Tangled Physics: Knots Strain Intuitive Physical Reasoning.

Whereas decades of research have cataloged striking errors in physical reasoning, a resurgence of interest in intuitive physics has revealed humans' remarkable ability to successfully predict the unfolding of physical scenes. A leading interpretation intended to resolve these opposing results is that physical reasoning recruits a general-purpose mechanism that reliably models physical scenarios (explaining recent successes), but overly contrived tasks or impoverished and ecologically invalid stimuli can produce poor performance (accounting for earlier failures). But might there be tasks that persistently strain physical understanding, even in naturalistic contexts? Here, we explore this question by introducing a new intuitive physics task: evaluating the strength of knots and tangles.

A Phone in a Basket Looks Like a Knife in a Cup: Role-Filler Independence in Visual Processing.

When a piece of fruit is in a bowl, and the bowl is on a table, we appreciate not only the individual objects and their features, but also the relations and , which abstract away from the particular objects involved. Independent representation of roles (e.g.

Caricaturing Shapes in Visual Memory.

When representing high-level stimuli, such as faces and animals, we tend to emphasize salient features-such as a face's prominent cheekbones or a bird's pointed beak. Such leaves traces in memory, which exaggerates these distinctive qualities. How broadly does this phenomenon extend? Here, in six experiments ( = 700 adults), we explored how memory automatically caricatures basic units of visual processing-simple geometric shapes-even without task-related demands to do so.

Seeing and understanding epistemic actions.

Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person's movements, what they are trying to learn? Here, five experiments explore .

Six ways of failing to see (and why the differences matter).

Sometimes we look but fail to see: our car keys on a cluttered desk, a repeated word in a carefully proofread email, or a motorcycle at an intersection. Wolfe and colleagues present a unifying, mechanistic framework for understanding these "Looked But Failed to See" errors, explaining how such misses arise from natural constraints on human visual processing. Here, we offer a conceptual taxonomy of six distinct ways we might be said to fail to see, and explore: how these relate to processes in Wolfe et al.

Compositionality in visual perception.

Quilty-Dunn et al.'s wide-ranging defense of the Language of Thought Hypothesis (LoTH) argues that vision traffics in abstract, structured representational formats. We agree: Vision, like language, is just as words compose into phrases, many visual representations contain discrete constituents that combine in systematic ways.

The perception of silence.

Auditory perception is traditionally conceived as the perception of sounds-a friend's voice, a clap of thunder, a minor chord. However, daily life also seems to present us with experiences characterized by the absence of sound-a moment of silence, a gap between thunderclaps, the hush after a musical performance. In these cases, do we positively silence? Or do we just , and merely judge or infer that it is silent? This longstanding question remains controversial in both the philosophy and science of perception, with prominent theories holding that sounds are the only objects of auditory experience and thus that our encounter with silence is cognitive, not perceptual.

When will AI misclassify? Intuiting failures on natural images.

Machine recognition systems now rival humans in their ability to classify natural images. However, their success is accompanied by a striking failure: a tendency to commit bizarre misclassifications on inputs specifically selected to fool them. What do ordinary people know about the nature and prevalence of such classification errors? Here, five experiments exploit the recent discovery of "natural adversarial examples" to ask whether naive observers can predict when and how machines will misclassify natural images.

Empirical evidence for perspectival similarity.

When a circular coin is rotated in depth, is there any sense in which it comes to resemble an ellipse? While this question is at the center of a rich and divided philosophical tradition (with some scholars answering affirmatively and some negatively), Morales et al. (2020, 2021) took an empirical approach, reporting 10 experiments whose results favor such perspectival similarity. Recently, Burge and Burge (2022) offered a vigorous critique of this work, objecting to its approach and conclusions on both philosophical and empirical grounds.

Problems and Mysteries of the Many Languages of Thought.

"What is the structure of thought?" is as central a question as any in cognitive science. A classic answer to this question has appealed to a Language of Thought (LoT). We point to emerging research from disparate branches of the field that supports the LoT hypothesis, but also uncovers diversity in LoTs across cognitive systems, stages of development, and species.

Beautiful on the inside: Aesthetic preferences and the skeletal complexity of shapes.

