Perceptual inference

Tug-of-Peace: Visual Rivalry and Atypical Visual Motion Processing in MECP2 Duplication Syndrome of Autism

Extracting common patterns of neural circuit computations in the autism spectrum and confirming them as a cause of specific core traits of autism is the first step toward identifying cell-level and circuit-level targets for effective clinical intervention. Studies in humans with autism have identified functional links and common anatomic substrates between core restricted behavioral repertoire, cognitive rigidity, and overstability of visual percepts during visual rivalry. To study these processes with single-cell precision and comprehensive neuronal population coverage, we developed the visual bistable perception paradigm for mice based on ambiguous moving plaid patterns consisting of two transparent gratings drifting at an angle of 120°. This results in spontaneous reversals of the perception between local component motion (plaid perceived as two separate moving grating components) and integrated global pattern motion (plaid perceived as a fused moving texture). This robust paradigm does not depend on the explicit report of the mouse, since the direction of the optokinetic nystagmus (OKN) is used to infer the dominant percept. Using this paradigm, we found that the rate of perceptual reversals between global and local motion interpretations is reduced in the methyl-CpG-binding protein 2 duplication syndrome (MECP2-ds) mouse model of autism. Moreover, the stability of local motion percepts is greatly increased in MECP2-ds mice at the expense of global motion percepts. Thus, our model reproduces a subclass of the core features in human autism (reduced rate of visual rivalry and atypical perception of visual motion). This further offers a well-controlled approach for dissecting neuronal circuits underlying these core features.

The Scent of Change: Sustainable Fragrances Through Industrial Biotechnology

Current environmental and safety considerations urge innovation to address the need for sustainable high-value chemicals that are embraced by consumers. This review discusses the concept of sustainable fragrances, as high-value, everyday and everywhere chemicals. Current and emerging technologies represent an opportunity to produce fragrances in an environmentally and socially responsible way. Biotechnology, including fermentation, biocatalysis, and genetic engineering, has the potential to reduce the environmental footprint of fragrance production while maintaining quality and consistency. Computational and in silico methods, including machine learning (ML), are also likely to augment the capabilities of sustainable fragrance production. Continued innovation and collaboration will be crucial to the future of sustainable fragrances, with a focus on developing novel sustainable ingredients, as well as ethical sourcing practices.

Combined action observation and motor imagery improves learning of activities of daily living in children with Developmental Coordination Disorder

Developmental coordination disorder (DCD) is characterised by poor motor coordination, which interferes with the ability to execute activities of daily living (ADLs). Combined action observation and motor imagery (AOMI) involves observing movement videos whilst imagining simultaneously the sensations of executing the same movement. Laboratory-based research indicates that AOMI can help improve movement coordination in children with DCD, but no previous research had investigated the efficacy of AOMI interventions for learning ADLs. This study investigated the efficacy of a home-based, parent-led, AOMI intervention for learning ADLs in children with DCD. Children with confirmed (n = 23) or suspected (n = 5) DCD (total sample n = 28), aged 7-12 years, were assigned to either an AOMI intervention or a control intervention (both n = 14). Participants attempted the following ADLs at pre-test (week 1), post-test (week 4), and retention test (week 6): shoelace tying, cutlery use, shirt buttoning, and cup stacking. Task completion times and movement techniques were recorded. The AOMI intervention produced significantly faster task completion times than the control intervention at post-test for shoelace tying, and significantly improved movement techniques for shoelace tying and cup stacking. Importantly, for children who could not tie shoelaces at pre-test (n = 9 per group), 89% of those following the AOMI intervention learnt the skill successfully by the end of the study, compared to only 44% of those following the control intervention. The findings indicate that home-based, parent-led, AOMI interventions can aid the learning of complex ADLs in children with DCD, and may be particularly effective for facilitating the learning of motor skills that do not currently exist within these children's motor repertoire.

