The features of an image can be represented at multiple levels—from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiments. In the first, different layers of a convolutional neural network (CNN), which represent progressively higher levels of features, were used to select the images that would be shown to 100 participants through a form of prospective assignment. Here, the discriminability/similarity of an image with others, according to different CNN layers dictated the images presented to different groups, who made a simple indoor versus outdoor judgment for each scene. We found that participants remember more scene images that were selected based on their low-level discriminability or high-level similarity. A second experiment replicated these results in an independent sample of 50 participants, with a different order of postencoding tasks. Together, these experiments provide evidence that both discriminability and similarity, at different visual levels, predict image memorability.
Acknowledgments:We thank Mac Shine for invaluable discussions during the conception of this project, and thank Heather Bruett, Griffin Koch, John Paulus and Xueying Ren for conversations related to the work. We are also grateful to Samuel Nastase and co-authors for making their dataset available. AbstractThe structure of information in the brain is crucial to cognitive function. The representational space of a brain region can be identified through Representational Similarity Analysis (RSA) applied to functional magnetic resonance imaging (fMRI) data. In its classic form, RSA collapses the time-series of each condition, eliminating fluctuations in similarity over time. We propose a method for identifying representational connectivity (RC) networks, which share fluctuations in representational strength, in an analogous manner to functional connectivity (FC), which tracks fluctuations in BOLD signal, and informational connectivity, which tracks fluctuations in pattern discriminability. We utilize jackknife resampling, a statistical technique in which observations are removed in turn to determine their influence. We applied the jackknife technique to an existing fMRI dataset collected as participants viewed videos of animals (Nastase et al., 2017). We used ventral temporal cortex (VT) as a seed region, and compared the resulting network to a second-order RSA, in which brain regions' representational spaces are compared, and to the network identified through FC. The novel representational connectivity analysis identified a network comprising regions associated with lower-level visual processing, spatial cognition, perceptual-motor integration, and visual attention, indicating that these regions shared fluctuations in representational similarity strength with VT. RC, second-order RSA and FC identified areas unique to each method, indicating that analyzing shared fluctuations in the strength of representational similarity reveals previously undetectable networks of regions. The RC analysis thus offers a new way to understand representational similarity at the network level.
The features of an image can be represented at multiple levels – from low-level visual properties to high-level meaning. What factors drive some images to be memorable while others are forgettable? Across three behavioral experiments, we addressed this question. In a first behavioral experiment, we combined a convolutional neural network (CNN) with behavioral prospective assignment, by using four CNN layers to select the scene images that each of one hundred participants experience. We found that participants remembered more images when they were assigned to view stimuli selected based on their discriminability in low-level CNN layers, or similarity in high-level layers. A second experiment replicated these results in an independent sample of participants. The third experiment investigated how memorability is influenced when images fall within a single semantic category (houses). We replicated results from the first two experiments at lower levels, but found that similarity predicted memorability at a mid-high level, rather than the highest level observed for scenes from multiple categories. This mid-high level contains representations for objects and object-parts, which are more important for discriminating images from the same category. Together, this research provides evidence that discriminability at different visual levels, modeled using a CNN, predicts image memorability.
Uncontrollable worry is a hallmark of generalized anxiety disorder and a transdiagnostic feature of psychopathology. Mindfulness-based strategies show promise for treating worry, but it is unknown which specific strategies are most beneficial, and how these skills might operate on a neurobiological level. We recruited 40 participants with clinically significant worry to undergo functional magnetic resonance imaging while engaging in real-time, idiographic worry and instructed disengagement using two mindfulness strategies (focused attention, acceptance) and one comparison strategy (suppression). Hypotheses were preregistered and partially supported. All disengagement strategies downregulated default mode and upregulated frontoparietal and salience networks, suggesting some shared mechanisms. Focused attention was most effective for promoting disengagement and elicited decreased activity in cognitive control and sensorimotor regions. Successful disengagement was associated with increased activity in rostrolateral prefrontal cortex and functional connectivity between posterior cingulate and primary somatosensory cortex. Findings support the role of cognitive control and somatosensory networks in disengagement from worry and suggest common and distinct mechanisms of disengagement, with focused attention a particularly promising strategy.
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