Excitatory versus inhibitory feedback in Bayesian formulations of scene construction

La investigación que se presenta en este trabajo, siendo de enfoque cuantitativo, tiene el objetivo de obtener la relación que hay entre las fases del neuromarketing y los elementos de la publicidad animada “Todo va a estar bien” de Rímac Seguros en los alumnos de la facultad de comunicaciones de la Universidad de Ciencias y Artes de América Latina (Lima 2017). Para su resultado, se utilizó el diseño correlacional, en un tipo de investigación no experimental. El nivel de ésta es descriptiva - correlacional y su método de investigación deductivo, inductivo y técnica estadística para conseguir los resultados precisos en una muestra de 50 estudiantes para su análisis utilizando la encuesta como instrumento de medición. Se llega a la conclusión de tener una relación entre las fases del neuromarketing y los elementos de la animación publicitaria de Rímac Seguros en este tipo de público estudiado, confirmando las hipótesis que se plantearon desde el principio

Excitatory versus inhibitory feedback in Bayesian formulations of scene construction

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects—as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures

Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation

Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation