Neural Representations of Post-Decision Accuracy and Reward Expectation in the Caudate Nucleus and Frontal Eye Field.

Performance monitoring that supports ongoing behavioral adjustments is often examined in the context of either choice confidence for perceptual decisions (i.e., "did I get it right?") or reward expectation for reward-based decisions (i.e., "what reward will I receive?"). However, our understanding of how the brain encodes these distinct evaluative signals remains limited because they are easily conflated, particularly in commonly used two-alternative tasks with symmetric rewards for correct choices. Previously we used a motion-discrimination task with asymmetric rewards to identify neural substrates of forming reward-biased perceptual decisions in the caudate nucleus (part of the striatum in the basal ganglia) and the frontal eye field (FEF, in prefrontal cortex). Here we leveraged this task design to partially decouple estimates of accuracy and reward expectation and examine their impacts on subsequent decisions and their representations in those two brain areas. We identified distinguishable representations of these two evaluative signals in individual caudate and FEF neurons, with regional differences in their distribution patterns and time courses. We observed that well-trained monkeys (both sexes) used both evaluative signals, infrequently but consistently, to adjust their subsequent decisions. We found further that these behavioral adjustments had reliable relationships with the neural representations of both evaluative signals in caudate, but not FEF. These results suggest that the cortico-striatal decision network may use diverse evaluative signals to monitor and adjust decision-making behaviors, adding to our understanding of the different roles that the FEF and caudate nucleus play in a diversity of decision-related computations.

Webcam-based eye tracking to detect mind wandering and comprehension errors.

Recent advances in computer vision have opened the door for scalable eye tracking using only a webcam. Such solutions are particularly useful for online educational technologies, in which a goal is to respond adaptively to students' ongoing experiences. We used WebGazer, a webcam-based eye-tracker, to automatically detect covert cognitive states during an online reading-comprehension task related to task-unrelated thought and comprehension. We present data from two studies using different populations: (1) a relatively homogenous sample of university students (N = 105), and (2) a more diverse sample from Prolific (N = 173, with < 20% White participants). Across both studies, the webcam-based eye-tracker provided sufficiently accurate and precise gaze measurements to predict both task-unrelated thought and reading comprehension from a single calibration. We also present initial evidence of predictive validity, including a positive correlation between predicted rates of task-unrelated thought and comprehension scores. Finally, we present slicing analyses to determine how performance changed under certain conditions (lighting, glasses, etc.) and generalizability of the results across the two datasets (e.g., training on the data Study 1 and testing on data from Study 2, and vice versa). We conclude by discussing results in the context of remote research and learning technologies.

On the nature and use of models in network neuroscience.

Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms.

Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions.

Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence.

Solutions to challenging inference problems are often subject to a fundamental trade-off between: 1) bias (being systematically wrong) that is minimized with complex inference strategies, and 2) variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to forms of inference based on suboptimal strategies. We examined inference problems applied to rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that varied in form and complexity. In general, subjects using more complex strategies tended to have lower bias and variance, but with a dependence on the form of strategy that reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but higher bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but lower, near-normative bias. Our results help define new principles that govern individual differences in behavior that depends on rare-event inference and, more generally, about the information-processing trade-offs that can be sensitive to not just the complexity, but also the optimality, of the inference process.

Factors impacting implementation of nutrition and physical activity policies in rural schools.

BACKGROUND: Rural Latino children have higher rates of obesity compared to non-Latino Whites. Schools are in a unique position to address rural childhood obesity through policies. While evidence exists on factors that promote or impede school-based physical activity (PA) and nutrition policies, only a fraction has been in rural communities. This study seeks to understand 1) the knowledge and perceptions of school nutrition and PA policies and 2) barriers and facilitators to their implementation among rural school stakeholders from Washington State. METHODS: We conducted 20 semi-structured, in-depth interviews with school stakeholders (e.g., principals and school nutrition directors) from four K-12 school districts in the Lower Yakima Valley of Eastern Washington State. Thematic analysis was conducted using inductive, constant comparison approach to identify themes around knowledge and perceptions of policies and barriers and facilitators of policy implementation. RESULTS: Three main themes were identified: perceptions and knowledge of school PA and nutrition policies, barriers to policy implementation, and facilitators of policy implementation. The majority of stakeholders were supportive of school-based policies promoting PA and a healthy diet, even when lacking a specific understanding of these policies. Four subthemes were identified as barriers to policy implementation: viewing PA as a low priority, misuse of recess time, funding constraints, and lack of strong leadership. Facilitators of implementation included strong leadership at the district level, creating healthy norms through school-community linkages and pooling community resources to improve nutrition and PA among children. CONCLUSIONS: Schools provide a unique setting to promote healthy diet and PA behaviors among children and their families. Study findings show that while knowledge of specific nutrition and PA policies may vary, support for such policies were high among rural stakeholders. Study findings can inform policy development and support strategies for policy implementation in rural settings. Future studies may want to examine whether implementation of strategies addressing the barriers and enhancing facilitators lead to success in rural school settings.

