talboger.bsky.social
second-year phd student at jhu psych | perception + cognition
https://talboger.github.io/
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Together, these results demonstrate multiple new phenomena of stylistic perception, and more generally introduce a scientific approach to the study of style. Stay tuned for more projects on this theme, including developmental work analyzing stylistic representation in kids!
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Finally, we sought a computational account of stylistic similarity. We found that an object recognition model (with no explicit knowledge of style) successfully predicts human judgments of similarity across styles.
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But these cases involve extracting style to discard it in a sense. Might we also use style to generate new representations? Suppose you’re shown the fork and spoon from a styled cutlery set; can you imagine the knife? We used a priming task to find exactly these representations.
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Next, we considered cases of ‘discounting’ in vision, like when we discount lighting conditions to discern an object’s color, or when we discount a cloth to discern the object beneath it. We found similar effects for style: Vision discounts style to discriminate images.
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First, we were inspired by ‘font tuning’, wherein the mind adapts to typefaces in ways that aid text comprehension. Might similar effects arise for style? In other words, might perception tune to the style of images in ways that aid scene comprehension? We show: Yes!
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So, we thought, let’s study style perception like we study those processes! We adapted a number of paradigms used in those literatures to study how the mind represents style.
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Here’s the idea: Seeing style involves ‘parsing’ content from form. The mind does this in other contexts too, like reading (separating typeface from letter identity) and maybe even color constancy (separating surface reflectance from lighting conditions).
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Style is the subject of considerable humanistic study, from art history to sociology to political theory. But a scientific account of style perception has remained elusive.
Using style transfer algorithms, we generated stimuli in various styles to use in psychophysics studies.
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Danny wolf transferred from Yale to Michigan, what is he, writing a friggin metaethics dissertation, folks?
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Check out the pre-print here (osf.io/preprints/ps...), and see all the experiments for yourself — including the illusory soccer balls — here (perceptionstudies.github.io/persistence).
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One fun thing that came up in the review process is that different people have vastly different definitions of what ‘object persistence’ is. We find this really interesting, so we wrote a full section devoted to spelling out these differences in hopes of opening up some discussion.
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We suggest that object persistence may be the simplest and best explanation for event completion. In our paper, we discuss how this might relate to other memory distortions (like representational momentum), and object/event cognition more broadly.
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We found large filling-in effects almost everywhere (not just due to inattention or an object-presence bias) — including when we disrupted cues previously proposed to create event completion. But abolishing object persistence made event completion effects disappear entirely.
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This allowed us to systematically disrupt various cues that have been proposed to create event completion effects. These included causality, continuity, familiarity, physical coherence, event coherence, and object persistence.
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We rendered animations in Blender — like the one you just saw — with an object either present or absent in each half. Participants watched these animations and simply had to complete a forced-choice judgment about whether the ball was present or absent in a given half.
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Event completion is a phenomenon where you falsely remember (i.e., ‘complete’) a part of an event that wasn’t really shown. People propose lots of interesting mechanisms for event completion (and other kinds of event-based distortions). But what really explains it?
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Thus, we think random behavior can — and should! — be viewed as trait-like. That is: Just like your personality traits are stable across contexts and time, so too is the way in which you may behave randomly.
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But for random behavior to be truly trait-like, it should also be stable over *time*. So, in Experiment 3, we tested the same participants from Experiment 2 one full year later (!). We found remarkably stable behavior across these two timepoints.
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This provided initial evidence for stable random behavior across tasks. However, numbers and one-dimensional locations share a representational format (i.e., a mental number line). In Experiment 2, we extended this to two-dimensional random locations and found the same pattern of results.
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In Experiment 1, we gave participants a number-generation and a one-dimensional location-generation task. Subjects’ sequences shared behavioral signatures across the two tasks; the model parameters were correlated across the two tasks; and our model accurately predicted choice-level behavior.
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Then, we developed a simple, 3-parameter model to describe these data. We asked whether the model could predict individual-level behavior in one task or timepoint after learning regularities from another task or timepoint.
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To approach this, we gave participants random generation tasks of different kinds (e.g., a number-generation and a two-dimensional location-generation task).
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A large research tradition explores how humans depart from true randomness. From that prior work, we know about stable group-level heuristics. But what about at the level of individuals; are there stable, unique patterns in YOUR random behavior?
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sorry about that! the full article is available for free here (talboger.github.io/files/BogerK...), where you'll find more discussion of these questions.
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I first presented this work at the
@socphilpsych.bsky.social joint SPP/ESPP meeting in Milan over 2 years ago, so I’m so excited to have it out!
See the final paper here: talboger.github.io/files/BogerK...
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We believe this sort of work relates to classic work on the richness of object representations; in the same way we may represent higher-level object features like causality and animacy, so too we may represent higher-level object complexities.
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Though most psychological research on object complexity focuses on the visual kind, we suggest that the mechanistic kind — the kind of complexity beneath an object’s surface — may be more consequential.
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In Experiments 4 and 5, we say: yes! We adapt two tasks used to study implicit effects of visual complexity: a visual search task and a visual working memory task. In both, we find that mechanistic complexity drives performance more than visual complexity.
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This may be expected, though. Maybe we judge and think about mechanistic complexity more than we do visual complexity. If mechanistic complexity is really so important, might it even drive visual processes more than visual complexity?
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First, we show that mechanistic complexity is more predictive of general intuitions of object complexity than visual complexity. This holds true both for numerical ratings and for forced-choice judgments.
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We carry out a set of experiments containing both explicit rating tasks and implicit vision tasks to show that mechanistic complexity is more important than visual complexity for judgments, visual attention, and visual working memory.