Confidence in Decision-Making during Probabilistic Tactile Learning Related to Distinct Thalamo–Prefrontal Pathways

Wang, Bin A.; Pleger, Burkhard

doi:10.1093/cercor/bhaa073

Cited by 9 publications

(18 citation statements)

References 62 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key model assumption from the rodent work was that the MD thalamus performs an intermediate-level computation rather than a sensory signal relay [ 20 , 21 ]. We first confirmed that this is the case for the human brain, by examining fMRI data of human participants performing a probabilistic inference task [ 32 ]. We then asked the neural model to solve components of the same probabilistic inference task and identified a role for the MD in rapid and flexible gating of human dlPFC strategy representations.…”

Section: Introductionmentioning

confidence: 80%

“…To draw direct parallels between the neural model and human data, we returned to our previously collected dataset [ 32 ] and took a different analytical approach from the published work. In mice, prefrontal inhibition impaired overall performance, while MD inhibition led to impairment specifically when animals were required to switch behavior [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

“…Code generated for this study has been deposited at <GitHub.com/hummosa/MD-reservoir>. Datasets generated during this study were previously published [ 32 ] and are available at < https://ruhr-uni-bochum.sciebo.de/s/xKBPyW7ZGLs2q2g >.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Thalamic regulation of frontal interactions in human cognitive flexibility

et al. 2022

Self Cite

View full text Add to dashboard Cite

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.

show abstract

Section: Introductionmentioning

confidence: 80%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Thalamic regulation of frontal interactions in human cognitive flexibility

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the learning mechanism that modifies these pathways is lacking. [21] showed similar functional connectivity in humans when looking at the activity of the thalamus, orbital frontal cortex, and the vmPFC during a tactile task. They found that when the subject switches strategy, both the thalamus and orbital frontal cortex are activated, while when the subject keeps the same strategy, both the thalamus and vmPFC area are activated.…”

Section: Introductionmentioning

confidence: 70%

Stable thalamocortical learning between medial-dorsal thalamus and cortical attractor networks captures cognitive flexibility

Qiu

2021

Preprint

View full text Add to dashboard Cite

Primates and rodents are able to continually acquire, adapt, and transfer knowledge and skill, and lead to goal-directed behavior during their lifespan. For the case when context switches slowly, animals learn via slow processes. For the case when context switches rapidly, animals learn via fast processes. We build a biologically realistic model with modules similar to a distributed computing system. Specifically, we are emphasizing the role of thalamocortical learning on a slow time scale between the prefrontal cortex (PFC) and medial dorsal thalamus (MD). Previous work [1] has already shown experimental evidence supporting classification of cell ensembles in the medial dorsal thalamus, where each class encodes a different context. However, the mechanism by which such classification is learned is not clear. In this work, we show that such learning can be self-organizing in the manner of an automaton (a distributed computing system), via a combination of Hebbian learning and homeostatic synaptic scaling. We show that in the simple case of two contexts, the network with hierarchical structure can do context-based decision making and smooth switching between different contexts. Our learning rule creates synaptic competition [2] between the thalamic cells to create winner-take-all activity. Our theory shows that the capacity of such a learning process depends on the total number of task-related hidden variables, and such a capacity is limited by system size N. We also theoretically derived the effective functional connectivity as a function of an order parameter dependent on the thalamo-cortical coupling structure.Significance StatementAnimals need to adapt to dynamically changing environments and make decisions based on changing contexts. Here we propose a combination of neural circuit structure with learning mechanisms to account for such behaviors. Specifically, we built a reservoir computing network improved by a Hebbian learning rule together with a synaptic scaling learning mechanism between the prefrontal cortex and the medial-dorsal (MD) thalamus. This model shows that MD thalamus is crucial in such context-based decision making. I also make use of dynamical mean field theory to predict the effective neural circuit. Furthermore, theoretical analysis provides a prediction that the capacity of such a network increases with the network size and the total number of tasks-related latent variables.

show abstract

“…The prefrontal cortex (PFC), and more specifically the orbitofrontal cortex (OFC), has long been implicated in the ability to respond adaptively and flexibly to obtain reward 4,5 . We recently identified the OFC in humans as a critical brain region related to updating the decision strategy based on newly accumulated evidence 6 . Recent studies emphasize that OFC supports value-guided behaviour by representing the predictions about specific outcomes associated with sensory stimuli 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Human orbitofrontal cortex signals decision outcomes to sensory cortex during flexible tactile learning

Wang

Veismann

Banerjee

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The ability to respond adaptively to an ever-changing environment relies on the orbitofrontal cortex (OFC). The OFC sends neuroanatomical projections to the primary sensory cortex - yet the contribution of this top-down feedback projection to behavioural flexibility in humans is unknown. Inspired from recent rodent studies, we here combined a probabilistic Go/No-Go tactile reversal learning task with functional magnetic resonance imaging (fMRI) in human participants to investigate how OFC interacts with the primary somatosensory cortex (S1) to promote flexible decision-making. We show a distinct task-dependent engagement of S1 and lateral OFC: while the lateral OFC responds saliently and transiently to rule-switches, activity in S1 reflects initial task learning and persistent engagement after each rule-switch event. Unlike the contralateral S1, which represents the sensory input, activity in ipsilateral S1 mirrors the outcome value during re-learning. Importantly, the implementation of this outcome-selectivity in ipsilateral S1 is dependent on top-down 'teaching' signals from lateral OFC. Overall, as in mice, we show comparable physiological and computational signatures of how dynamic interactions between OFC and sensory cortex support flexible decision-making in humans.

show abstract

Confidence in Decision-Making during Probabilistic Tactile Learning Related to Distinct Thalamo–Prefrontal Pathways

Cited by 9 publications

References 62 publications

Thalamic regulation of frontal interactions in human cognitive flexibility

Thalamic regulation of frontal interactions in human cognitive flexibility

Stable thalamocortical learning between medial-dorsal thalamus and cortical attractor networks captures cognitive flexibility

Human orbitofrontal cortex signals decision outcomes to sensory cortex during flexible tactile learning

Contact Info

Product

Resources

About