A tutorial on joint models of neural and behavioral measures of cognition

Palestro, James J.; Bahg, Giwon; Sederberg, Per B.; Lü, Zhong-Lin; Steyvers, Mark; Turner, B. E.

doi:10.1016/j.jmp.2018.03.003

Cited by 71 publications

(70 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an effort to make this type of method more approachable, we have provided the JAGS code for the hyperbolic, direct difference, Luce choice / bet utility, and joint models at osf.io/e46zj. This approach is closely related to the joint modeling of neural and behavioral data as well as cognitive latent variable models of personality and behavioral data; tutorials on these procedures can be found in Palestro et al (2018) and Vandekerckhove (2014), respectively. Our hope is that this provides an effective method for factor comparisons using cognitive models as the predictors of behavioral data, allowing for new and interesting inferences about common latent processes that generate decision behavior.…”

Section: Discussionmentioning

confidence: 99%

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Kvam¹,

Romeu²,

Turner³

et al. 2021

Psychological Methods

Self Cite

View full text Add to dashboard Cite

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use.

show abstract

Section: Discussionmentioning

confidence: 99%

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Kvam¹,

Romeu²,

Turner³

et al. 2021

Psychological Methods

Self Cite

View full text Add to dashboard Cite

show abstract

“…In neurocognitive modelling are proposed two relevant architectures to account to the modelling of relationships between different sources of data: the Directed Approach and the Covariance Approach [11,45].…”

Section: Joint Modellingmentioning

confidence: 99%

“…Advances in the understanding of this relation are due to the development of different computational tools, allowing for a finer analysis of several sources of information. Some examples are: (1) cognitive modelling [4,5] which formally accounts for the generative cognitive processes which are assumed to produce the observed data; (2) Bayesian graphical models [6,7] which provide a powerful and flexible way to perform hierarchical Bayesian analysis, allowing to account for group and individual differences; (3) joint neurocognitive modelling [1,[8][9][10][11] which provides a framework to simultaneously model and analyze neural and behavioural data by allowing the latter to be informative for the former, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data

et al. 2020

View full text Add to dashboard Cite

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information.In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

show abstract

“…In the future, a powerful methodology would involve using both psychophysics and neuroimaging data together to infer changes in encoding, perhaps using hierarchical Bayesian modeling, in which multiple types/sources of data for each participant can be used simultaneously to make inferences about a single set of model parameters [73].…”

Section: Recommendations For Researchersmentioning

confidence: 99%

Changes within neural population codes can be inferred from psychophysical threshold studies

Hays

Soto

2020

Preprint

View full text Add to dashboard Cite

The use of population encoding models has come to dominate the study of human visual neuroscience, serving as a primary tool for making inferences about neural code changes based on indirect measurements. A popular approach in computational neuroimaging is to use such models to obtain estimates of neural population responses via inverted encoding modeling. Recent research suggests that this approach may be prone to identifiability problems, with multiple mechanisms of encoding change producing similar changes in the estimated population responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. However, computational work aimed at determining to what extent different mechanisms can be differentiated using psychophysics is lacking. Here, we used simulation to explore exactly which of a number of changes in neural population codes could be differentiated from observed changes in psychophysical thresholds. Eight mechanisms of encoding change were under study, chosen because they have been proposed in the previous literature as mechanisms for improved task performance (e.g., due to attention or learning): specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. We simulated psychophysical thresholds as a function of both external noise (TvN curves) or stimulus value (TvS curves) for a number of variations of each one of the models. With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling, and provide strong constraints on inferences based on the latter. We use our results to provide recommendations for interested researchers and to re-interpret previous psychophysical data in terms of mechanisms of encoding change.

show abstract

A tutorial on joint models of neural and behavioral measures of cognition

Cited by 71 publications

References 66 publications

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data

Changes within neural population codes can be inferred from psychophysical threshold studies

Contact Info

Product

Resources

About