Bayesian Decision Models: A Primer

Adaptive radiotherapy triggering for nasopharyngeal cancer based on bayesian decision model

Objective.To develop a Bayesian decision model for adaptive radiotherapy (ART) in nasopharyngeal cancer (NPC) that balances clinical capacity of ART and inter-fraction dosimetric changes.Approach.A retrospective analysis was conducted on 84 fractions from 17 NPC patients treated with intensity-modulated radiotherapy using a CT-Linac. Fourteen patients were included for the model construction, and three for validation. Daily diagnostic-level CT images were rigidly registered to the planning CT for regions of interest and treatment plan propagation. The propagated contours were reviewed and refined by radiation oncologists. For each daily CT, percentage differences in 27 dose metrics were compared to the original plan. Composite scores of dose differences were developed using factor analysis on planning target volume (PTV) and organ at risk (OAR) dose metrics. These scores were integrated into a Bayesian decision model, which incorporated a subjective trigger rate to determine the initiation of ART.Main results.The model generated individualized re-plan strategies based on composite scores for PTV or OAR, with trigger rates ranging from 10% to 60%. In the validation with 14 fractions, significant anatomical and dosimetric variations were identified. At a 30% trigger rate, only one fraction was misclassified.Significance.It is feasible to employ a Bayesian decision model for ART, merging subjective clinical insights with objective dosimetric data to refine re-planning decisions.

Reduced latency in manual interception with anticipatory smooth eye movements

Delays in visuomotor processing can cause the image of a moving object to fall outside the limited range of central vision, resulting in a blurred perception. To overcome these delays, anticipatory smooth eye movements (ASEMs) occur in the expected direction of future object motion before the object begins to move. This study demonstrated the functional benefits of ASEMs in rapid visually guided behaviors, highlighting their role beyond merely compensating for visuomotor delays. By experimentally facilitating ASEMs independent of participants' predictions regarding future object motion, we showed that ASEMs themselves expedite the initiation of interception movements. Our results revealed a quantitative relationship between ASEM velocity and interception latency on a single-trial level, which was more pronounced in participants employing predictive control compared to those relying on reactive control. These findings suggest that ASEMs enhance the feedforward control of interception movements by providing extraretinal signals rather than retinal signals.

FrAMBI: A Software Framework for Auditory Modeling Based on Bayesian Inference

Research in hearing science often relies on auditory models to describe listener's behaviour and its neural underpinning in acoustic environments. These models gather empirical evidence from behavioural data to address research questions on the neural mechanisms underlying sound perception. Despite seemingly similar statistical methods, auditory models are often implemented for each study separately, which hinders reproducibility and across-study comparisons, thus limiting the advancement at a field level. Here, we introduce a framework for studying neural mechanisms of sound perception by employing auditory modeling based on Bayesian inference (FrAMBI), a MATLAB/Octave toolbox. FrAMBI provides a standardized structure to implement an auditory model following the perception-action cycle and enables the automatic application of statistical analysis with behavioural data. We show FrAMBI's capabilities in several examples with increasing levels of complexity within the context of sound source localisation tasks: a basic implementation for a static scenario, iterating over the perception-action cycle with a moving sound source, the definition of multiple model variants testing different neural mechanisms, and the procedure for parameter estimation and model comparison. Being integrated into the widely used auditory modelling toolbox (AMT), FrAMBI is planned to be maintained in the long term and expanded accordingly, fostering reproducible research in the field of neuroscience.

Deep learning-based multimodal integration of imaging and clinical data for predicting surgical approach in percutaneous transforaminal endoscopic discectomy

BACKGROUND

For cases of multilevel lumbar disc herniation (LDH), selecting the surgical approach for Percutaneous Transforaminal Endoscopic Discectomy (PTED) presents significant challenges and heavily relies on the physician's judgment. This study aims to develop a deep learning (DL)-based multimodal model that provides objective and referenceable support by comprehensively analyzing imaging and clinical data to assist physicians.

METHODS

This retrospective study collected imaging and clinical data from patients with multilevel LDH. Each segmental MR scan was concurrently fed into a multi-input ResNet 50 model to predict the target segment. The target segment scan was then input to a custom model to predict the PTED approach direction. Clinical data, including the patient's lower limb sensory and motor functions, were used as feature variables in a machine learning (ML) model for prediction. Bayesian optimization was employed to determine the optimal weights for the fusion of the two models.

