Quantifying decision-making in dynamic, continuously evolving environments

During perceptual decision-making tasks, centroparietal electroencephalographic (EEG) potentials report an evidence accumulation-to-bound process that is time locked to trial onset. However, decisions in real-world environments are rarely confined to discrete trials; they instead unfold continuously, with accumulation of time-varying evidence being recency-weighted towards its immediate past. The neural mechanisms supporting recency-weighted continuous decision-making remain unclear. Here, we use a novel continuous task design to study how the centroparietal positivity (CPP) adapts to different environments that place different constraints on evidence accumulation. We show that adaptations in evidence weighting to these different environments are reflected in changes in the CPP. The CPP becomes more sensitive to fluctuations in sensory evidence when large shifts in evidence are less frequent, and the potential is primarily sensitive to fluctuations in decision-relevant (not decision-irrelevant) sensory input. A complementary triphasic component over occipito-parietal cortex encodes the sum of recently accumulated sensory evidence, and its magnitude covaries with parameters describing how different individuals integrate sensory evidence over time. A computational model based on leaky evidence accumulation suggests that these findings can be accounted for by a shift in decision threshold between different environments, which is also reflected in the magnitude of pre-decision EEG activity. Our findings reveal how adaptations in EEG responses reflect flexibility in evidence accumulation to the statistics of dynamic sensory environments

Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Such dysfunction may be caused by aberrant interactions of different neuromodulators with NMDARs, which could explain clinical heterogeneity among patients. In two studies (N = 81 each), we used a double-blind placebo-controlled between-subject design to systematically test whether auditory mismatch responses under varying levels of environmental stability are sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. We found a significant drug × mismatch interaction: while the muscarinic acetylcholine receptor antagonist biperiden delayed and topographically shifted mismatch responses, particularly during high stability, this effect could not be detected for amisulpride, a dopamine D2/D3 receptor antagonist. Neither galantamine nor levodopa, which elevate acetylcholine and dopamine levels, respectively, exerted significant effects on MMN. This differential MMN sensitivity to muscarinic versus dopaminergic receptor function may prove useful for developing tests that predict individual treatment responses in schizophrenia

Como crianças de nível sócio-econômico médio e em situação de rua desenham uns aos outros?

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Como crianças de nível sócio-econômico médio e em situação de rua desenham uns aos outros?

09

The generalized Hierarchical Gaussian Filter

Hierarchical Bayesian models of perception and learning feature prominently in contemporary cognitive neuroscience where, for example, they inform computational concepts of mental disorders. This includes predictive coding and hierarchical Gaussian filtering (HGF), which differ in the nature of hierarchical representations. Predictive coding assumes that higher levels in a given hierarchy influence the state (value) of lower levels. In HGF, however, higher levels determine the rate of change at lower levels. Here, we extend the space of generative models underlying HGF to include a form of nonlinear hierarchical coupling between state values akin to predictive coding and artificial neural networks in general. We derive the update equations corresponding to this generalization of HGF and conceptualize them as connecting a network of (belief) nodes where parent nodes either predict the state of child nodes or their rate of change. This enables us to (1) create modular architectures with generic computational steps in each node of the network, and (2) disclose the hierarchical message passing implied by generalized HGF models and to compare this to comparable schemes under predictive coding. We find that the algorithmic architecture instantiated by the generalized HGF is largely compatible with that of predictive coding but extends it with some unique predictions which arise from precision and volatility related computations. Our developments enable highly flexible implementations of hierarchical Bayesian models for empirical data analysis and are available as open source software

A developmental framework for embodiment research

Embodiment research is at a turning point. There is an increasing amount of data and studies investigating embodiment phenomena and their role in mental processing and functions from across a wide range of disciplines and theoretical schools within the life sciences. However, the integration of behavioral data with data from different biological levels is challenging for the involved research fields such as movement psychology, social and developmental neuroscience, computational psychosomatics, social and behavioral epigenetics, human-centered robotics, and many more. This highlights the need for an interdisciplinary framework of embodiment research. In addition, there is a growing need for a cross-disciplinary consensus on level-specific criteria of embodiment. We propose that a developmental perspective on embodiment is able to provide a framework for overcoming such pressing issues, providing analytical tools to link timescales and levels of embodiment specific to the function under study, uncovering the underlying developmental processes, clarifying level-specific embodiment criteria, and providing a matrix and platform to bridge disciplinary boundaries among the involved research fields

Analysis Plan: Investigating group differences in visual MMN responses in persons with multiple sclerosis

<p>This analysis plan describes the planned analysis of an EEG data set of a visual mismatch paradigm conducted in persons with multiple sclerosis (MS) and healthy controls. The goal of the analysis is to investigate differences in the visual mismatch response between persons with MS and healthy controls In addition, the aim is to predict the future level of fatigue in the MS sample. A second file contains the preprocessing plan that describes the preprocessing of the EEG data. Note that this plan was written earlier (February 2023) and time stamped on an internal git repository at ETH. It is added here to provide a complete description of the analysis. </p><p>Note: A twin experiment was conducted using an auditory instead of a visual mismatch paradigm. The corresponding analysis plan is separately uploaded and can be found here: <a href="https://zenodo.org/doi/10.5281/zenodo.10166109">10.5281/zenodo.10166109</a></p&gt

Analysis Plan: Investigating group differences in auditory MMN responses in persons with multiple sclerosis

<p>This analysis plan describes the planned analysis of an EEG data set of an auditory mismatch paradigm conducted in persons with multiple sclerosis (MS) and healthy controls. The goal of the analysis is to investigate differences in the auditory mismatch response between persons with MS and healthy controls. In addition, the aim is to predict the future level of fatigue in the MS sample. A second file contains the preprocessing plan that describes the preprocessing of the EEG data. Note that this plan was written earlier (February 2023) and time stamped on an internal git repository. It is added here to provide a complete description of the analysis. </p><p>Note: A twin experiment was conducted using a visual instead of an auditory mismatch paradigm. The corresponding analysis plan is separately uploaded and can be found here: <a href="https://zenodo.org/doi/10.5281/zenodo.10169143">10.5281/zenodo.10169143</a></p&gt