A System Computational Model of Implicit Emotional Learning

Use of cognitive correction training improves learning for children with mathematics learning disability

Children with normal intelligence may experience varying degrees of mathematics learning disabilities (MD). This study aims to conduct training to improve the brain's cognitive ability for mathematics learning by focusing on two important mathematical cognitive abilities. This was a prospective study of 70 children in grades 2-5 from two primary schools in Changzhou and with MD enrolled from June 2015 to February 2017. The children were randomized 1:1 to the training and control groups. A training cycle included 40 sessions (5/weeks) (30 min each session). The efficacy of learning was assessed by assessing number learning and graph reasoning, and by using the Raven standard reasoning test score. In the training group, backward number memory (from 6.1 ± 1.8 to 6.7 ± 1.3, P = 0.02), number sequential connection (from 54.4 ± 14.5 to 47.1 ± 12.1, P < 0.01), and rapid graph judgment (from 531.9 ± 76.3 to 557.8 ± 85.7, P = 0.04) were improved by training, while there was no effect on forward number memory (P = 0.13). There were significant differences in total score and scores of b, c, and e series before and after training (all P < 0.05). The children in the control group had no improvement after 8 weeks. There was a correlation between the ability of rapid graphic judgment before and after training and the score of the Raven E series (r = 0.384, P = 0.024), and between the score of the Raven C series and the score of the Raven D series (r = 0.468, P = 0.013). Cognitive correction training improved the sensitivity to numbers and mathematics learning in children with MD.

Understanding and exploiting prediction errors minimization within the brain in pharmacological treatments

The human brain can be conceptualized as an inference machine that actively predicts and explains its sensations and perceptions: it makes predictions through a probabilistic model. Such a model is continuously and implicitly updated by the computation and minimization of weighted prediction errors, as shown by numerous studies and experimental results. Nevertheless, such an algorithmic functioning of the brain has not been exploited in the neuropharmacological practice. In this manuscript, we show by theoretical analysis and model fitting of previously published data in two different contexts, how it is possible to increase the effectiveness of neuropharmacological and immunosuppressive drugs, through the modulation of the weighted prediction errors. Moreover, on the basis of the proposed model, we derive an optimized drug administration schedule able to increase the drug effectiveness of one order of magnitude, in psoriasis treatment. We make important testable predictions, evidencing the impact and the potential benefit of prediction errors modulation within the brain, in the pharmacotherapeutic practice. Finally, our results lead to a novel formal theory of implicit learning, and shed lights on the actual roles of classical conditioning and UCS revaluation in behavioral and pharmacological conditioning experiments. The potential practical implications of our results are many: the reduction of drugs side effects; the maximization of the therapeutic outcome; a more effective treatment for chronic pain, certain neuropsychiatric diseases, autoimmune diseases and allergic diseases.

Placebo Response is Driven by UCS Revaluation: Evidence, Neurophysiological Consequences and a Quantitative Model

Despite growing scientific interest in the placebo effect and increasing understanding of neurobiological mechanisms, theoretical modeling of the placebo response remains poorly developed. The most extensively accepted theories are expectation and conditioning, involving both conscious and unconscious information processing. However, it is not completely understood how these mechanisms can shape the placebo response. We focus here on neural processes which can account for key properties of the response to substance intake. It is shown that placebo response can be conceptualized as a reaction of a distributed neural system within the central nervous system. Such a reaction represents an integrated component of the response to open substance administration (or to substance intake) and is updated through “unconditioned stimulus (UCS) revaluation learning”. The analysis leads to a theorem, which proves the existence of two distinct quantities coded within the brain, these are the expected or prediction outcome and the reactive response. We show that the reactive response is updated automatically by implicit revaluation learning, while the expected outcome can also be modulated through conscious information processing. Conceptualizing the response to substance intake in terms of UCS revaluation learning leads to the theoretical formulation of a potential neuropharmacological treatment for increasing unlimitedly the effectiveness of a given drug.