In this study, we investigated the information processing stages underlying associative recognition. We recorded EEG data while participants performed a task that involved deciding whether a probe word triple matched any previously studied triple. We varied the similarity between probes and studied triples. According to a model of associative recognition developed in the Adaptive Control of Thought-Rational cognitive architecture, probe similarity affects the duration of the retrieval stage: Retrieval is fastest when the probe is similar to a studied triple. This effect may be obscured, however, by the duration of the comparison stage, which is fastest when the probe is not similar to the retrieved triple. Owing to the opposing effects of probe similarity on retrieval and comparison, overall RTs provide little information about each stage's duration. As such, we evaluated the model using a novel approach that decomposes the EEG signal into a sequence of latent states and provides information about the durations of the underlying information processing stages. The approach uses a hidden semi-Markov model to identify brief sinusoidal peaks (called bumps) that mark the onsets of distinct cognitive stages. The analysis confirmed that probe type has opposite effects on retrieval and comparison stages.

In this study, we investigated the stages of information processing in associative recognition. We recorded EEG data while participants performed an associative recognition task that involved manipulations of word length, associative fan, and probe type, which were hypothesized to affect the perceptual encoding, retrieval, and decision stages of the recognition task, respectively. Analyses of the behavioral and EEG data, supplemented with classification of the EEG data using machine-learning techniques, provided evidence that generally supported the sequence of stages assumed by a computational model developed in the Adaptive Control of Thought-Rational cognitive architecture. However, the results suggested a more complex relationship between memory retrieval and decision-making than assumed by the model. Implications of the results for modeling associative recognition are discussed. The study illustrates how a classifier approach, in combination with focused manipulations, can be used to investigate the timing of processing stages.

Much research focuses on how people acquire concrete stimulus–response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individual's representation of a task's states and actions shapes behavior as well as the FRN.

Part- and whole-task conditions were created by manipulating the presence of certain components of the Space Fortress video game. A cognitive model was created for two-part games that could be combined into a model that performed the whole game. The model generated predictions both for behavioral patterns and activation patterns in various brain regions. The activation predictions concerned both tonic activation that was constant in these regions during performance of the game and phasic activation that occurred when there was resource competition. The model's predictions were confirmed about how tonic and phasic activation in different regions would vary with condition. These results support the Decomposition Hypothesis that the execution of a complex task can be decomposed into a set of information-processing components and that these components combine unchanged in different task conditions. In addition, individual differences in learning gains were predicted by individual differences in phasic activation in those regions that displayed highest tonic activity. This individual difference pattern suggests that the rate of learning of a complex skill is determined by capacity limits.

A functional magnetic resonance imaging (fMRI) study was performed in which participants performed a complex series of mental calculations that spanned about 2 min. An Adaptive Control of Thought—Rational (ACT-R) model [Anderson, J. R. How can the human mind occur in the physical universe? New York: Oxford University Press, 2007] was developed that successfully fit the distribution of latencies. This model generated predictions for the fMRI signal in six brain regions that have been associated with modules in the ACT-R theory. The model's predictions were confirmed for a fusiform region that reflects the visual module, for a prefrontal region that reflects the retrieval module, and for an anterior cingulate region that reflects the goal module. In addition, the only significant deviations to the motor region that reflects the manual module were anticipatory hand movements. In contrast, the predictions were relatively poor for a parietal region that reflects an imaginal module and for a caudate region that reflects the procedural module. Possible explanations of these poor fits are discussed. In addition, exploratory analyses were performed to find regions that might correspond to the predictions of the modules.

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.

Previous research has found three brain regions for tracking components of the ACT-R cognitive architecture: a posterior parietal region that tracks changes in problem representation, a prefrontal region that tracks retrieval of task-relevant information, and a motor region that tracks the programming of manual responses. This prior research has used relatively simple tasks to incorporate a slow event-related procedure, allowing the blood oxygen level-dependent (BOLD) response to go back to baseline after each trial. The research described here attempts to extend these methods to tracking problem solving in a complex task, the Tower of Hanoi, which involves many complex steps of cognition and motor actions in rapid succession. By tracking the activation patterns in these regions, it is possible to predict with intermediate accuracy when participants are planning a future sequence of moves. The article describes a cognitive model in the ACT-R architecture that is capable of explaining both the latency data in move generation and the BOLD responses in these three regions.

This research tests a model of the computational role of three cortical regions in tasks like algebra equation solving. The model assumes that there is a left parietal region-of-interest (ROI) where the problem expression is represented and transformed, a left prefrontal ROI where information for solving the task is retrieved, and a motor ROI where hand movements to produce the answer are programmed. A functional magnetic resonance imaging (fMRI) study of an abstract symbolmanipulation task was performed to articulate the roles of these three regions. Participants learned to associate words with instructions for transforming strings of letters. The study manipulated the need to retrieve these instructions, the need to transform the strings, and whether there was a delay between calculation of the answer and the output of the answer. As predicted, the left parietal ROI mainly reflected the need for a transformation and the left prefrontal ROI the need for retrieval. Homologous right ROIs showed similar but weaker responses. Neither the prefrontal nor the parietal ROIs responded to delay, but the motor ROI did respond to delay, implying motor rehearsal over the delay. Except for the motor ROI, these patterns of activity did not vary with response hand. In an ACT-R model, it was shown that the activity of an imaginal buffer predicted the blood oxygen level-dependent (BOLD) response of the parietal ROI, the activity of a retrieval buffer predicted the response of the prefrontal ROI, and the activity of a manual buffer predicted the response of the motor ROI.