Learning at Variable Attentional Load Requires Cooperation of Working Memory, Meta-learning and Attention-augmented Reinforcement Learning

Flexible learning of changing reward contingencies can be realized with different strategies. A fast learning strategy involves using working memory of recently rewarded objects to guide choices. A slower learning strategy uses prediction errors to gradually update value expectations to improve choices. How the fast and slow strategies work together in scenarios with real-world stimulus complexity is not well known. Here, we aim to disentangle their relative contributions in rhesus monkeys while they learned the relevance of object features at variable attentional load. We found that learning behavior across six monkeys is consistently best predicted with a model combining (i) fast working memory and (ii) slower reinforcement learning from differently weighted positive and negative prediction errors as well as (iii) selective suppression of nonchosen feature values and (iv) a meta-learning mechanism that enhances exploration rates based on a memory trace of recent errors. The optimal model parameter settings suggest that these mechanisms cooperate differently at low and high attentional loads. Whereas working memory was essential for efficient learning at lower attentional loads, enhanced weighting of negative prediction errors and meta-learning were essential for efficient learning at higher attentional loads. Together, these findings pinpoint a canonical set of learning mechanisms and suggest how they may cooperate when subjects flexibly adjust to environments with variable real-world attentional demands.

Healthy and Balanced Nutrition for Children through Physical Education Classes

Introduction. In recent years, the rate of childhood obesity has been on the rise, currently standing at levels close to 20%. This means that one in five children is more likely to suffer from cardiovascular or metabolic diseases. Physical Education classes are therefore an ideal way to raise awareness among children and their families about healthy and balanced eating habits. Method. A total of 113 primary school students, aged 9–12 years, participated in the study. In order to analyze the data, a structural equation model (SEM) was used to analyze the influence between the variables. Results. The SEM results revealed that a controlling social context showed a negative prediction of psychological need satisfaction and a positive prediction of frustration. However, an autonomy supportive social context showed a negative prediction of psychological need satisfaction and a positive prediction of psychological need satisfaction. Frustration of psychological needs was negatively related to motivation, whereas satisfaction was positively related to motivation. In turn, motivation was positively related to each of the factors of the theory of planned behaviour. Finally, intention to follow a healthy diet was positively related to the Mediterranean diet. Discussion. These results revealed the importance of social context and physical education classes in the adoption of a balanced diet.

The Neural Correlates of Cued Reward Omission

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla–Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor (CI). Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold.

Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence, and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, that is the temporal difference error. A major difference between areas was the overall activity level of reward responses: reward responses in dorsolateral striatum were positively shifted, lacking inhibitory responses to negative prediction errors. The differences in dopamine signals put specific constraints on the properties of behaviors controlled by dopamine in these regions.

Desempeño del índice proteína/creatinina para predecir proteinuria significativa en pacientes con sospecha de preeclampsia

Objective: To assess the performance of the protein/creatinine index to predict proteinuria significantly and secondarily, to find the best cut-off point that had better sensitivity and specificity. Methods: Diagnostic test study developed in a cross-sectional design of patients exposed to the hypertensive syndrome of pregnancy who did or did not develop posterior preeclampsia. 173 patients were selected non-probabilistically. The new cutoff of protein/creatinine index was found using the ROC curve, we calculated sensitivity, specificity, positive and negative prediction value, positive and negative likelihood ratios and find the Spearman correlation with 24 hours urine proteinuria. Data analysis was performed using STATA software version 13. Results: The new cutoff was 0.39 with 68.3% sensitivity, specificity 73.5%, positive prediction value 91.3%, negative prediction value 36.2%, likelihood ratios (+) 2.58, likelihood ratios (-) 0.43 and an area under the curve of 0.7799. Excellent correlation was obtained with 24 hours proteinuria (rs =0.9308, p=0.000). Conclusion: Although the new cutoff for the protein/creatinine index is different from conventional, this conventional this is more specific and therefore may be useful in the outpatient management on patients with low suspicious of preeclampsia in our population. Keywords: Preeclampsia, Protein/Creatinine index, 24 hours proteinuria.

CORRELATION OF D-DIMER LEVEL WITH THE PRESENCE AND SEVERITY OF PULMONARY EMBOLISM ON COMPUTED TOMOGRAPHY PULMONARY ANGIOGRAPHY

Introduction: Early diagnosis of pulmonary embolism can reduce morbidity and motility. D-dimer is well known parameter having high negative prediction value. This study focused on role of D-dimer in early prediction of presence and severity of pulmonary embolism. Material and Methods: Thirty patients with clinical suspicion of pulmonary embolism along with high D-dimer value were included in this study. All selected patients underwent computed tomography pulmonary angiography assessment. D-dimer value was correlated with presence and proximity of pulmonary embolism. Results: Out of thirty selected patients 50% had pulmonary embolism on computed tomography pulmonary angiography assessment. D-dimer value correlated well with presence and proximity of pulmonary embolism. Conclusion: D-dimer value more than 4000 ng/ml had high positive prediction value (79%) in suspected clinical cases. Value more than 8000 ng/ml further improve value to nearly 100% in suspected cases.

Proposal and Investigation of an Artificial Intelligence (AI)-Based Cloud Resource Allocation Algorithm in Network Function Virtualization Architectures

The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed resource prediction with the minimization of symmetric loss functions like Mean Squared Error. When inevitable prediction errors are made, the prediction methodologies are not able to differently weigh positive and negative prediction errors that could impact the total network cost. In fact if the predicted traffic is higher than the real one then an over allocation cost, referred to as over-provisioning cost, will be paid by the network operator; conversely, in the opposite case, Quality of Service degradation cost, referred to as under-provisioning cost, will be due to compensate the users because of the resource under allocation. In this paper we propose and investigate a resource allocation strategy based on a Long Short Term Memory algorithm in which the training operation is based on the minimization of an asymmetric cost function that differently weighs the positive and negative prediction errors and the corresponding over-provisioning and under-provisioning costs. In a typical traffic and network scenario, the proposed solution allows for a cost saving by 30% with respect to the case of solution with symmetric cost function.