Sensory Prediction Research Articles

Sensory evaluation and prediction of bulk wine by physicochemical indicators based on PCA‐PSO‐LSSVM method

It is difficult to implement the sensory quality evaluation of bulk wine before and after transportation due to geographical, time, cost, and personnel limitation. This study aimed to use the physicochemical indicators of bulk wine to construct an evaluation model of sensory quality. First, this paper designed and carried out the simulation experiment of bulk wine transportation and obtained experimental data on physicochemical and sensory indicators of wine. Afterwards, principal component analysis (PCA) was used to reduce the dimension and fuse features of the physicochemical index. Then, the extracted features were used as the input of the evaluation model, least square support vector machine (LSSVM) was constructed for sensory evaluation, and particle swarm optimization (PSO) algorithm was selected to optimize LSSVM parameters. Finally, the PCA-PSO-LSSVM model was practically applied in sampled winery. The results showed that comparing models’ accuracy the PCA-PSO-LSSVM model was superior to traditional models LSSVM and PSO-LSSVM, there is significant difference between PCA-PSO-LSSVM and LSSVM, and the predictive accuracy in appearance and taste attributes was high and the overall prediction accuracy was 86.6%. The application results revealed the changes of sensory quality for bulk wine after transportation. This research provides a set of simple and feasible methods for the sensory evaluation of bulk wine. Practical applications To study the method of using the basic physicochemical index of wine to intelligently evaluate the sensory attributes of bulk wine, so as to realize the accurate evaluation of the quality of bulk wine after transportation. By comparing the comprehensive and accurate quality evaluation before and after the bulk wine transportation, it is helpful for wine companies to understand the impact of the transportation link on the wine quality, further improve the transportation method of bulk wine, and ensure the quality of the wine has important practical significance.

Abnormal global-brain functional connectivity and its relationship with cognitive deficits in drug-naive first-episode adolescent-onset schizophrenia.

Abnormal functional connectivity (FC) has been reported in drug-naive first-episode adolescent-onset schizophrenia (AOS) with inconsistent results due to differently selected regions of interest. The voxel-wise global-brain functional connectivity (GFC) analysis can help explore abnormal FC in an unbiased way in AOS. A total of 48 drug-naive first-episode AOS as well as 31 sex-, age- and education-matched healthy controls were collected. Data were subjected to GFC, correlation analysis and support vector machine analyses. Compared with healthy controls, the AOS group exhibited increased GFC in the right middle frontal gyrus (MFG), and decreased GFC in the right inferior temporal gyrus, left superior temporal gyrus (STG)/precentral gyrus/postcentral gyrus, right posterior cingulate cortex /precuneus and bilateral cuneus. After the Benjamini-Hochberg correction, significantly negative correlations between GFC in the bilateral cuneus and Trail-Making Test: Part A (TMT-A) scores (r=-0.285, p=0.049), between GFC in the left STG/precentral gyrus/postcentral gyrus and TMT-A scores (r=-0.384, p=0.007), and between GFC in the right MFG and the fluency scores (r=-0.335, p=0.020) in the patients. GFC in the left STG/precentral gyrus/postcentral gyrus has a satisfactory accuracy (up to 86.08%) in classifying patients from controls. AOS shows abnormal GFC in the brain areas of multiple networks, which bears cognitive significance. These findings suggest potential abnormalities in processing self-monitoring and sensory prediction, which further elucidate the pathophysiology of AOS.

Mechanisms of Human Motor Learning Do Not Function Independently.

Human motor learning is governed by a suite of interacting mechanisms each one of which modifies behavior in distinct ways and rely on different neural circuits. In recent years, much attention has been given to one type of motor learning, called motor adaptation. Here, the field has generally focused on the interactions of three mechanisms: sensory prediction error SPE-driven, explicit (strategy-based), and reinforcement learning. Studies of these mechanisms have largely treated them as modular, aiming to model how the outputs of each are combined in the production of overt behavior. However, when examined closely the results of some studies also suggest the existence of additional interactions between the sub-components of each learning mechanism. In this perspective, we propose that these sub-component interactions represent a critical means through which different motor learning mechanisms are combined to produce movement; understanding such interactions is critical to advancing our knowledge of how humans learn new behaviors. We review current literature studying interactions between SPE-driven, explicit, and reinforcement mechanisms of motor learning. We then present evidence of sub-component interactions between SPE-driven and reinforcement learning as well as between SPE-driven and explicit learning from studies of people with cerebellar degeneration. Finally, we discuss the implications of interactions between learning mechanism sub-components for future research in human motor learning.

