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Knowledge of cause and effect allows people to navigate and understand the complex systems of the world. Despite the importance of causal knowledge to everyday reasoning, little is known about how people transfer causal knowledge learned in one situation to novel contexts. In two experiments, we examine when people choose to generalize two types of causal knowledge, causal mechanisms (Experiment 1) and causal strength (Experiment 2), across various domains. We find that people willingly transfer causal knowledge to novel contexts when the entities in those contexts share high categorical relatedness with the source of the causal knowledge. The extent to which people are willing to transfer causal knowledge decreases as category similarity decreases. We discuss future research that could delineate the boundaries of causal transfer.

CogSci 2022

Transferring Novel Causal Knowledge

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Detecting analogy is an important high-level cognitive skill that is involved in many aspects of human reasoning. While Structure Mapping Theory (Gentner, 1983) is a well-recognized high-level theory of analogy, it lacks a neural process implementation that links to perception and attention. Avoiding algorithmic computation on ungrounded symbols, we present a dynamic neural architecture built from interacting neural populations that establishes analogy between objects in two visually presented scenes. Consistent with SMT, it accounts for how humans find such analogies.


A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs

Perceptual-motor tasks offer redundant solutions to achieve a goal. However, not all solutions are equally robust to error-producing noise or variability and thus, skill learning can be viewed as a search process to identify behaviors that are error-tolerant. Throwing a ball to hit a target is one such example of a complex perceptual-motor skill that has been studied in the laboratory via the virtual “skittles” task, a simplified 2D task involving throwing a tetherball around a pole to hit a target. We implemented the task as a Unity3D environment (code here: https://github.com/ShortFox/SkittlesTaskEnvironment/) which enables participants to complete the task with a computer mouse and replicated key findings from previous research. Our implementation allows for remote data collection and can serve as a pedagogical tool to teach concepts in skill acquisition. Future work will use this task to explore human versus machine skill acquisition by leveraging Unity’s MLAgents reinforcement learning package.

A computer mouse-based throwing task to study perceptual-motor skill learning in humans and machines

Object interactions – collisions, scraping and rolling – create many of the sounds that we hear in the world around us. These sounds are generated via lawful physical dynamics. Anecdotally, humans possess some intuitive knowledge of the physical generative processes underlying sound production, but little is known about the extent and nature of this knowledge. In this work, we study the human ability to make inferences about physical properties like mass, size, velocity and surface roughness from contact sounds, and test whether it is consistent with the use of a generative model of the underlying physics. Additionally, we propose a computational model that uses analysis-by-synthesis to infer object properties and their physical configurations using contact sounds as input, and compare it to human behavior.

Inferring object interactions from scraping and rolling sounds

In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe the results of the agent attempting to stack various other objects based on the same trained policy. By extracting embedding vectors from a convolutional neural net trained over the results of the aforementioned stacking “play,” we can determine the similarity of a given object to known object types, and determine if the given object is likely dissimilar enough to the known types to be considered a novel class of object. We present the results of this method on two datasets gathered using two different policies and demonstrate what information the agent needs to extract from its environ- ment to make these novelty judgments.

Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction

Mental simulation is a powerful cognitive capacity that underlies people's ability to draw inferences about what happened in the past from the present. Recent work suggests that eye-tracking can be used as a window through which one can study the process of mental simulation in intuitive physics tasks. In our experiment, participants have to figure out in which of three holes a ball was dropped in a virtual Plinko box. We develop a computational model of human intuitive physical reasoning in Plinko that runs repeated simulations in a noisy physics simulator in order to infer in which hole the ball was dropped. We evaluate our model's behavior against multiple human data signals: trial judgments, response times, and eye-movement data. We find that a model that sequentially samples simulations while balancing uncertainty and reward best explains the patterns of participant behavior we observe in these three signals.

Looking into the past: Eye-tracking mental simulation in physical inference

Previous research using motor dual tasks and TMS has suggested that interference with cortical motor information impacts word reading. This research offers support for theories of grounded cognition by suggesting a functional role of sensorimotor information in conceptual representation. However, motor dual tasks and TMS are limited to partial interference of body representations. To address this, the current electroencephalography (EEG) study induces body illusions to interfere with the broader representation of body information during verb reading. During this task, participants categorized words related to hands or feet while we measured EEG. With spatiotemporal representational similarity analysis (RSA), we demonstrate that sensorimotor information is decodable in normal conditions and is delayed during illusion conditions. Our results suggest that during illusion conditions, sensorimotor information is disrupted by body illusions. This supports theories of grounded cognition for word reading.

The role of body representations in higher order cognition

Self-directed learners are often described as ‘intuitive scientists’, yet they also tend to struggle in assessments of their scientific reasoning. We investigate a novel explanation for this apparent gap between formal and informal scientific inquiry. Specifically, we consider whether learners’ documented failure to correctly apply the control of variables strategy might stem from a mismatch between their causal intuitions and task presentation. Children (7- and 9-year-olds) and adults were tested on a version of a traditional multivariate reasoning task (Tschirgi, 1980) that we modified to clarify ambiguous elements of the causal logic. A significant majority of participants in all age groups selected informative experiments on this modified task, avoiding confounded actions with positive tangible outcomes. This finding contrasts with the longstanding claim that learners do not correctly employ control of variables without extensive training and suggests that self-directed scientific inquiry may be intuitively suited to support causal learning goals.

Clarifying the Causal Logic of a Classic Control of Variables Task

Suppose we observe something happen in an interaction be- tween two objects A and B. Can we then predict what will hap- pen in an interaction between A and C, or between B and C? Recent research, inspired by work on the “causal asymmetry”, suggests that people use cues to causal agency to guide object- based generalization decisions, even in relatively abstract set- tings. When object A possesses cues to causal agency (e.g. it moves, remains stable throughout the interaction), people tend to predict that what happened will probably also occur in an interaction between A and C, but not between B and C. Here we replicate and extend this work, with the goal of identify- ing the cues that people use to determine that an object is a causal agent. In four experiments, we manipulate three prop- erties of the agent and recipient objects. We find that people anchor their inductive generalizations around the agent object when that object possesses all three cues to causal agency, but removing either cue abolishes the asymmetry.

Dissecting causal asymmetries in inductive generalization

The same causal system can be accurately described in many ways. What governs the evaluation of these choices? We propose a novel, formal account of causal evaluation according to which evaluations of causal claims reflect the joint demands of maximal informativeness and maximal compression. Across two experiments, we show that evaluations of more and less compressed causal claims are sensitive to the amount of information lost by choosing the more compressed causal claim over a less compressed one, regardless of whether the compression is realized by coarsening a single variable or by eliding a background condition. This offers a unified account of two dimensions along which causal claims are evaluated (proportionality and stability), and contributes to a more general picture of human cognition according to which the capacity to create compressed (causal) representations plays a central role.

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A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs

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