John R. Anderson

YNIMG Journal 2020 Journal Article

Reconstructing fine-grained cognition from brain activity

• John R. Anderson
• Shawn Betts
• Jon M. Fincham
• Ryan Hope
• Mathew W. Walsh

We describe the Sketch-and-Stitch method for bringing together a cognitive model and EEG to reconstruct the cognition of a subject. The method was tested in the context of a video game where the actions are highly interdependent and variable: simply changing whether a key was pressed or not for a 30th of a second can lead to a very different outcome. The Sketch level identifies the critical events in the game and the Stitch level fills in the detailed actions between these events. The critical events tend to produce robust EEG signals and the cognitive model provides probabilities of various transitions between critical events and the distribution of intervals between these events. This information can be combined in a hidden semi-Markov model that identifies the most probable sequence of critical events and when they happened. The Stitch level selects detailed actions from an extensive library of model games to produce these critical events. The decision about which sequence of actions to select from the library is made on the basis of how well they would produce weaker aspects of the EEG signal. The resulting approach can produce quite compelling replays of actual games from the EEG of a subject.

YNIMG Journal 2018 Journal Article

Mapping working memory retrieval in space and in time: A combined electroencephalography and electrocorticography approach

• Qiong Zhang
• Marieke van Vugt
• Jelmer P. Borst
• John R. Anderson

In this study, we investigated the time course and neural correlates of the retrieval process underlying visual working memory. We made use of a rare dataset in which the same task was recorded using both scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. This allowed us to examine with great spatial and temporal detail how the retrieval process works, and in particular how the medial temporal lobe (MTL) is involved. In each trial, participants judged whether a probe face had been among a set of recently studied faces. With a method that combines hidden semi-Markov models and multivariate pattern analysis, the neural signal was decomposed into a sequence of latent cognitive stages with information about their durations on a trial-by-trial basis. Analyzed separately, EEG and ECoG data yielded converging results on discovered stages and their interpretation, which reflected 1) a brief pre-attention stage, 2) encoding the stimulus, 3) retrieving the studied set, and 4) making a decision. Combining these stages with the high spatial resolution of ECoG suggested that activity in the temporal cortex reflected item familiarity in the retrieval stage; and that once retrieval is complete, there is active maintenance of the studied face set in the decision stage in the MTL. During this same period, the frontal cortex guides the decision by means of theta coupling with the MTL. These observations generalize previous findings on the role of MTL theta from long-term memory tasks to short-term memory tasks.

YNIMG Journal 2017 Journal Article

When math operations have visuospatial meanings versus purely symbolic definitions: Which solving stages and brain regions are affected?

• Aryn A. Pyke
• Jon M. Fincham
• John R. Anderson

How does processing differ during purely symbolic problem solving versus when mathematical operations can be mentally associated with meaningful (here, visuospatial) referents? Learners were trained on novel math operations (↓, ↑), that were defined strictly symbolically or in terms of a visuospatial interpretation (operands mapped to dimensions of shaded areas, answer = total area). During testing (scanner session), no visuospatial representations were displayed. However, we expected visuospatially-trained learners to form mental visuospatial representations for problems, and exhibit distinct activations. Since some solution intervals were long (~10s) and visuospatial representations might only be instantiated in some stages during solving, group differences were difficult to detect when treating the solving interval as a whole. However, an HSMM-MVPA process (Anderson and Fincham, 2014a) to parse fMRI data identified four distinct problem-solving stages in each group, dubbed: 1) encode; 2) plan; 3) compute; and 4) respond. We assessed stage-specific differences across groups. During encoding, several regions implicated in general semantic processing and/or mental imagery were more active in visuospatially-trained learners, including: bilateral supramarginal, precuneus, cuneus, parahippocampus, and left middle temporal regions. Four of these regions again emerged in the computation stage: precuneus, right supramarginal/angular, left supramarginal/inferior parietal, and left parahippocampal gyrus. Thus, mental visuospatial representations may not just inform initial problem interpretation (followed by symbolic computation), but may scaffold on-going computation. In the second stage, higher activations were found among symbolically-trained solvers in frontal regions (R. medial and inferior and L. superior) and the right angular and middle temporal gyrus. Activations in contrasting regions may shed light on solvers’ degree of use of symbolic versus mental visuospatial strategies, even in absence of behavioral differences.

