Mapping shape to visuomotor mapping: learning and generalisation of sensorimotor behaviour based on contextual information.

The accuracy of visually guided motor movements largely depends on the stability of the sensory environment that defines the required response mapping. Thus, as the environment keeps changing we constantly have to adapt our motor responses to stay accurate. The more sensory information we receive about the current state of the environment the more accurate we may be. Recruitment of additional cues that correlate with the environment can therefore aid in this adaptation process. It has previously been shown that subjects recruit previously irrelevant cues to help them switch between 2 specific visuomotor mappings (e.g. Martin et al., 1996; van Dam et al., 2008). However, in rapidly changing environments additional cues will only be of real benefit if it is possible to learn a more continuous correlation between the cue and required visuomotor response. Here we investigate transfer of explicitly trained cue-element/response-mapping combinations to other cue elements from the same continuous scale (a shape morph). In our experiment subjects performed a rapid pointing task to targets for which we manipulated the visuomotor mapping. During training subject simultaneously learned two mappings to two different target shapes. The target shapes were taken from a set of shape morphs (we morphed between spiky and circular shapes). After five sessions of 180 training trials, using catch trials, we tested subjects' performance on different target shape morphs that could either come from an interpolation or an extrapolation along the shape morph axis. Results show that for 7 out of the 12 subjects learning is not restricted to the trained shapes but interpolates and partially also extrapolates to other shapes along the morph axis. We conclude that participants learned implicitly the newly defined shape axis when trained with two distinct visuomotor mappings and they generalize their visuomotor mappings to this new dimension.

The concept of forward models in the brain, classically applied to describing on-line motor control, can in principle be extended to action planning, i.e. assuming forward sensory predictions are issued during the mere preparation of movements. To test this idea, we combined a delayed movement task with a virtual reality based manipulation of visuomotor congruence during functional magnetic resonance imaging. Participants executed simple hand movements after a delay. During the delay, two aspects of the upcoming movement could be cued: the movement type and the visuomotor mapping (i.e. congruence of executed hand movements and visual movement feedback by a glove-controlled virtual hand). Frontoparietal areas showed increased delay period activity when preparing pre-specified movements (cued > uncued). The cerebellum showed increased activity during the preparation for incongruent > congruent visuomotor mappings. The left anterior intraparietal sulcus showed an interaction effect, responding most strongly when a pre-specified (cued) movement was prepared under expected visuomotor incongruence. These results suggest that motor planning entails a forward prediction of visual body movement feedback, which can be adjusted in anticipation of nonstandard visuomotor mappings, and which is likely computed by the cerebellum and integrated with state estimates for (planned) control in the anterior intraparietal sulcus.

This study proposes a weight optimization-based ensemble framework aimed at improving genomic prediction accuracy. It incorporates 8 Bayesian models—BayesA, BayesB, BayesC, BayesBpi, BayesCpi, BayesR, BayesL, and BayesRR in the ensemble framework, where the weight assigned to each model was optimized using genetic algorithm method. The performance of the ensemble model, named EnBayes, was evaluated on 18 datasets from 4 crop species, showing improved prediction accuracy compared to individual Bayesian models. New objective functions were proposed to improve prediction accuracy in terms of both Pearson's correlation coefficient and mean square error. The accuracy of the ensemble model was found to be associated with the number of models considered in the framework, where a few more accurate models achieved similar accuracy as that of more number of less accurate models. Additionally, over-bias and under-bias models also influenced the biasness of the ensemble model's accuracy. The study also explored a meta-learning approach using Bayesian models as base learners and random forest, quantile regression forest, and ridge regression as meta-learners, with the EnBayes model outperforming this approach. While traditional genomic prediction models GBLUP and rrBLUP and machine learning models support vector machine, random forest, extreme gradient boosting, and light gradient boosting were included in the ensemble framework in addition to Bayesian models, the ensemble model achieved higher accuracy as compared to the individual Bayesian, BLUP, and machine learning models. We believe that EnBayes would contribute significantly to ongoing efforts on improving genomic prediction accuracy.

