Towards structural systematicity in distributed,statically bound visual representations

The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships. We argue that the main desiderata of a representational system—productivity and systematicity—can (indeed, for a number of reasons, should) be achieved without recourse to the classical, proposition‐like compositionality. We show how this can be done, by describing a systematic and productive model of the representation of visual structure, which relies on static rather than dynamic binding and uses coarsely coded rather than atomic shape primitives.     

Criteria for consciousness in humans and other mammals

The standard behavioral index for human consciousness is the ability to report events with accuracy. While this method is routinely used for scientific and medical applications in humans, it is not easy to generalize to other species. Brain evidence may lend itself more easily to comparative testing. Human consciousness involves widespread, relatively fast low-amplitude interactions in the thalamocortical core of the brain, driven by current tasks and conditions. These features have also been found in other mammals, which suggests that consciousness is a major biological adaptation in mammals. We suggest more than a dozen additional properties of human consciousness that may be used to test comparative predictions. Such homologies are necessarily more remote in non-mammals, which do not share the thalamocortical complex. However, as we learn more we may be able to make "deeper" predictions that apply to some birds, reptiles, large-brained invertebrates, and perhaps other species.     

Towards a computational theory of experience

A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must satisfy for it to constitute not just a sufficient but a necessary, and therefore naturalistic and intrinsic, explanation of qualia. We show that a common assumption behind many neurocomputational theories of the mind, according to which mind states can be formalized solely in terms of instantaneous vectors of activities of representational units such as neurons, does not meet the requisite conditions, in part because it relies on inactive units to shape presently experienced qualia and implies a homogeneous representation space, which is devoid of intrinsic structure. We then sketch a naturalistic computational theory of qualia, which posits that experience is realized by dynamical activity-space trajectories (rather than points) and that its richness is measured by the representational capacity of the trajectory space in which it unfolds.