Reinforcement learning with associative or discriminative generalization across states and actions: fMRI at 3 T and 7 T.

The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single reward-prediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversal-learning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With high-resolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.

Chronic thromboembolic pulmonary hypertension in Australia and New Zealand: An analysis of the PHSANZ registry.

BACKGROUND AND OBJECTIVE: Chronic thromboembolic pulmonary hypertension (CTEPH) is a serious condition occurring in 2%-4% of patients after acute pulmonary embolism. Pulmonary endarterectomy (PEA) is a potential cure for technically operable disease. The epidemiology and long-term outcomes of CTEPH have not been previously described in Australia and New Zealand. METHODS: Data were extracted from the Pulmonary Hypertension Society of Australia and New Zealand (PHSANZ) registry for patients diagnosed with CTEPH between January 2004 and March 2020. Baseline characteristics, treatment strategies, outcome data and long-term survival are reported. RESULTS: A total of 386 patients were included with 146 (37.8%) undergoing PEA and 240 (62.2%) in the non-PEA group. PEA patients were younger (55 ± 16 vs. 62 ± 16 years, p < 0.001) with higher baseline 6-min walk distance (6MWD; 405 ± 122 vs. 323 ± 146 m, p = 0.021), whilst both groups had similar baseline pulmonary haemodynamics. Pulmonary hypertension-specific therapy was used in 54% of patients post-PEA and 88% in the non-PEA group. The 1-, 3- and 5-year survival rates were 93%, 87% and 84% for the PEA group compared to 86%, 73% and 62%, respectively, for the non-PEA group (p < 0.001). Multivariate survival analysis showed baseline 6MWD was an independent predictor of survival in both operated and medically managed patients. CONCLUSION: In this first multicentre report of CTEPH in Australia and New Zealand, long-term survival is comparable to that in other contemporary CTEPH registries. However, PEA was only performed in a minority of CTEPH patients (37.8%) and significantly less than overseas reports. Greater awareness of PEA and improved patient access to experienced CTEPH centres are important priorities.

Impacts of Transcranial Direct Current Stimulation on the Action Observation Network and Sports Anticipation Task.

Effective anticipation skills in sporting cognition have been shown to facilitate expertise in sports. Transcranial direct current stimulation (tDCS) has shown to improve motor and cognitive functioning. Therefore, this study aimed to determine the assistive effects of tDCS on the action observer network in both novice and expert gamers during an occlusion task, as well as the related electroencephalographic spectral power response. Twenty-three novice and 23 expert video gamers received either sham or active tDCS with a right parietal anode and left frontal cathode. Only experts demonstrated a significant improvement in predicting ball direction for the overall and early occlusions after tDCS. Spectral power results revealed significant changes in theta, high-gamma, and delta frequencies. The findings indicate that tDCS was able to modulate anticipatory behavior and cortical activity in experts compared with novice participants, suggesting a facilitatory role for tDCS to improve anticipatory effects and assist as a neurocognitive training technique.

Individual Neurons in the Cingulate Cortex Encode Action Monitoring, Not Selection, during Adaptive Decision-Making.

The cingulate cortex contributes to complex, adaptive behaviors, but the exact nature of its contributions remains unresolved. Proposals from previous studies, including evaluating past actions or selecting future ones, have been difficult to distinguish in part because of an incomplete understanding of the task-relevant variables that are encoded by individual cingulate neurons. In this study, we recorded from individual neurons in parts of both the anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC) in 2 male rhesus monkeys performing a saccadic reward task. The task required them to use adaptive, feedback-driven strategies to infer the spatial location of a rewarded saccade target in the presence of different forms of uncertainty. We found that task-relevant, spatially selective feedback signals were encoded by the activity of individual neurons in both brain regions, with stronger selectivity for spatial choice and reward-target signals in PCC and stronger selectivity for feedback in ACC. Moreover, neurons in both regions were sensitive to sequential effects of feedback that partly reflected sequential behavioral patterns. However, neither brain region exhibited systematic modulations by the blockwise conditions that governed the reliability of the trial-by-trial feedback and drove adaptive behavioral patterns. There was also little evidence that single-neuron responses in either brain region directly predicted the extent to which feedback and contextual information were used to inform choices on the subsequent trial. Thus, certain cingulate neurons encode diverse, evaluative signals needed for adaptive, feedback-driven decision-making, but those signals may be integrated elsewhere in the brain to guide actions.SIGNIFICANCE STATEMENT Effective decision-making in dynamic environments requires adapting to changes in feedback and context. The anterior and posterior cingulate cortex have been implicated in adaptive decision-making, but the exact nature of their respective roles remains unresolved. Here we compare patterns of task-driven activity of subsets of individual neurons from parts of the two brain regions in monkeys performing a saccadic task with dynamically changing reward locations. We find evidence for regional specializations in neural representations of choice and feedback, including task-relevant modulations of activity that could be used for performance monitoring. However, we find little evidence that these neural representations are used directly to adjust choice behavior, which thus likely requires integration of these signals elsewhere in the brain.

