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 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.

We build an ACT-R model of a geometry-proof task based on behavioral, verbal Traveling 2 protocol, eye movement, from proficient adult participants and then assess the model using neuroimaging data. Our geometry proof problems differed in terms of the number of steps of inference required to find a proof. Participants ’ verbal protocols and eye movements indicated that they first encoded the goal statement, then made a number of inferences, and finally came to a conclusion and made their response. In the imaging study we determined the involvement of a number of brain regions in the various stages of the task. The ACT-R model provided reasonable fits to participant accuracy, latency, and the BOLD response in most of the brain regions. However, there was unexpected activity in the motor region early in the problem solution, unexpected activity in the fusiform late in problem solution, and unexpected anterior prefrontal activity after the problem solution. We conclude that the model underestimates the perceptual-motor involvement in geometry learning just as does much of the educational curriculum.

A methodology is described for developing cognitive science theories which produce numerical predictions. This is done by adopting methodology from mathematical models in physics, and adapting it for use with the more complex computational models. Bootstrap confidence intervals and equivalence testing are introduced, and parameter fitting is shown to be an intermediate step before prediction. To ensure replication and exploration by other researchers, publication of the source code for the model, experimental situation, and data analysis is required. To assist in this process, we have developed a freely available tool suite, covering creation of models, running parallel simulations, parameter exploration, data analysis, and Internet-based access to all data.

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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 Taatgen’s (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.

In this paper, a model-based analysis method for fMRI is used with a high-level symbolic process model. Participants performed a triple-task in which intermediate task information needs to be updated frequently. Previous work has shown that the associated resource – the problem state resource – acts as a bottleneck in multitasking. The model-based method was used to locate the neural correlates of ‘problem state replacements’. To analyze the fMRI data, we fit the computational process model to the behavioral data and regressed the model’s activity against the fMRI data. The brain region responsible for the temporary representation of problem states, the inferior parietal lobule, and the brain region responsible for long-term storage of problem states, the inferior frontal gyrus were thus identified. These results show that model-based fMRI analyses can be performed using high-level symbolic cognitive models, enabling fine-grained exploratory fMRI research.

We build an ACT-R model of a geometry-proof task based on behavioral, verbal protocol, eye movement, from proficient adult participants and then assess the model using neuroimaging data. Our geometry proof problems differed in terms of the number of steps of inference required to find a proof. Participants ’ verbal protocols and eye movements indicated that they first encoded the goal statement, then made a number of inferences, and finally came to a conclusion and made their response. In the imaging study we determined the involvement of a number of brain regions in the various stages of the task. The ACT-R model provided reasonable fits to participant accuracy, latency, and the BOLD response in most of the brain regions. However, there was unexpected activity in the motor region early in the problem solution, unexpected activity in the fusiform late in problem solution, and unexpected anterior prefrontal activity after the problem solution. We conclude that the model underestimates the perceptual-motor involvement in geometry learning just as does much of the educational curriculum. Traveling 3 John Bruer (1997) argued that applying neuroscience directly to educational questions

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