Advances in techniques for imposing reciprocity in brain-behavior relations

Turner, B. E.; Palestro, James J.; Miletić, Steven; Forstmann, Birte U.

doi:10.1016/j.neubiorev.2019.04.018

Cited by 35 publications

(24 citation statements)

References 111 publications

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“…In this article, we extended an existing framework for connecting mind, brain, and behavior by specifying Gaussian process linking functions for the dynamics of both latent and manifest variables. This extension allows a few key advantages that will be important in the field of model-based cognitive neuroscience (4,7,8,44). First, our approach inherits all of the advantages that a hierarchical structure provides, such as statistical reciprocity through conditionally independent variables within a global model and flexibly accounting for missing observations (9,45,46).…”

Section: Discussionmentioning

confidence: 99%

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Gaussian process linking functions for mind, brain, and behavior

Bahg

Evans

Galdo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain–behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model’s performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.

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Section: Discussionmentioning

confidence: 99%

“…Despite these innovations, the key to understanding what the mind is and ultimately how it produces complex patterns of thought and decisions lies in our ability to explain the structure between manifest variables of the brain and manifest variables of behavior. The set of possible links connecting these variables is known as linking propositions (1)(2)(3)(4).…”

mentioning

confidence: 99%

Gaussian process linking functions for mind, brain, and behavior

Bahg

Evans

Galdo

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…In our opinion, addressing these sorts of issues may require the development of more sophisticated computational approaches that can treat behavioral and neural data in a statistically symmetrical manner (Rigoux and Daunizeau, 2015;Turner et al, 2013Turner et al, , 2016Turner et al, , 2019. We intend to pursue this type of approach in subsequent publications.…”

Section: Discussionmentioning

confidence: 99%

Synaptic plasticity in the orbitofrontal cortex explains how risk attitude adapts to the range of risk prospects

Brochard

Daunizeau

2020

Preprint

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Computational investigations of learning and decision making suggest that systematic deviations to adaptive behavior may be the incidental outcome of biological constraints imposed on neural information processing. In particular, recent studies indicate that range adaptation, i.e., the mechanism by which neurons dynamically tune their output firing properties to match the changing statistics of their inputs, may drive plastic changes in the brain’s decision system that induce systematic deviations to rationality. Here, we ask whether behaviorally-relevant neural information processing may be distorted by other incidental, hard-wired, biological constraints, in particular: Hebbian plasticity. One of our main contributions is to propose a simple computational method for identifying (and comparing) the neural signature of such biological mechanisms or constraints. Using ANNs (i.e., artificial neural network models) and RSA (i.e., representational similarity analysis), we compare the neural signatures of two types of hard-wired biological mechanisms/constraints: namely, range adaptation and Hebbian plasticity. We apply the approach to two different open fMRI datasets acquired when people make decisions under risk. In both cases, we show that although peoples’ apparent indifferent choices are well explained by biologically-constrained ANNs, choice data alone does not discriminate between range adaptation and Hebbian plasticity. However, RSA shows that neural activity patterns in bilateral Striatum and Amygdala are more compatible with Hebbian plasticity. Finally, the strength of evidence for Hebbian plasticity in these structures predicts inter-individual differences in choice inconsistency.

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“…The copyright holder for this preprint this version posted December 22, 2020. ; https://doi.org/10.1101/2020.12.21.423791 doi: bioRxiv preprint neural patterns are linked to cognitive mechanisms through computational models (e.g., [47,69]).…”

Section: Discussionmentioning

confidence: 99%

Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

Rac-Lubashevsky

Frank

2020

Preprint

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Adaptive cognitive-control is achieved through a hierarchical cortico-striatal gating system that supports selective updating, maintenance, and retrieval of useful cognitive and motor information. Here, we developed a novel task that independently manipulated selective gating operations of working-memory (input), from working-memory (output), and in response (motor) and tested the neural dynamics and computational principles that support them. Increases in gating demands, captured by gate switches, were expressed by distinct EEG correlates at each gating level that evolved dynamically in partially overlapping time windows. EEG decoding analysis further showed that neural indexes of working-memory (category) and motor (action) representations were prioritized particularly when the corresponding gate was switching. Finally, the control mechanisms involved in gate switches were quantified by the drift diffusion model, showing elevated motor decision threshold in all gating levels. Together these results support the notion that cognitive gating operations scaffold on top of mechanisms involved in motor gating.

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Advances in techniques for imposing reciprocity in brain-behavior relations

Cited by 35 publications

References 111 publications

Gaussian process linking functions for mind, brain, and behavior

Gaussian process linking functions for mind, brain, and behavior

Synaptic plasticity in the orbitofrontal cortex explains how risk attitude adapts to the range of risk prospects

Analogous computations in working memory input, output and motor gating: Electrophysiological and computational modeling evidence

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