A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multi-dimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction especially in clinical populations. Computational modeling may address the challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent works suggest that Bayesian adaptive design optimization (ADO) is a promising way to address the challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual’s characteristics. In this review, we first describe the ADO methodology and then summarize recent works demonstrating that ADO increases reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and propose we develop ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.
Previous literature suggests that a balance between Pavlovian and instrumental decision-making systems is critical for optimal decision-making. Pavlovian bias (i.e., approach toward reward-predictive stimuli and avoid punishment-predictive stimuli) often contrasts with the instrumental response. Although recent neuroimaging studies have identified brain regions that may be related to Pavlovian bias, including the dorsolateral prefrontal cortex (dlPFC), it is unclear whether a causal relationship exists. Therefore, we investigated whether upregulation of the dlPFC using transcranial current direct stimulation (tDCS) would reduce Pavlovian bias. In this double-blind study, participants were assigned to the anodal or the sham group; they received stimulation over the right dlPFC for 3 successive days. On the last day, participants performed a reinforcement learning task known as the orthogonalized go/no-go task; this was used to assess each participant’s degree of Pavlovian bias in reward and punishment domains. We used computational modeling and hierarchical Bayesian analysis to estimate model parameters reflecting latent cognitive processes, including Pavlovian bias, go bias, and choice randomness. Several computational models were compared; the model with separate Pavlovian bias parameters for reward and punishment domains demonstrated the best model fit. When using a behavioral index of Pavlovian bias, the anodal group showed significantly lower Pavlovian bias in the punishment domain, but not in the reward domain, compared with the sham group. In addition, computational modeling showed that Pavlovian bias parameter in the punishment domain was lower in the anodal group than in the sham group, which is consistent with the behavioral findings. The anodal group also showed a lower go bias and choice randomness, compared with the sham group. These findings suggest that anodal tDCS may lead to behavioral suppression or change in Pavlovian bias in the punishment domain, which will help to improve comprehension of the causal neural mechanism.Author summaryA decision-making bias guided by the Pavlovian system (i.e., approach reward and avoid punishment) is often useful and predominant across species but it is also related to several psychiatric conditions. The dorsolateral prefrontal cortex (dlPFC) is known to be related to such “Pavlovian bias” but it is unclear whether a causal relationship exists between them. Here, we evaluated whether decision-making biases including Pavlovian bias could be modulated by exogenous brain stimulation, transcranial current direct stimulation, over the right dlPFC for 3 successive days. A combination of behavioral analysis and computational modeling revealed that the anodal group had lower Pavlovian bias in the punishment domain compared with the sham group. In addition, the anodal group showed lower go bias and choice randomness than the sham group, which can also hamper instrumental learning. These findings suggest a causal role for the dlPFC in modulating the balance between the Pavlovian and instrumental decision-making systems.
Previous literature suggests that a balance between Pavlovian and instrumental decision-making systems is critical for optimal decision-making. Pavlovian bias (i.e., approach toward reward-predictive stimuli and avoid punishment-predictive stimuli) often contrasts with the instrumental response. Although recent neuroimaging studies have identified brain regions that may be related to Pavlovian bias, including the dorsolateral prefrontal cortex (dlPFC), it is unclear whether a causal relationship exists. Therefore, we investigated whether upregulation of the dlPFC using transcranial current direct stimulation (tDCS) would reduce Pavlovian bias. In this double-blind study, participants were assigned to the anodal or the sham group; they received stimulation over the right dlPFC for 3 successive days. On the last day, participants performed a reinforcement learning task known as the orthogonalized go/no-go task; this was used to assess each participant’s degree of Pavlovian bias in reward and punishment domains. We used computational modeling and hierarchical Bayesian analysis to estimate model parameters reflecting latent cognitive processes, including Pavlovian bias, go bias, and choice randomness. Several computational models were compared; the model with separate Pavlovian bias parameters for reward and punishment domains demonstrated the best model fit. When using a behavioral index of Pavlovian bias, the anodal group showed significantly lower Pavlovian bias in the punishment domain, but not in the reward domain, compared with the sham group. In addition, computational modeling showed that Pavlovian bias parameter in the punishment domain was lower in the anodal group than in the sham group, which is consistent with the behavioral findings. The anodal group also showed a lower go bias and choice randomness, compared with the sham group. These findings suggest that anodal tDCS may lead to behavioral suppression or change in Pavlovian bias in the punishment domain, which will help to improve comprehension of the causal neural mechanism.
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