Cerebellum and Emotion Memory.

Fear is an important emotion for survival, and the cerebellum has been found to contribute not only to innate affective and defensive behavior, but also to learned fear responses. Acquisition and retention of fear conditioned bradycardia and freezing have been shown to depend on the integrity of the cerebellar vermis in rodents. There is a considerable number of brain imaging studies, which observe activation of the human cerebellum in fear conditioning paradigms. Different to what one may expect based on the initial cerebellar lesion studies, activations related to the learned prediction of threat go well beyond the vermis, and are most prominent in the lateral cerebellum. Different parts of the cerebellum likely contribute to learning of autonomic, motor, emotional and cognitive responses involved in classical fear conditioning. The neural operation which is performed in the various parts of the cerebellum is frequently assumed to be the same. One hypothesis is that the cerebellum acts as, or is part of, a predictive device. More recent findings will be discussed that thecerebellum may not only be involved in the processing of sensory prediction errors, but also in the processing of reward and reward prediction errors, which may play a central role in emotions and emotional learning. Current knowledge about the intrinsic learning mechanisms underlying fear memory in the cerebellum, and its connections with subcortical and cortical fear circuitry will be presented. The chapter will conclude with a discussion on how disordered cerebellar fear learning may contribute to affective disorders.

Sound omission related brain responses in children

Action is an important way for children to learn about the world. Recent theories suggest that action is inherently accompanied by the sensory prediction of its effects. Such predictions can be revealed by rarely omitting the expected sensory consequence of the action, resulting in an omission response that is observable in the EEG. Although prediction errors play an important role in models of learning and development, little is known about omission-related brain responses in children.This study used a motor-auditory omission paradigm, testing a group of 6–8-year-old children and an adult group (N = 31 each). In an identity-specific condition, the sound coupled to the motor action was predictable, while in an identity unspecific condition the sound was unpredictable.Results of a temporal principal component analysis revealed that sound-related brain responses underlying the N1-complex differed considerably between age groups. Despite these developmental differences, omission responses (oN1) were similar between age groups. Two subcomponents of the oN1 were differently affected by specific and unspecific predictions.Results demonstrate that children, independent from the maturation of sound processing mechanisms, can implement specific and unspecific predictions as flexibly as adults. This supports theories that regard action and prediction error as important drivers of cognitive development.

Audio and visual speech emotion activate the left pre-supplementary motor area.

Sensorimotor brain areas have been implicated in the recognition of emotion expressed on the face and through nonverbal vocalizations. However, no previous study has assessed whether sensorimotor cortices are recruited during the perception of emotion in speech-a signal that includes both audio (speech sounds) and visual (facial speech movements) components. To address this gap in the literature, we recruited 24 participants to listen to speech clips produced in a way that was either happy, sad, or neutral in expression. These stimuli also were presented in one of three modalities: audio-only (hearing the voice but not seeing the face), video-only (seeing the face but not hearing the voice), or audiovisual. Brain activity was recorded using electroencephalography, subjected to independent component analysis, and source-localized. We found that the left presupplementary motor area was more active in response to happy and sad stimuli than neutral stimuli, as indexed by greater mu event-related desynchronization. This effect did not differ by the sensory modality of the stimuli. Activity levels in other sensorimotor brain areas did not differ by emotion, although they were greatest in response to visual-only and audiovisual stimuli. One possible explanation for the pre-SMA result is that this brain area may actively support speech emotion recognition by using our extensive experience expressing emotion to generate sensory predictions that in turn guide our perception.