Investigating the relationship between parental weight stigma and feeding practices.

Promoting a healthy diet in children remains a prominent public health priority. Parents have been shown to be a major influence on their children's eating behaviors, but limited research has been devoted to exploring the factors that lead parents to select certain feeding practices over others. Past research has demonstrated a link between weight stigma (i.e., prejudicial attitudes or discriminatory behavior targeted at individuals who carry excess weight) and an individual's own weight-related behaviors and outcomes, but no study has examined how parental levels of weight stigma maybe associated with a parent's preferred feeding practices. The primary objective of this study was to examine the cross-sectional associations between parental levels of weight-based stigmatization with parental feeding practices. Responses were collected on Amazon's Mechanical Turk website for n = 406 parents who 1) had at least one child aged 5-10 and 2) perceived themselves to be overweight or obese. After adjusting for relevant covariates, parental weight stigma was shown to be significantly associated with restrictive feeding practices, and verbal modeling of eating behaviors (all ps < .05). A priori exploratory mediation analysis identified concern about child weight as a significant mediator between weight stigma and parental feeding practices. A discussion of the potential limitations of this study, future directions of research, and implications of these findings are included.

Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems.

[This corrects the article DOI: 10.1371/journal.pcbi.1003150.].

Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment.

Much of what we know about how the brain forms decisions comes from studies of saccadic eye movements. However, saccadic decisions are often studied in isolation, which limits the insights that they can provide about real-world decisions with complex interdependencies. Here, we used a serial reaction time (RT) task to show that prior expectations affect RTs via interdependent, normative decision processes that operate within and across saccades. We found that human subjects performing the task generated saccades that were governed by a rise-to-threshold decision process with a starting point that reflected expected state-dependent transition probabilities. These probabilities depended on decisions about the current state (the correct target) that, under some conditions, required the accumulation of information across saccades. Without additional feedback, this information was provided by each saccadic decision threshold, which represented the total evidence in favor of the chosen target. Therefore, the output of the within-saccade process was used, not only to generate the saccade, but also to provide input to the across-saccade process. This across-saccade process, in turn, helped to set the starting point of the next within-saccade process. These results imply a novel role for functional information-processing loops in optimizing saccade generation in dynamic environments.SIGNIFICANCE STATEMENT Saccades are the rapid, ballistic eye movements that we make approximately three times every second to scan the visual scene for interesting things to look at. The apparent ease with which we produce saccades belies their computational sophistication, which can be studied quantitatively in the laboratory to provide insights into how our brain manages the interplay between sensory input and motor output. The present work is important because we show for the first time how this interplay operates both within and across saccades to ensure that these eye movements are guided effectively by learned expectations in dynamic environments. More generally, this study shows how sensory-motor decision processes, typically studied in isolation, interact via functional information-processing loops in the brain to produce complex, adaptive behaviors.

Phasic activation of individual neurons in the locus ceruleus/subceruleus complex of monkeys reflects rewarded decisions to go but not stop.

Neurons in the brainstem nucleus locus ceruleus (LC) often exhibit phasic activation in the context of simple sensory-motor tasks. The functional role of this activation, which leads to the release of norepinephrine throughout the brain, is not yet understood in part because the conditions under which it occurs remain in question. Early studies focused on the relationship of LC phasic activation to salient sensory events, whereas more recent work has emphasized its timing relative to goal-directed behavioral responses, possibly representing the end of a sensory-motor decision process. To better understand the relationship between LC phasic activation and sensory, motor, and decision processing, we recorded spiking activity of neurons in the LC+ (LC and the adjacent, norepinephrine-containing subceruleus nucleus) of monkeys performing a countermanding task. The task required the monkeys to occasionally withhold planned, saccadic eye movements to a visual target. We found that many well isolated LC+ units responded to both the onset of the visual cue instructing the monkey to initiate the saccade and again after saccade onset, even when it was initiated erroneously in the presence of a stop signal. Many of these neurons did not respond to saccades made outside of the task context. In contrast, neither the appearance of the stop signal nor the successful withholding of the saccade elicited an LC+ response. Therefore, LC+ phasic activation encodes sensory and motor events related to decisions to execute, but not withhold, movements, implying a functional role in goal-directed actions, but not necessarily more covert forms of processing.

