersons (1985) identical productions model. The theory also offers explanations for far transfer, in which the 2 tasks have no surface characteristics in common, which I demonstrate with 2 models in the domain of cognitive control, where training on either task-switching or working memory control led to an improvement of performance on other control tasks. The theory can therefore help evaluate the effectiveness of cognitive training that has the goal to improve general cognitive abilities.

The acquisition of robust and flexible cognitive skills.

The authors introduce a model of skill acquisition that incorporates elements of both traditional models and models based on embedded cognition by striking a balance between top-down and bottom-up control. A knowledge representation is used in which pre- and postconditions are attached to actions. This model captures improved performance due to learning not only in terms of shorter solution times and lower error rates during the task but also in an increased flexibility to solve similar problems and robustness against unexpected events. In 3 experiments using a complex aviation task, the authors contrasted instructions that explicitly stated pre- and postconditions with conventional instructions that did not. The instructions with pre- and postconditions led to better and more robust performance than other instructions, especially on problems that required transfer. The parameters of the model were estimated to obtain a quantitative fit of the results of Experiment 1, which was then successfully used to predict the results of Experiments 2 and 3.