A computational framework of human causal generalization

How do people decide how general a causal relationship is, in terms of the entities or situations it applies to? How can people make these difficult judgments in a fast, efficient way? To address these questions, I designed a novel online experiment interface that systematically measures how people generalize causal relationships, and developed a computational modeling framework that combines program induction (about the hidden causal laws) with non-parametric category inference (about their domains of influence) to account for unique patterns in human causal generalization. In particular, by introducing adaptor grammars to standard Bayesian-symbolic models, this framework formalizes conceptual bootstrapping as a general online inference algorithm that gives rise to compositional causal concepts. Chapter 2 investigates one-shot causal generalization, where I find that participants’ inferences are shaped by the order of the generalization questions they are asked. Chapter 3 looks into few-shot cases, and finds an asymmetry in the formation of causal categories: participants preferentially identify causal laws with features of the agent objects rather than recipients, but this asymmetry disappears when visual cues to causal agency are challenged. The proposed modeling approach can explain both the generalizationorder effect and the causal asymmetry, outperforming a naïve Bayesian account while providing a computationally plausible mechanism for real-world causal generalization. Chapter 4 further extends this framework with adaptor grammars, using a dynamic conceptual repertoire that is enriched over time, allowing the model to cache and later reuse elements of earlier insights. This model predicts systematically different learned concepts when the same evidence is processed in different orders, and across four experiments people’s learning outcomes indeed closely resembled this model’s, differing significantly from alternative accounts

Dissecting causal asymmetries in inductive generalization

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

A rational model of spatial neglect

Spatial neglect has been a phenomenon of interest for perceptual and neuropsychological researchers for decades. However, the underlying cognitive processes remain unclear. We provide a Bayesian framework for the classic line bisection task in spatial neglect, regarding it as rational inferences in the face of uncertain information. A Bayesian observer perceives the left and right endpoints of a line with uncertainty, and leverages prior expectations about line lengths to compensate for this uncertainty. This Bayesian model provides a basis for characterizing different patterns of behavior. Our model also captures the paradoxical cross-over effect observed in earlier studies as a natural outcome when uncertainty is high and the observer falls back on priors. It provides measures that correlate well with measures from other neglect tests, and can accurately distinguish stroke patients from healthy controls. It has the potential to facilitate spatial neglect studies and inform clinical decisions

Understanding spatial neglect:A Bayesian perspective

Spatial neglect has been a phenomenon of interest for perceptual and neuropsychological researchers for decades. However, the underlying cognitive processes remain unclear. We provide a Bayesian framework for the classic line bisection task in spatial neglect, regarding bisection responses as rational inferences in the face of uncertain information. A Bayesian observer perceives the left and right endpoints of a line with uncertainty, and leverages prior expectations about line lengths to compensate for this uncertainty. This Bayesian model provides a basis for characterizing different patterns of neglect behavior. Our model also captures the paradoxical cross-over effect observed in earlier studies. It provides measures that correlate well with measures from other neglect tests, and can accurately distinguish stroke patients from healthy controls

Categorizing perceived causal events

Over the last few decades, Causal Model Theory (CMT) has become a dominant framework for human causal-based reasoning, including categorization and inference. CMT prescribes how people should reason about probabilistic events in terms of causal models. In typical causal-based categorization experiments, subjects are provided with verbal descriptions of causally linked features, generally including probabilistic information. Another line of research focuses on perceived or experienced causal events, rather than on verbal descriptions. In this work we asked whether effects which are consistent with CMT, and that have been obtained with verbal descriptions, generalize to visually perceived events. In two experiments, we presented subjects with videos of a 3D A→B causal event rather than verbal descriptions. In Exp. 1, we found that subjects who saw the causal event did not show the coherence effect in categorization (i.e., subjects tend to rate the null ¬A¬B event as a category member). However, subjects who did see the null event during training did show the effect. In Exp. 2, we ruled out the possibility that Exp. 1’s results were simply an effect of how frequently events were experienced during training. We conclude that a one-shot perceived causal event is not sufficient for people to show causal-based reasoning as CMT predicts

Catalytic Isomerization of Olefins and Their Derivatives: A Brief Overview

Carbon–carbon double bond (CCDB) isomerization is a method for synthesizing new organic compounds from olefins and their derivatives, which was based on C=C migration along carbon chain and cis/trans transform, and it plays a vital role in the fields of organic synthesis, synthesis of daily chemicals, raw oil’s development and synthesis of natural products and so on. In this paper, advances of five types of catalytic methods for CCDB of olefins and their derivatives since the 1960s were discussed in detail; Based on his recent work, the author mainly introduces the application and development of photocatalysis in CCDB of olefins and their derivatives