A healthy fear of the unknown: perspectives on the interpretation of parameter fits from computational models in neuroscience.

Fitting models to behavior is commonly used to infer the latent computational factors responsible for generating behavior. However, the complexity of many behaviors can handicap the interpretation of such models. Here we provide perspectives on problems that can arise when interpreting parameter fits from models that provide incomplete descriptions of behavior. We illustrate these problems by fitting commonly used and neurophysiologically motivated reinforcement-learning models to simulated behavioral data sets from learning tasks. These model fits can pass a host of standard goodness-of-fit tests and other model-selection diagnostics even when the models do not provide a complete description of the behavioral data. We show that such incomplete models can be misleading by yielding biased estimates of the parameters explicitly included in the models. This problem is particularly pernicious when the neglected factors are unknown and therefore not easily identified by model comparisons and similar methods. An obvious conclusion is that a parsimonious description of behavioral data does not necessarily imply an accurate description of the underlying computations. Moreover, general goodness-of-fit measures are not a strong basis to support claims that a particular model can provide a generalized understanding of the computations that govern behavior. To help overcome these challenges, we advocate the design of tasks that provide direct reports of the computational variables of interest. Such direct reports complement model-fitting approaches by providing a more complete, albeit possibly more task-specific, representation of the factors that drive behavior. Computational models then provide a means to connect such task-specific results to a more general algorithmic understanding of the brain.

A mixture of delta-rules approximation to bayesian inference in change-point problems.

Error-driven learning rules have received considerable attention because of their close relationships to both optimal theory and neurobiological mechanisms. However, basic forms of these rules are effective under only a restricted set of conditions in which the environment is stable. Recent studies have defined optimal solutions to learning problems in more general, potentially unstable, environments, but the relevance of these complex mathematical solutions to how the brain solves these problems remains unclear. Here, we show that one such Bayesian solution can be approximated by a computationally straightforward mixture of simple error-driven 'Delta' rules. This simpler model can make effective inferences in a dynamic environment and matches human performance on a predictive-inference task using a mixture of a small number of Delta rules. This model represents an important conceptual advance in our understanding of how the brain can use relatively simple computations to make nearly optimal inferences in a dynamic world.

Normative evidence weighting and accumulation in correlated environments.

The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighting and accumulation of evidence. The results highlight the robustness of our brain's ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.

The subthalamic nucleus contributes causally to perceptual decision-making in monkeys.

The subthalamic nucleus (STN) plays critical roles in the motor and cognitive function of the basal ganglia (BG), but the exact nature of these roles is not fully understood, especially in the context of decision-making based on uncertain evidence. Guided by theoretical predictions of specific STN contributions, we used single-unit recording and electrical microstimulation in the STN of healthy monkeys to assess its causal, computational roles in visual-saccadic decisions based on noisy evidence. The recordings identified subpopulations of STN neurons with distinct task-related activity patterns that related to different theoretically predicted functions. Microstimulation caused changes in behavioral choices and response times that reflected multiple contributions to an 'accumulate-to-bound'-like decision process, including modulation of decision bounds and evidence accumulation, and to non-perceptual processes. These results provide new insights into the multiple ways that the STN can support higher brain function.

Sleep duration in middle-aged years of life predicts the age of diagnosis of Parkinson's disease.

Introduction: Chronic short sleep duration (i.e., <7 h sleep daily) could reduce the brain's ability to attenuate toxin and protein accumulation, which may contribute to Parkinson's disease (PD). The purpose of this study was to characterize the relationship between self-reported sleep duration from adolescence to adulthood and the age of diagnosis in people with PD. A secondary purpose was to characterize the interaction between sleep duration and physical activity through the lifespan on the age of PD diagnosis. Methods: A secondary data analysis was performed using the Fox Insight data set. Multiple regression analysis was used to determine the age range that sleep duration best predicted the age of diagnosis of PD. Hierarchical linear multiple regression was performed to assess if self-reported sleep duration, physical activity, and their interaction predicted the age of diagnosis for PD, after accounting for sociodemographic factors. Results: Both sleep (p < 0.001) and physical activity time (p = 0.013) significantly predicted the of age of onset of PD. In contrast, there was no evidence to support an interaction of sleep by physical activity on the age of diagnosis of PD. Sleep duration at 46-55 years maintained significance after controlling for education, income, race, ethnicity, and sex (p < 0.001). Weekly duration of time spent performing moderate-intensity physical activity was added as an input variable. Conclusion: Sleep duration significantly predicts the age of diagnosis of PD, with shorter sleep duration associated with a younger age of diagnosis of PD.