RESULT

The predictive performance of the multimodal model significantly outperformed the DL and ML models. For PTED target segment prediction, the multimodal model achieved an accuracy of 93.8%, while the DL and ML models achieved accuracies of 87.7% and 87.0%, respectively. Regarding the PTED approach direction, the multimodal model had an accuracy of 89.3%, significantly higher than the DL model's 87.8% and the ML model's 87.6%.

CONCLUSION

The multimodal model demonstrated excellent performance in predicting PTED target segments and approach directions. Its predictive performance surpassed that of the individual DL and ML models.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.

Machine learning-based prediction model for hypofibrinogenemia after tigecycline therapy

BACKGROUND

In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.

OBJECTIVE

We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.

METHODS

This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort.

RESULTS

Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668-0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652-0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups.

CONCLUSIONS

The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

Rats rely on airflow cues for self-motion perception

Self-motion perception is a vital skill for all species. It is an inherently multisensory process that combines inertial (body-based) and relative (with respect to the environment) motion cues. Although extensively studied in human and non-human primates, there is currently no paradigm to test self-motion perception in rodents using both inertial and relative self-motion cues. We developed a novel rodent motion simulator using two synchronized robotic arms to generate inertial, relative, or combined (inertial and relative) cues of self-motion. Eight rats were trained to perform a task of heading discrimination, similar to the popular primate paradigm. Strikingly, the rats relied heavily on airflow for relative self-motion perception, with little contribution from the (limited) optic flow cues provided-performance in the dark was almost as good. Relative self-motion (airflow) was perceived with greater reliability vs. inertial. Disrupting airflow, using a fan or windshield, damaged relative, but not inertial, self-motion perception. However, whiskers were not needed for this function. Lastly, the rats integrated relative and inertial self-motion cues in a reliability-based (Bayesian-like) manner. These results implicate airflow as an important cue for self-motion perception in rats and provide a new domain to investigate the neural bases of self-motion perception and multisensory processing in awake behaving rodents.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Bayesian Reinforcement Learning With Limited Cognitive Load

All biological and artificial agents must act given limits on their ability to acquire and process information. As such, a general theory of adaptive behavior should be able to account for the complex interactions between an agent's learning history, decisions, and capacity constraints. Recent work in computer science has begun to clarify the principles that shape these dynamics by bridging ideas from reinforcement learning, Bayesian decision-making, and rate-distortion theory. This body of work provides an account of capacity-limited Bayesian reinforcement learning, a unifying normative framework for modeling the effect of processing constraints on learning and action selection. Here, we provide an accessible review of recent algorithms and theoretical results in this setting, paying special attention to how these ideas can be applied to studying questions in the cognitive and behavioral sciences.

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Path integration, rather than being suppressed, is used to update spatial views in familiar environments with constantly available landmarks

This project tested three hypotheses conceptualizing the interaction between path integration based on self-motion and piloting based on landmarks in a familiar environment with persistent landmarks. The first hypothesis posits that path integration functions automatically, as in environments lacking persistent landmarks (environment-independent hypothesis). The second hypothesis suggests that persistent landmarks suppress path integration (suppression hypothesis). The third hypothesis proposes that path integration updates the spatial views of the environment (updating-spatial-views hypothesis). Participants learned a specific object's location. Subsequently, they undertook an outbound path originating from the object and then indicated the object's location (homing). In Experiments 1&1b, there were landmarks throughout the first 9 trials. On some later trials, the landmarks were presented during the outbound path but unexpectedly removed during homing (catch trials). On the last trials, there were no landmarks throughout (baseline trials). Experiments 2-3 were similar but added two identical objects (the original one and a rotated distractor) during homing on the catch and baseline trials. Experiment 4 replaced two identical objects with two groups of landmarks. The results showed that in Experiments 1&1b, homing angular error on the first catch trial was significantly larger than the matched baseline trial, undermining the environment-independent hypothesis. Conversely, in Experiment 2-4, the proportion of participants who recognized the original object or landmarks was similar between the first catch and the matched baseline trial, favoring the updating-spatial-views hypothesis over the suppression hypothesis. Therefore, while mismatches between updated spatial views and actual views of unexpected removal of landmarks impair homing performance, the updated spatial views help eliminate ambiguous targets or landmarks within the familiar environment.