A previous study found that as monkeys learned novel mappings between visual cues and responses, neuronal activity patterns evolved at approximately the same time in both the dorsal premotor cortex (PMd) and the putamen. Here we report that, in both regions, the population activity for novel mappings came to resemble that for familiar ones as learning progressed. Both regions showed activity differences on trials with correct responses versus those with incorrect ones. In addition to these common features, we observed two noteworthy differences between PMd and putamen activity during learning. After a response choice had been made, but prior to feedback about the correctness of that choice (reward or nonreward), the putamen showed a sustained activity increase in activity, whereas PMd did not. Also in the putamen, this prereward activity was highly selective for the specific visuomotor mapping that had just been performed, and this selectivity was maintained until the time of the reward. After performance reached an asymptote, the degree of this selectivity decreased markedly to the level typical for familiar visuomotor mappings. These findings support the hypothesis that neurons in the striatum play a pivotal role in associative learning.

Don’t watch where you’re going: The neural correlates of decoupling eye and arm movements

This paper examines the effect of tobacco advertising bans on youth smoking prevalence in developing countries. Despite little direct empirical analysis of this issue, public health professionals frequently propose advertising bans as a first step in dealing with youth smoking. Using a Bayesian learning model, the paper first develops a plausible mechanism for an effect of advertising bans on youth smoking. Advertising is hypothesized to alter youth perceptions of the ubiquity of smoking and the risks associated with this behavior. Using survey-based data, a cross-country linear probability model of youth smoking prevalence is estimated, including gender results, pooled results, and results by frequency of smoking (30-day, ever-smoked). Explanatory variables are included for income, demographics, advertising bans, and several other important policy variables (health warnings, prohibition of sales to minors, school classes, antismoking messages, access in retail stores). The empirical results indicate that advertising bans have no effect on youth smoking prevalence in developing countries. This result holds for complete bans as well as partial bans. Some of the results suggest that school classes and other education efforts would be effective as a means to alter or affect youth perceptions of the risks associated with smoking.

Region map is the sparse representation of a high-resolution synthetic aperture radar (SAR) image on the middle-level semantic layer in its semantic space. Based on the semantic information of the region map, the high-resolution SAR image is divided into hybrid, structural, and homogeneous pixel subspaces. The segmentation of SAR images can be divided into these three subspaces segmentation, of which the segmentation of hybrid subspace has more challenge because of complex structures. There are often many extremely inhomogeneous areas in the hybrid pixel subspace. Are these nonconnected areas in the same or different classes? To solve this problem, a Bayesian learning model with the constraint of sketch characteristic and an initialization method is proposed to construct a structural vector that can reflect the essential features of each extremely inhomogeneous area. Then, the unsupervised segmentation of the hybrid pixel subspace can be realized by using the structural vectors of these areas in this paper. Theoretical analysis and experimental results show that the performance of the hybrid pixel subspace segmentation realized by the structural vectors based on the Bayesian learning model proposed in the paper is better than that only used by hand designing features.

Neural Correlates of Associative Learning in Trichotillomania and Skin-Picking Disorder

Effects of aging on pointing movements under restricted visual feedback conditions

