Spatial and feature-selective attention have distinct effects on population-level tuning

Abstract

1Selective attention is fundamental to cognitive activity and can be deployed in 2 different ways. Non-human primate data suggests that spatial and feature-based 3 visual attention have qualitatively different effects on neural tuning, but this has 4 been challenging to assess in humans. Using multivariate decoding of MEG data, 5we tracked the effects of spatial and feature-selective attention on population-level 6 coding of novel objects. We found that spatial and feature-selective attention 7 interacted multiplicatively to enhance object representation. Moreover, the two 8 types of attention induced qualitatively different patterns of enhancement in 9 occipital cortex, and these differences were accounted for by the principles of 10 response-gain and tuning curve sharpening derived from single-unit work. A novel 11 information flow analysis further showed that stimulus representations in occipital 12 cortex were Granger-caused by coding in frontal cortices earlier in time. We find 13 that human spatial and feature-selective attention rely on qualitatively different, 14interacting, neural mechanisms. 15At any moment, there is far more information available from our senses than we can 16 possibly process at once. Accordingly, only a subset of the available information is 17 processed to a high level, making it crucial that brain can dynamically devote greatest 18 processing resources to the most relevant information. Our ability to selectively attend 19 to relevant information is remarkably flexible. For instance, we can adapt our 20 attentional state by directing our attention in space (spatial attention, e.g. attend left), 21 to a specific feature dimension (feature-selective attention, e.g. detect changes in color 22 across a scene) or based on a particular feature value along that feature dimension 23 (feature-based attention, e.g. find all the red objects), using the definitions of Chen et 24 al. (2012). Each of these types of attention can change behavior, improving 25 performance related to the attended location or feature-dimension, while decreasing 26 performance on the ignored dimension/location (Pestilli and Carrasco, 2005; Rossi and

Neural signatures of vigilance decrements predict behavioural errors before they occur

“…The manipulation of attention showed a strong overall effect with enhanced representation of both the less important direction of approach and the most task-relevant distance to object information for cued dots, regardless of how frequent the targets were (Figure 3). The improved representation of information under attention extends previous findings from us and others (Woolgar et al, 2015b;Goddard et al, 2019;Nastase et al, 2017) to moving displays, in which the participants monitor multiple objects simultaneously.…”

“…The method we used here evaluated the correlation between representational dissimilarity matrices, which has provided high-dimensional information about distance to object, obtained from multiple sensors across the brain areas. This makes the analysis more sensitive to capturing subtle connectivity and also aligns with a major recent shift in literature from univariate to multivariate informational connectivity analyses (Goddard et al, 2016;Goddard et al, 2019;Karimi-Rouzbahani et al, 2019;Karimi-Rouzbahani, 2017;Anzellotti & Coutanche, 2018;Basti et al, 2020).…”

Concurrent neuroimaging and neurostimulation reveals a causal role for dlPFC in coding of task-relevant information

“…Non-human primate research has shown that these regions preferentially code task-relevant information (Rao, Rainer, & Miller, 1997;Freedman et al, 2001;Cromer et al, 2010;Roy et al, 2010;Kadohisa et al, 2013), a result mirrored in human neuroimaging data (Woolgar et al, 2015;Jackson et al, 2017;Jackson & Woolgar, 2018). This selective representation may support preferential coding in other brain regions (Desimone & Duncan, 1995;Miller & Cohen, 2001), as indicated by previous work (Baldauf & Desimone, 2014;Goddard, Carlson, & Woolgar, 2019), yielding a plausible mechanism for a brain-wide focus on relevant information and a key component of cognitive control. For example, Baldauf and Desimone (2014) combined magnetoencephalography and fMRI to show that the inferior frontal junction appears to direct object-based attentional inputs to the inferior-temporal cortex.…”

Perceptual difficulty modulates the direction of information flow in familiar face recognition

“…We developed a novel connectivity method based on RSA to quantify the relationships between the evolution of information based on perioccipital EEG electrodes and those of the peri-frontal electrodes. As an advantage to previous Granger causality methods (Goddard et al, 2016;Goddard et al, 2019;Karimi-Rouzbahani et al, 2019), the connectivity method developed here allowed us to check whether the transferred signals contained specific aspects of stimulus information.…”

“…Timing information is also crucial in evaluating the flows of feed-forward and feedback information as these processes often differ in the temporal dynamics. With the advent of the concept of informational connectivity analysis, we now have the potential to examine the interaction of information between feed-forward and feedback mechanisms to characterize their potential spatiotemporal contribution to familiar face recognition (Goddard et al, 2016;Goddard et al, 2019;Anzellotti and Coutanche, 2018;Basti et al, 2020;Karimi-Rouzbahani et al, 2020). However, this requires novel methods to track the flow of familiarity information from a given brain area to a destination area and link this flow to the behavioural task goals to confirm its biological relevance.…”