Recent accounts of large-scale cortical organisation suggest that the default mode network (DMN) is positioned at the top of a principal gradient, reflecting the separation between heteromodal and unimodal sensory-motor regions in patterns of connectivity and in geodesic distance along the cortical surface (Margulies et al., 2016). This isolation of DMN from external inputs might allow the integration of disparate sources of information that can constrain subsequent cognition. We tested this hypothesis by manipulating the degree to which semantic decisions for ambiguous words (e.g. jam ) were constrained by preceding visual cues depicting relevant spatial contexts (e.g. supermarket or road ) and/or facial emotions (e.g. happy vs. frustrated ). We contrasted (i) the effects of a single preceding cue with a no-cue condition employing scrambled images, and (ii) convergent spatial and emotion cues with single cues. Single cues elicited stronger activation in the multiple demand network relative to no cues, consistent with the requirement to maintain information in working memory. The availability of two convergent cues elicited stronger activation within DMN regions (bilateral angular gyrus, middle temporal gyrus, medial prefrontal cortex, and posterior cingulate), even though behavioural performance was unchanged by cueing – consequently task difficulty is unlikely to account for the observed differences in brain activation. A regions-of-interest analysis along the unimodal-to-heteromodal principal gradient revealed maximal activation for the convergent cue condition at the heteromodal end, corresponding to the DMN. Our findings are consistent with the view that regions of DMN support states of information integration that constrain ongoing cognition and provide a framework for understanding the location of these effects at the heteromodal end of the principal gradient.

Highlights d Supervised learning mechanisms are involved in VPL of complex natural images d Detailed feedback is required for long-term retention of VPL of natural images d VPL improves the detection of subtle lesions in mammograms d VPL can be used to develop clinical training tools for radiologists

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