Multisensory Calibration: A Variety of Slow and Fast Brain Processes Throughout the Lifespan

From before we are born, throughout development, adulthood, and aging, we are immersed in a multisensory world. At each of these stages, our sensory cues are constantly changing, due to body, brain, and environmental changes. While integration of information from our different sensory cues improves precision, this only improves accuracy if the underlying cues are unbiased. Thus, multisensory calibration is a vital and ongoing process. To meet this grand challenge, our brains have evolved a variety of mechanisms. First, in response to a systematic discrepancy between sensory cues (without external feedback) the cues calibrate one another (unsupervised calibration). Second, multisensory function is calibrated to external feedback (supervised calibration). These two mechanisms superimpose. While the former likely reflects a lower level mechanism, the latter likely reflects a higher level cognitive mechanism. Indeed, neural correlates of supervised multisensory calibration in monkeys were found in higher level multisensory cortical area VIP, but not in the relatively lower level multisensory area MSTd. In addition, even without a cue discrepancy (e.g., when experiencing stimuli from different sensory cues in series) the brain monitors supra-modal statistics of events in the environment and adapts perception cross-modally. This too comprises a variety of mechanisms, including confirmation bias to prior choices, and lower level cross-sensory adaptation. Further research into the neuronal underpinnings of the broad and diverse functions of multisensory calibration, with improved synthesis of theories is needed to attain a more comprehensive understanding of multisensory brain function.

From Multisensory Integration to Multisensory Decision-Making

Organisms live in a dynamic environment in which sensory information from multiple sources is ever changing. A conceptually complex task for the organisms is to accumulate evidence across sensory modalities and over time, a process known as multisensory decision-making. This is a new concept, in terms of that previous researches have been largely conducted in parallel disciplines. That is, much efforts have been put either in sensory integration across modalities using activity summed over a duration of time, or in decision-making with only one sensory modality that evolves over time. Recently, a few studies with neurophysiological measurements emerge to study how different sensory modality information is processed, accumulated, and integrated over time in decision-related areas such as the parietal or frontal lobes in mammals. In this review, we summarize and comment on these studies that combine the long-existed two parallel fields of multisensory integration and decision-making. We show how the new findings provide insight into our understanding about neural mechanisms mediating multisensory information processing in a more complete way.

Introspective inference counteracts perceptual distortion

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Corrective feedback guides human perceptual decision-making by informing about the world state rather than rewarding its choice

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs "reinforcement learning," a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these "feedback-as-reward" and "feedback-as-evidence" hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.

Nonhuman primates exploit the prior assumption that the visual world is vertical

When human subjects tilt their heads in dark surroundings, the noisiness of vestibular information impedes precise reports on objects' orientation with respect to Earth's vertical axis. This difficulty is mitigated if a vertical visual background is available. Tilted visual backgrounds induce feelings of head tilt in subjects who are in fact upright. This is often explained as a result of the brain resorting to the prior assumption that natural visual backgrounds are vertical. Here, we tested whether monkeys show comparable perceptual mechanisms. To this end we trained two monkeys to align a visual arrow to a vertical reference line that had variable luminance across trials, while including a large, clearly visible background square whose orientation changed from trial to trial. On ∼20% of all trials, the vertical reference line was left out to measure the subjective visual vertical (SVV). When the frame was upright, the monkeys' SVV was aligned with the gravitational vertical. In accordance with the perceptual reports of humans, however, when the frame was tilted it induced an illusion of head tilt as indicated by a bias in SVV toward the frame orientation. Thus all primates exploit the prior assumption that the visual world is vertical.NEW & NOTEWORTHY Here we show that the principles that characterize the human perception of the vertical are shared by another old world primate species, the rhesus monkey, suggesting phylogenetic continuity. In both species the integration of visual and vestibular information on the orientation of the head relative to the world is similarly constrained by the prior assumption that the visual world is vertical in the sense of having an orientation that is congruent with the gravity vector.

