Real-time Adaptive Design Optimization Within Functional MRI Experiments

Bahg, Giwon; Sederberg, Per B.; Myung, Jay I.; Li, Xiangrui; Pitt, Mark A.; Lü, Zhong-Lin; Turner, B. E.

doi:10.1007/s42113-020-00079-7

Cited by 7 publications

(5 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, many of its applications add computational complexity above and beyond that of calculating the expensive DDM likelihood. Such costly applications have included Bayesian hierarchical extensions (Vandekerckhove et al, 2011), latent variable structures (Vandekerckhove, 2014), applications to large population samples (Lucio et al, 2017;Salum et al, 2014aSalum et al, , 2014b, use of DDMs as a component in numerical experiments (Stafford, Pirrone, Croucher, & Krystalli, 2020), real-time adaptive design optimization (Bahg et al, 2020), and model comparisons that involve high-dimensional numerical integration (Boehm et al, 2023;Gronau, Heathcote, & Matzke, 2019).…”

Section: Discussionmentioning

confidence: 99%

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

Chávez De la Peña,

Vandekerckhove

2023

Preprint

View full text Add to dashboard Cite

The EZ-diffusion model is a simplification of the popular drift diffusion model of choice response times that allows researchers to calculate diffusion model parameters directly from data with no need for expensive computations. The EZ-diffusion model is based on a system of equations in which the diffusion model's drift rate, boundary separation, and nondecision time parameters are jointly used to predict three summary statistics (the accuracy rate and the mean and variance of the correct response times). These equations can then be inverted to obtain estimators for the three parameters from these summary statistics. Here, we describe a probabilistic formulation of the EZ-diffusion model that can serve as a hyper-efficient proxy model to the drift diffusion model. The new formulation is based on sampling distributions of summary statistics and consists only of normal and binomial distributions. It can easily be implemented in any probabilistic programming language. We demonstrate the validity of the proxy model through extensive simulation studies and provide multiple examples (via osf.io/bzkpn), including an implementation in JASP. We conclude that, although the recovery of some parameters with the proxy model is biased, the recovery of regression parameters is good, making the method useful for explanatory cognitive modeling. Casting the EZ-diffusion model in the broad family of Bayesian generative models allows us to benefit from mature implementations, practical workflows, and powerful extensions that are not possible without a probabilistic implementation and not feasible with the regular drift diffusion model.

show abstract

Section: Discussionmentioning

confidence: 99%

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

Chávez De la Peña,

Vandekerckhove

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Preliminary results suggest that the superior performance of ADO observed here generalizes to other tasks. In addition, our recent work also demonstrates that ADO can be used to optimize the sequencing of stimuli and improve functional magnetic resonance imaging (fMRI) measurements 51 , which can reduce the cost of data acquisition and improve the quality of neuroimaging data.…”

Section: Discussionmentioning

confidence: 99%

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Ahn

Shen

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. in three populations (college students, patients with a substance use disorder, and Amazon Mechanical turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADo), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 min of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting. Delay discounting, one dimension of impulsivity 1 , assesses how individuals make trade-offs between small but immediately available rewards versus large but delayed rewards. Delay discounting is broadly linked to normative cognitive and behavioral processes, such as financial decision making 2 , social decision making 3 , and personality 4 , among others. Also, individual differences in delay discounting are associated with several cognitive capacities, including working memory 5 , intelligence 6 , and top-down regulation of impulse control mediated by the prefrontal cortex 7,8. Delay discounting is a strong candidate endophenotype for a wide range of maladaptive behaviors, including addictive disorders 9,10 and health risk behaviors for a review, see 11. Studies of test-retest reliability of delay discounting demonstrate reliability both for adolescents 12 and adults 13 , and genetics studies indicate that delay discounting may be a heritable trait 9. As such, delay discounting has received attention in the developing field of precision medicine in mental health as a potentially rapid and reliable (bio)marker of individual differences relevant for treatment outcomes 14-16. The construct validity of delay discounting has been demonstrated in numerous studies. For example, the delay discounting task is widely used to assess (altered) temporal impulsivity of various psychiatric disorders, including patients with substance use disorders e.g., 11 , schizophrenia 13,14 , and bipolar disorder 14. Therefore, improved assessment of delay discounting may be beneficial to many fields, including psychology, neuroscience, medicine 15 , and economics. To link decision making tasks to mental functioning is a formidable challenge that requires simultaneously achieving multiple measurement goals. We focus on three aspects of measurement: reliability, precision, and efficiency. Reliable measurement of latent neurocognitive constructs or biological processes, such as impulsivity, reward sensitivity, or learning rate, is difficult. Recent advancements in neuroscience and computational psychiatry 16...

show abstract

“…Preliminary results suggest that the superior reliability, precision, and efficiency of ADO compared to conventional methods generalize to other tasks. In addition, our recent work also demonstrates that ADO can be used to optimize the sequencing of stimuli and improve functional magnetic resonance imaging (fMRI) measurement 51 , which can reduce the cost of data acquisition and improve the quality of neuroimaging data.…”

Section: Discussionmentioning

confidence: 99%

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Ahn

Shen

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Number of words in the abstract: 135Number of words in the main text (including references): 7,600 AbstractMachine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 minutes of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.

show abstract

Real-time Adaptive Design Optimization Within Functional MRI Experiments

Cited by 7 publications

References 59 publications

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

An EZ Bayesian hierarchical drift diffusion model for response time and accuracy

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm

Contact Info

Product

Resources

About