The Contribution of Parietal Cortex to Visual Salience

Unique stimuli stand out. In spite of an abundance of competing sensory stimuli, the detection of the most salient ones occurs without effort, and that detection contributes to the guidance of adaptive behavior. Neurons sensitive to the salience of visual stimuli are widespread throughout the primate visual system and are thought to shape the selection of visual targets. However, the source of the salience computation has remained elusive. Among the possible candidates are areas within posterior parietal cortex, which appear to be crucial in the control of visual attention and are thought to play a unique role in representing stimulus salience. Here we show that reversible inactivation of parietal cortex not only selectively reduces the representation of visual salience within the brain, but it also diminishes the influence of salience on visually guided behavior. These results demonstrate a distinct contribution of parietal areas to vision and visual attention

The Contribution of Parietal Cortex to Visual Salience

Unique stimuli stand out. In spite of an abundance of competing sensory stimuli, the detection of the most salient ones occurs without effort, and that detection contributes to the guidance of adaptive behavior. Neurons sensitive to the salience of visual stimuli are widespread throughout the primate visual system and are thought to shape the selection of visual targets. However, the source of the salience computation has remained elusive. Among the possible candidates are areas within posterior parietal cortex, which appear to be crucial in the control of visual attention and are thought to play a unique role in representing stimulus salience. Here we show that reversible inactivation of parietal cortex not only selectively reduces the representation of visual salience within the brain, but it also diminishes the influence of salience on visually guided behavior. These results demonstrate a distinct contribution of parietal areas to vision and visual attention

Emotional arousal amplifies the effects of biased competition in the brain

The arousal-biased competition model predicts that arousal increases the gain on neural competition between stimuli representations. Thus, the model predicts that arousal simultaneously enhances processing of salient stimuli and impairs processing of relatively less-salient stimuli. We tested this model with a simple dot-probe task. On each trial, participants were simultaneously exposed to one face image as a salient cue stimulus and one place image as a non-salient stimulus. A border around the face cue location further increased its bottom-up saliency. Before these visual stimuli were shown, one of two tones played: one that predicted a shock (increasing arousal) or one that did not. An arousal-by-saliency interaction in category-specific brain regions (fusiform face area for salient faces and parahippocampal place area for non-salient places) indicated that brain activation associated with processing the salient stimulus was enhanced under arousal whereas activation associated with processing the non-salient stimulus was suppressed under arousal. This is the first functional magnetic resonance imaging study to demonstrate that arousal can enhance information processing for prioritized stimuli while simultaneously impairing processing of non-prioritized stimuli. Thus, it goes beyond previous research to show that arousal does not uniformly enhance perceptual processing, but instead does so selectively in ways that optimizes attention to highly salient stimuli

Specialized Signals for Spatial Attention in the Ventral and Dorsal Visual Streams

Neuroscientists have traditionally conceived the visual system as having a ventral stream of vision for perception and a dorsal one associated with vision for action. However functional differences between them have become relatively blurred in recent years, not the least by the systematic parallel mapping of functions allowed by functional magnetic resonance imaging (fMRI). Here, using fMRI to simultaneously monitor several brain regions, we first studied a hallmark ventral stream computation: the processing of faces. We did so by probing responses to motion, an attribute whose processing is typically associated with the dorsal stream. In humans, it is known that face-selective regions in the superior temporal sulcus (STS) show enhanced responses to facial motion that are absent in the rest of the face-processing system. In macaques, face areas also exist, but their functional specializations for facial motion are unknown. We showed static and moving face and non-face objects to macaques and humans in an fMRI experiment in order to isolate potential functional specializations in the ventral stream face-processing system and to motivate putative homologies across species. Our results revealed all macaque face areas showed enhanced responses to moving faces. There was a difference between more dorsal face areas in the fundus of the STS, which are embedded in motion responsive cortex and ventral ones, where enhanced responses to motion interacted with object category and could not be explained by their proximity to motion responsive cortex. In humans watching the same stimuli, only the STS face area showed an enhancement for motion. These results suggest specializations for motion exist in the macaque face-processing network but they do not lend themselves to a direct equalization between human and macaque face areas. We then proceeded to compare ventral and dorsal stream functions in terms of their code for spatial attention, whose control was typically associated with the dorsal stream and prefrontal areas. We took advantage of recent fMRI studies that provide a systematic map of cortical areas modulated by spatial attention and suggest PITd, a ventral stream area in the temporal lobe, can support endogenous attention control. Covert attention and stimulus selection by saccades are represented in the same maps of visual space in attention control areas. Difficulties interpreting this multiplicity of functions led to the proposal that they encode priority maps, where multiple sources are summed to form a single priority signal, agnostic as to its eventual use by downstream areas. Using a paradigm that dissociates covert attention and response selection, we test this hypothesis with fMRI-guided electrophysiology in two cortical areas: parietal area LIP, where the priority map was first proposed to apply, and temporal area PITd. Our results indicate LIP sums disparate signals, but as a consequence independent channels of spatial information exist for attention and response planning. PITd represents relevant locations and, rather than summing signals, contains a single map for covert attention. Our findings have the potential to resolve a longstanding controversy about the nature of spatial signals in LIP and establish PITd as a robust map for covert attention in the ventral stream. Together, our results suggest that while the distribution of labor between ventral stream and dorsal stream areas is less linear than what a what a rough depiction of them can suggest, it is illuminated by their proposed function as supporting vision for perception and vision for action respectively

Identifying the Computational Requirements of an Integrated Top-Down-Bottom-Up Model for Overt Visual Attention within an Active Vision System.