Statistically Optimal Cue Integration During Human Spatial Navigation

In 2007, Cheng and colleagues published their influential review wherein they analyzed the literature on spatial cue interaction during navigation through a Bayesian lens, and concluded that models of optimal cue integration often applied in psychophysical studies could explain cue interaction during navigation. Since then, numerous empirical investigations have been conducted to assess the degree to which human navigators are optimal when integrating multiple spatial cues during a variety of navigation-related tasks. In the current review, we discuss the literature on human cue integration during navigation that has been published since Cheng et al.'s original review. Evidence from most studies demonstrate optimal navigation behavior when humans are presented with multiple spatial cues. However, applications of optimal cue integration models vary in their underlying assumptions (e.g., uninformative priors and decision rules). Furthermore, cue integration behavior depends in part on the nature of the cues being integrated and the navigational task (e.g., homing versus non-home goal localization). We discuss the implications of these models and suggest directions for future research.

Neural correlates of valence-dependent belief and value updating during uncertainty reduction: An fNIRS study

Selective use of new information is crucial for adaptive decision-making. Combining a gamble bidding task with assessing cortical responses using functional near-infrared spectroscopy (fNIRS), we investigated potential effects of information valence on behavioral and neural processes of belief and value updating during uncertainty reduction in young adults. By modeling changes in the participants' expressed subjective values using a Bayesian model, we dissociated processes of (i) updating beliefs about statistical properties of the gamble, (ii) updating values of a gamble based on new information about its winning probabilities, as well as (iii) expectancy violation. The results showed that participants used new information to update their beliefs and values about the gambles in a quasi-optimal manner, as reflected in the selective updating only in situations with reducible uncertainty. Furthermore, their updating was valence-dependent: information indicating an increase in winning probability was underweighted, whereas information about a decrease in winning probability was updated in good agreement with predictions of the Bayesian decision theory. Results of model-based and moderation analyses showed that this valence-dependent asymmetry was associated with a distinct contribution of expectancy violation, besides belief updating, to value updating after experiencing new positive information regarding winning probabilities. In line with the behavioral results, we replicated previous findings showing involvements of frontoparietal brain regions in the different components of updating. Furthermore, this study provided novel results suggesting a valence-dependent recruitment of brain regions. Individuals with stronger oxyhemoglobin responses during value updating was more in line with predictions of the Bayesian model while integrating new information that indicates an increase in winning probability. Taken together, this study provides first results showing expectancy violation as a contributing factor to sub-optimal valence-dependent updating during uncertainty reduction and suggests limitations of normative Bayesian decision theory.

Multisensory decisions from self to world

Classic Bayesian models of perceptual inference describe how an ideal observer would integrate 'unisensory' measurements (multisensory integration) and attribute sensory signals to their origin(s) (causal inference). However, in the brain, sensory signals are always received in the context of a multisensory bodily state-namely, in combination with other senses. Moreover, sensory signals from both interoceptive sensing of one's own body and exteroceptive sensing of the world are highly interdependent and never occur in isolation. Thus, the observer must fundamentally determine whether each sensory observation is from an external (versus internal, self-generated) source to even be considered for integration. Critically, solving this primary causal inference problem requires knowledge of multisensory and sensorimotor dependencies. Thus, multisensory processing is needed to separate sensory signals. These multisensory processes enable us to simultaneously form a sense of self and form distinct perceptual decisions about the external world. In this opinion paper, we review and discuss the similarities and distinctions between multisensory decisions underlying the sense of self and those directed at acquiring information about the world. We call attention to the fact that heterogeneous multisensory processes take place all along the neural hierarchy (even in forming 'unisensory' observations) and argue that more integration of these aspects, in theory and experiment, is required to obtain a more comprehensive understanding of multisensory brain function. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

The presence of irrelevant alternatives paradoxically increases confidence in perceptual decisions

Confidence in perceptual decisions is thought to reflect the probability of being correct. According to this view, confidence should be unaffected or minimally reduced by the presence of irrelevant alternatives. To test this prediction, we designed five experiments. In Experiment 1, participants had to identify the largest geometrical shape among two or three alternatives. In the three-alternative condition, one of the shapes was much smaller than the other two, being a clearly incorrect option. Counter-intuitively, confidence was higher when the irrelevant alternative was present, evidencing that confidence construction is more complex than previously thought. Four computational models were tested, only one of them accounting for the results. This model predicts that confidence increases monotonically with the number of irrelevant alternatives, a prediction we tested in Experiment 2. In Experiment 3, we evaluated whether this effect replicated in a categorical task, but we did not find supporting evidence. Experiments 4 and 5 allowed us to discard stimuli presentation time as a factor driving the effect. Our findings suggest that confidence models cannot ignore the effect of multiple, possibly irrelevant alternatives to build a thorough understanding of confidence.