Longitudinal functional imaging of VIP interneurons reveals sup-population specific effects of stroke that are rescued with chemogenetic therapy

Stroke profoundly disrupts cortical excitability which impedes recovery, but how it affects the function of specific inhibitory interneurons, or subpopulations therein, is poorly understood. Interneurons expressing vasoactive intestinal peptide (VIP) represent an intriguing stroke target because they can regulate cortical excitability through disinhibition. Here we chemogenetically augmented VIP interneuron excitability in a murine model of photothrombotic stroke and show that it enhances somatosensory responses and improves recovery of paw function. Using longitudinal calcium imaging, we discovered that stroke primarily disrupts the fidelity (fraction of responsive trials) and predictability of sensory responses within a subset of highly active VIP neurons. Partial recovery of responses occurred largely within these active neurons and was not accompanied by the recruitment of minimally active neurons. Importantly, chemogenetic stimulation preserved sensory response fidelity and predictability in highly active neurons. These findings provide a new depth of understanding into how stroke and prospective therapies (chemogenetics), can influence subpopulations of inhibitory interneurons.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing.

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

Bidirectional interaction between visual and motor generative models using Predictive Coding and Active Inference

In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories. We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories. We furthermore inquire the effects of bidirectional interactions between the motor and the visual modules. The architecture is tested on the control of a simulated robotic arm learning to reproduce handwritten letters.

Acoustic stimulation increases implicit adaptation in sensorimotor adaptation.

Sensorimotor adaptation is an important part of our ability to perform novel motor tasks (i.e., learning of motor skills). Efforts to improve adaptation in healthy and clinical patients using non-invasive brain stimulation methods have been hindered by inter-individual and intra-individual variability in brain susceptibility to stimulation. Here, we explore unpredictable loud acoustic stimulation as an alternative method of modulating brain excitability to improve sensorimotor adaptation. In two experiments, participants moved a cursor towards targets, and adapted to a 30º rotation of cursor feedback, either with or without unpredictable acoustic stimulation. Acoustic stimulation improved initial adaptation to sensory prediction errors in Study 1, and improved overnight retention of adaptation in Study 2. Unpredictable loud acoustic stimulation might thus be a potent method of modulating sensorimotor adaptation in healthy adults.

Evaluation of the Effect of Essential Oil Addition on the Quality Parameters and Predicted Shelf Life of Potato Yogurt

Potato, the third most important food crop worldwide, is rich in nutrients but low in protein. In contrast, milk is rich in protein. Yogurt produced through the cofermentation of potatoes and milk is a highly nutritious food. The quality and shelf life of yogurt are important topics in the dairy industry. The objective of this study was to explore the effect of the addition of essential oil (EO) on the shelf life and quality of potato yogurt. The antimicrobial effects of several EOs, the effect of perilla leaf EO (PLEO) concentration on potato yogurt, and the volatile flavor components of PLEO and PLEO potato yogurt were evaluated. The effects of storage time and temperature on the pH, microbial counts, and sensory characteristics of PLEO potato yogurt also were analyzed to establish a shelf-life model. PLEO had an antimicrobial effect and was the appropriate EO for use in the potato yogurt. A total of 69 compounds were detected in PLEO, and limonene was the main compound. PLEO had an effect on the pH, sensory characteristics, and viable bacterial counts of potato yogurt during storage. The optimal concentration of added PLEO was 0.04%. PLEO had considerable influence on volatile flavor components, and the consumer acceptance of 0.04% PLEO potato yogurt was higher than that of potato yogurt without PLEO in the later stage of storage. The shelf life of potato yogurt with PLEO was 6 days longer than that of the control yogurt. PLEO also improved the concentrations of active terpene substances in potato yogurt. The prediction models based on pH and sensory scores at 5°C were established as A = A0e0.00323t and A = A0e0.00355t, respectively. Comparison of the accuracy factor and the deviation factor of the models revealed that the sensory prediction model was more accurate than the pH prediction model. The results of this study provide theoretical and data support for the industrial development of yogurt with EOs, including extension and prediction of its shelf life.

Human Somatosensory Cortex Is Modulated during Motor Planning.