Computational visual attention systems have been constructed in order for robots and other devices to detect and locate regions of interest in their visual world. Such systems often attempt to take account of what is known of the human visual system and employ concepts, such as ‘active vision’, to gain various perceived advantages. However, despite the potential for gaining insights from such experiments, the computational requirements for visual attention processing are often not clearly presented from a biological perspective. This was the primary objective of this study, attained through two specific phases of investigation: 1) conceptual modeling of a top-down-bottom-up framework through critical analysis of the psychophysical and neurophysiological literature, 2) implementation and validation of the model into robotic hardware (as a representative of an active vision system). Seven computational requirements were identified: 1) transformation of retinotopic to egocentric mappings, 2) spatial memory for the purposes of medium-term inhibition of return, 3) synchronization of ‘where’ and ‘what’ information from the two visual streams, 4) convergence of top-down and bottom-up information to a centralized point of information processing, 5) a threshold function to elicit saccade action, 6) a function to represent task relevance as a ratio of excitation and inhibition, and 7) derivation of excitation and inhibition values from object-associated feature classes. The model provides further insight into the nature of data representation and transfer between brain regions associated with the vertebrate ‘active’ visual attention system. In particular, the model lends strong support to the functional role of the lateral intraparietal region of the brain as a primary area of information consolidation that directs putative action through the use of a ‘priority map’

Out of focus – brain attention control deficits in adult ADHD

Modern environments are full of information, and place high demands on the attention control mechanisms that allow the selection of information from one (focused attention) or multiple (divided attention) sources, react to changes in a given situation (stimulus-driven attention), and allocate effort according to demands (task-positive and task-negative activity). We aimed to reveal how attention deficit hyperactivity disorder (ADHD) affects the brain functions associated with these attention control processes in constantly demanding tasks. Sixteen adults with ADHD and 17 controls performed adaptive visual and auditory discrimination tasks during functional magnetic resonance imaging (fMRI). Overlapping brain activity in frontoparietal saliency and default-mode networks, as well as in the somato-motor, cerebellar, and striatal areas were observed in all participants. In the ADHD participants, we observed exclusive activity enhancement in the brain areas typically considered to be primarily involved in other attention control functions: During auditory-focused attention, we observed higher activation in the sensory cortical areas of irrelevant modality and the default-mode network (DMN). DMN activity also increased during divided attention in the ADHD group, in turn decreasing during a simple button-press task. Adding irrelevant stimulation resulted in enhanced activity in the salience network. Finally, the irrelevant distractors that capture attention in a stimulus-driven manner activated dorsal attention networks and the cerebellum. Our findings suggest that attention control deficits involve the activation of irrelevant sensory modality, problems in regulating the level of attention on demand, and may encumber top-down processing in cases of irrelevant information. (C) 2018 Elsevier B.V. All rights reserved.Peer reviewe

Processing of quantitative information, investigated with fMRI.

Ever since the discovery of the ‘number neurons’, the neural representation of quantity in the brain has been thought of as a number-selective coding system. In such a system, the neuron is activated by a specific quantity but numerically close quantities also activate the neuron. Recent fMRI studies also confirmed the existence of a number-selective system in humans. Several computational modelling studies predicted a number-sensitive coding stage as a necessary preceding stage to the number-selective neurons (Verguts & Fias, 2004). In this coding scheme, the coding is analogous to the number it represents. This can be implemented by neurons that respond monotonically to number (e.g., more strongly for larger numbers). Recently, the biological reality of such a system has been demonstrated by use of single-cell recording, in the lateral intraparietal area (LIP) of the macaque monkey. In this thesis, we searched for evidence of number-sensitive coding in humans. Using a priming paradigm, we found behavioural evidence for a number-sensitive system in humans for small non-symbolic numerosities (1 to 5). Using event-related fMRI, we showed number-sensitive activation in the human LIP area in the same number range. Remarkably, we could not extend these results for larger numerosities (2 to 64). Whereas the lack of results in the behavioural priming experiment could be due to an insensitivity of the method, this was not a plausible explanation in the fMRI experiment, as the activity measured in human LIP significantly decreased for numerosities larger than 8. We therefore concluded that the number-sensitive system is liable to a capacity limit for higher numerosities, which could be caused by the use of lateral inhibition. We further suggest that the implementation of this lateral inhibition is dependent on the particular task set, and that the capacity limit is not present (or less stringent) when numerosity is not behaviourally relevant. This could explain the finding of number-sensitive neurons for larger numerosities in monkeys. Finally, we suggest that a different mechanism is employed when numerical value of large numerosities is relevant. This leads to the conclusion that dot patterns in the small and large number range are processed differently

Cognitive effort modulates connectivity between dorsal anterior cingulate cortex and task-relevant cortical areas

Investment of cognitive effort is required in everyday life and has received ample attention in recent neurocognitive frameworks. The neural mechanism of effort investment is thought to be structured hierarchically, with dorsal anterior cingulate cortex (dACC) at the highest level, recruiting task-specific upstream areas. In the current fMRI study, we tested whether dACC is generally active when effort demand is high across tasks with different stimuli, and whether connectivity between dACC and task-specific areas is increased depending on the task requirements and effort level at hand. For that purpose, a perceptual detection task was administered that required male and female human participants to detect either a face or a house in a noisy image. Effort demand was manipulated by adding little (low effort) or much (high effort) noise to the images. Results showed a network of dACC, anterior insula (AI), and intraparietal sulcus (IPS) to be more active when effort demand was high, independent of the performed task (face or house detection). Importantly, effort demand modulated functional connectivity between dACC and face-responsive or house-responsive perceptual areas, depending on the task at hand. This shows that dACC, AI, and IPS constitute a general effort-responsive network and suggests that the neural implementation of cognitive effort involves dACC-initiated sensitization of task-relevant areas