Proposal of a Decision Support System and Model to Mitigate Scope Variability for New Product Development

This paper proposes a decision support model (DSM) to be used by project managers (PM) in the research of new products within multinational companies to select the right option to mitigate scope variability during critical junctures like project continuity. On-the-shelf DSM are leveraged to overcome their shortcomings with more effective and relevant solutions. The proposed DSM is a consolidation of on-the-shelf DSM and methodologies such as balanced scorecard; quality function deployment; analytic hierarchy process; strengths, weaknesses, opportunities, and threats; and agile methodology, leading to an application in a decision support system (DSS) using progressive web applications. Feedback and input were collected through interviews with experienced PMs. Performance indicators were then developed to be reviewed by the PM to verify their applicability. Based on the feedback obtained, the developed model contributes to the engineering research industry as it mitigates common challenges that PMs face by efficiently providing a solution-seeking guideline.

Contextual inference in learning and memory

Context is widely regarded as a major determinant of learning and memory across numerous domains, including classical and instrumental conditioning, episodic memory, economic decision-making, and motor learning. However, studies across these domains remain disconnected due to the lack of a unifying framework formalizing the concept of context and its role in learning. Here, we develop a unified vernacular allowing direct comparisons between different domains of contextual learning. This leads to a Bayesian model positing that context is unobserved and needs to be inferred. Contextual inference then controls the creation, expression, and updating of memories. This theoretical approach reveals two distinct components that underlie adaptation, proper and apparent learning, respectively referring to the creation and updating of memories versus time-varying adjustments in their expression. We review a number of extensions of the basic Bayesian model that allow it to account for increasingly complex forms of contextual learning.

A normative model of peripersonal space encoding as performing impact prediction

Accurately predicting contact between our bodies and environmental objects is paramount to our evolutionary survival. It has been hypothesized that multisensory neurons responding both to touch on the body, and to auditory or visual stimuli occurring near them—thus delineating our peripersonal space (PPS)—may be a critical player in this computation. However, we lack a normative account (i.e., a model specifying how we ought to compute) linking impact prediction and PPS encoding. Here, we leverage Bayesian Decision Theory to develop such a model and show that it recapitulates many of the characteristics of PPS. Namely, a normative model of impact prediction (i) delineates a graded boundary between near and far space, (ii) demonstrates an enlargement of PPS as the speed of incoming stimuli increases, (iii) shows stronger contact prediction for looming than receding stimuli—but critically is still present for receding stimuli when observation uncertainty is non-zero—, (iv) scales with the value we attribute to environmental objects, and finally (v) can account for the differing sizes of PPS for different body parts. Together, these modeling results support the conjecture that PPS reflects the computation of impact prediction, and make a number of testable predictions for future empirical studies.

The brain has neurons that respond to touch on the body, as well as to auditory or visual stimuli occurring near the body. These neurons delineate a graded boundary between the near and far space. Here, we aim at understanding whether the function of these neurons is to predict future impact between the environment and body. To do so, we build a mathematical model that is statistically optimal at predicting future impact, taking into account the costs incurred by an impending collision. Then we examine if its properties are similar to those of the above-mentioned neurons. We find that the model (i) differentiates between the near and far space in a graded fashion, predicts different near/far boundary depths for different (ii) body parts, (iii) object speeds and (iv) directions, and (v) that this boundary scales with the value we attribute to environmental objects. These properties have all been described in behavioral studies and ascribed to neurons responding to objects near the body. Together, these findings suggest why the brain has neurons that respond only to objects near the body: to compute predictions of impact.

