Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach

2019 ◽  
Vol 31 (9) ◽  
pp. 1751-1788 ◽  
Author(s):  
Ali Yousefi ◽  
Ishita Basu ◽  
Angelique C. Paulk ◽  
Noam Peled ◽  
Emad N. Eskandar ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Goodness Of Fit ◽  
Spectral Power ◽  
Field Potential ◽  
High Dimensional ◽  
Cognitive State ◽  
Modeling Framework ◽  
Neural Data ◽  
Space Modeling ◽  
The Relationship

Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavioral readouts. This framework allows us to decompose the hard problem of modeling the relationship between neural and behavioral data into separable encoding-decoding approaches. We first use a state-space modeling framework, the behavioral decoder, to articulate the relationship between an objective behavioral readout (e.g., response times) and cognitive state. The second step, the neural encoder, involves using a generalized linear model (GLM) to identify the relationship between the cognitive state and neural signals, such as local field potential (LFP). We then use the neural encoder model and a Bayesian filter to estimate cognitive state using neural data (LFP power) to generate the neural decoder. We provide goodness-of-fit analysis and model selection criteria in support of the encoding-decoding result. We apply this framework to estimate an underlying cognitive state from neural data in human participants ([Formula: see text]) performing a cognitive conflict task. We successfully estimated the cognitive state within the 95% confidence intervals of that estimated using behavior readout for an average of 90% of task trials across participants. In contrast to previous encoder-decoder models, our proposed modeling framework incorporates LFP spectral power to encode and decode a cognitive state. The framework allowed us to capture the temporal evolution of the underlying cognitive processes, which could be key to the development of closed-loop experiments and treatments.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

2021 ◽  
pp. 1-31
Author(s):  
Yalda Amidi ◽  
Behzad Nazari ◽  
Saeid Sadri ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Point Process ◽  
Goodness Of Fit ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Synaptic Connections ◽  
Synaptic Coupling ◽  
Modeling Framework ◽  
Space Modeling ◽  
Spiking Activity

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

scholarly journals Bayesian Decoder Models with a Discriminative Observation Process

2020 ◽  
Author(s):  
Mohammad R. Rezaei ◽  
Alex E. Hadjinicolaou ◽  
Sydney S. Cash ◽  
Uri T. Eden ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Neural Activity ◽  
Model Development ◽  
High Dimensional ◽  
Modeling Framework ◽  
Movement Trajectories ◽  
State Process ◽  
Observation Process ◽  
Space Modeling ◽  
Low Dimensional

AbstractThe Bayesian state-space neural encoder-decoder modeling framework is an established solution to reveal how changes in brain dynamics encode physiological covariates like movement or cognition. Although the framework is increasingly being applied to progress the field of neuroscience, its application to modeling high-dimensional neural data continues to be a challenge. Here, we propose a novel solution that avoids the complexity of encoder models that characterize high-dimensional data as a function of the underlying state processes. We build a discriminative model to estimate state processes as a function of current and previous observations of neural activity. We then develop the filter and parameter estimation solutions for this new class of state-space modeling framework called the “direct decoder” model. We apply the model to decode movement trajectories of a rat in a W-shaped maze from the ensemble spiking activity of place cells and achieve comparable performance to modern decoding solutions, without needing an encoding step in the model development. We further demonstrate how a dynamical auto-encoder can be built using the direct decoder model; here, the underlying state process links the high-dimensional neural activity to the behavioral readout. The dynamical auto-encoder can optimally estimate the low-dimensional dynamical manifold which represents the relationship between brain and behavior.

scholarly journals Early Education Care from Its Practitioners to Achieve Sustainability

2021 ◽  
Vol 13 (6) ◽  
pp. 3396
Author(s):  
Óscar Gavín-Chocano ◽  
David Molero ◽  
Inmaculada García-Martínez
Keyword(s):  
Early Intervention ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Satisfaction With Life ◽  
Well Being ◽  
Satisfaction With Life Scale ◽  
Life Scale ◽  
Fit Index ◽  
The Relationship ◽  
Early Intervention Professionals

