Jon M. Fincham

Neural mechanisms of planning: A computational analysis using event-related fMRI

To investigate the neural mechanisms of planning, we used a novel adaptation of the Tower of Hanoi (TOH) task and event-related functional MRI. Participants were trained in applying a specific strategy to an isomorph of the five-disk TOH task. After training, participants solved novel problems during event-related functional MRI. A computational cognitive model of the task was used to generate a reference time series representing the expected blood oxygen level-dependent response in brain areas involved in the manipulation and planning of goals. This time series was used as one term within a general linear modeling framework to identify brain areas in which the time course of activity varied as a function of goal-processing events. Two distinct time courses of activation were identified, one in which activation varied parametrically with goal-processing operations, and the other in which activation became pronounced only during goal-processing intensive trials. Regions showing the parametric relationship comprised a frontoparietal system and include right dorsolateral prefrontal cortex [Brodmanns area (BA 9)], bilateral parietal (BA 40/7), and bilateral premotor (BA 6) areas. Regions preferentially engaged only during goal-intensive processing include left inferior frontal gyrus (BA 44). The implications of these results for the current model, as well as for our understanding of the neural mechanisms of planning and functional specialization of the prefrontal cortex, are discussed.

Acquisition of procedural skills from examples.

Three experiments were run in which Ss first memorized examples of input-output pairs and then generated the outputs for a series of new inputs by analogy to the original examples. Ss first performed these mappings by explicit analogy to an example, but with practice they learned to make these input-output mappings directly without reference to the examples. Ss sped up as a power function of practice over a day (Experiment 1) or days (Experiments 2 and 3). Ss developed a directional asymmetry such that they were slower to calculate the input from the output than the output from the input (whereas initially they had not been). Ss showed similar speed up in their ability to recall the original examples but did not show the same directional asymmetry. Initially, there was some transfer from practicing the procedure to recalling the examples, but this diminished over days.

A central circuit of the mind

The methodologies of cognitive architectures and functional magnetic resonance imaging can mutually inform each other. For example, four modules of the ACT-R (adaptive control of thought - rational) cognitive architecture have been associated with four brain regions that are active in complex tasks. Activity in a lateral inferior prefrontal region reflects retrieval of information in a declarative module; activity in a posterior parietal region reflects changes to problem representations in an imaginal module; activity in the anterior cingulate cortex reflects the updates of control information in a goal module; and activity in the caudate nucleus reflects execution of productions in a procedural module. Differential patterns of activation in such central regions can reveal the time course of different components of complex cognition.

Practice and Retention: A Unifying Analysis

What is the strength of a memory trace that has received various practices at times tj in the past? The strength accumulation equation proposes the following: strength = sigma tj-d, where the summation is over the practices of the trace. This equation predicts both the power law of practice and the power law of retention. This article reports the fits of the predictions of this equation to 5 experiments. Across these experiments, participants received as many as 240 trials of practice distributed over intervals as long as 400 days. The experiments also varied whether participants were just practicing retrieving an item or practicing applying a relatively complex rule. A model based on this equation successfully fit all the data when it was assumed that the passage of psychological time slowed after the experimental session. The strength accumulation equation was compared with other conceptions of the retention function and the relationship of the retention function to the practice function.

Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-paced Tower of Hanoi

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.

Distinct roles of the anterior cingulate and prefrontal cortex in the acquisition and performance of a cognitive skill

The purpose of the present work is to study the functional roles of two predefined regions of interest: one in the left anterior cingulate cortex (ACC) that seems to reflect goal-relevant control demand, and one in the left prefrontal cortex (PFC) that reflects memory retrieval demand. Two slow event-related brain imaging experiments were conducted, adapting a cognitive skill acquisition paradigm. Experiment 1 found that both left ACC and left PFC activity increased parametrically with task difficulty. Using a slight modification of the same basic paradigm, Experiment 2 attempted to decouple retrieval and control demands over the course of learning. Participants were imaged early in training and again several days later, after substantial additional training in the task. There was a clear dissociation between activity in the left PFC and the left ACC. Although the PFC region showed a substantial decrease in activity over the course of learning, reflecting greater ease of retrieval, the ACC showed the opposite pattern of results with significantly greater activity after training, reflecting increased control demand. Moreover, the increased response in the ACC occurred when errors and latencies were smallest.

Cognitive and metacognitive activity in mathematical problem solving: prefrontal and parietal patterns

Students were taught an algorithm for solving a new class of mathematical problems. Occasionally in the sequence of problems, they encountered exception problems that required that they extend the algorithm. Regular and exception problems were associated with different patterns of brain activation. Some regions showed a Cognitive pattern of being active only until the problem was solved and no difference between regular or exception problems. Other regions showed a Metacognitive pattern of greater activity for exception problems and activity that extended into the post-solution period, particularly when an error was made. The Cognitive regions included some of parietal and prefrontal regions associated with the triple-code theory of (Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487–506) and associated with algebra equation solving in the ACT-R theory (Anderson, J. R. (2005). Human symbol manipulation within an 911 integrated cognitive architecture. Cognitive science, 29, 313–342. Metacognitive regions included the superior prefrontal gyrus, the angular gyrus of the triple-code theory, and frontopolar regions.

Neural imaging to track mental states while using an intelligent tutoring system

Hemodynamic measures of brain activity can be used to interpret a students mental state when they are interacting with an intelligent tutoring system. Functional magnetic resonance imaging (fMRI) data were collected while students worked with a tutoring system that taught an algebra isomorph. A cognitive model predicted the distribution of solution times from measures of problem complexity. Separately, a linear discriminant analysis used fMRI data to predict whether or not students were engaged in problem solving. A hidden Markov algorithm merged these two sources of information to predict the mental states of students during problem-solving episodes. The algorithm was trained on data from 1 day of interaction and tested with data from a later day. In terms of predicting what state a student was in during a 2-s period, the algorithm achieved 87% accuracy on the training data and 83% accuracy on the test data. The results illustrate the importance of integrating the bottom-up information from imaging data with the top-down information from a cognitive model.

Lateral inferior prefrontal cortex and anterior cingulate cortex are engaged at different stages in the solution of insight problems

Two studies used puzzles that required participants to find a word that satisfied a set of constraints. The first study used a remote-association task, where participants had to find a word that would form compound words with 3 other words. The second study required participants to complete a word fragment with an associate of another word. Both studies produced distinct patterns of activity in the lateral inferior prefrontal cortex (LIPFC) and the anterior cingulate cortex (ACC). Activation in the LIPFC rose only as long as the participants were trying to retrieve the solution and dropped off as soon as the solution was obtained. However, activation in the ACC increased upon the retrieval of a solution, reflecting the need to process that solution. The data of the second experiment are fit by an information-processing model that interprets the activity in the LIPFC as reflecting retrieval operations and the activity in the ACC as reflecting subgoal setting.

Discovering the Sequential Structure of Thought.

Multi-voxel pattern recognition techniques combined with Hidden Markov models can be used to discover the mental states that people go through in performing a task. The combined method identifies both the mental states and how their durations vary with experimental conditions. We apply this method to a task where participants solve novel mathematical problems. We identify four states in the solution of these problems: Encoding, Planning, Solving, and Respond. The method allows us to interpret what participants are doing on individual problem-solving trials. The duration of the planning state varies on a trial-to-trial basis with novelty of the problem. The duration of solution stage similarly varies with the amount of computation needed to produce a solution once a plan is devised. The response stage similarly varies with the complexity of the answer produced. In addition, we identified a number of effects that ran counter to a prior model of the task. Thus, we were able to decompose the overall problem-solving time into estimates of its components and in way that serves to guide theory.