Why is “blindsight” blind? A new perspective on primary visual cortex, recurrent activity and visual awareness

The neuropsychological phenomenon of blindsight has been taken to suggest that the primary visual cortex (V1) plays a unique role in visual awareness, and that extrastriate activation needs to be fed back to V1 in order for the content of that activation to be consciously perceived. The aim of this review is to evaluate this theoretical framework and to revisit its key tenets. Firstly, is blindsight truly a dissociation of awareness and visual detection? Secondly, is there sufficient evidence to rule out the possibility that the loss of awareness resulting from a V1 lesion simply reflects reduced extrastriate responsiveness, rather than a unique role of V1 in conscious experience? Evaluation of these arguments and the empirical evidence leads to the conclusion that the loss of phenomenal awareness in blindsight may not be due to feedback activity in V1 being the hallmark awareness. On the basis of existing literature, an alternative explanation of blindsight is proposed. In this view, visual awareness is a “global” cognitive function as its hallmark is the availability of information to a large number of perceptual and cognitive systems; this requires inter-areal long-range synchronous oscillatory activity. For these oscillations to arise, a specific temporal profile of neuronal activity is required, which is established through recurrent feedback activity involving V1 and the extrastriate cortex. When V1 is lesioned, the loss of recurrent activity prevents inter-areal networks on the basis of oscillatory activity. However, as limited amount of input can reach extrastriate cortex and some extrastriate neuronal selectivity is preserved, computations involving comparison of neural firing rates within a cortical area remain possible. This enables “local” read-out from specific brain regions, allowing for the detection and discrimination of basic visual attributes. Thus blindsight is blind due to lack of “global” long-range synchrony, and it functions via “local” neural readout from extrastriate areas.

Network mechanisms of intentional learning

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flex- ible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ven- tral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple dis- tinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

Fractionating Human Intelligence

What makes one person more intellectually able than another? Can the entire distribution of human intelligence be accounted for by just one general factor? Is intelligence supported by a single neural system? Here, we provide a perspective on human intelligence that takes into account how general abilities or ‘‘factors’’ reflect the functional organiza- tion of the brain. By comparing factor models of individual differences in performance with factor models of brain functional organization, we demon- strate that different components of intelligence have their analogs in distinct brain networks. Using simulations based on neuroimaging data, we show that the higher-order factor ‘‘g’’ is accounted for by cognitive tasks corecruiting multiple networks. Finally, we confirm the independence of these com- ponents of intelligence by dissociating them using questionnaire variables. We propose that intelli- gence is an emergent property of anatomically distinct cognitive systems, each of which has its own capacity.