scholarly journals A flexible and generalizable model of online latent-state learning

2018 ◽  
Author(s):  
Amy L Cochran ◽  
Josh M Cisler
Keyword(s):  
Likelihood Estimation ◽  
Brain Regions ◽  
Prior Beliefs ◽  
Learning Agents ◽  
Return Of Fear ◽  
Memory Modification ◽  
State Learning ◽  
Approximate Bayesian ◽  
Correct Association

AbstractMany models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to rewards (or penalties) and infer which index (i.e. latent state) is presently active. Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments. The resulting model combines a Rescorla-Wagner rule, for which updates to associations are proportional to prediction error, with an approximate Bayesian rule, for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations. In simulation, we demonstrate the model’s ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model, including rapid return of fear after extinction, the Hall-Pearce effect, partial reinforcement extinction effect, backwards blocking, and memory modification. Lastly, we derive our model as an online algorithm to maximum likelihood estimation, demonstrating it is an efficient approach to outcome prediction. Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts.Author summaryComputational researchers are increasingly interested in a structured form of learning known as latent-state inferences. Latent-state inferences is a type of learning that involves categorizing, generalizing, and recalling disparate associations between observations in one’s environment and is used in situations when the correct association is latent or unknown. This type of learning has been used to explain overgeneralization of a fear memory and the cognitive role of certain brain regions important to cognitive neuroscience and psychiatry. Accordingly, latent-state inferences are an important area of inquiry. Through simulation and theory, we establish a new model of latent-state inferences. Moving forward, we aim to use this framework to measure latent-state inferences in healthy and psychiatric populations.

scholarly journals Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

AbstractAccording to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

scholarly journals Disentangling Predictive Processing in the Brain: A Meta-analytic Study in Favour of a Predictive Network

2021 ◽  
Author(s):  
Linda Ficco ◽  
Lorenzo Mancuso ◽  
Jordi Manuello ◽  
Alessia Teneggi ◽  
Donato Liloia ◽  
...  
Keyword(s):  
Large Scale ◽  
Inferior Frontal Gyrus ◽  
Predictive Coding ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Predictive Processing ◽  
Spatial Convergence ◽  
Large Scale Networks ◽  
The Brain

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signal. Despite extensive research has investigated the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a task-based meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

Neural Underpinnings of Numerical and Spatial Cognition: An fMRI Meta-Analysis of Brain Regions Associated with Symbolic Number, Arithmetic, and Mental Rotation

2019 ◽  
Author(s):  
Zachary Hawes ◽  
H Moriah Sokolowski ◽  
Chuka Bosah Ononye ◽  
Daniel Ansari
Keyword(s):  
Mental Rotation ◽  
Parietal Lobe ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Brain Regions ◽  
Angular Gyrus ◽  
Number Representation ◽  
Neural Underpinnings ◽  
The Right ◽  
Symbolic Number

Where and under what conditions do spatial and numerical skills converge and diverge in the brain? To address this question, we conducted a meta-analysis of brain regions associated with basic symbolic number processing, arithmetic, and mental rotation. We used Activation Likelihood Estimation (ALE) to construct quantitative meta-analytic maps synthesizing results from 86 neuroimaging papers (~ 30 studies/cognitive process). All three cognitive processes were found to activate bilateral parietal regions in and around the intraparietal sulcus (IPS); a finding consistent with shared processing accounts. Numerical and arithmetic processing were associated with overlap in the left angular gyrus, whereas mental rotation and arithmetic both showed activity in the middle frontal gyri. These patterns suggest regions of cortex potentially more specialized for symbolic number representation and domain-general mental manipulation, respectively. Additionally, arithmetic was associated with unique activity throughout the fronto-parietal network and mental rotation was associated with unique activity in the right superior parietal lobe. Overall, these results provide new insights into the intersection of numerical and spatial thought in the human brain.

Reducing misperceptions through news stories with data visualization: The role of readers’ prior knowledge and prior beliefs

Journalism ◽  
2021 ◽  
pp. 146488492110287
Author(s):  
Paul Mena
Keyword(s):  
Experimental Design ◽  
Prior Knowledge ◽  
Data Visualization ◽  
Accurate Information ◽  
Graphical Representations ◽  
Prior Beliefs ◽  
News Stories ◽  
Issue Involvement

Amid the global discussion on ways to fight misinformation, journalists have been writing stories with graphical representations of data to expose misperceptions and provide readers with more accurate information. Employing an experimental design, this study explored to what extent news stories correcting misperceptions are effective in reducing them when the stories include data visualization and how influential readers’ prior beliefs, issue involvement and prior knowledge may be in that context. The study found that the presence of data visualization in news articles correcting misperceptions significantly enhanced the reduction of misperceptions among news readers with less than average prior knowledge about an issue. In addition, it was found that prior beliefs had a significant effect on news readers’ misperceptions regardless of the presence or absence of data visualization. In this way, this research offers some support for the notion that data visualization may be useful to decrease misperceptions under certain circumstances.