Temporal integration of feature probability distributions

Humans are surprisingly good at learning the statistical characteristics of their visual environment. Recent studies have revealed that not only can the visual system learn repeated features of visual search distractors, but also their actual probability distributions. Search times were determined by the frequency of distractor features over consecutive search trials. The search displays applied in these studies involved many exemplars of distractors on each trial and while there is clear evidence that feature distributions can be learned from large distractor sets, it is less clear if distributions are well learned for single targets presented on each trial. Here, we investigated potential learning of probability distributions of single targets during visual search. Over blocks of trials, observers searched for an oddly colored target that was drawn from either a Gaussian or a uniform distribution. Search times for the different target colors were clearly influenced by the probability of that feature within trial blocks. The same search targets, coming from the extremes of the two distributions were found significantly slower during the blocks where the targets were drawn from a Gaussian distribution than from a uniform distribution indicating that observers were sensitive to the target probability determined by the distribution shape. In Experiment 2, we replicated the effect using binned distributions and revealed the limitations of encoding complex target distributions. Our results demonstrate detailed internal representations of target feature distributions and that the visual system integrates probability distributions of target colors over surprisingly long trial sequences.

Towards a cross-level understanding of Bayesian inference in the brain

Perception emerges from unconscious probabilistic inference, which guides behaviour in our ubiquitously uncertain environment. Bayesian decision theory is a prominent computational model that describes how people make rational decisions using noisy and ambiguous sensory observations. However, critical questions have been raised about the validity of the Bayesian framework in explaining the mental process of inference. Firstly, some natural behaviours deviate from Bayesian optimum. Secondly, the neural mechanisms that support Bayesian computations in the brain are yet to be understood. Taking Marr's cross level approach, we review the recent progress made in addressing these challenges. We first review studies that combined behavioural paradigms and modelling approaches to explain both optimal and suboptimal behaviours. Next, we evaluate the theoretical advances and the current evidence for ecologically feasible algorithms and neural implementations in the brain, which may enable probabilistic inference. We argue that this cross-level approach is necessary for the worthwhile pursuit to uncover mechanistic accounts of human behaviour.

Comparison of efficacy and safety of traditional Chinese patent medicines for diabetic nephropathy: A protocol for Bayesian network meta-analysis

BACKGROUND

Diabetic nephropathy (DN) is one of the most serious complications of diabetes. It has become a global public health problem among humans. DN is the leading cause of end-stage renal disease. At present, there is no specific medicine or modern medicinal therapy. In recent years, studies have shown that traditional Chinese patent medicines have been effective in treating DN, with few side effects. There is no systematic review on the treatment of DN with Chinese patent medicines. The current systematic review aims to evaluate the efficacy and safety of Chinese patent medicines for the treatment of DN.

METHODS

We will develop a search strategy to search major Chinese and English databases from inception to February 25, 2022 for randomized controlled trials examining the use of traditional Chinese patent medicine for the treatment of DN. The search will be conducted in accordance with the participants, interventions, comparisons, outcomes (PICOS) framework. Two researchers will use EndnoteX9 software to extract data and independently evaluate the quality of the included trials. Finally, the Bayesian network meta-analysis will be carried out by using software such as ReviewManager, Stata16.0, and WinBUGS1.4.3.

RESULTS

The primary outcomes will be urine albumin excretion rate, urea nitrogen, serum creatinine, total effective rate, and adverse events, and the secondary outcomes will be body mass index, fasting blood glucose, and 2-hPG during 75-g OGTT. These outcomes will be examined to provide a reliable basis for the treatment of DN with different traditional Chinese patent medicines.

CONCLUSION

This review will compare the efficacy and safety of different traditional Chinese patent medicines for treating DN. The results of the study will provide a basis for the selection of adjuvant treatment options for DN.

Cognitive, Systems, and Computational Neurosciences of the Self in Motion

Navigating by path integration requires continuously estimating one's self-motion. This estimate may be derived from visual velocity and/or vestibular acceleration signals. Importantly, these senses in isolation are ill-equipped to provide accurate estimates, and thus visuo-vestibular integration is an imperative. After a summary of the visual and vestibular pathways involved, the crux of this review focuses on the human and theoretical approaches that have outlined a normative account of cue combination in behavior and neurons, as well as on the systems neuroscience efforts that are searching for its neural implementation. We then highlight a contemporary frontier in our state of knowledge: understanding how velocity cues with time-varying reliabilities are integrated into an evolving position estimate over prolonged time periods. Further, we discuss how the brain builds internal models inferring when cues ought to be integrated versus segregated-a process of causal inference. Lastly, we suggest that the study of spatial navigation has not yet addressed its initial condition: self-location.