(1) Background: Early intervention professionals are involved in the reconceptualisation of their service due to the exceptional situation caused by the COVID-19 epidemic, within the family context and aware of the children’s needs, with an impact on their emotional well-being to ensure sustainability. An analysis of their socio–emotional profile and training is increasingly needed to face their professional development effectively; (2) Methods: In this study, 209 early intervention professionals participated (n = 209), with an average age of 37.62 (±9.02). The following instruments were used: Satisfaction with Life Scale (SWLS), Wong Law Emotional Intelligence Scale (WLEIS-S) and the Utrecht Work Engagement Scale (UWES-9). The purpose of the study was to examine the relationship between early intervention (EI) and engagement as predictors of greater life satisfaction using Structural Equation Modelling (SEM). (3) Results: There exists a relationship between some dimensions of the instruments used (p < 0.01). The model obtained good structural validity (χ² = 3.264; Root Mean Square Error of Approximation (RMSEA) =.021; Goodness-of-Fit Index (GFI) = 0.991; Comparative Goodness of Fit Index (CFI) = 0.999; Incremental Fit Index (IFI) = 0.999). Subsequently, the results described above were verified through Bayesian statistics, thereby reinforcing the evidence provided; (4) Conclusions: Findings highlight the importance of providing professionals with emotional tools and strategies, from the educational context, in order to carry out their activity effectively and ensure the sustainability within the current situation, while remaining fully engaged.

scholarly journals Sensory feedback-dependent coding of arm position in local field potentials of the posterior parietal cortex

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paul VanGilder ◽  
Ying Shi ◽  
Gregory Apker ◽  
Christopher A. Buneo
Keyword(s):  
Local Field ◽  
Sensory Feedback ◽  
Posterior Parietal Cortex ◽  
Spectral Power ◽  
Field Potential ◽  
Beta Activity ◽  
Beta Band ◽  
Arm Position ◽  
Multisensory Interactions ◽  
Spiking Activity

AbstractAlthough multisensory integration is crucial for sensorimotor function, it is unclear how visual and proprioceptive sensory cues are combined in the brain during motor behaviors. Here we characterized the effects of multisensory interactions on local field potential (LFP) activity obtained from the superior parietal lobule (SPL) as non-human primates performed a reaching task with either unimodal (proprioceptive) or bimodal (visual-proprioceptive) sensory feedback. Based on previous analyses of spiking activity, we hypothesized that evoked LFP responses would be tuned to arm location but would be suppressed on bimodal trials, relative to unimodal trials. We also expected to see a substantial number of recording sites with enhanced beta band spectral power for only one set of feedback conditions (e.g. unimodal or bimodal), as was previously observed for spiking activity. We found that evoked activity and beta band power were tuned to arm location at many individual sites, though this tuning often differed between unimodal and bimodal trials. Across the population, both evoked and beta activity were consistent with feedback-dependent tuning to arm location, while beta band activity also showed evidence of response suppression on bimodal trials. The results suggest that multisensory interactions can alter the tuning and gain of arm position-related LFP activity in the SPL.

scholarly journals Characterizing Bilingual Effects on Cognition: The Search for Meaningful Individual Differences

2021 ◽  
Vol 11 (1) ◽  
pp. 81
Author(s):  
Kristina C. Backer ◽  
Heather Bortfeld
Keyword(s):  
Individual Differences ◽  
Cognitive Processes ◽  
Language Background ◽  
Bilingual Advantage ◽  
The Past ◽  
General Movement ◽  
And Performance ◽  
Task Irrelevant ◽  
Analytical Approaches ◽  
The Relationship

A debate over the past decade has focused on the so-called bilingual advantage—the idea that bilingual and multilingual individuals have enhanced domain-general executive functions, relative to monolinguals, due to competition-induced monitoring of both processing and representation from the task-irrelevant language(s). In this commentary, we consider a recent study by Pot, Keijzer, and de Bot (2018), which focused on the relationship between individual differences in language usage and performance on an executive function task among multilingual older adults. We discuss their approach and findings in light of a more general movement towards embracing complexity in this domain of research, including individuals’ sociocultural context and position in the lifespan. The field increasingly considers interactions between bilingualism/multilingualism and cognition, employing measures of language use well beyond the early dichotomous perspectives on language background. Moreover, new measures of bilingualism and analytical approaches are helping researchers interrogate the complexities of specific processing issues. Indeed, our review of the bilingualism/multilingualism literature confirms the increased appreciation researchers have for the range of factors—beyond whether someone speaks one, two, or more languages—that impact specific cognitive processes. Here, we highlight some of the most salient of these, and incorporate suggestions for a way forward that likewise encompasses neural perspectives on the topic.

