Functional integration processes underlying the instruction-based learning of novel goal-directed behaviors

Ruge, Hannes; Wolfensteller, Uta

doi:10.1016/j.neuroimage.2012.12.003

Cited by 46 publications

(69 citation statements)

References 68 publications

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“…Such areas interact with MFC and LFC (Danielmeier, Eichele, Forstmann, Tittgemeyer, & Ullsperger, 2011;Egner & Hirsch, 2005), in specific frequency bands (Baldauf & Desimone, 2014). The response area in the processing module may correspond to premotor cortex, which strongly interacts with (other) decision-making areas (Ruge & Wolfensteller, 2012;Cisek & Kalaska, 2010). Finally, the integrator module may correspond to primary motor areas that collect evidence in favor of relevant response options (Hare, Schultz, Camerer, O'Doherty, & Rangel, 2011).…”

Section: Discussionmentioning

confidence: 99%

Binding by Random Bursts: A Computational Model of Cognitive Control

Verguts

2017

Journal of Cognitive Neuroscience

138

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Abstract■ A neural synchrony model of cognitive control is proposed. It construes cognitive control as a higher-level action to synchronize lower-level brain areas. Here, a controller prefrontal area (medial frontal cortex) can synchronize two cortical processing areas. The synchrony is achieved by a random theta frequency-locked neural burst sent to both areas. The choice of areas that receive this burst is determined by lateral frontal cortex. As a result of this synchrony, communication between the two areas becomes more efficient. The model is tested on the classical Stroop cognitive control task, and its operation is explored in several simulations. Both reactive and proactive controls are implemented via theta power modulation. Increasing theta power improves behavioral performance; furthermore, via theta-gamma phase-amplitude coupling, theta also increases gamma frequency power and synchrony in posterior processing areas. Thus, the model solves a central computational problem for cognitive control (how to allow rapid communication between arbitrary brain areas), while making rich contact with behavioral and neurophysiological data. ■

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Section: Discussionmentioning

confidence: 99%

Binding by Random Bursts: A Computational Model of Cognitive Control

Verguts

2017

Journal of Cognitive Neuroscience

138

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“…However, only few neuroimaging studies have actually employed sparse regression or classification methods in order to gain new insights into neural processes. In the present study, a large fMRI sample (N = 70) (Ruge and Wolfensteller, 2013) was used to provide evidence that previously undetected task-driven effects can be identified by sparse classification techniques in spite of missing univariate effects. Furthermore, a recent refinement of sparse regression exploiting the 3D-structure of fMRI data was employed to potentially obtain higher classification accuracies and weight maps with better interpretability (Grosenick et al, 2013).…”

Section: Overviewmentioning

confidence: 99%

“…In spite of being an important characteristic of human cognitive capabilities, only recently several studies have examined this topic using fMRI (Cole et al, 2012 andRuge, 2012). While most of these studies have been concerned with encoding and execution of novel S-R rules, a recent study by Ruge and Wolfensteller (2013) explored how outcomes are integrated into rule representation.…”

Section: Instruction-based Learning and Outcome Integrationmentioning

confidence: 99%

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Sparse regularization techniques provide novel insights into outcome integration processes

et al. 2015

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By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account. Here, these advanced classification methods were applied to a large fMRI sample (N=70) in order to gain novel insights into the functional localization of outcome integration processes. While the beneficial effect of differential outcomes is well-studied in trial-and-error learning, outcome integration in the context of instruction-based learning has remained largely unexplored. In order to examine neural processes associated with outcome integration in the context of instruction-based learning, two groups of subjects underwent functional imaging while being presented with either differential or ambiguous outcomes following the execution of varying stimulus-response instructions. While no significant univariate group differences were found in the resulting fMRI dataset, L1-regularized (sparse) classifiers performed significantly above chance and also clearly outperformed the standard L2-regularized (dense) Support Vector Machine on this whole-brain between-subject classification task. Moreover, additional L2-regularization via the Elastic Net and spatial regularization by the Graph Net improved interpretability of discriminative weight maps but were accompanied by reduced classification accuracies. Most importantly, classification based on sparse regularization facilitated the identification of highly specific regions differentially engaged under ambiguous and differential outcome conditions, comprising several prefrontal regions previously associated with probabilistic learning, rule integration and reward processing. Additionally, a detailed post-hoc analysis of these regions revealed that distinct activation dynamics underlay the processing of ambiguous relative to differential outcomes. Together, these results show that L1-regularization can improve classification performance while simultaneously providing highly specific and interpretable discriminative activation patterns.

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“…This suggests that individuals learn through a process of linking behavior with the expected outcome of that behavior and that such behaviors may be influenced through subtle cues while other aspects of behavior are based upon habitual patterns. 31 This evidence suggests that confidence in the performance of a motor task, such as TJM, may be facilitated through the use of teaching strategies that include subtle cueing before, during, and after the task, that provide the learner with clear expectations regarding the outcome of the task. Methods that create habitual patterns of activity through repetition and allow the learner to experience successful task performance may also assist with confidence development.…”

Section: Confidence Developmentmentioning

confidence: 99%

A model for teaching and learning spinal thrust manipulation and its effect on participant confidence in technique performance

Wise

Schenk

Lattanzi

2016

Journal of Manual & Manipulative Therapy

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Background: Despite emerging evidence to support the use of high velocity thrust manipulation in the management of lumbar spinal conditions, utilization of thrust manipulation among clinicians remains relatively low. One reason for the underutilization of these procedures may be related to disparity in training in the performance of these techniques at the professional and post professional levels. Purpose: To assess the effect of using a new model of active learning on participant confidence in the performance of spinal thrust manipulation and the implications for its use in the professional and postprofessional training of physical therapists. Methods: A cohort of 15 DPT students in their final semester of entry-level professional training participated in an active training session emphasizing a sequential partial task practice (SPTP) strategy in which participants engaged in partial task practice over several repetitions with different partners. Participants' level of confidence in the performance of these techniques was determined through comparison of preand post-training session surveys and a post-session open-ended interview. Results: The increase in scores across all items of the individual pre-and post-session surveys suggests that this model was effective in changing overall participant perception regarding the effectiveness and safety of these techniques and in increasing student confidence in their performance. Interviews revealed that participants greatly preferred the SPTP strategy, which enhanced their confidence in technique performance. Conclusion: Results indicate that this new model of psychomotor training may be effective at improving confidence in the performance of spinal thrust manipulation and, subsequently, may be useful for encouraging the future use of these techniques in the care of individuals with impairments of the spine. Inasmuch, this method of instruction may be useful for training of physical therapists at both the professional and post-professional levels.

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Functional integration processes underlying the instruction-based learning of novel goal-directed behaviors

Cited by 46 publications

References 68 publications

Binding by Random Bursts: A Computational Model of Cognitive Control

Binding by Random Bursts: A Computational Model of Cognitive Control

Sparse regularization techniques provide novel insights into outcome integration processes

A model for teaching and learning spinal thrust manipulation and its effect on participant confidence in technique performance

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