YNIMG Journal 2016 Journal Article

Tracking cognitive processing stages with MEG: A spatio-temporal model of associative recognition in the brain

• Jelmer P. Borst
• Avniel S. Ghuman
• John R. Anderson

In this study, we investigated the cognitive processing stages underlying associative recognition using MEG. Over the last four decades, a model of associative recognition has been developed in the ACT-R cognitive architecture. This model was first exclusively based on behavior, but was later evaluated and improved based on fMRI and EEG data. Unfortunately, the limited spatial resolution of EEG and the limited temporal resolution of fMRI have made it difficult to fully understand the spatiotemporal dynamics of associative recognition. We therefore conducted an associative recognition experiment with MEG, which combines excellent temporal resolution with reasonable spatial resolution. To analyze the data, we applied non-parametric cluster analyses and a multivariate classifier. This resulted in a detailed spatio-temporal model of associative recognition. After the visual encoding of the stimuli in occipital regions, three separable memory processes took place: a familiarity process (temporal cortex), a recollection process (temporal cortex and supramarginal gyrus), and a representational process (dorsolateral prefrontal cortex). A late decision process (superior parietal cortex) then acted upon the recollected information represented in the prefrontal cortex, culminating in a late response process (motor cortex). We conclude that existing theories of associative recognition, including the ACT-R model, should be adapted to include these processes.

YNIMG Journal 2015 Journal Article

The discovery of processing stages: Analyzing EEG data with hidden semi-Markov models

• Jelmer P. Borst
• John R. Anderson

In this paper we propose a new method for identifying processing stages in human information processing. Since the 1860s scientists have used different methods to identify processing stages, usually based on reaction time (RT) differences between conditions. To overcome the limitations of RT-based methods we used hidden semi-Markov models (HSMMs) to analyze EEG data. This HSMM-EEG methodology can identify stages of processing and how they vary with experimental condition. By combining this information with the brain signatures of the identified stages one can infer their function, and deduce underlying cognitive processes. To demonstrate the method we applied it to an associative recognition task. The stage-discovery method indicated that three major processes play a role in associative recognition: a familiarity process, an associative retrieval process, and a decision process. We conclude that the new stage-discovery method can provide valuable insight into human information processing.

YNIMG Journal 2014 Journal Article

Discovering the structure of mathematical problem solving

• John R. Anderson
• Hee Seung Lee
• Jon M. Fincham

The goal of this research is to discover the stages of mathematical problem solving, the factors that influence the duration of these stages, and how these stages are related to the learning of a new mathematical competence. Using a combination of multivariate pattern analysis (MVPA) and hidden Markov models (HMM), we found that participants went through 5 major phases in solving a class of problems: A Define Phase where they identified the problem to be solved, an Encode Phase where they encoded the needed information, a Compute Phase where they performed the necessary arithmetic calculations, a Transform Phase where they performed any mathematical transformations, and a Respond Phase where they entered an answer. The Define Phase is characterized by activity in visual attention and default network regions, the Encode Phase by activity in visual regions, the Compute Phase by activity in regions active in mathematical tasks, the Transform Phase by activity in mathematical and response regions, and the Respond phase by activity in motor regions. The duration of the Compute and Transform Phases were the only ones that varied with condition. Two features distinguished the mastery trials on which participants came to understand a new problem type. First, the duration of late phases of the problem solution increased. Second, there was increased activation in the rostrolateral prefrontal cortex (RLPFC) and angular gyrus (AG), regions associated with metacognition. This indicates the importance of reflection to successful learning.

YNIMG Journal 2012 Journal Article

Using brain imaging to track problem solving in a complex state space

• John R. Anderson
• Jon M. Fincham
• Darryl W. Schneider
• Jian Yang

This paper describes how behavioral and imaging data can be combined with a Hidden Markov Model (HMM) to track participants' trajectories through a complex state space. Participants completed a problem-solving variant of a memory game that involved 625 distinct states, 24 operators, and an astronomical number of paths through the state space. Three sources of information were used for classification purposes. First, an Imperfect Memory Model was used to estimate transition probabilities for the HMM. Second, behavioral data provided information about the timing of different events. Third, multivoxel pattern analysis of the imaging data was used to identify features of the operators. By combining the three sources of information, an HMM algorithm was able to efficiently identify the most probable path that participants took through the state space, achieving over 80% accuracy. These results support the approach as a general methodology for tracking mental states that occur during individual problem-solving episodes.

YNIMG Journal 2007 Journal Article

The roles of prefrontal and posterior parietal cortex in algebra problem solving: A case of using cognitive modeling to inform neuroimaging data

• Jared F. Danker
• John R. Anderson

In naturalistic algebra problem solving, the cognitive processes of representation and retrieval are typically confounded, in that transformations of the equations typically require retrieval of mathematical facts. Previous work using cognitive modeling has associated activity in the prefrontal cortex with the retrieval demands of algebra problems and activity in the posterior parietal cortex with the transformational demands of algebra problems, but these regions tend to behave similarly in response to task manipulations (Anderson, J. R. , Qin, Y. , Sohn, M. -H. , Stenger, V. A. , Carter, C. S. , 2003. An information-processing model of the BOLD response in symbol manipulation tasks. Psychon. Bull. Rev. 10, 241–261; Qin, Y. , Carter, C. S. , Silk, E. M. , Stenger, A. , Fissell, K. , Goode, A. , Anderson, J. R. , 2004. The change of brain activation patterns as children learn algebra equation solving. Proc. Natl. Acad. Sci. 101, 5686–5691). With this study we attempt to isolate activity in these two regions by using a multi-step algebra task in which transformation (parietal) is manipulated in the first step and retrieval (prefrontal) is manipulated in the second step. Counter to our initial predictions, both brain regions were differentially active during both steps. We designed two cognitive models, one encompassing our initial assumptions and one in which both processes were engaged during both steps. The first model provided a poor fit to the behavioral and neural data, while the second model fit both well. This simultaneously emphasizes the strong relationship between retrieval and representation in mathematical reasoning and demonstrates that cognitive modeling can serve as a useful tool for understanding task manipulations in neuroimaging experiments.

YNIMG Journal 2005 Journal Article

An information-processing model of three cortical regions: evidence in episodic memory retrieval

• Myeong-Ho Sohn
• Adam Goode
• V. Andrew Stenger
• Kwan-Jin Jung
• Cameron S. Carter
• John R. Anderson

ACT-R (Anderson, J. R. , et al. , 2003. An information-processing model of the BOLD response in symbol manipulation tasks. Psychon. Bull. Rev. 10, 241–261) relates the inferior dorso-lateral prefrontal cortex to a retrieval buffer that holds information retrieved from memory and the posterior parietal cortex to an imaginal buffer that holds problem representations. Because the number of changes in a problem representation is not necessarily correlated with retrieval difficulties, it is possible to dissociate prefrontal–parietal activations. In two fMRI experiments, we examined this dissociation using the fan effect paradigm. Experiment 1 compared a recognition task, in which representation requirement remains the same regardless of retrieval difficulty, with a recall task, in which both representation and retrieval loads increase with retrieval difficulty. In the recognition task, the prefrontal activation revealed a fan effect but not the parietal activation. In the recall task, both regions revealed fan effects. In Experiment 2, we compared visually presented stimuli and aurally presented stimuli using the recognition task. While only the prefrontal region revealed the fan effect, the activation patterns in the prefrontal and the parietal region did not differ by stimulus presentation modality. In general, these results provide support for the prefrontal–parietal dissociation in terms of retrieval and representation and the modality-independent nature of the information processed by these regions. Using ACT-R, we also provide computational models that explain patterns of fMRI responses in these two areas during recognition and recall.