How the conception of control influences our understanding of actions

Wilful movement requires neural control. Commonly, neural computations are thought to generate motor commands that bring the musculoskeletal system - that is, the plant - from its current physical state into a desired physical state. The current state can be estimated from past motor commands and from sensory information. Modelling movement on the basis of this concept of plant control strives to explain behaviour by identifying the computational principles for control signals that can reproduce the observed features of movements. From an alternative perspective, movements emerge in a dynamically coupled agent-environment system from the pursuit of subjective perceptual goals. Modelling movement on the basis of this concept of perceptual control aims to identify the controlled percepts and their coupling rules that can give rise to the observed characteristics of behaviour. In this Perspective, we discuss a broad spectrum of approaches to modelling human motor control and their notions of control signals, internal models, handling of sensory feedback delays and learning. We focus on the influence that the plant control and the perceptual control perspective may have on decisions when modelling empirical data, which may in turn shape our understanding of actions.

Spatial summation of cold and warm detection: Evidence for increased precision when brisk stimuli are delivered over larger area

Cold and warm stimuli delivered over a larger skin area tend to be more easily detected/elicit stronger sensations, a phenomenon referred to as spatial summation. The aim of the present study was to clarify how stimulation area affects thermal detection processes by evaluating whether increasing the stimulation area simply reduces the detection threshold or also reduces the uncertainty of the detection process. Psychometric functions were fitted to the detection performance of 16 healthy subjects. Stimuli (duration: 200 ms; rate of change: 300 °C/s) were delivered to the volar forearm using a Peltier-effect contact thermode and three different stimulation surfaces (23 mm², 69 mm², and 116 mm). Stimulation intensities were selected trial-by-trial by the psi marginal method to optimize estimation of slope and threshold parameters of the psychometric function. The raw data (100 stimulus-response pairs per subject per surface and per modality) was used to fit group-level hierarchical models of cold and warm detection, allowing to assess the effect of stimulation surface and account for inter-individual variability. Increasing stimulation area led to a compression of the psychometric function towards baseline skin temperature (reduced threshold and steeper slope), suggesting that spatial summation reflects a change in the precision of the neural representation of the stimulus which in turn influences the ability of the nervous system to distinguish true stimuli from sensory noise. Regardless of area, with the stimulation settings used in this study, cold detection appeared easier than warm detection, possibly because of structural and functional differences between cold- and warm-sensitive afferents.

Bayes' Theorem in Neurocritical Care: Principles and Practice

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders.

Start-Ups as Adaptable Stable Systems Based on Synchronous Business Models

Business models have been a popular topic in research and practice for more than twenty years. During this time, frameworks for formulating business models have been developed, such as the business model canvas. Moreover, different business model frameworks have been proposed for different sectors. Yet, these frameworks have the fundamental shortcoming of not addressing directly and persistently the primary objective of start-ups: to survive in changing environments. The aim of the action research reported in this paper is to overcome that fundamental shortcoming. This is an important topic because the majority of start-ups do not survive. In this paper, first principles for survival in changing environments are related to business models. In particular, action research to reframe start-ups as adaptable stable systems based on synchronous business models is reported. The paper provides three principal contributions. The contribution to business model theory building is to relate survival first principles revealed through natural science research to business models. Reference to first principles highlight that survival depends on maintaining both external adaptability and internal stability through synchronization with changing environments. The second contribution is to business model practice through describing a simple business modeling method that is based on the scientific first principles. The third contribution is to provide an example that bridges the rigor–relevance gap between scientific research and business practice.

Perceptual inference, accuracy, and precision in temporal reproduction in schizophrenia

Accumulating evidence suggests that deficits in perceptual inference account for symptoms of schizophrenia. One manifestation of perceptual inference is the central bias, i.e., the tendency to put emphasis on prior experiences over actual events in perceiving incoming sensory stimuli. Using an interval reproduction task, this study aimed to determine whether patients with schizophrenia show a stronger central bias than participants without schizophrenia. In the interval reproduction task, participants were shown a cross on a screen. The cross was replaced with a Gaussian patch for a predetermined time interval, and participants were required to reproduce the interval duration by pressing and releasing the space key. We manipulated the uncertainty of prior information using different interval distributions. We found no difference in the influence of prior information on interval reproduction between patients and controls. However, patients with SZ showed a stronger central bias than healthy participants in the intermediate interval range (approximately 450 ms to 900 ms). It is possible that the patients in SZ have non-uniform deficits associated with interval range or uncertainty of prior information in perceptual inference. Further, the severity of avolition and alogia was correlated with the strength of central bias in SZ. This study provides some insights into the mechanisms underlying the association between schizophrenic symptoms and perceptual inference.

