Christof Koch

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment’s causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ’Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats’ adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity.

Panpsychism shares many intuitions with integrated information theory (IIT), in particular that consciousness is an intrinsic fundamental property of reality, is graded, and can be found in small amounts in simple physical systems. Unlike panpsychism, however, IIT clearly articulates which systems are conscious and which ones are not (resolving panpsychism's combination problem) and why consciousness can be adaptive. The systemic weakness of panpsychism, or any other -ism, is that they fail to offer a protracted conceptual, let alone empirical, research programme that yields novel insights or proposes new experiments. Without those, progress on the mind–body problem will not occur.

Preface : consciousness redux -- What is consciousness? -- Who is conscious? -- Animal consciousness -- Consciousness and the rest -- Consciousness and the brain -- Tracking the footprints of consciousness -- Why we need a theory of consciousness -- Of wholes -- Tools to measure consciousness -- The uber-mind and pure consciousness -- Does consciousness have a function? -- Computationalism and experience -- Computers can't simulate experience -- Consciousness : here, there but not everywhere -- Coda : why this matters -- References -- Notes.

Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is a crucial element of attentional deployment. Fourth, attention and eye movements tightly interplay, posing computational challenges with respect to the coordinate system used to control attention. And last, scene understanding and object recognition strongly constrain the selection of attended locations. Insights from these five key areas provide a framework for a computational and neurobiological understanding of visual attention.

The target article misrepresents the foundations of integrated information theory and ignores many essential publications. It, thus, falls to this lead commentary to outline the axioms and postulates of IIT and correct major misconceptions. The commentary also explains why IIT starts from phenomenology and why it predicts that only select physical substrates can support consciousness. Finally, it highlights that IIT's account of experience – a cause–effect structure quantified by integrated information – has nothing to do with “information transfer.”

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness a sh one that says what experience is and what type of physical systems can have it. Integrated information theory does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience, and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing.

Illusions that produce perceptual suppression despite constant retinal input are used to manipulate visual consciousness. Here we report on a powerful variant of existing techniques, Continuous Flash Suppression. Distinct images flashed successively around 10 Hz into one eye reliably suppress an image presented to the other eye. Compared to binocular rivalry, the duration of perceptual suppression increased more than 10-fold. Using this tool we show that the strength of the negative afterimage of an adaptor was reduced by half when it was perceptually suppressed by input from the other eye. The more likely the adaptor was completely suppressed, the larger the reduction of the afterimage intensity. Paradoxically, trial-to-trial visibility of the adaptor did not correlate with the degree of suppression. Our results imply that formation of afterimages involves neuronal structures that access input from both eyes, but that do not correspond directly to the neuronal correlates of perceptual awareness

What is the relationship between a visual percept and the underlying neuronal activity in parts of the brain? This manifesto reviews the theoretical framework of Crick and Kochfor answering these questions based on the neuroanatomy and physiology of mammalian cortex and associated subcortical structures. This evidence suggests that primates are not directly aware of neural activity in primary visual cortex, although they may be aware of such activity in extrastriate cortical areas. Psychophysical evidence in humans supporting this hypothesis is discussed.