scholarly journals Optimistic amnesia: how online and offline processing shape belief updating and memory biases in immediate and long-term optimism biases

2021 ◽  
Author(s):  
Ziqing Yao ◽  
Xuanyi Lin ◽  
Xiaoqing Hu
Keyword(s):  
Active Role ◽  
Prior Beliefs ◽  
Belief Updating ◽  
Estimation Task ◽  
Memory Biases ◽  
Brain Potentials ◽  
Adverse Life Event ◽  
Offline Processing

Abstract When people are confronted with feedback that counters their prior beliefs, they preferentially rely on desirable rather than undesirable feedback in belief updating, i.e. an optimism bias. In two pre-registered EEG studies employing an adverse life event probability estimation task, we investigated the neurocognitive processes that support the formation and the change of optimism biases in immediate and 24 h delayed tests. We found that optimistic belief updating biases not only emerged immediately but also became significantly larger after 24 h, suggesting an active role of valence-dependent offline consolidation processes in the change of optimism biases. Participants also showed optimistic memory biases: they were less accurate in remembering undesirable than desirable feedback probabilities, with inferior memories of undesirable feedback associated with lower belief updating in the delayed test. Examining event-related brain potentials (ERPs) revealed that desirability of feedback biased initial encoding: desirable feedback elicited larger P300s than undesirable feedback, with larger P300 amplitudes predicting both higher belief updating and memory accuracies. These results suggest that desirability of feedback could bias both online and offline memory-related processes such as encoding and consolidation, with both processes contributing to the formation and change of optimism biases.

scholarly journals The G Protein-Coupled Glutamate Receptors as Novel Molecular Targets in Schizophrenia Treatment—A Narrative Review

2021 ◽  
Vol 10 (7) ◽  
pp. 1475
Author(s):  
Waldemar Kryszkowski ◽  
Tomasz Boczek
Keyword(s):  
Glutamate Receptors ◽  
Metabotropic Glutamate Receptors ◽  
Glutamate Release ◽  
Molecular Targets ◽  
Brain Regions ◽  
Psychotic Symptoms ◽  
Unknown Etiology ◽  
Metabotropic Glutamate ◽  
G Protein Coupled

Schizophrenia is a severe neuropsychiatric disease with an unknown etiology. The research into the neurobiology of this disease led to several models aimed at explaining the link between perturbations in brain function and the manifestation of psychotic symptoms. The glutamatergic hypothesis postulates that disrupted glutamate neurotransmission may mediate cognitive and psychosocial impairments by affecting the connections between the cortex and the thalamus. In this regard, the greatest attention has been given to ionotropic NMDA receptor hypofunction. However, converging data indicates metabotropic glutamate receptors as crucial for cognitive and psychomotor function. The distribution of these receptors in the brain regions related to schizophrenia and their regulatory role in glutamate release make them promising molecular targets for novel antipsychotics. This article reviews the progress in the research on the role of metabotropic glutamate receptors in schizophrenia etiopathology.

scholarly journals The Protective Role of Brain CYP2J in Parkinson’s Disease Models

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yueran Li ◽  
Jinhua Wu ◽  
Xuming Yu ◽  
Shufang Na ◽  
Ke Li ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Brain Regions ◽  
Protective Role ◽  
Neural Cells ◽  
Endogenous Substrates ◽  
6 Hydroxydopamine ◽  
Myd88 Signaling ◽  
Lps Treatment

CYP2J proteins are present in the neural cells of human and rodent brain regions. The aim of this study was to investigate the role of brain CYP2J in Parkinson’s disease. Rats received right unilateral injection with lipopolysaccharide (LPS) or 6-hydroxydopamine (6-OHDA) in the substantia nigra following transfection with or without the CYP2J3 expression vector. Compared with LPS-treated rats, CYP2J3 transfection significantly decreased apomorphine-induced rotation by 57.3% at day 12 and 47.0% at day 21 after LPS treatment; moreover, CYP2J3 transfection attenuated the accumulation of α-synuclein. Compared with the 6-OHDA group, the number of rotations by rats transfected with CYP2J3 decreased by 59.6% at day 12 and 43.5% at day 21 after 6-OHDA treatment. The loss of dopaminergic neurons and the inhibition of the antioxidative system induced by LPS or 6-OHDA were attenuated following CYP2J3 transfection. The TLR4-MyD88 signaling pathway was involved in the downregulation of brain CYP2J induced by LPS, and CYP2J transfection upregulated the expression of Nrf2 via the inhibition of miR-340 in U251 cells. The data suggest that increased levels of CYP2J in the brain can delay the pathological progression of PD initiated by inflammation or neurotoxins. The alteration of the metabolism of the endogenous substrates (e.g., AA) could affect the risk of neurodegenerative disease.

