A new aspect of sign and its implications for the Theory of Communication

Este artigo propõe que a semiótica peirceana pode oferecer bases tanto lógicas quanto epistemológicas para a busca de uma teoria geral da comunicação. No entanto, o desenvolvimento de uma teoria semiótica da comunicação depende, em primeiro lugar, de uma melhor compreensão dos aspectos formais do signo, tarefa atribuída por Peirce à gramática, o primeiro ramo de sua semiótica. Nós apresentamos uma análise das relações do signo, revelando um aspecto não trabalhado por Peirce, ampliando seu número para onze. Este novo aspecto é a relação triádica entre signo, objeto dinâmico e interpretante dinâmico (S-OD-ID). Nós defendemos que esta relação é essencial para a compreensão da comunicação como semiose, por dar conta da repetição ou redundância do signo comunicativo, quando se cria ou transmite informação. O artigo pretende dar um passo a mais na direção de uma teoria da comunicação verdadeiramente universal, através do vínculo entre a semiótica peirceana e a moderna filosofia da linguagem.

Two-Item Sentence Comprehension by a Dog (Canis familiaris)

Syntax use by non-human animals remains a controversial issue. We present here evidence that a dog may respond to verbal requests composed of two independent terms, one referring to an object and the other to an action to be performed relative to the object. A female mongrel dog, Sofia, was initially trained to respond to action (point and fetch) and object (ball, key, stick, bottle and bear) terms which were then presented as simultaneous, combinatorial requests (e. g. ball fetch, stick point). Sofia successfully responded to object-action requests presented as single sentences, and was able to flexibly generalize her performance across different contexts. These results provide empirical evidence that dogs are able to extract the information contained in complex messages and to integrate it in directed performance, an ability which is shared with other linguistically trained animals and may represent a forerunner of syntactic functioning.

DTI voxelwise analysis did not differentiate older depressed patients from older subjects without depression

Introduction: Neuroimaging has been widely used in studies to investigate depression in the elderly because it is a noninvasive technique, and it allows the detection of structural and functional brain alterations. Fractional anisotropy (FA) and mean diffusivity (MD) are neuroimaging indexes of the microstructural integrity of white matter, which are measured using diffusion tensor imaging (DTI). The aim of this study was to investigate differences in FA or MD in the entire brain without a previously determined region of interest (ROI) between depressed and non-depressed elderly patients. Method: Brain magnetic resonance imaging scans were obtained from 47 depressed elderly patients, diagnosed according to DSM-IV criteria, and 36 healthy elderly patients as controls. Voxelwise statistical analysis of FA data was performed using tract-based spatial statistics (TBSS). Results: After controlling for age, no significant differences among FA and MD parameters were observed in the depressed elderly patients. No significant correlations were found between cognitive performance and FA or MD parameters. Conclusion: There were no significant differences among FA or MD values between mildly or moderately depressed and non-depressed elderly patients when the brain was analyzed without a previously determined ROI. (C) 2012 Elsevier Ltd. All rights reserved.

Emergence of sensory selection mechanisms in Artificial Life simulations

Background The evolutionary advantages of selective attention are unclear. Since the study of selective attention began, it has been suggested that the nervous system only processes the most relevant stimuli because of its limited capacity [1]. An alternative proposal is that action planning requires the inhibition of irrelevant stimuli, which forces the nervous system to limit its processing [2]. An evolutionary approach might provide additional clues to clarify the role of selective attention. Methods We developed Artificial Life simulations wherein animals were repeatedly presented two objects, "left" and "right", each of which could be "food" or "non-food." The animals' neural networks (multilayer perceptrons) had two input nodes, one for each object, and two output nodes to determine if the animal ate each of the objects. The neural networks also had a variable number of hidden nodes, which determined whether or not it had enough capacity to process both stimuli (Table 1). The evolutionary relevance of the left and the right food objects could also vary depending on how much the animal's fitness was increased when ingesting them (Table 1). We compared sensory processing in animals with or without limited capacity, which evolved in simulations in which the objects had the same or different relevances. Table 1. Nine sets of simulations were performed, varying the values of food objects and the number of hidden nodes in the neural networks. The values of left and right food were swapped during the second half of the simulations. Non-food objects were always worth -3. The evolution of neural networks was simulated by a simple genetic algorithm. Fitness was a function of the number of food and non-food objects each animal ate and the chromosomes determined the node biases and synaptic weights. During each simulation, 10 populations of 20 individuals each evolved in parallel for 20,000 generations, then the relevance of food objects was swapped and the simulation was run again for another 20,000 generations. The neural networks were evaluated by their ability to identify the two objects correctly. The detectability (d') for the left and the right objects was calculated using Signal Detection Theory [3]. Results and conclusion When both stimuli were equally relevant, networks with two hidden nodes only processed one stimulus and ignored the other. With four or eight hidden nodes, they could correctly identify both stimuli. When the stimuli had different relevances, the d' for the most relevant stimulus was higher than the d' for the least relevant stimulus, even when the networks had four or eight hidden nodes. We conclude that selection mechanisms arose in our simulations depending not only on the size of the neuron networks but also on the stimuli's relevance for action.