CogSci

[CogSci19] Decomposing Human Causal Learning: Bottom-up Associative Learning and Top-down Schema Reasoning

Transfer learning is fundamental for intelligence; agents expected to operate in novel and unfamiliar environments must be able to transfer previously learned knowledge to new domains or problems. However, knowledge transfer manifests at different …

[CogSci18] Human Causal Transfer: Challenges for Deep Reinforcement Learning

Discovery and application of causal knowledge in novel problem contexts is a prime example of human intelligence. As new information is obtained from the environment during interactions, people develop and refine causal schemas to establish a …

[CogSci17] Consistent Probabilistic Simulation Underlying Human Judgment in Substance Dynamics

A growing body of evidence supports the hypothesis that humans infer future states of perceived physical situations by propagating noisy representations forward in time using rational (approximate) physics. In the present study, we examine whether …

[CogSci16] Probabilistic Simulation Predicts Human Performance on Viscous Fluid-Pouring Problem

The physical behavior of moving fluids is highly complex, yet people are able to interact with them in their everyday lives with relative ease. To investigate how humans achieve this remarkable ability, the present study extended the classical …

[CogSci15] Evaluating Human Cognition of Containing Relations with Physical Simulation

Containers are ubiquitous in daily life. By container, we consider any physical object that can contain other objects, such as bowls, bottles, baskets, trash cans, refrigerators, etc. In this paper, we are interested in following questions: What is a …