Bayesian decision theory and navigation

Spatial navigation is a complex cognitive activity that depends on perception, action, memory, reasoning, and problem-solving. Effective navigation depends on the ability to combine information from multiple spatial cues to estimate one's position and the locations of goals. Spatial cues include landmarks, and other visible features of the environment, and body-based cues generated by self-motion (vestibular, proprioceptive, and efferent information). A number of projects have investigated the extent to which visual cues and body-based cues are combined optimally according to statistical principles. Possible limitations of these investigations are that they have not accounted for navigators' prior experiences with or assumptions about the task environment and have not tested complete decision models. We examine cue combination in spatial navigation from a Bayesian perspective and present the fundamental principles of Bayesian decision theory. We show that a complete Bayesian decision model with an explicit loss function can explain a discrepancy between optimal cue weights and empirical cues weights observed by (Chen et al. Cognitive Psychology, 95, 105-144, 2017) and that the use of informative priors to represent cue bias can explain the incongruity between heading variability and heading direction observed by (Zhao and Warren 2015b, Psychological Science, 26[6], 915-924). We also discuss (Petzschner and Glasauer's , Journal of Neuroscience, 31(47), 17220-17229, 2011) use of priors to explain biases in estimates of linear displacements during visual path integration. We conclude that Bayesian decision theory offers a productive theoretical framework for investigating human spatial navigation and believe that it will lead to a deeper understanding of navigational behaviors.

Neural implementations of Bayesian inference

Bayesian inference has emerged as a general framework that captures how organisms make decisions under uncertainty. Recent experimental findings reveal disparate mechanisms for how the brain generates behaviors predicted by normative Bayesian theories. Here, we identify two broad classes of neural implementations for Bayesian inference: a modular class, where each probabilistic component of Bayesian computation is independently encoded and a transform class, where uncertain measurements are converted to Bayesian estimates through latent processes. Many recent experimental neuroscience findings studying probabilistic inference broadly fall into these classes. We identify potential avenues for synthesis across these two classes and the disparities that, at present, cannot be reconciled. We conclude that to distinguish among implementation hypotheses for Bayesian inference, we require greater engagement among theoretical and experimental neuroscientists in an effort that spans different scales of analysis, circuits, tasks, and species.

Uncertainty is maintained and used in working memory

What are the contents of working memory? In both behavioral and neural computational models, a working memory representation is typically described by a single number, namely, a point estimate of a stimulus. Here, we asked if people also maintain the uncertainty associated with a memory and if people use this uncertainty in subsequent decisions. We collected data in a two-condition orientation change detection task; while both conditions measured whether people used memory uncertainty, only one required maintaining it. For each condition, we compared an optimal Bayesian observer model, in which the observer uses an accurate representation of uncertainty in their decision, to one in which the observer does not. We find that this “Use Uncertainty” model fits better for all participants in both conditions. In the first condition, this result suggests that people use uncertainty optimally in a working memory task when that uncertainty information is available at the time of decision, confirming earlier results. Critically, the results of the second condition suggest that this uncertainty information was maintained in working memory. We test model variants and find that our conclusions do not depend on our assumptions about the observer's encoding process, inference process, or decision rule. Our results provide evidence that people have uncertainty that reflects their memory precision on an item-specific level, maintain this information over a working memory delay, and use it implicitly in a way consistent with an optimal observer. These results challenge existing computational models of working memory to update their frameworks to represent uncertainty.

Deciphering human decision rules in motion discrimination

We investigated the eight decision rules for a same-different task, as summarized in Petrov (Psychonomic Bulletin & Review, 16(6), 1011-1025, 2009). These rules, including the differencing (DF) rule and the optimal independence rule, are all based on the standard model in signal detection theory. Each rule receives two stimulus values as inputs and uses one or two decision criteria. We proved that the false alarm rate p(F) ≤ 1/2 for four of the rules. We also conducted a same-different rating experiment on motion discrimination (n = 54), with 4^∘ or 8^∘ directional difference. We found that the human receiver operating characteristic (ROC) spanned its full range [0,1] in p(F), thus rejecting these four rules. The slope of the human Z-ROC was also < 1, further confirming that the independence rule was not used. We subsequently fitted in the four-dimensional (p_AA, p_AB, p_BA, p_BB) space the human data to the remaining four rules-DF and likelihood ratio rules, each with one or two criteria, where p_XY = p(responding "different" given stimulus sequence XY). We found that, using residual distribution analysis, only the two criteria DF rule (DF2) could account for the human data.

Increased influence of prior choices on perceptual decisions in autism

Autism spectrum disorder (ASD) manifests sensory and perceptual atypicalities. Recent theories suggest that these may reflect a reduced influence of prior information in ASD. Some studies have found reduced adaptation to recent sensory stimuli in ASD. However, the effects of prior stimuli and prior perceptual choices can counteract one-another. Here, we investigated this using two different tasks (in two different cohorts): (i) visual location discrimination and (ii) multisensory (visual-vestibular) heading discrimination. We fit the data using a logistic regression model to dissociate the specific effects of prior stimuli and prior choices. In both tasks, perceptual decisions were biased toward recent choices. Notably, the 'attractive' effect of prior choices was significantly larger in ASD (in both tasks and cohorts), while there was no difference in the influence of prior stimuli. These results challenge theories of reduced priors in ASD, and rather suggest an increased consistency bias for perceptual decisions in ASD.

Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior

Our senses provide us with a rich experience of a detailed visual world, yet the empirical results seem to suggest severe limitations on our ability to perceive and remember. In recent attempts to reconcile the contradiction between what is experienced and what can be reported, it has been argued that the visual world is condensed to a set of summary statistics, explaining both the rich experience and the sparse reports. Here, we show that explicit reports of summary statistics underestimate the richness of ensemble perception. Our observers searched for an odd-one-out target among heterogeneous distractors and their representation of distractor characteristics was tested explicitly or implicitly. Observers could explicitly distinguish distractor sets with different mean and variance, but not differently-shaped probability distributions. In contrast, the implicit assessment revealed that the visual system encodes the mean, the variance, and even the shape of feature distributions. Furthermore, explicit measures had common noise sources that distinguished them from implicit measures. This suggests that explicit judgments of stimulus ensembles underestimate the richness of visual representations. We conclude that feature distributions are encoded in rich detail and can guide behavior implicitly, even when the information available for explicit summary judgments is coarse and limited.

Comparative efficacy and safety of traditional Chinese patent medicine for the treatment of type 2 diabetes mellitus: A Bayesian network meta-analysis protocol

BACKGROUND

At present, the prevalence of type 2 diabetes mellitus (T2DM) has become a major public health issue throughout the world, especially in developing countries. Notably, traditional Chinese patent medicines (TCPMs) are of great significance in the treatment of T2DM combined with conventional Western medicine therapy. However, there is a lack of comparison among all the current common TCPMs for treating T2DM. Therefore, this study intends to explore the efficacy and safety of different TCPMs against T2DM through the Bayesian network meta-analysis (NMA).

METHODS

We will conduct a comprehensive and systematic search for randomized controlled trials (RCTs) of TCPM for the treatment of T2DM in both Chinese and English databases published till August 2020. Two researchers will be responsible for screening eligible literature, extracting data, and assessing the risk of bias of included studies independently. Then, pairwise meta-analyses and Bayesian network meta-analyses will be conducted to assess all available evidence. In the end, data will be analyzed using STATA15.0 and WinBUGS1.4.3 software.

CONCLUSION

This study will compare the efficacy and safety of different TCPMs against T2DM in detail. Our findings will provide a reliable evidence for selecting clinical treatment program and guideline development of T2DM.

A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception

Human perceptual decisions can be repelled away from (repulsive adaptation) or attracted towards recent visual experience (attractive serial dependence). It is currently unclear whether and how these repulsive and attractive biases interact during visual processing and what computational principles underlie these history dependencies. Here we disentangle repulsive and attractive biases by exploring their respective timescales. We find that perceptual decisions are concurrently attracted towards the short-term perceptual history and repelled from stimuli experienced up to minutes into the past. The temporal pattern of short-term attraction and long-term repulsion cannot be captured by an ideal Bayesian observer model alone. Instead, it is well captured by an ideal observer model with efficient encoding and Bayesian decoding of visual information in a slowly changing environment. Concurrent attractive and repulsive history biases in perceptual decisions may thus be the consequence of the need for visual processing to simultaneously satisfy constraints of efficiency and stability.