Recent data and motor control theory argues that movement planning involves preparing the neural state of primary motor cortex (M1) for forthcoming action execution. Theories related to internal models, feedback control, and predictive coding also emphasize the importance of sensory prediction (and processing) before (and during) the movement itself, explaining why motor-related deficits can arise from damage to primary somatosensory cortex (S1). Motivated by this work, here we examined whether motor planning, in addition to changing the neural state of M1, changes the neural state of S1, preparing it for the sensory feedback that arises during action. We tested this idea in two human functional MRI studies (N = 31, 16 females) involving delayed object manipulation tasks, focusing our analysis on premovement activity patterns in M1 and S1. We found that the motor effector to be used in the upcoming action could be decoded, well before movement, from neural activity in M1 in both studies. Critically, we found that this effector information was also present, well before movement, in S1. In particular, we found that the encoding of effector information in area 3b (S1 proper) was linked to the contralateral hand, similarly to that found in M1, whereas in areas 1 and 2 this encoding was present in both the contralateral and ipsilateral hemispheres. Together, these findings suggest that motor planning not only prepares the motor system for movement but also changes the neural state of the somatosensory system, presumably allowing it to anticipate the sensory information received during movement.SIGNIFICANCE STATEMENT Whereas recent work on motor cortex has emphasized the critical role of movement planning in preparing neural activity for movement generation, it has not investigated the extent to which planning also modulates the activity in the adjacent primary somatosensory cortex. This reflects a key gap in knowledge, given that recent motor control theories emphasize the importance of sensory feedback processing in effective movement generation. Here, we find through a convergence of experiments and analyses, that the planning of object manipulation tasks, in addition to modulating the activity in the motor cortex, changes the state of neural activity in different subfields of the human S1. We suggest that this modulation prepares the S1 for the sensory information it will receive during action execution.

Along the fringes of Agency: neurodevelopmental account of the obsessive mind.

The experiential core of the obsessive mind rests on subtle, primary mental phenomena (such as obsessions and so called "sensory phenomena") which precede and trigger behavioral compulsions. Converging evidence supports a possible pathophysiological role for altered corollary discharge (phenotypically expressed in sensorimotor symptoms and contributing to a reduced Sense of Agency [SoA]), in the neurodevelopment of obsessions and "sensory phenomena." In phenomenological terms, "sensory phenomena" may represent the subjective experiential resonance of an individual history of persistent inaccurate sensory predictions, whereas accompanying manifestations, such as the obsessive need for order and symmetry, may represent a compensatory attempt to mitigate "sensory phenomena" (eg, by increasing the sensory predictability of the surrounding world). Since disturbances of both SoA and Sense of Ownership have been thematized as potential pathogenetic factors in the neurodevelopment of the psychotic mind, a dimensional account of altered sensorimotor prediction may partly explain the affinities (and high comorbidity) between obsessive-compulsive disorder and schizophrenia spectrum disorders.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers.

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly's future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Sensory block level prediction of spinal anaesthesia with 0.5% hyperbaric bupivacaine: a retrospective study

There is still no consensus on how to determine the dose of spinal anaesthesia with adequate sensory block for a planned surgery. This retrospective study aimed to explore the associations of miscellaneous factors with peak sensory block level after spinal anaesthesia with hyperbaric bupivacaine, and to construct a predictive model for single-shot spinal anaesthesia. We collected the records of 401 non-pregnant adults who underwent spinal anaesthesia with 0.5% hyperbaric bupivacaine at the L3–4 or L4–5 intervertebral space for lower body surgeries. Multiple linear regression analysis was used to investigate predictors of the block level and build up the predictive model. Five variables were identified as independent predictors of the peak sensory block level, including bupivacaine dose, height, weight, gender and age. The predictive model for peak block level after spinal anaesthesia could be expressed as a formula with these five variables and the estimated predictive power was 0.72. Based on this model, it is possible to determine a reasonable dose of hyperbaric bupivacaine for spinal anaesthesia, which gives adequate sensory block required for diverse surgical procedures in various patients and could be considered as a dose reference for sensory block height in spinal anaesthesia.

