Humans make choices every day, which are often intended to lead to desirable outcomes. While we often have some degree of control over the outcomes of our actions, in many cases this control remains limited. Here, we investigate the effect of control over outcomes on the neural correlates of outcome valuation and implementation of behavior, as desired outcomes can only be reached if choices are implemented as intended. In a value-based decision-making task, reward outcomes were either contingent on trial-by-trial choices between two different tasks, or were unrelated to these choices. Using fMRI, multivariate pattern analysis, and model-based neuroscience methods, we identified reward representations in a large network including the striatum, dorso-medial prefrontal cortex (dmPFC) and parietal cortex. These representations were amplified when rewards were contingent on subjects9 choices. We further assessed the implementation of chosen tasks by identifying brain regions encoding tasks during a preparation or maintenance phase, and found them to be encoded in the dmPFC and parietal cortex. Importantly, outcome contingency did not affect neural coding of chosen tasks. This suggests that controlling choice outcomes selectively affects the neural coding of these outcomes, but has no effect on the means to reach them. Overall, our findings highlight the role of the dmPFC and parietal cortex in processing of value-related and task-related information, linking motivational and control-related processes in the brain. These findings inform current debates on the neural basis of motivational and cognitive control, as well as their interaction.
When the human mind wanders, it engages in episodes during which attention is focused on self-generated thoughts rather than on external task demands. Although the sustained attention to response task is commonly used to examine relationships between mind wandering and executive functions, limited executive resources are required for optimal task performance. In the current study, we aimed to investigate the relationship between mind wandering and executive functions more closely by employing a recently developed finger-tapping task to monitor fluctuations in attention and executive control through task performance and periodical experience sampling during concurrent functional magnetic resonance imaging (fMRI) and pupillometry. Our results show that mind wandering was preceded by increases in finger-tapping variability, which was correlated with activity in dorsal and ventral attention networks. The entropy of random finger-tapping sequences was related to activity in frontoparietal regions associated with executive control, demonstrating the suitability of this paradigm for studying executive functioning. The neural correlates of behavioral performance, pupillary dynamics, and self-reported attentional state diverged, thus indicating a dissociation between direct and indirect markers of mind wandering. Together, the investigation of these relationships at both the behavioral and neural level provided novel insights into the identification of underlying mechanisms of mind wandering.
Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.
To describe loss of cortical thickness and ventricular enlargement during normal aging in a sample of 525 neurologically and psychiatrically inconspicuous subjects (17-68 years old).An automated segmentation algorithm was applied to assess cortical thickness and compared with conventional measurements of ventricular indices (ventricular body index (VBI), anterior horn index (AHI), and third ventricular width) as performed in clinical practice. Regression analysis was performed to elucidate the relationship between a decrease of the cortical mantle and increase in ventricular width with aging.Cortical thickness decreases with age (r = -0.49, P < 0.01; r = -0.502 in male and r = -0.461 in female subjects). Regarding the ventricular indices, we found a significant correlation with age for both the whole sample and the subdivision by gender. Cortical thickness and ventricular width are closely correlated (r = -0.43 in women, r = -0.468 in men, P < 0.001 each). The bandwidth of variance scales up with aging in all parameters. The results are discussed in terms of the underlying mechanisms of normal aging.Our findings suggest that a decrease in cortical thickness and increase in ventricular width occur with normal aging. The enlargement of the third ventricle correlates the most strongly with age.
Studying individual differences in psychology often involves examiningcorrelations across various measures. However, research involving high-dimensional data—such as in task batteries or neuroscience—often targetslatent constructs rather than individual correlations. Furthermore, the num-ber of correlations grows quadratically with increasing dimensionality, po-tentially leading to overfitting and spurious inference. Therefore, researcherscommonly use factor analysis to study individual differences. However, con-ventional approaches ignore the hierarchical structure of the data and over-look measurement error, leading to attenuated factor loadings. In this pa-per, we introduce a Bayesian framework that integrates hierarchical mod-eling to account for measurement error with factor analysis to infer latentstructures. We employ a post-hoc processing algorithm that removes theneed for conventional constraints on factor loadings, thereby avoiding po-tential bias in their estimation. Additionally, we utilize a shrinkage priorto automatically identify and exclude unsupported factors. The accompa-nying software enables the creation of generative models at the individuallevel, supporting a wide range of hypotheses—from descriptive to theory-driven models—and facilitating robust group-level inferences grounded inpsychological theory. Through simulations and empirical applications, wedemonstrate that our hierarchical factor analysis method flexibly and reli-ably estimates latent structures in high-dimensional data, offering a valuabletool for individual-differences research in psychology and neuroscience.
Reinforcement learning models of error-driven learning and sequential-sampling models of decision making have provided significant insight into the neural basis of a variety of cognitive processes. Until recently, model-based cognitive neuroscience research using both frameworks has evolved separately and independently. Recent efforts have illustrated the complementary nature of both modelling traditions and showed how they can be integrated into a unified theoretical framework, explaining trial-by-trial dependencies in choice behavior as well as response time distributions. Here, we review a theoretical background of integrating the two classes of models, and review recent empirical efforts towards this goal. We furthermore argue that the integration of both modelling traditions provides mutual benefits for both fields, and highlight promises of this approach for cognitive modelling and model-based cognitive neuroscience.
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