Organizational Neuroscience of Industrial Adaptive Behavior

Organizational neuroscience is recognized in organizational behavior literature as offering an interpretive framework that can shed new light on existing organizational challenges. In this paper, findings from neuroscience studies concerned with adaptive behavior for ecological fitness are applied to explore industrial adaptive behavior. This is important because many companies are not able to manage dynamics between adaptability and stability. The reported analysis relates business-to-business signaling in competitive environments to three levels of inference. In accordance with neuroscience studies concerned with adaptive behavior, trade-offs between complexity and accuracy in business-to-business signaling and inference are explained. In addition, signaling and inference are related to risks and ambiguities in competitive industrial markets. Overall, the paper provides a comprehensive analysis of industrial adaptive behavior in terms of relevant neuroscience constructs. In doing so, the paper makes a contribution to the field of organizational neuroscience, and to research concerned with industrial adaptive behavior. The reported analysis is relevant to organizational adaptive behavior that involves combining human intelligence and artificial intelligence.

Interoception as modeling, allostasis as control

The brain regulates the body by anticipating its needs and attempting to meet them before they arise - a process called allostasis. Allostasis requires a model of the changing sensory conditions within the body, a process called interoception. In this paper, we examine how interoception may provide performance feedback for allostasis. We suggest studying allostasis in terms of control theory, reviewing control theory's applications to related issues in physiology, motor control, and decision making. We synthesize these by relating them to the important properties of allostatic regulation as a control problem. We then sketch a novel formalism for how the brain might perform allostatic control of the viscera by analogy to skeletomotor control, including a mathematical view on how interoception acts as performance feedback for allostasis. Finally, we suggest ways to test implications of our hypotheses.

Implicit learning in 3-year-olds with high and low likelihood of autism shows no evidence of precision weighting differences

Predictive Processing accounts of autism claim that autistic individuals assign higher precision to their prediction errors than non-autistic individuals, that is, autistic individuals update their predictions more readily when faced with unexpected sensory input. Since setting the level of precision is a fundamental part of perception and learning, we propose that such differences should be detectable in various domains at a very early age, before clinical symptoms have fully emerged. We therefore tested 3-year-old younger siblings of autistic children, with a high likelihood of later receiving an autism diagnosis themselves, and low-likelihood children with an older sibling without autism. We used a novel implicit learning paradigm to examine the effect of sensory noise on the predictions participants built. In order to learn a sequence, our participants had to select which visual information to attend to and disregard low-level prediction errors caused by the sensory noise, which the theory claims is more difficult for autistic individuals. Contrary to the proposed higher precision-weighting of prediction errors in autism, the high-likelihood children did not show signs of updating their predictions more readily when we added sensory noise compared to the low-likelihood children, either in their reaction times or in the recurrence and determinism of their response locations. These results raise challenges for Predictive Processing theories of autism, specifically for the notion that prediction errors are inflexibly highly weighted by individuals with autism.

The extrafoveal preview paradigm as a measure of predictive, active sampling in visual perception

A key feature of visual processing in humans is the use of saccadic eye movements to look around the environment. Saccades are typically used to bring relevant information, which is glimpsed with extrafoveal vision, into the high-resolution fovea for further processing. With the exception of some unusual circumstances, such as the first fixation when walking into a room, our saccades are mainly guided based on this extrafoveal preview. In contrast, the majority of experimental studies in vision science have investigated "passive" behavioral and neural responses to suddenly appearing and often temporally or spatially unpredictable stimuli. As reviewed here, a growing number of studies have investigated visual processing of objects under more natural viewing conditions in which observers move their eyes to a stationary stimulus, visible previously in extrafoveal vision, during each trial. These studies demonstrate that the extrafoveal preview has a profound influence on visual processing of objects, both for behavior and neural activity. Starting from the preview effect in reading research we follow subsequent developments in vision research more generally and finally argue that taking such evidence seriously leads to a reconceptualization of the nature of human visual perception that incorporates the strong influence of prediction and action on sensory processing. We review theoretical perspectives on visual perception under naturalistic viewing conditions, including theories of active vision, active sensing, and sampling. Although the extrafoveal preview paradigm has already provided useful information about the timing of, and potential mechanisms for, the close interaction of the oculomotor and visual systems while reading and in natural scenes, the findings thus far also raise many new questions for future research.