A plain, blank canvas does not look very beautiful; to make it aesthetically appealing requires adding structure and complexity. But how much structure is best? In other words, what is the relationship between beauty and complexity? It has long been hypothesized that complexity and beauty meet at a "sweet spot," such that the most beautiful images are neither too simple nor too complex. Here, we take a novel experimental approach to this question, using an information-theoretic approach to object representation based on an internal "skeletal" structure.

Visual cognition: A new perspective on mental rotation.

Manipulating an object in one's mind has long been thought to mirror physically manipulating that object in allocentric three-dimensional space. A new study revises and clarifies this foundational assumption, identifying a previously unknown role for the observer's point-of-view.

Can You Hear Me Now? Sensitive Comparisons of Human and Machine Perception.

The rise of machine-learning systems that process sensory input has brought with it a rise in comparisons between human and machine perception. But such comparisons face a challenge: Whereas machine perception of some stimulus can often be probed through direct and explicit measures, much of human perceptual knowledge is latent, incomplete, or unavailable for explicit report. Here, we explore how this asymmetry can cause such comparisons to misestimate the overlap in human and machine perception.

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 .

Deepfake detection by human crowds, machines, and machine-informed crowds.

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model's prediction are more accurate than either alone, but inaccurate model predictions often decrease participants' accuracy.

Seeing and speaking: How verbal "description length" encodes visual complexity.

What is the relationship between complexity in the world and complexity in the mind? Intuitively, increasingly complex objects and events should give rise to increasingly complex mental representations (or perhaps a plateau in complexity after a certain point). However, a counterintuitive possibility with roots in information theory is an inverted-U-shaped relationship between the "objective" complexity of some stimulus and the complexity of its mental representation, because excessively complex patterns might be characterized by surprisingly short computational descriptions (e.g.

"Impossible" Somatosensation and the (Ir)rationality of Perception.

Impossible figures represent the world in ways it cannot be. From the work of M. C.

Curious Objects: How Visual Complexity Guides Attention and Engagement.

Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experience-and why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration.

The Perception of Relations.

The world contains not only objects and features (red apples, glass bowls, wooden tables), but also relations holding between them (apples contained in bowls, bowls supported by tables). Representations of these relations are often developmentally precocious and linguistically privileged; but how does the mind extract them in the first place? Although relations themselves cast no light onto our eyes, a growing body of work suggests that even very sophisticated relations display key signatures of automatic visual processing. Across physical, eventive, and social domains, relations such as support, fit, cause, chase, and even socially interact are extracted rapidly, are impossible to ignore, and influence other perceptual processes.

Physically Implied Surfaces.

In addition to seeing objects that are directly in view, we also represent objects that are merely implied (e.g., by occlusion, motion, and other cues).

Hi-def memories of lo-def scenes.

The study of visual memory is typically concerned with an image's content: How well, and with what precision, we can recall which objects, people, or features we have seen in the past. But images also vary in their quality: The same object or scene may appear in an image that is sharp and highly resolved, or it may appear in an image that is blurry and faded. How do we remember those properties? Here six experiments demonstrate a new phenomenon of "vividness extension": a tendency to (mis)remember images as though they are "enhanced" versions of themselves - that is, sharper and higher quality than they actually appeared at the time of encoding.

Performance vs. competence in human-machine comparisons.

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach "human-level" accuracy in an astounding variety of domains, and even predict human brain activity-raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines in strange and "unhumanlike" ways, threatening their status as models of our minds. How can we know when human-machine behavioral differences reflect deep disparities in their underlying capacities, vs.

Sustained representation of perspectival shape.

Arguably the most foundational principle in perception research is that our experience of the world goes beyond the retinal image; we perceive the distal environment itself, not the proximal stimulation it causes. Shape may be the paradigm case of such "unconscious inference": When a coin is rotated in depth, we infer the circular object it truly is, discarding the perspectival ellipse projected on our eyes. But is this really the fate of such perspectival shapes? Or does a tilted coin retain an elliptical appearance even when we know it's circular? This question has generated heated debate from Locke and Hume to the present; but whereas extant arguments rely primarily on introspection, this problem is also open to empirical test.