Integration of predictions and afferent signals in body ownership

We aimed at investigating the contribution of sensory predictions triggered by the sight of an object moving towards the body for the sense of body ownership. We used a recently developed psychophysical discrimination task to assess body ownership in the rubber hand illusion. In this task, the participants had to choose which of the two right rubber hands in view felt most like their own, and the ownership discriminations were fitted to psychometric curves. In the current study, we occluded the visual impressions of the object moving towards one of the rubber hands (during the first two-thirds of the path) and only revealed the final third of the object's movement trajectory when it touched the rubber hand (approach-occluded condition). Alternatively, we occluded only the final part so that the main part of the movement towards the model hand was visible (touch-occluded). We compared these two conditions to an illusion baseline condition where the object was visible during the entire trajectory and contact (no-occlusion). The touch-occluded condition produced equally strong hand ownership as the baseline condition with no occlusion, while ownership perception was significantly reduced when vision of the object approaching the rubber hand was occluded (approach-occluded). Our results show that tactile predictions generated from seeing an object moving towards the body are temporally exact, and they contribute to the rubber hand illusion by integrating with temporally congruent afferent sensory signals. This finding highlights the importance of multisensory predictions in peripersonal space, object permanence, and the interplay between bottom-up sensory signals and top-down predictions in body ownership.

Speech auditory-motor adaptation to formant-shifted feedback lacks an explicit component: Reduced adaptation in adults who stutter reflects limitations in implicit sensorimotor learning.

The neural mechanisms underlying stuttering remain poorly understood. A large body of work has focused on sensorimotor integration difficulties in individuals who stutter, including recently the capacity for sensorimotor learning. Typically, sensorimotor learning is assessed with adaptation paradigms in which one or more sensory feedback modalities are experimentally perturbed in real time. Our own previous work on speech with perturbed auditory feedback revealed substantial auditory-motor learning limitations in both children and adults who stutter (AWS). It remains unknown, however, which subprocesses of sensorimotor learning are impaired. Indeed, new insights from research on upper limb motor control indicate that sensorimotor learning involves at least two distinct components: (a) an explicit component that includes intentional strategy use and presumably is driven by target error and (b) an implicit component that updates an internal model without awareness of the learner and presumably is driven by sensory prediction error. Here, we attempted to dissociate these components for speech auditory-motor learning in AWS versus adults who do not stutter (AWNS). Our formant-shift auditory-motor adaptation results replicated previous findings that such sensorimotor learning is limited in AWS. Novel findings are that neither control nor stuttering participants reported any awareness of changing their productions in response to the auditory perturbation and that neither group showed systematic drift in auditory target judgments made throughout the adaptation task. These results indicate that speech auditory-motor adaptation to formant-shifted feedback relies exclusively on implicit learning processes. Thus, limited adaptation in AWS reflects poor implicit sensorimotor learning. Speech auditory-motor adaptation to formant-shifted feedback lacks an explicit component: Reduced adaptation in adults who stutter reflects limitations in implicit sensorimotor learning.

The encoding of stochastic regularities is facilitated by action-effect predictions

Our brains continuously build and update predictive models of the world, sources of prediction being drawn for example from sensory regularities and/or our own actions. Yet, recent results in the auditory system indicate that stochastic regularities may not be easily encoded when a rare medium pitch deviant is presented between frequent high and low pitch standard sounds in random order, as reflected in the lack of sensory prediction error event-related potentials [i.e., mismatch negativity (MMN)]. We wanted to test the implication of the predictive coding theory that predictions based on higher-order generative models—here, based on action intention, are fed top-down in the hierarchy to sensory levels. Participants produced random sequences of high and low pitch sounds by button presses in two conditions: In a “specific” condition, one button produced high and the other low pitch sounds; in an “unspecific” condition, both buttons randomly produced high or low-pitch sounds. Rare medium pitch deviants elicited larger MMN and N2 responses in the “specific” compared to the “unspecific” condition, despite equal sound probabilities. These results thus demonstrate that action-effect predictions can boost stochastic regularity-based predictions and engage higher-order deviance detection processes, extending previous notions on the role of action predictions at sensory levels.

Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

Predictive learning as a network mechanism for extracting low-dimensional latent space representations

Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.