arXiv subjects

Wilma A. Bainbridge

Publications and source records attributed to Wilma A. Bainbridge.

4 recordsLinked to original sources

arXiv2025

Image memorability predicts social media virality and externally-associated commenting

Visual content on social media plays a key role in entertainment and information sharing, yet some images gain more engagement than others. We propose that image memorability - the ability to be remembered - may predict viral potential. Using 1,247 Reddit image posts across three timepoints, we assessed memorability with neural network ResMem and correlated the predicted memorability scores with virality metrics. Memorable images were consistently associated with more comments, even after controlling for image categories with ResNet-152. Semantic analysis revealed that memorable images relate to more neutral-affect comments, suggesting a distinct pathway to virality from emotional content. Additionally, visual consistency analysis showed that memorable posts inspired diverse, externally-associated comments. By analyzing ResMem's layers, we found semantic distinctiveness was key to both memorability and virality. This study highlights memorability as a unique correlate of social media virality, offering insights into how visual features and human cognitive behavioral interactions are associated with online engagement.

cs.HC↗

arXiv2022

Embracing New Techniques in Deep Learning for Estimating Image Memorability

Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work has used now-outdated deep learning architectures to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate five alternative deep learning models which exploit developments in the field from the last five years, largely the introduction of residual neural networks, which are intended to allow the model to use semantic information in the memorability estimation process. These new models were tested against the prior state of the art with a combined dataset built to optimize both within-category and across-category predictions. Our findings suggest that the key prior memorability network had overstated its generalizability and was overfit on its training set. Our new models outperform this prior model, leading us to conclude that Residual Networks outperform simpler convolutional neural networks in memorability regression. We make our new state-of-the-art model readily available to the research community, allowing memory researchers to make predictions about memorability on a wider range of images.

cs.CV↗

arXiv2021

Shared memories driven by the intrinsic memorability of items

When we experience an event, it feels like our previous experiences, our interpretations of that event (e.g., aesthetics, emotions), and our current state will determine how we will remember it. However, recent work has revealed a strong sway of the visual world itself in influencing what we remember and forget. Certain items -- including certain faces, words, images, and movements -- are intrinsically memorable or forgettable across observers, regardless of individual differences. Further, neuroimaging research has revealed that the brain is sensitive to memorability both rapidly and automatically during late perception. These strong consistencies in memory across people may reflect the broad organizational principles of our sensory environment, and may reveal how the brain prioritizes information before encoding items into memory. In this chapter, I will discuss our current state-of-the-art understanding of memorability for visual information, and what these findings imply about how we perceive and remember visual events.

q-bio.NC↗

arXiv2017

The Resiliency of Memorability: A Predictor of Memory Separate from Attention and Priming

When we encounter a new person or place, we may easily encode it into our memories, or we may quickly forget it. Recent work finds that this likelihood of encoding a given entity - memorability - is highly consistent across viewers and intrinsic to an image; people tend to remember and forget the same images. However, several forces influence our memories beyond the memorability of the stimulus itself - for example, how attention-grabbing the stimulus is, how much attentional resources we dedicate to the task, or how primed we are for that stimulus. How does memorability interact with these various phenomena, and could any of them explain the effects of memorability found in prior work? This study uses five psychophysical experiments to explore the link between memorability and three attention-related phenomena: 1) bottom-up attention (through testing spatial cueing and visual search), 2) top-down attention (through testing cognitive control and depth of encoding), and 3) priming. These experiments find that memorability remains resilient to all of these phenomena - none are able to explain memorability effects or overcome the strong effects memorability has on determining memory performance. Thus, memorability is truly an independent, intrinsic attribute of an image that works in conjunction with these phenomena to determine if an event will ultimately be remembered.

q-bio.NC↗