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

The selective attention for identification model (SAIM): Simulating visual search in natural colour images

We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM. © Springer-Verlag Berlin Heidelberg 2007