Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains.

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

Modelling decision-making biases.

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate.

Echoes from Intrinsic Connectivity Networks in the Subcortex.

Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.

7T functional MRI finds no evidence for distinct functional subregions in the subthalamic nucleus during a speeded decision-making task.

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). Thirty-four participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of the STN along the dorsolateral-ventromedial axis, identified using manually delineated high resolution anatomical images and based on a previously published atlas. Using these paradigms, all segments responded equally to the experimental manipulations, and the tasks did not provide evidence for the tripartite model.

Charting human subcortical maturation across the adult lifespan with in vivo 7 T MRI.

The human subcortex comprises hundreds of unique structures. Subcortical functioning is crucial for behavior, and disrupted function is observed in common neurodegenerative diseases. Despite their importance, human subcortical structures continue to be difficult to study in vivo. Here we provide a detailed account of 17 prominent subcortical structures and ventricles, describing their approximate iron and myelin contents, morphometry, and their age-related changes across the normal adult lifespan. The results provide compelling insights into the heterogeneity and intricate age-related alterations of these structures. They also show that the locations of many structures shift across the lifespan, which is of direct relevance for the use of standard magnetic resonance imaging atlases. The results further our understanding of subcortical morphometry and neuroimaging properties, and of normal aging processes which ultimately can improve our understanding of neurodegeneration.

A new model of decision processing in instrumental learning tasks.

Learning and decision-making are interactive processes, yet cognitive modeling of error-driven learning and decision-making have largely evolved separately. Recently, evidence accumulation models (EAMs) of decision-making and reinforcement learning (RL) models of error-driven learning have been combined into joint RL-EAMs that can in principle address these interactions. However, we show that the most commonly used combination, based on the diffusion decision model (DDM) for binary choice, consistently fails to capture crucial aspects of response times observed during reinforcement learning. We propose a new RL-EAM based on an advantage racing diffusion (ARD) framework for choices among two or more options that not only addresses this problem but captures stimulus difficulty, speed-accuracy trade-off, and stimulus-response-mapping reversal effects. The RL-ARD avoids fundamental limitations imposed by the DDM on addressing effects of absolute values of choices, as well as extensions beyond binary choice, and provides a computationally tractable basis for wider applications.

The interpretation of behavior-model correlations in unidentified cognitive models.

The rise of computational modeling in the past decade has led to a substantial increase in the number of papers that report parameter estimates of computational cognitive models. A common application of computational cognitive models is to quantify individual differences in behavior by estimating how these are expressed in differences in parameters. For these inferences to hold, models need to be identified, meaning that one set of parameters is most likely, given the behavior under consideration. For many models, model identification can be achieved up to a scaling constraint, which means that under the assumption that one parameter has a specific value, all remaining parameters are identified. In the current note, we argue that this scaling constraint implies a strong assumption about the cognitive process that the model is intended to explain, and warn against an overinterpretation of the associative relations found in this way. We will illustrate these points using signal detection theory, reinforcement learning models, and the linear ballistic accumulator model, and provide suggestions for a clearer interpretation of modeling results.

fMRI protocol optimization for simultaneously studying small subcortical and cortical areas at 7 ​T.

Most fundamental cognitive processes rely on brain networks that include both cortical and subcortical structures. Studying such networks using functional magnetic resonance imaging (fMRI) requires a data acquisition protocol that provides blood-oxygenation-level dependent (BOLD) sensitivity across the entire brain. However, when using standard single echo, echo planar imaging protocols, researchers face a tradeoff between BOLD-sensitivity in cortex and in subcortical areas. Multi echo protocols avoid this tradeoff and can be used to optimize BOLD-sensitivity across the entire brain, at the cost of an increased repetition time. Here, we empirically compare the BOLD-sensitivity of a single echo protocol to a multi echo protocol. Both protocols were designed to meet the specific requirements for studying small, iron rich subcortical structures (including a relatively high spatial resolution and short echo times), while retaining coverage and BOLD-sensitivity in cortical areas. The results indicate that both sequences lead to similar BOLD-sensitivity across the brain at 7 â€‹T.

Mutual benefits: Combining reinforcement learning with sequential sampling models.

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.

The functional microscopic neuroanatomy of the human subthalamic nucleus.

The subthalamic nucleus (STN) is successfully used as a surgical target for deep brain stimulation in the treatment of movement disorders. Interestingly, the internal structure of the STN is still incompletely understood. The objective of the present study was to investigate three-dimensional (3D) immunoreactivity patterns for 12 individual protein markers for GABA-ergic, serotonergic, dopaminergic as well as glutamatergic signaling. We analyzed the immunoreactivity using optical densities and created a 3D reconstruction of seven postmortem human STNs. Quantitative modeling of the reconstructed 3D immunoreactivity patterns revealed that the applied protein markers show a gradient distribution in the STN. These gradients were predominantly organized along the ventromedial to dorsolateral axis of the STN. The results are of particular interest in view of the theoretical underpinning for surgical targeting, which is based on a tripartite distribution of cognitive, limbic and motor function in the STN.

Core body temperature speeds up temporal processing and choice behavior under deadlines.

Evidence suggests that human timing ability is compromised by heat. In particular, some studies suggest that increasing body temperature speeds up an internal clock, resulting in faster time perception. However, the consequences of this speed-up for other cognitive processes remain unknown. In the current study, we rigorously tested the speed-up hypothesis by inducing passive hyperthermia through immersion of participants in warm water. In addition, we tested how a change in time perception affects performance in decision making under deadline stress. We found that participants underestimate a prelearned temporal interval when body temperature increases, and that their performance in a two-alternative forced-choice task displays signatures of increased time pressure. These results show not only that timing plays an important role in decision-making, but also that this relationship is mediated by temperature. The consequences for decision-making in job environments that are demanding due to changes in body temperature may be considerable.

Advances in techniques for imposing reciprocity in brain-behavior relations.

To better understand human behavior, the emerging field of model-based cognitive neuroscience seeks to anchor psychological theory to the biological substrate from which behavior originates: the brain. Despite complex dynamics, many researchers in this field have demonstrated that fluctuations in brain activity can be related to fluctuations in components of cognitive models, which instantiate psychological theories. In this review, we discuss a number of approaches for relating brain activity to cognitive models, and expand on a framework for imposing reciprocity in the inference of mental operations from the combination of brain and behavioral data.

Caution in decision-making under time pressure is mediated by timing ability.

The time available to inform decisions is often limited, for example because of a response deadline. In such circumstances, accurate knowledge of the amount of time available for a decision is crucial for optimal choice behavior. However, the relation between temporal cognition and decision-making under time pressure is poorly understood. Here, we test how the precision of the internal representation of time affects choice behavior when decision time is limited by a deadline. We show that participants with a precise internal representation of time respond more cautiously in decision-making. Furthermore, we provide an empirical test of theoretical accounts of decision-making that argue that it is optimal to commit to a decision based on increasingly less evidence as the deadline approaches (so-called 'collapsing decision bounds'). These theories entail that the speed of collapse of the decision bound should depend on the precision of the internal representation of the deadline. However, although we find evidence that participants collapse decision bounds, we found no relation between the amount of collapse and the internal representation of time.

How to control for confounds in decoding analyses of neuroimaging data.

Over the past decade, multivariate "decoding analyses" have become a popular alternative to traditional mass-univariate analyses in neuroimaging research. However, a fundamental limitation of using decoding analyses is that it remains ambiguous which source of information drives decoding performance, which becomes problematic when the to-be-decoded variable is confounded by variables that are not of primary interest. In this study, we use a comprehensive set of simulations as well as analyses of empirical data to evaluate two methods that were previously proposed and used to control for confounding variables in decoding analyses: post hoc counterbalancing and confound regression. In our empirical analyses, we attempt to decode gender from structural MRI data while controlling for the confound "brain size". We show that both methods introduce strong biases in decoding performance: post hoc counterbalancing leads to better performance than expected (i.e., positive bias), which we show in our simulations is due to the subsampling process that tends to remove samples that are hard to classify or would be wrongly classified; confound regression, on the other hand, leads to worse performance than expected (i.e., negative bias), even resulting in significant below chance performance in some realistic scenarios. In our simulations, we show that below chance accuracy can be predicted by the variance of the distribution of correlations between the features and the target. Importantly, we show that this negative bias disappears in both the empirical analyses and simulations when the confound regression procedure is performed in every fold of the cross-validation routine, yielding plausible (above chance) model performance. We conclude that, from the various methods tested, cross-validated confound regression is the only method that appears to appropriately control for confounds which thus can be used to gain more insight into the exact source(s) of information driving one's decoding analysis.