Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Ghaderi-Kangavari, Amin; Rad, Jamal Amani; Parand, Kourosh; Nunez, Michael D.

doi:10.1016/j.jmp.2022.102725

Cited by 6 publications

(21 citation statements)

References 87 publications

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“…One example is to find single-trial ERPs using a weighted map resulting from singular value decomposition (SVD) of the traditional trial-averaged ERPs at every electrode. This procedure has been successful in our previous work (Nunez et al, 2019;Ghaderi-Kangavari et al, 2022). In other work, a single-trial multivariate linear discriminant analysis (LDA) has been used to determine linear spatial weightings of the EEG electrodes for maximal discrimination of target (faces) and non-target (cars) trials during a simple categorization task (Philiastides et al, 2006;Diaz et al, 2017).…”

Section: Single-trial Erp Analysismentioning

confidence: 99%

“…Even though some joint models incorporate neural dynamics in classical DDMs, only some models have been developed that consider EEG signatures as a participant response similar to reaction time (RT) and accuracy (van Ravenzwaaij et al, 2017). There are however many studies linking event-related potential (ERP) components and behavioral performance (O'Connell et al, 2012a;Gluth et al, 2013;Philiastides et al, 2014;Loughnane et al, 2016), as well as using EEG to constrain models' parameters at the level of the experimental trial (Frank, 2015;Nunez et al, 2017;Ghaderi-Kangavari et al, 2022). Models should be further developed that explain ERP data at the trial level with generative models similar to response times (RTs) and accuracy.…”

Section: Motivationmentioning

confidence: 99%

“…Other models constrain evidence accumulation rate (i.e., drift-rate) parameters to make better parameter estimates (see Fig, 1). With these models, we can test research questions about correlates of behavioral performance and neural data (e.g., O'Connell et al, 2012a, new research questions (such as about spatial attention, see Ghaderi-Kangavari et al, 2022), and explain and predict ERP measures jointly with behavioral measures. Another benefit is that these models are mea-surement models in that the estimated parameters reflect interpretations of cognitive processes.…”

Section: Motivationmentioning

confidence: 99%

“…Furthermore, Nunez et al found evidence that the latency of the negative N200 between 150 and 275 after stimulus onset in the occipito-parietal electrodes reflects the VET needed for figure-ground segregation before the accumulation of evidence (Nunez et al, 2019). Finally, Ghaderi-Kangavari et al used hierarchical Bayesian models that included single-trial N200 latencies to identify the effects of spatial attention on perceptual decisionmaking and found evidence that perceptual decision-making manipulates both VET before evidence accumulation and other non-decision time process after or during evidence accumulation (Ghaderi-Kangavari et al, 2022).…”

Section: N200 Latencies Can Reflect Visual Encoding Timementioning

confidence: 99%

“…Note that a regression model the linking function allows EEG measures to be on any non-standardized scale for mapping from φ to θ (e.g., in the original scale of milliseconds for N200 latencies or micro-volts μV per second for CPP latencies). Directed neurocognitive models have been successfully used for studying the effect of visual attention, noise suppression, and spatial attention in perceptual decision-making tasks (Nunez et al, 2017(Nunez et al, , 2019Ghaderi-Kangavari et al, 2022) as well as deployed to answer any other questions in related to the cognitive control of single-trial EEG in decisionmaking (Wiecki et al, 2013;Frank, 2015;Yau et al, 2021). Note that directed joint models can often produce better estimate parameter relationships between EEG measures and cognitive parameters than other methods because these models fit in a single step, as opposed to methods that fit cognitive models and linear regression sequentially (Ghaderi-Kangavari et al, 2021).…”

Section: Types Of Joint Modelsmentioning

confidence: 99%

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A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials

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Despite advances in techniques for exploring reciprocity in brain-behavior relations, few studies focus on building neurocognitive models that describe both human EEG and behavioral modalities at the single-trial level. Here, we introduce a new integrative joint modeling framework for the simultaneous description of single-trial EEG measures and cognitive modeling parameters of decision-making. As specific examples, we formalized how single-trial N200 latencies and centro-parietal positivities (CPPs) are predicted by changing single-trial parameters of various drift-diffusion models (DDMs). We trained deep neural networks to learn Bayesian posterior distributions of unobserved neurocognitive parameters based on model simulations. These models do not have closed-form likelihoods and are not easy to fit using Markov chain Monte Carlo (MCMC) methods because nuisance parameters on single trials are shared in both behavior and neural activity. We then used parameter recovery assessment and model misspecification to ascertain how robustly the models’ parameters can be estimated. Moreover, we fit the models to three different real datasets to test their applicability. Finally, we provide some evidence that single-trial integrative joint models are superior to traditional integrative models. The current single-trial paradigm and the simulation-based (likelihood-free) approach for parameter recovery can inspire scientists and modelers to conveniently develop new neurocognitive models for other neural measures and to evaluate them appropriately.

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Section: Single-trial Erp Analysismentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: N200 Latencies Can Reflect Visual Encoding Timementioning

confidence: 99%

Section: Types Of Joint Modelsmentioning

confidence: 99%

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A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials

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A Human–Machine Joint Learning Framework to Boost Endogenous BCI Training

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Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks

Schreiber,

Hewitt,

Seymour

et al. 2024

Wellcome Open Res

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Bayesian statistics are popular in human cognitive neuroscience research because they can incorporate prior knowledge. Although well established for retrospective analysis, the application of Bayesian methods to prospective analysis is less well developed, especially when used in combination with computational model-based analysis of behavioural data. It is therefore important to establish effective methods for testing and optimising experimental designs for these purposes. One potential framework for a prospective approach is Bayes factor design analysis (BFDA), which can be used alongside latent variable modelling to evaluate and visualise the distribution of Bayes factors for a given experimental design. This paper provides a tutorial-style analysis combining BFDA with latent variable modelling to evaluate exploration-exploitation trade-offs in the binary multi-armed bandit task (MAB). This is a particularly tricky example of human decision-making with which to investigate the feasibility of differentiating latent variables between groups as a function of different design parameters. We examined how sample size, number of games per participant and effect size affect the strength of evidence supporting a difference in means between two groups. To further assess how these parameters affect experimental results, metrics of error were evaluated. Using simulations, we demonstrated how BFDA can be combined with latent variable modelling to evaluate and optimise parameter estimation of exploration in the MAB task, allowing effective inference of the mean degree of random exploration in a population, as well as between groups. However, BFDA indicated that, even with large samples and effect sizes, there may be some circumstances where there is a high likelihood of errors and a low probability of detecting evidence in favour of a difference when comparing random exploration between two groups performing the bandit task. In summary, we show how BFDA can prospectively inform design and power of human behavioural tasks.

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Neuro-cognitive models of single-trial EEG measures describe latent effects of spatial attention during perceptual decision making

Cited by 6 publications

References 87 publications

A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials

A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials

A Human–Machine Joint Learning Framework to Boost Endogenous BCI Training

Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks

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