Cognitive biases, environmental, patient and personal factors associated with critical care decision making: A scoping review

PURPOSE

Cognitive biases and factors affecting decision making in critical care can potentially lead to life-threatening errors. We aimed to examine the existing evidence on the influence of cognitive biases and factors on decision making in critical care.

MATERIALS AND METHODS

We conducted a scoping review by searching MEDLINE for articles from 2004 to November 2020. We included studies conducted in physicians that described cognitive biases or factors associated with decision making. During the study process we decided on the method to summarize the evidence, and based on the obtained studies a descriptive summary of findings was the best fit.

RESULTS

Thirty heterogenous studies were included. Four main biases or factors were observed, e.g. cognitive biases, personal factors, environmental factors, and patient factors. Six (20%) studies reported biases associated with decision making comprising omission-, status quo-, implicit-, explicit-, outcome-, and overconfidence bias. Nineteen (63%) studies described personal factors, twenty-two (73%) studies described environmental factors, and sixteen (53%) studies described patient factors.

CONCLUSIONS

The current evidence on cognitive biases and factors is heterogenous, but shows they influence clinical decision. Future studies should investigate the prevalence of cognitive biases and factors in clinical practice and their impact on clinical outcomes.

A Compositional Neural Architecture for Language

Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de)compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations, and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.

Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity

The brain uses its intrinsic dynamics to actively predict observed sensory inputs, especially under perceptual ambiguity. However, it remains unclear how this inference process is neurally implemented in biasing perception of ambiguous inputs towards the predicted percepts. The process of perceptual inference can be well illustrated by the phenomenon of bistable apparent motion in the Ternus display, in which subjective perception spontaneously alternates between element motion (EM) and group motion (GM) percepts depending on whether two consecutively presented frames are grouped over time or not. The frequency of alpha-band oscillations has long been hypothesized to gate the temporal window of perceptual grouping over time. Under this hypothesis, variation in the intrinsic alpha frequency should predict perceptual outcome of the bistable Ternus display. Moreover, we hypothesize that the perception system employs this prior knowledge on intrinsic alpha frequency to resolve perceptual ambiguity, by shifting perceptual inference towards the predicted percepts. Using electroencephalography and intracranial recordings, we showed that both between and within subjects, lower prestimulus alpha frequencies (PAFs) predicted the EM percepts since the two frames fell in the same alpha cycle and got temporally integrated, while higher PAFs predicted the GM percepts since the two frames fell in different alpha cycles. Multivariate decoding analysis between the EM percepts with lower PAFs and the GM percepts with higher PAFs further revealed a representation of the subsequently reported bistable percept in the neural signals shortly before the actual appearance of the second frame. Therefore, perceptual inference, based on variation in intrinsic PAFs, biases poststimulus neural representations by inducing preactivation of the predicted percepts. In addition, enhanced prestimulus blood-oxygen-level-dependent (BOLD) signals and network dynamics in the frontoparietal network, together with reduced prestimulus alpha power, upon perceiving the EM percepts suggest that temporal grouping is an attention-demanding process.

