Distorted body representations are robust to differences in experimental instructions

Several recent reports have shown that even healthy adults maintain highly distorted representations of the size and shape of their body. These distortions have been shown to be highly consistent across different study designs and dependent measures. However, previous studies have found that visual judgments of size can be modulated by the experimental instructions used, for example, by asking for judgments of the participant’s subjective experience of stimulus size (i.e., apparent instructions) versus judgments of actual stimulus properties (i.e., objective instructions). Previous studies investigating internal body representations have relied exclusively on ‘apparent’ instructions. Here, we investigated whether apparent versus objective instructions modulate findings of distorted body representations underlying position sense (Exp. 1), tactile distance perception (Exp. 2), as well as the conscious body image (Exp. 3). Our results replicate the characteristic distortions previously reported for each of these tasks and further show that these distortions are not affected by instruction type (i.e., apparent vs. objective). These results show that the distortions measured with these paradigms are robust to differences in instructions and do not reflect a dissociation between perception and belief

From Computer Metaphor to Computational Modeling: The Evolution of Computationalism

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year