Extending problem-solving procedures through reflection

Anderson, John R.; Fincham, Jon M.

doi:10.1016/j.cogpsych.2014.06.002

Cited by 45 publications

(64 citation statements)

References 46 publications

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“…Fig. 1 shows a swimlane representation of the activity of Anderson and Fincham's (2014b) ACT-R model for the solution of the regular problem 7$4 = X, which took the model 10.91 s to solve, close to the participant's average time of 11.24 s on this problem. The ACT-R cognitive architecture is organized into modules, each of which processes a different type of information and has its own buffer to hold information.…”

Section: Pyramid Problems and The Use Of Hidden Markov Modelsmentioning

confidence: 99%

“…Each term in the sequence is one less than the previous-so 8$3 = 8 + 7 + 6 = 21. Anderson and Fincham (2014b) compared problems with positive single digits for base and height (regular problems) to problems that required participants to extend their knowledge (exception problems). Exception problems contained features such as problems with negative or variable bases (e.g.…”

Section: Pyramid Problems and The Use Of Hidden Markov Modelsmentioning

confidence: 99%

“…We assume durations of stages are sampled from Gamma distributions. As in Anderson and Fincham (2014b), we estimated just one scale parameter per cognitive stage for each learning phase (i.e., if there are N learning phases, we estimated 3 Â N scale parameters), but we also estimated a separate shape parameter to vary for each of the 21 problems (i.e. 3 Â 21 Â N shape parameters if we assume N learning phases).…”

Section: Bottom-up Stage Analysis: Cognitive Stages Within Learning Pmentioning

confidence: 99%

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Phases of learning: How skill acquisition impacts cognitive processing

Tenison

Fincham

Anderson

2016

Cognitive Psychology

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This fMRI study examines the changes in participants' information processing as they repeatedly solve the same mathematical problem. We show that the majority of practice-related speedup is produced by discrete changes in cognitive processing. Because the points at which these changes take place vary from problem to problem, and the underlying information processing steps vary in duration, the existence of such discrete changes can be hard to detect. Using two converging approaches, we establish the existence of three learning phases. When solving a problem in one of these learning phases, participants can go through three cognitive stages: Encoding, Solving, and Responding. Each cognitive stage is associated with a unique brain signature. Using a bottom-up approach combining multi-voxel pattern analysis and hidden semi-Markov modeling, we identify the duration of that stage on any particular trial from participants brain activation patterns. For our top-down approach we developed an ACT-R model of these cognitive stages and simulated how they change over the course of learning. The Solving stage of the first learning phase is long and involves a sequence of arithmetic computations. Participants transition to the second learning phase when they can retrieve the answer, thereby drastically reducing the duration of the Solving stage. With continued practice, participants then transition to the third learning phase when they recognize the problem as a single unit and produce the answer as an automatic response. The duration of this third learning phase is dominated by the Responding stage.

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Section: Pyramid Problems and The Use Of Hidden Markov Modelsmentioning

confidence: 99%

Section: Pyramid Problems and The Use Of Hidden Markov Modelsmentioning

confidence: 99%

Section: Bottom-up Stage Analysis: Cognitive Stages Within Learning Pmentioning

confidence: 99%

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Phases of learning: How skill acquisition impacts cognitive processing

Tenison

Fincham

Anderson

2016

Cognitive Psychology

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“…Anderson & Fincham, 2014). These efforts differ from the current effort in that there is not an external definition of the mental state.…”

Section: Discussionmentioning

confidence: 95%

“…We now investigate whether we can train a classifier to recognize the state a participant is in on a scan from the pattern of fMRI activity on that scan. To classify the states of specific scans we used a linear discriminant analysis (LDA), a technique we have used to classify such sequential states in other efforts (e.g., Anderson et al, 2010, Anderson et al, 2012Anderson et al, 2014). As described in the methods, we used the first 100 dimensions of standardized PCA scores and the PCA was done on the scan activity deconvolved using a Weiner filter.…”

Section: Step 2: Classification Of States From Brain Activationmentioning

confidence: 99%

The sequential structure of brain activation predicts skill

et al. 2016

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Abstract:In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players.Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation.

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2020

Cognitive Mechanisms of Learning

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Extending problem-solving procedures through reflection

Cited by 45 publications

References 46 publications

Phases of learning: How skill acquisition impacts cognitive processing

Phases of learning: How skill acquisition impacts cognitive processing

The sequential structure of brain activation predicts skill

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