BackgroundThe preoperative prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma (LUAD) is crucial for selecting the appropriate surgical approach and improving patient outcomes. Previous research has confirmed that there is a significant correlation between consolidation-to-tumor ratio (CTR) and STAS. This study aimed to develop a Bayesian deep learning (DL) model based on the CTR prior to predict STAS in patients with stage IA LUAD.MethodsThis large-scale diagnostic study included patients with solitary primary invasive LUAD who underwent complete resection between November 2017 and October 2023. Enrolled patients were randomly assigned to training, validation, and test cohorts in a 7:2:1 ratio. Using a variational Bayesian inference framework, we developed a DL model based on the CTR prior (STAS-DLPrior CTR). The performance of STAS-DLPrior CTR was compared with another DL model without the CTR prior (STAS-DLNon-prior CTR) using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsA total of 1,374 patients were included, with 961 in the training cohort, 275 in the validation cohort, and 138 in the test cohort. The results showed that CTR in the STAS-positive group was significantly higher than that in the STAS-negative group [0.63 (interquartile range, 0.36, 0.98) vs. 0.35 (interquartile range, 0.19, 0.60), P<0.001]. Compared to STAS-DLNon-prior CTR, the area under the ROC curve (AUC) tends to be higher for STAS-DLPrior CTR (0.831 vs. 0.731, P=0.06) in the validation cohort, and STAS-DLPrior CTR demonstrated a significantly higher AUC (0.858 vs. 0.637, P=0.008) in the test cohort. Additionally, the calibration curve suggested better calibration for STAS-DLPrior CTR. DCA and CIC also indicated that STAS-DLPrior CTR conferred higher clinical net benefit.ConclusionsThe proposed model based on the CTR prior offers significant advantages in predicting STAS in patients with stage IA LUAD, and incorporating doctors’ knowledge as priors can effectively guide the development of DL models.

The assumptions that agents have rational expectations, know the true structure of the economy, and have access to the same information have been dropped in favour of models of expectations formation which make less stringent assumptions and demands of agents. Chief amongst these are models which stress the role of information rigidities: agents are assumed to act as rational expectations forecasters given limited information. Either agents do not always update their forecasts to reflect the latest economic news and statistics, or they receive noisy signals regarding the current state of the economy. Because agents are assumed to act rationally given their information sets, they would be expected to pass standard tests of forecast efficiency, such as the orthogonality of their forecasts and forecast errors. But the implications of the informational rigidity models are testable on aggregate quantities, as are extensions to the basic informational rigidity models, and the evidence is reviewed in this chapter. The modelling of individual forecaster behaviour using a Bayesian learning model is also reviewed, including the possibility that forecasters interpret public information differently and that some forecasters might receive superior (more precise) signals.

Bayesian learning models with revision of evidence

The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.

The issue of services composition to offer seamless access to a variety of high level services has received widespread attention in recent years. The proposed approaches have been mainly issued from the research undertaken jointly on Workflow and AI-based planning techniques. These methods are based in general on strong assumptions: static environment, deterministic invocation of the services. However, the number of service invocation failures increase considerably due to the dynamic and uncertain nature of the ubiquitous environment. These failures can cause a degradation of the quality of the provided services and affect their continuity. Moreover, to select the best adapted service to the user's context among functionally equivalent services, a quality based selection mechanism is needed. To bring a solution to these problems, we propose in this paper a quality-based services selection approach, coupled with a learning mechanism for ensuring a flexible and failure-tolerant service composition. Our approach is based on the Markov Decision Processes (MDPs) and the Bayesian learning models. The results obtained in the different tests of simulation, show clearly the feasibility of the adopted method and the convergence of the learning mechanism.

The information that individuals can hold in working memory is quite limited, but researchers have typically studied this capacity using simple objects or letter strings with no associations between them. However, in the real world there are strong associations and regularities in the input. In an information theoretic sense, regularities introduce redundancies that make the input more compressible. The current study shows that observers can take advantage of these redundancies, enabling them to remember more items in working memory. In 2 experiments, covariance was introduced between colors in a display so that over trials some color pairs were more likely to appear than other color pairs. Observers remembered more items from these displays than from displays where the colors were paired randomly. The improved memory performance cannot be explained by simply guessing the high-probability color pair, suggesting that observers formed more efficient representations to remember more items. Further, as observers learned the regularities, their working memory performance improved in a way that is quantitatively predicted by a Bayesian learning model and optimal encoding scheme. These results suggest that the underlying capacity of the individuals' working memory is unchanged, but the information they have to remember can be encoded in a more compressed fashion.