CONSCIOUSNESS
Download - Center for Consciousness Studies - University of Arizona
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2. Neuroscience 115<br />
2.15 Specific brain areas<br />
145 Two Legs, Two Arms, One Head. Who am I? H. Henrik Ehrsson (Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden)<br />
Ask any child if his hands belong to him and the answer will be “Of course!” But how<br />
does the brain actually identify its own body? In this talk I will describe how cognitive neuroscientists<br />
have recently begun to address this fundamental question. It has been known for<br />
some time that patients with damage to their premotor or parietal lobes can fail to recognize<br />
their own limbs (e.g. after stroke). This suggests that these brain regions are involved in<br />
generating a sense of body ownership, but says nothing about how this is achieved. In my<br />
presentation I will present experiments that suggest that multisensory mechanisms are crucial<br />
for how we come to experience that we own our body. The hypothesis is that parts of the<br />
body are distinguished from the external world by the patterns they produce of correlated<br />
information from different sensory modalities (vision, touch and muscle sense). These correlations<br />
are hypothesized to be detected by neuronal populations that integrate multisensory<br />
information from the space near the body. We have recently used a combination of functional<br />
magnetic resonance imaging and human behavioral experiments to test these predictions. To<br />
change the feeling of body ownership, perceptual illusions were used where healthy individuals<br />
experienced that a rubber hand was their own, that a mannequin was their body, or, that<br />
they are outside their physical body and inside the body of other individual. Our behavioral<br />
results demonstrate that ownership of limbs and entire bodies depend on the temporal and<br />
spatial congruency of visual, tactile and proprioceptive signals in body-centered reference<br />
frames, and that the visual information from first person perspective plays a crucial role. Our<br />
imaging data show that neuronal populations in the premotor and intraparietal cortex are active<br />
when humans sense they own limbs, which supports the hypothesis that the integration of<br />
multisensory information in body-centered coordinates is crucial for ownership. These results<br />
are of fundamental importance because they identify the brain mechanisms that produce the<br />
feeling of ownership of one’s entire body. By clarifying how the normal brain produces a<br />
sense of body ownership, we can learn to project ownership onto artificial bodies and simulated<br />
virtual ones; and even make two people have the experience of swapping bodies with<br />
one another. This could have ground-breaking applications in the fields of virtual reality and<br />
neuro-prosthetics. PL3<br />
2.16 Miscellaneous<br />
146 A Systems Model for Selectivity Between Default and Task Modes Kevin Brown,<br />
Jonathan Smallwood; Jonathan W. Schooler; Jean M. Carlson <br />
(Physics, University of California Santa Barbara, Santa Barbara, CA)<br />
Neuroimaging data suggests that the human brain has at least two attentional states - a<br />
task-focused mode in which attention is focused externally on the task at hand and a less<br />
constrained internal mode dubbed the default state. An essential step to understanding the<br />
distinct processes governed by these different states is to understand the way in which the<br />
brain divides finite cognitive resources and selects among externally- and internally-driven<br />
goal states. We present a coarse-grained, systems-level dynamical model which explores the<br />
temporal nature of the interaction between these different modes, in which we examine the<br />
role of the locus coeruleus in modulating between external events and internally generated<br />
signals. We discuss the implications of the model for understanding mindwandering and attentional<br />
failures, which we characterize as partitioning of cognitive effort away from externallydriven<br />
tasks to internally-generated ones. We also discuss the potential of directly fitting the<br />
dynamical model to multimodal dynamical measurements (surface EEG, BOLD fMRI and<br />
pupillometry data). C3