Yulin Qin

An integrated theory of the mind.

Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.

Laboratory replication of scientific discovery processes

Fourteen subjects were tape-recorded while they undertook to find a law to summarize numerical doto they were given. The source of the data was not identified, nor were the variables lobeled semantically. Unknown to the subjects. the data were measurements of the distances of the planets from the sun and the periods of their revolutions obout it-equivalent to the doto used by Johonnes Kepler to discover his third low of planetary motion. Four of the 14 subjects discovered the some low as Kepler did (the period varies OS the 3/2 power of the distance), and a fifth come very close to the answer. The subiects’ protocols provide a detatled picture of the problem-solving searches they engoged in, which were mainly, but not exclusively, in the space of possible functions for fitting the data. and provide explanations OS to why some succeeded and the others foiled. The scorch heuristics used by the subjects ore similar to those embodied in the BACON program, a computer simulation of certain scientific discovery processes. The experiment demonstrates the feasibility of examining some of the processes of scientific discovery by recreating, in the laboratory, discovery situations of substontiol historical relevance. It demonstrates also. that under conditions rather similar to those of the originol discoverer, a low can be rediscovered by persons of ordinory intelligence (i.e., the intelligence needed for academic success in a good university). The data for the successful subjects reveal no “creative” processes in this kind of o discovery situation different from those that are regulorly observed in all kinds of problem-solving settings.

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.

An information-processing model of the BOLD response in symbol manipulation tasks.

Two imaging experiments were performed—one involving an algebraic transformation task studied by Anderson, Reder, and Lebiere (1996) and the other an abstraction symbol manipulation task studied by Blessing and Anderson (1996). ACT-R models exist that predict the latency patterns in these tasks. These models require activity in an imaginal buffer to represent changes to the problem representation, in a retrieval buffer to hold information from declarative memory, and in a manual buffer to hold information about motor behavior. A general theory is described about how to map activity in these buffers onto the fMRI blood oxygen level dependent (BOLD) response. This theory claims that the BOLD response is integrated over the duration that a buffer is active and can be used to predict the observed BOLD function. Activity in the imaginal buffer is shown to predict the BOLD response in a left posterior parietal region; activity in the retrieval buffer is shown to predict the BOLD response in a left prefrontal region; and activity in the manual buffer is shown to predict activity in a motor region. More generally, this article shows how to map a large class of information-processing theories (not just ACT-R) onto the BOLD response and provides a precise interpretation of the cognitive significance of the BOLD response.

Predicting the practice effects on the blood oxygenation level-dependent (BOLD) function of fMRI in a symbolic manipulation task.

Based on adaptive control of thought-rational (ACT-R), a cognitive architecture for cognitive modeling, researchers have developed an information-processing model to predict the blood oxygenation level-dependent (BOLD) response of functional MRI in symbol manipulation tasks. As an extension of this research, the current event-related functional MRI study investigates the effect of relatively extensive practice on the activation patterns of related brain regions. The task involved performing transformations on equations in an artificial algebra system. This paper shows that the base-level activation learning in the ACT-R theory can predict the change of the BOLD response in practice in a left prefrontal region reflecting retrieval of information. In contrast, practice has relatively little effect on the form of BOLD response in the parietal region reflecting imagined transformations to the equation or the motor region reflecting manual programming.

The Relationship of Three Cortical Regions to an Information-Processing Model

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.

Information-processing modules and their relative modality specificity

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.

Using brain imaging to extract the structure of complex events at the rational time band

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 ThoughtRational (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 models 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.

Brain regions engaged by part-and whole-task performance in a video game: A model-based test of the decomposition hypothesis

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 models 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.

Extraction of overt verbal response from the acoustic noise in a functional magnetic resonance imaging scan by use of segmented active noise cancellation

A method to extract the subjects overt verbal response from the obscuring acoustic noise in an fMRI scan is developed by applying active noise cancellation with a conventional MRI microphone. Since the EPI scanning and its accompanying acoustic noise in fMRI are repetitive, the acoustic noise in one time segment was used as a reference noise in suppressing the acoustic noise in subsequent segments. However, the acoustic noise from the scanner was affected by the subjects movements, so the reference noise was adaptively adjusted as the scanners acoustic properties varied in time. This method was successfully applied to a cognitive fMRI experiment with overt verbal responses. Magn Reson Med 53:739–744, 2005.