YNIMG Journal 2001 Journal Article

Effects of Changes in Experimental Design on PET Studies of Isometric Force

• Suraj A. Muley
• Stephen C. Strother
• James Ashe
• Sally A. Frutiger
• John R. Anderson
• John J. Sidtis
• David A. Rottenberg

Based on single-cell recordings in primates, the relationship between neuronal activity and force magnitude is thought to be monotonic, at least for a subset of pyramidal cells in the motor cortex. Functional neuroimaging studies have also suggested a monotonic relationship between cerebral activation and force magnitude. In order to more precisely define this relationship and to characterize the activation pattern(s) associated with the modulation of static force, we studied 40 normal subjects using [15O]water PET and a simple visuomotor task—application of static force on a micro force sensor with the thumb and index finger of the right hand. When our experimental design did not produce the expected result (evidence of a relationship between cerebral activation and force magnitude in ten subjects), we made serial changes in the experimental protocol, including the addition of control (baseline) trials, and increased the number of subjects in an effort to increase our sensitivity to variations in force magnitude. We compared univariate and multivariate data-analytic strategies, but we relied on our multivariate results to elucidate the interaction of attentional and motor networks. We found that increasing the number of subjects from 10 to 20 resulted in an increase in statistical power and a more stable (i. e. , more replicable) but qualitatively similar result, and that the inclusion of control trials in a 10-subject group did not enhance our ability to discern significant brain-behavior relationships. Our results suggest that sample sizes greater than 20 may be required to detect parametric variation in some instances and that failure to detect such variation may result from unanticipated neurobehavioral effects.

ICML Conference 2000 Conference Paper

Achieving Efficient and Cognitively Plausible Learning in Backgammon

• Scott Sanner
• John R. Anderson
• Christian Lebiere
• Marsha C. Lovett

AIJ Journal 1990 Journal Article

Cognitive modeling and intelligent tutoring

• John R. Anderson
• C.Franklin Boyle
• Albert T. Corbett
• Matthew W. Lewis

AIJ Journal 1989 Journal Article

A theory of the origins of human knowledge

• John R. Anderson

The PUPS theory and its ACT∗ predecessor are computational embodiments of psychology's effort to develop a theory of the origins of knowledge. The theories contain proposals for extraction of knowledge from the environment, a strength-based prioritization of knowledge, knowledge compilation mechanisms for forming use-specific versions of knowledge, and induction mechanisms for extending knowledge. PUPS differs from ACT∗ basically in its principles of induction which include analogy-based generalization, a discrimination mechanism, and principles of making causal inferences. The knowledge in these theories can be classified into the knowledge level, algorithm level, and implementation level. Knowledge at the knowledge level consists of information acquired from the environment and innate principles of induction and problem solving. Knowledge at the algorithm level consists of internal deductions, inductions, and compilation. Knowledge at the implementation level takes the form of setting strengths for the encoding of specific pieces of information

IJCAI Conference 1985 Conference Paper

Dynamic Student Modelling in an Intelligent Tutor for LISP Programming

• Brian J. Reiser
• John R. Anderson
• Robert G. Farrell

IJCAI Conference 1985 Conference Paper

The Geometry Tutor

• John R. Anderson
• C. Franklin Boyle
• GREGG YOST

AIJ Journal 1984 Journal Article

Cognitive psychology

• John R. Anderson

IJCAI Conference 1983 Conference Paper

Learning to Program

• John R. Anderson