Melting Ice With Your Mind: Representational Momentum for Physical States.

When a log burns, it transforms from a block of wood into a pile of ash. Such state changes are among the most dramatic ways objects change, going beyond mere changes of position or orientation. How does the mind represent changes of state? A foundational result in visual cognition is that memory extrapolates the positions of moving objects-a distortion called representational momentum. Here, five experiments (N = 400 adults) exploited this phenomenon to investigate mental representations in state space. Participants who viewed objects undergoing state changes (e.g., ice melting, logs burning, or grapes shriveling) remembered them as more changed (e.g., more melted, burned, or shriveled) than they actually were. This pattern extended to several types of state changes, went beyond their low-level properties, and even adhered to their natural trajectories in state space. Thus, mental representations of objects actively incorporate how they change-not only in their relation to their environment, but also in their essential qualities.

Reading a graph is like reading a paragraph.

Vision provides rapid processing for some tasks, but encounters strong constraints from others. Although many tasks encounter a capacity limit of processing four visual objects at once, some evidence suggests far lower limits for processing relationships among objects. What is our capacity limit for relational processing? If it is indeed limited, then people may miss important relationships between data values in a graph. To test this question, we asked people to explore graphs of trivially simple 2 × 2 data sets and found that half of the viewers missed surprising and improbable relationships (e.g., a child's height decreasing over time). These relationships were spotted easily in a control condition, which implicitly directed viewers to prioritize inspecting the key relationships. Thus, a severe limit on relational processing, combined with a cascade of other capacity-limited operations (e.g., linking values to semantic content), makes understanding a graph more like slowly reading a paragraph then immediately recognizing an image. These results also highlight the practical importance of "data storytelling" techniques, where communicators design graphs that help their audience prioritize the most important relationships in data. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What is "Where": Physical Reasoning Informs Object Location.

A central puzzle the visual system tries to solve is: "what is where?" While a great deal of research attempts to model object recognition ("what"), a comparatively smaller body of work seeks to model object location ("where"), especially in perceiving everyday objects. How do people locate an object, right now, in front of them? In three experiments collecting over 35,000 judgements on stimuli spanning different levels of realism (line drawings, real images, and crude forms), participants clicked "where" an object is, as if pointing to it. We modeled their responses with eight different methods, including both human response-based models (judgements of physical reasoning, spatial memory, free-response "click anywhere" judgements, and judgements of where people would grab the object), and image-based models (uniform distributions over the image, convex hull, saliency map, and medial axis). Physical reasoning was the best predictor of "where," performing significantly better than even spatial memory and free-response judgements. Our results offer insight into the perception of object locations while also raising interesting questions about the relationship between physical reasoning and visual perception.

An approximate representation of objects underlies physical reasoning.

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the body representation), as opposed to fine-grained forms for visual recognition (the shape representation). We used three classic psychophysical tasks (causality perception, time-to-collision, and change detection) in novel settings that dissociate body and shape. People's behavior across tasks indicates that they rely on coarse bodies for physical reasoning, which lies between convex hulls and fine-grained shapes. Our empirical and computational findings shed light on basic representations people use to understand everyday dynamics, and how these representations differ from those used for recognition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).