scholarly journals High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in long-term care facilities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pablo M. De Salazar ◽  
Nicholas B. Link ◽  
Karuna Lamarca ◽  
Mauricio Santillana
Keyword(s):  
Long Term Care ◽  
Mitigation Strategies ◽  
Key Factors ◽  
Modeling Framework ◽  
High Coverage ◽  
Term Care ◽  
Early Effect ◽  
Care Facilities ◽  
The Relationship

Abstract Background Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Measuring the vaccine effectiveness among the most vulnerable in these settings is essential to monitor and improve mitigation strategies. Methods We evaluate the early effect of the administration of BNT162b2-mRNA vaccine to individuals older than 64 years residing in LTCFs in Catalonia, Spain. We monitor all the SARS-CoV-2 documented infections and deaths among LTCFs residents once more than 70% of them were fully vaccinated (February–March 2021). We develop a modeling framework based on the relationship between community and LTCFs transmission during the pre-vaccination period (July–December 2020). We compute the total reduction in SARS-CoV-2 documented infections and deaths among residents of LTCFs over time, as well as the reduction in the detected transmission for all the LTCFs. We compare the true observations with the counterfactual predictions. Results We estimate that once more than 70% of the LTCFs population are fully vaccinated, 74% (58–81%, 90% CI) of COVID-19 deaths and 75% (36–86%, 90% CI) of all expected documented infections among LTCFs residents are prevented. Further, detectable transmission among LTCFs residents is reduced up to 90% (76–93%, 90% CI) relative to that expected given transmission in the community. Conclusions Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death among LTCFs residents. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic conditional on key factors such as vaccine escape, roll out and coverage.

scholarly journals Quanto Pricing beyond Black–Scholes

2021 ◽  
Vol 14 (3) ◽  
pp. 136
Author(s):  
Holger Fink ◽  
Stefan Mittnik
Keyword(s):  
Option Pricing ◽  
Goodness Of Fit ◽  
Calibration Procedure ◽  
Barrier Options ◽  
Pricing Models ◽  
Modeling Framework ◽  
Double Barrier ◽  
Parameter Stability ◽  
Comprehensive Data ◽  
Black Scholes

Since their introduction, quanto options have steadily gained popularity. Matching Black–Scholes-type pricing models and, more recently, a fat-tailed, normal tempered stable variant have been established. The objective here is to empirically assess the adequacy of quanto-option pricing models. The validation of quanto-pricing models has been a challenge so far, due to the lack of comprehensive data records of exchange-traded quanto transactions. To overcome this, we make use of exchange-traded structured products. After deriving prices for composite options in the existing modeling framework, we propose a new calibration procedure, carry out extensive analyses of parameter stability and assess the goodness of fit for plain vanilla and exotic double-barrier options.

Relation between understanding and agreeing in response to one-sentence assertion*

2014 ◽  
Vol 7 (1) ◽  
pp. 36-67 ◽  
Author(s):  
YASUHIRO OZURU ◽  
DAVID BOWIE ◽  
GIULIA KAUFMAN
Keyword(s):  
Cognitive Processes ◽  
Experimental Studies ◽  
Cognitive Response ◽  
Response Options ◽  
Evaluative Response ◽  
Quasi Experimental ◽  
The Relationship

abstractThree quasi-experimental studies were conducted to investigate the relationship between the evaluative (i.e., agree/true) and the meta-cognitive (i.e., understand) response, and to determine which type of response people are more likely to provide when responding to one-sentence assertive statements. In Studies 1 and 2, participants performed two separate tasks in which they were asked to indicate the levels of: (i) understanding and (ii) agreement / perceived truthfulness of 126 one-sentence statements. The results indicated that participants were likely to provide a negative evaluative response (i.e., disagree/false) to a statement that they did not understand. In Study 3, participants were asked to evaluate the same 126 statements and choose between four response options: agree, disagree, understand, do not understand. The results indicated that people are more likely provide an evaluative response regardless of the understandability of a statement. The results of these studies are discussed in relation to (i) pragmatic perspective of how people infer speakers’ meaning, and (ii) cognitive processes underlying evaluative and meta-cognitive response.

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