The capacity limits of moving objects in the imagination.

People have capacity limits when tracking objects in direct perception. But how many objects can people track in their imagination? In nine pre-registered experiments (N = 313 total), we examine the capacity limits of mentally simulating the movement of objects in the mind's eye. In a novel Imagined Objects Tracking task, participants continue the motion of animated objects in their mind up to a pre-defined point.

Resource bounds on mental simulations: Evidence from a liquid-reasoning task.

People are able to reason about the physical dynamics of everyday objects. Bute there are theoretical disagreements about the computations that underlie this ability. One proposal is that people are running an approximate mental simulation of their environment.

Physics versus graphics as an organizing dichotomy in cognition.

People build world models that simulate the dynamics of the real world. They do so in engineered systems for the purposes of scientific understanding or recreation, as well as in intuitive reasoning to predict and explain the environment. On the basis of a major split in the simulation of real-time dynamics in engineered systems, we argue that people's intuitive mental simulation includes a basic split between physical simulation and graphical rendering.

Loopholes: A window into value alignment and the communication of meaning.

Intentional misunderstandings take advantage of the ambiguity of language to do what someone said, instead of what they actually wanted. These purposeful misconstruals or loopholes are a familiar facet of fable, law, and everyday life. Engaging with loopholes requires a nuanced understanding of goals (your own and those of others), ambiguity, and social alignment.

The detection of automatic behavior in other people.

The standard model of theory of mind posits that we attribute mental states to other people to explain their behavior. However, what of cases in which we think the other person is being scripted, acting automatically with no goals or beliefs to recover? While a great deal of past work has distinguished between automatic and reflective behaviors in one's own decision making, here we argue that reasoning about automatic behavior in other people is an important and largely unexplored area in research into theory of mind. We report results from two studies (N = 4,528 total) that examine the detection of automatic behavior in others.

Electrophysiology Reveals That Intuitive Physics Guides Visual Tracking and Working Memory.

Starting in early infancy, our perception and predictions are rooted in strong expectations about the behavior of everyday objects. These intuitive physics expectations have been demonstrated in numerous behavioral experiments, showing that even pre-verbal infants are surprised when something impossible happens (e.g.

Blending simulation and abstraction for physical reasoning.

How are people able to understand everyday physical events with such ease? One hypothesis suggests people use an approximate probabilistic simulation of the world. A contrasting hypothesis is that people use a collection of abstractions or features. While it has been noted that the two hypotheses explain complementary aspects of physical reasoning, there has yet to be a model of how these two modes of reasoning can be used together.

Ambivalence by design: A computational account of loopholes.

Loopholes offer an opening. Rather than comply or directly refuse, people can subvert an intended request by an intentional misunderstanding. Such behaviors exploit ambiguity and under-specification in language.

Genies, lawyers, and smart-asses: Extending proxy failures to intentional misunderstandings.

We propose that the logic of a genie - an agent that exploits an ambiguous request to intentionally misunderstand a stated goal - underlies a common and consequential phenomenon, well within what is currently called proxy failures. We argue that such intentional misunderstandings are not covered by the current proposed framework for proxy failures, and suggest to expand it.

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 representation), as opposed to fine-grained forms for visual recognition (the representation).

Non-commitment in mental imagery.

We examine non-commitment in the imagination. Across 5 studies (N > 1, 800), we find that most people are non-committal about basic aspects of their mental images, including features that would be readily apparent in real images. While previous work on the imagination has discussed the possibility of non-commitment, this paper is the first, to our knowledge, to examine this systematically and empirically.

Rethink reporting of evaluation results in AI.

Aggregate metrics and lack of access to results limit understanding.

Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents' Action Plans.

Do infants appreciate that other people's actions may fail, and that these failures endow risky actions with varying degrees of negative utility (i.e., danger)? Three experiments, including a pre-registered replication, addressed this question by presenting 12- to 15-month-old infants ( = 104, 52 female, majority White) with an animated agent who jumped over trenches of varying depth towards its goals.

Partial mental simulation explains fallacies in physical reasoning.

People can reason intuitively, efficiently, and accurately about everyday physical events. Recent accounts suggest that people use mental simulation to make such intuitive physical judgments. But mental simulation models are computationally expensive; how is physical reasoning relatively accurate, while maintaining computational tractability? We suggest that people make use of , mentally moving forward in time only parts of the world deemed relevant.

Plans or Outcomes: How Do We Attribute Intelligence to Others?

Humans routinely make inferences about both the contents and the workings of other minds based on observed actions. People consider what others want or know, but also how intelligent, rational, or attentive they might be. Here, we introduce a new methodology for quantitatively studying the mechanisms people use to attribute intelligence to others based on their behavior.

Heroes of our own story: Self-image and rationalizing in thought experiments.

Cushman's rationalization account can be extended to cover another part of his portrayal of representational exchange: thought experiments that lead to conclusions about the self. While Cushman's argument is compelling, a full account of rationalization as adaptive will need to account for the divergence in rationalizing one's actions compared to the actions of others.

Sticking to the Evidence? A Behavioral and Computational Case Study of Micro-Theory Change in the Domain of Magnetism.

Constructing an intuitive theory from data confronts learners with a "chicken-and-egg" problem: The laws can only be expressed in terms of the theory's core concepts, but these concepts are only meaningful in terms of the role they play in the theory's laws; how can a learner discover appropriate concepts and laws simultaneously, knowing neither to begin with? We explore how children can solve this chicken-and-egg problem in the domain of magnetism, drawing on perspectives from computational modeling and behavioral experiments. We present 4- and 5-year-olds with two different simplified magnet-learning tasks. Children appropriately constrain their beliefs to two hypotheses following ambiguous but informative evidence.

Lucky or clever? From expectations to responsibility judgments.

How do people hold others responsible for the consequences of their actions? We propose a computational model that attributes responsibility as a function of what the observed action reveals about the person, and the causal role that the person's action played in bringing about the outcome. The model first infers what type of person someone is from having observed their action. It then compares a prior expectation of how a person would behave with a posterior expectation after having observed the person's action.

Learning physical parameters from dynamic scenes.

Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time.

Ingredients of intelligence: From classic debates to an engineering roadmap.

We were encouraged by the broad enthusiasm for building machines that learn and think in more human-like ways. Many commentators saw our set of key ingredients as helpful, but there was disagreement regarding the origin and structure of those ingredients. Our response covers three main dimensions of this disagreement: nature versus nurture, coherent theories versus theory fragments, and symbolic versus sub-symbolic representations.