Humans remarkably can perceive 3D surfaces that are literally invisible – when their shapes are implied through dynamic physical interaction with various materials (liquid, granular, elastic). To study this ability and its interaction with perception of material properties, we produced videos simulating unfamiliar non-rigid or non-solid materials interacting with differently shaped rigid objects and containers. Crucially, videos rendered only the materials, while the rigids remained invisible. Observers correctly identified the underlying surface shape from two alternatives in most, but not all cases; observers also simultaneously inferred the internal properties of the material and identified a matching sample from how it interacted with novel surfaces. In fact, judgments about materials were comparable to when observers saw fully visible renderings of all objects in the scenes. Our results highlight the role of an internal physics model in the joint perception of shape and material properties, and in the loop of perception more generally.

In science, we use common graphical representations to indicate changes in events over time, independent of domain. Are children also sensitive to abstract patterns in the ways events change over time? In a series of four experiments, we show that young children (range: 48-84 months) distinguish different function families (Exp 1). Children can also distinguish specific function types within function families (e.g., between linear and sigmoid monotonic functions; Exp 2); nonetheless, they will group different function types within a family together, rather than with functions in a different family (Exp 3). Finally, we show that children’s sensitivity to functions is abstract, allowing them to match observable causes to verbal descriptions of their effects (Exp 4). These results suggest that although some aspects of function understanding, like learning how to interpret graphs, requires formal education, the ability to identify abstract functional relationships is intuitive and early emerging.

In three experiments, we investigated the effect of repeated exposure and social interaction on adults' tendency to make sense of novel events. Specifically, we examined whether, across trials, participants' observations shifted from descriptive to explanatory, from specific to generic, became more inclined to reference causes, and more evaluative. We found that while there was an effect of repeated exposure on generalization and of social interaction on both explanation and generalization, the intervention that was most likely to shift adults' sense-making behavior was a communicative context of small groups in which each participant had partial and different knowledge. We suggest that this is because social contexts inherently motivate individuals to integrate new information, reconcile discrepancies, and forge efficient, generalizable concepts.

We introduce a novel experimental paradigm for studying multi-modal integration in causal in-ference. Our experiments feature a physically realistic Plinko machine in which a ball is droppedthrough one of three holes and comes to rest at the bottom after colliding with a number of ob-stacles. We develop a hypothetical simulation model which postulates that people figure out whathappened by integrating visual and auditory evidence through mental simulation. We test themodel in a series of three experiments. In Experiment 1, participants only receive visual infor-mation and either predict where the ball will land, or infer in what hole it was dropped based onwhere it landed. In Experiment 2, participants receive both visual and auditory information – theyhear what sounds the dropped ball makes. We find that participants are capable of integratingboth sources of information, and that the sounds help them figure out what happened. In Exper-iment 3, we show strong cue integration: even when vision and sound are individually completelynon-diagnostic, participants succeed by combining both sources of evidence.

We tailor the explanations we give depending on the person asking for them – you would explain why an event happened differently depending on which of the contributing causes the listener already knows. While significant prior work focuses on how causal structure in the world influences explanation, we focus on how explanation production is modulated by listener belief. We propose a computational model framing explanation as rational communication about causal events, using a recursive theory-of-mind and language production framework to choose amongst possible explanatory utterances that minimize the divergence between speaker and listener belief about a why an event happened. We evaluate our model using some partial observer stimuli, which manipulate the listener's stated prior knowledge about an event, and find that our model well-predicts human judgements about which of several contributing causes is the best explanation for a speaker to provide by modeling their communicative value to the listener.

Conventional theories of visual object recognition treat objectseffectively as abstract, arbitrary patterns of image features.They do not explicitly represent objects as physical entities inthe world, with physical properties such as three-dimensionalshape, mass, stiffness, elasticity, surface friction, and so on.However, for many purposes, an object’s physical existence iscentral to our ability to recognize it and think about it. Thisis certainly true for recognition via haptic perception, i.e., per-ceiving objects by touch, but even in the visual domain an ob-ject’s physical properties may directly determine how it looksand thereby how we recognize it. Here we show how a physi-cal object representation can allow the solution of visual prob-lems, like perceiving an object under a cloth, that are other-wise difficult to accomplish without extensive experience, andwe provide behavioral and computational evidence that peoplecan use such a representation.

Studies suggest that children’s exploratory behavior is sensitive to uncertainty; however, few have approached thiswith sufficient precision to model quantitatively. Across three experiments, children (mean age=70 months) were asked to shakea box to identify which of two sets of marbles, differing in numerosity, were hidden inside. The sets’ numerosities varied in theirdiscriminability indices – the degree to which listeners can distinguish the sets based on the acoustic information generated.The time children spent shaking the box varied systematically with the discriminability of the alternative hypotheses they wereasked to distinguish, even though they heard only one set for each contrast. This suggests that children represent the uncertaintyin their own perceptual discrimination abilities (an ability we refer to as an intuitive psychophysics) and their exploratorybehavior is precisely calibrated to their degree of uncertainty about alternative hypotheses that might explain unobserved causesof perceptual data.

Causal relationships can generate many different kinds of correlations among variables. However, research on children’s causal reasoning has focused almost exclusively on just one kind of regularity: the temporal covariation between candidate causes and effects, and in particular, the covariation between interventions and outcomes. Here we show that young children recognize more abstract correlations – in the ways that object properties are distributed over sets, or change over time – and constrain their causal hypotheses accordingly. Specifically, we show that children (range: 48-84 months) distinguish candidate causes based on correlations in the distribution of discrete (set size, arity, and proportion) and continuous (mean and mode) properties of sets (Experiment 1), and also within monotonic, quadratic, and periodic functions (Experiment 2). Keywords: children; causal reasoning; abstract concepts; sets; functions

Humans can successfully interpret images even when significant image transformations have distorted them. Such images aid in differentiating existing computational models for perception because models which predict similar results for typical stimuli may diverge when confronted with atypical stimuli. We propose an explanation of people’s ability to perceive a specific class of degraded stimuli that require extreme generalization capabilities: Mooney, or two-tone images of faces. We provide a parametric and controllable method to generate Mooney faces and hypothesize that the human visual system generalizes under extreme cases because it reverses the naturalistic image generation process. We show that Mooney faces are approximated under extreme illumination conditions; a computer graphics perspective of the Mooney formation process suggests inverse graphics as a model for perceptual generalization. With a number of demonstrations, we argue that Mooney faces are important to understand the role of generative models and inverse graphics in human visual processing.