RESUMEN

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.

Asunto(s)

RESUMEN

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.

RESUMEN

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.

Asunto(s)