Preface
There is one thing the photograph must contain, the humanity of the moment.
—Robert Frank
Computational models of objective visual properties such as semantic content
and geometric relationships have made significant breakthroughs using the latest
achievements in machine learning and large-scale data collection. There has also
been limited but important work exploiting these breakthroughs to improve compu-
tational modelling of subjective visual properties such as interestingness, affective
values and emotions, aesthetic values, memorability, novelty, complexity, visual
composition and stylistic attributes, and creativity. Researchers that apply machine
learning to model these subjective properties are often motivated by the wide range
of potential applications of such models, including for content retrieval and search,
storytelling, targeted advertising, education and learning, and content filtering. The
performance of such machine learning-based models leaves significant room for
improvement and indicates a need for fundamental breakthroughs in our approach
to understanding such highly complex phenomena.
Largely in parallel to these efforts in the machine learning community, recent
years have witnessed important advancements in our understanding of the psycho-
logical underpinnings of these same subjective properties of visual stimuli. Early
focuses in the vision sciences were on the processing of simple visual features
like orientations, eccentricities, and edges. However, utilizing new neuroimaging
techniques such as functional magnetic resonance imaging, breakthroughs through
the 1990s and 2000s uncovered specialized processing in the brain for high-level
visual information, such as image categories (e.g., faces, scenes, tools, objects) and
more complex image properties (e.g., real-world object size, emotions, aesthetics).
Recent work in the last decade has leveraged machine learning techniques to
allow researchers to probe the specific content of visual representations in the
brain. In parallel, the widespread advent of the Internet has allowed for large-scale
crowd-sourced experiments, allowing psychologists to go beyond small samples
with limited, controlled stimulus sets to study images at a large scale. With the
combination of these advancements, psychology is now able to take a fresh look at
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