We present a detailed process theory of the moment-by-moment working-memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The resulting theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles are formalized in computational form in the Adaptive Control of Thought–Rational (ACT–R) architecture, and our process model is realized in ACT–R. We present the results of 6 sets of simulations: 5 simulation sets provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden-path structures. A final simulation set provides a graded taxonomy of double center embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model’s complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information. All fits were obtained with only 1 free scaling parameter fixed across the simulations; all other parameters were ACT–R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. We contrast the theory and empirical predictions with several related accounts of sentence-processing complexity.

As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the particular domain but do not generalize easily to other domains. This article outlines a general executive for the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture that, given independent models of individual tasks, schedules and interleaves the models ’ behavior into integrated multitasking behavior. To demonstrate the power of the proposed approach, the article describes an application to the domain of driving, showing how the general executive can interleave component subtasks of the driving task (namely, control and monitoring) and interleave driving with in-vehicle secondary tasks (radio tuning and phone dialing). Keywords: Multitasking, Cognitive architectures, ACT–R, Driving 1.

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions and experience, and that bottom-up processing is used whenever possible. In a model of learning perfect time-sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom-up activation of instructions can explain parallel behavior. In a model of a

The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of differential probabilities, magnitudes, variabilities, and delay of reinforcement. The model can also produce the violation of independence, preference reversals, and the goal gradient of reinforcement in maze learning. An experiment was conducted to study learning of action sequences in a multistep task. The fit of the model to the data demonstrated its ability to account for complex skill learning. The advantages of incorporating the mechanism into a larger cognitive architecture are discussed.

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.

This research uses fMRI to understand the role of eight cortical regions in a relatively complex information-processing task. Modality of input (visual versus auditory) and modality of output (manual versus vocal) are manipulated. Two perceptual regions (auditory cortex and fusiform gyrus) only reflected perceptual encoding. Two motor regions were involved in information rehearsal as well as programming of overt actions. Two cortical regions (parietal and prefrontal) performed processing (retrieval and representational change) independent of input and output modality. The final two regions (anterior cingulate and caudate) were involved in control of cognition independent of modality of input or output and content of the material. An information-processing model, based on the ACT-R theory, is described that predicts the BOLD response in these regions. Different modules in the theory vary in the degree to which they are modality-specific and the degree to which they are involved in central versus peripheral cognitive processes.

& The roles of prefrontal and anterior cingulate cortices have been widely studied, yet little is known on how they interact to enable complex cognitive abilities. We investigated this issue in a complex yet well-defined symbolic paradigm: algebraic problem solving. In our experimental problems, the demands for retrieving arithmetic facts and maintaining intermediate problem representations were manipulated separately. An analysis of functional brain images acquired while participants were solving the problems confirmed that prefrontal regions were affected by the retrieval of arithmetic facts, but only scarcely by the need to manipulate intermediate forms of the equations, hinting at a specific role in memory retrieval. Hemodynamic activity in the dorsal cingulate, on the contrary, increased monotonically as more information processing steps had to be taken, independent of their nature. This pattern was essentially mimicked in the caudate nucleus, suggesting a related functional role in the control of cognitive actions. We also implemented a computational model within the Adaptive Control of Thought—Rational (ACT-R) cognitive architecture, which was able to reproduce both the behavioral data and the time course of the hemodynamic activity in a number of relevant regions of interest. Therefore, imaging results and computer simulation provide evidence that symbolic cognition can be explained by the functional interaction of medial structures supporting control and serial execution, and prefrontal cortices engaged in the on-line retrieval of specific relevant information. &

The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers. We suggest that such a mechanism provides an account for several cognitive functions of the basal ganglia. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus–response associations by internalizing transferred cortical representations in the striatum. We discuss how the model is related to production systems and cognitive architectures. A series of simulations is presented to illustrate how the model can perform simple stimulus–response tasks, develop automatic behaviors, and provide an account of impairments in Parkinson’s and Huntington’s diseases.

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell’s own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture’s research program.

One of the most important applications of grounded cognition theories is to science and mathematics education, where the primary goal is to foster knowledge and skills that are widely transportable to new situations. This presents a challenge to those grounded cognition theories that tightly tie knowledge to the specifics of a single situation. In this