The lateral intraparietal sulcus takes viewpoint changes into account during memory-guided attention in natural scenes

Negative emotions enhance memory-guided attention in a visual search task by increasing frontoparietal, insular, and parahippocampal cortical activity

Previous literature demonstrated that long-term memory representations guide spatial attention during visual search in real-world pictures. However, it is currently unknown whether memory-guided visual search is affected by the emotional content of the picture. During functional magnetic resonance imaging (fMRI), participants were asked to encode the position of high-contrast targets embedded in emotional (negative or positive) or neutral pictures. At retrieval, they performed a visual search for targets presented at the same location as during encoding, but at a much lower contrast. Behaviorally, participants detected more accurately targets presented in negative pictures compared to those in positive or neutral pictures. They were also faster in detecting targets presented at encoding in emotional (negative or positive) pictures than in neutral pictures, or targets not presented during encoding (i.e., memory-guided attention effect). At the neural level, we found increased activation in a large circuit of regions involving the dorsal and ventral frontoparietal cortex, insular and parahippocampal cortex, selectively during the detection of targets presented in negative pictures during encoding. We propose that these regions might form an integrated neural circuit recruited to select and process previously encoded target locations (i.e., memory-guided attention sustained by the frontoparietal cortex) embedded in emotional contexts (i.e., emotional contexts recollection supported by the parahippocampal cortex and emotional monitoring supported by the insular cortex). Ultimately, these findings reveal that negative emotions can enhance memory-guided visual search performance by increasing neural activity in a large-scale brain circuit, contributing to disentangle the complex relationship between emotion, attention, and memory.

A large-scale neurocomputational model of spatial cognition integrating memory with vision

We introduce a large-scale neurocomputational model of spatial cognition called 'Spacecog', which integrates recent findings from mechanistic models of visual and spatial perception. As a high-level cognitive ability, spatial cognition requires the processing of behaviourally relevant features in complex environments and, importantly, the updating of this information during processes of eye and body movement. The Spacecog model achieves this by interfacing spatial memory and imagery with mechanisms of object localisation, saccade execution, and attention through coordinate transformations in parietal areas of the brain. We evaluate the model in a realistic virtual environment where our neurocognitive model steers an agent to perform complex visuospatial tasks. Our modelling approach opens up new possibilities in the assessment of neuropsychological data and human spatial cognition.

Using Natural Scenes to Enhance our Understanding of the Cerebral Cortex's Role in Visual Search

Using natural scenes is an approach to studying the visual and eye movement systems approximating how these systems function in everyday life. This review examines the results from behavioral and neurophysiological studies using natural scene viewing in humans and monkeys. The use of natural scenes for the study of cerebral cortical activity is relatively new and presents challenges for data analysis. Methods and results from the use of natural scenes for the study of the visual and eye movement cortex are presented, with emphasis on new insights that this method provides enhancing what is known about these cortical regions from the use of conventional methods.