Cerebellum, Predictions and Errors

Making predictions and validating the predictions against actual sensory information is thought to be one of the most fundamental functions of the nervous system. A growing body of evidence shows that the neural mechanisms controlling behavior, both in motor and non-motor domains, rely on prediction errors, the discrepancy between predicted and actual information. The cerebellum has been viewed as a key component of the motor system providing predictions about upcoming movements and receiving feedback about motor errors. Consequentially, studies of cerebellar function have focused on the motor domain with less consideration for the wider context in which movements are generated. However, motor learning experiments show that cognition makes important contributions to motor adaptation that involves the cerebellum. One of the more successful theoretical frameworks for understanding motor control and cerebellar function is the forward internal model which states that the cerebellum predicts the sensory consequences of the motor commands and is involved in computing sensory prediction errors by comparing the predictions to the sensory feedback. The forward internal model was applied and tested mainly for effector movements, raising the question whether cerebellar encoding of behavior reflects task performance measures associated with cognitive involvement. Electrophysiological studies based on pseudo-random tracking in monkeys show that the discharge of Purkinje cell, the sole output neurons of the cerebellar cortex, encodes predictive and feedback signals not only of the effector kinematics but also of task performance. The implications are that the cerebellum implements both effector and task performance forward models and the latter are consistent with the cognitive contributions observed during motor learning. The implications of these findings include insights into recent psychophysical observations on moving with reduced feedback and motor learning. The findings also support the cerebellum's place in hierarchical generative models that work in concert to refine predictions about behavior and the world. Therefore, cerebellar representations bridge motor and non-motor domains and provide a better understanding of cerebellar function within the functional architecture of the brain.

Evidence that neural information flow is reversed between object perception and object reconstruction from memory

Remembering is a reconstructive process, yet little is known about how the reconstruction of a memory unfolds in time in the human brain. Here, we used reaction times and EEG time-series decoding to test the hypothesis that the information flow is reversed when an event is reconstructed from memory, compared to when the same event is initially being perceived. Across three experiments, we found highly consistent evidence supporting such a reversed stream. When seeing an object, low-level perceptual features were discriminated faster behaviourally, and could be decoded from brain activity earlier, than high-level conceptual features. This pattern reversed during associative memory recall, with reaction times and brain activity patterns now indicating that conceptual information was reconstructed more rapidly than perceptual details. Our findings support a neurobiologically plausible model of human memory, suggesting that memory retrieval is a hierarchical, multi-layered process that prioritises semantically meaningful information over perceptual details.

Data gathering ability contributes to visual organization and probabilistic reasoning

Individuals use data gathering methods to inform judgments and behaviors. Effective interaction with the environment depends on these having high accuracy and low noise, but when they become abnormal, aberrant thoughts and perceptions can occur. In this study, we examined if data gathering methods were consistent across tasks that relied on different cognitive abilities, specifically visual perception and probabilistic reasoning. Thirty-four non-clinical participants engaged in the Ebbinghaus Illusion and the Jumping to Conclusions tasks, while also completing questionnaires concerning aspects of delusion formation. A significant, positive correlation was observed between performance on the Ebbinghaus Illusion and the Jumping to Conclusions tasks. Both tasks were negatively correlated with the General Conspiracy Belief Scale. The results suggest an underlying mechanism for data gathering that is consistent across behavioral domains and exists on a continuum in the general population.

Predictive cues for auditory stream formation in humans and monkeys

Auditory perception is improved when stimuli are predictable, and this effect is evident in a modulation of the activity of neurons in the auditory cortex as shown previously. Human listeners can better predict the presence of duration deviants embedded in stimulus streams with fixed interonset interval (isochrony) and repeated duration pattern (regularity), and neurons in the auditory cortex of macaque monkeys have stronger sustained responses in the 60-140 ms post-stimulus time window under these conditions. Subsequently, the question has arisen whether isochrony or regularity in the sensory input contributed to the enhancement of the neuronal and behavioural responses. Therefore, we varied the two factors isochrony and regularity independently and measured the ability of human subjects to detect deviants embedded in these sequences as well as measuring the responses of neurons the primary auditory cortex of macaque monkeys during presentations of the sequences. The performance of humans in detecting deviants was significantly increased by regularity. Isochrony enhanced detection only in the presence of the regularity cue. In monkeys, regularity increased the sustained component of neuronal tone responses in auditory cortex while isochrony had no consistent effect. Although both regularity and isochrony can be considered as parameters that would make a sequence of sounds more predictable, our results from the human and monkey experiments converge in that regularity has a greater influence on behavioural performance and neuronal responses.