Electroencephalographic Recordings of Focal Seizures in Patients Affected by Periventricular Nodular Heterotopia: Role of the Heterotopic Nodules in the Genesis of Epileptic Discharges

2004 ◽  
Vol 19 (3) ◽  
pp. 369-377
Author(s):  
Giorgio Battaglia ◽  
Silvana Franceschetti ◽  
Luisa Chiapparini ◽  
Elena Freri ◽  
Stefania Bassanini ◽  
...  
Keyword(s):  
Brain Regions ◽  
Periventricular Nodular Heterotopia ◽  
Focal Seizures ◽  
Ictal Eeg ◽  
Epileptic Discharges ◽  
Nodular Heterotopia ◽  
Eeg Recordings ◽  
Drug Resistant Epilepsy ◽  
Video Eeg

Patients affected by periventricular nodular heterotopia are frequently characterized by focal drug-resistant epilepsy. To investigate the role of periventricular nodules in the genesis of seizures, we analyzed the electroencephalographic (EEG) features of focal seizures recorded by means of video-EEG in 10 patients affected by different types of periventricular nodular heterotopia and followed for prolonged periods of time at the epilepsy center of our institute. The ictal EEG recordings with surface electrodes revealed common features in all patients: all seizures originated from the brain regions where the periventricular nodular heterotopia were located; EEG patterns recorded on the leads exploring the periventricular nodular heterotopia were very similar both at the onset and immediately after the seizure's end in all patients. Our data suggest that seizures are generated by abnormal anatomic circuitries, including the heterotopic nodules and adjacent cortical areas. The major role of heterotopic neurons in the genesis and propagation of epileptic discharges must be taken into account when planning surgery for epilepsy in patients with periventricular nodular heterotopia. ( J Child Neurol 2005;20:369—377).

scholarly journals Activity in the brain’s valuation and mentalizing networks is associated with propagation of online recommendations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisa C. Baek ◽  
Matthew Brook O’Donnell ◽  
Christin Scholz ◽  
Rui Pei ◽  
Javier O. Garcia ◽  
...  
Keyword(s):  
Social Media ◽  
Word Of Mouth ◽  
Mental States ◽  
Brain Regions ◽  
Prior Work ◽  
Opinion Change ◽  
Wide Range ◽  
The Mind ◽  
Mentalizing System

AbstractWord of mouth recommendations influence a wide range of choices and behaviors. What takes place in the mind of recommendation receivers that determines whether they will be successfully influenced? Prior work suggests that brain systems implicated in assessing the value of stimuli (i.e., subjective valuation) and understanding others’ mental states (i.e., mentalizing) play key roles. The current study used neuroimaging and natural language classifiers to extend these findings in a naturalistic context and tested the extent to which the two systems work together or independently in responding to social influence. First, we show that in response to text-based social media recommendations, activity in both the brain’s valuation system and mentalizing system was associated with greater likelihood of opinion change. Second, participants were more likely to update their opinions in response to negative, compared to positive, recommendations, with activity in the mentalizing system scaling with the negativity of the recommendations. Third, decreased functional connectivity between valuation and mentalizing systems was associated with opinion change. Results highlight the role of brain regions involved in mentalizing and positive valuation in recommendation propagation, and further show that mentalizing may be particularly key in processing negative recommendations, whereas the valuation system is relevant in evaluating both positive and negative recommendations.

scholarly journals Possible Clues for Brain Energy Translation via Endolysosomal Trafficking of APP-CTFs in Alzheimer’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Senthilkumar Sivanesan ◽  
Ravi Mundugaru ◽  
Jayakumar Rajadas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Ketone Bodies ◽  
Brain Regions ◽  
Pentose Phosphate ◽  
Lactate Shuttle ◽  
Carboxy Terminal ◽  
Brain Energy

Vascular dysfunctions, hypometabolism, and insulin resistance are high and early risk factors for Alzheimer’s disease (AD), a leading neurological disease associated with memory decline and cognitive dysfunctions. Early defects in glucose transporters and glycolysis occur during the course of AD progression. Hypometabolism begins well before the onset of early AD symptoms; this timing implicates the vulnerability of hypometabolic brain regions to beta-secretase 1 (BACE-1) upregulation, oxidative stress, inflammation, synaptic failure, and cell death. Despite the fact that ketone bodies, astrocyte-neuron lactate shuttle, pentose phosphate pathway (PPP), and glycogenolysis compensate to provide energy to the starving AD brain, a considerable energy crisis still persists and increases during disease progression. Studies that track brain energy metabolism in humans, animal models of AD, and in vitro studies reveal striking upregulation of beta-amyloid precursor protein (β-APP) and carboxy-terminal fragments (CTFs). Currently, the precise role of CTFs is unclear, but evidence supports increased endosomal-lysosomal trafficking of β-APP and CTFs through autophagy through a vague mechanism. While intracellular accumulation of Aβ is attributed as both the cause and consequence of a defective endolysosomal-autophagic system, much remains to be explored about the other β-APP cleavage products. Many recent works report altered amino acid catabolism and expression of several urea cycle enzymes in AD brains, but the precise cause for this dysregulation is not fully explained. In this paper, we try to connect the role of CTFs in the energy translation process in AD brain based on recent findings.

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