Beyond the Information Given: Causal Models in Learning and Reasoning

ABSTRACT— The philosopher David Hume's conclusion that causal induction is solely based on observed associations still presents a puzzle to psychology. If we only acquired knowledge about statistical covariations between observed events without accessing deeper information about causality, we would be unable to understand the differences between causal and spurious relations, between prediction and diagnosis, and between observational and interventional inferences. All these distinctions require a deep understanding of causality that goes beyond the information given. We report a number of recent studies that demonstrate that people and rats do not stick to the superficial level of event covariations but reason and learn on the basis of deeper causal representations. Causal-model theory provides a unified account of this remarkable competence.     

SAD as a mathematical assistant—how should we go from here to there?

The System for Automated Deduction (SAD) is developed in the framework of the Evidence Algorithm research project and is intended for automated processing of mathematical texts. The SAD system works on three levels of reasoning: (a) the level of text presentation where proofs are written in a formal natural-like language for subsequent verification; (b) the level of foreground reasoning where a particular theorem proving problem is simplified and decomposed; (c) the level of background deduction where exhaustive combinatorial inference search in classical first-order logic is applied to prove end subgoals.

We present an overview of SAD describing the ideas behind the project, the system's design, and the process of problem formalization in the fashion of SAD. We show that the choice of classical first-order logic as the background logic of SAD is not too restrictive. For example, we can handle binders like Σ or lim without resort to second order or to a full-powered set theory. We illustrate our approach with a series of examples, in particular, with the classical problem .     

White matter integrity and pictorial reasoning in high-functioning children with autism

The current study investigated the neurobiological role of white matter in visuospatial versus linguistic processing abilities in autism using diffusion tensor imaging. We examined differences in white matter integrity between high-functioning children with autism (HFA) and typically developing controls (CTRL), in relation to the groups’ response times (RT) on a pictorial reasoning task under three conditions: visuospatial, V, semantic, S, and V + S, a hybrid condition allowing language use to facilitate visuospatial transformations.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

What’s in the name? Or how rocks and stones are different from bunnies and rabbits

Labels have been shown to play an important role in inductive generalization; however, the mechanism by which labels contribute to generalization early in development remains unclear. We investigated two factors that may influence the inductive potential of labels: semantic similarity and co-occurrence probability. Results suggested that adults and 6-year-olds rely on semantic similarity of labels and that their generalizations are not affected by co-occurrence probability. Specifically, generalization patterns were qualitatively similar for co-occurring semantically similar labels (e.g., bunny-rabbit) and non-co-occurring semantically similar labels (e.g., rock-stone) in 6-year-olds and adults. Unlike 6-year-olds and adults, 4-year-olds were likely to generalize co-occurring labels but not non-co-occurring labels. Possible mechanisms by which co-occurrence probability may influence label generalization in young children are discussed.     

Intrinsic cognitive models

Theories concerning the structure, or format, of mental representation should (1) be formulated in mechanistic, rather than metaphorical terms; (2) do justice to several philosophical intuitions about mental representation; and (3) explain the human capacity to predict the consequences of worldly alterations (i.e., to think before we act). The hypothesis that thinking involves the application of syntax‐sensitive inference rules to syntactically structured mental representations has been said to satisfy all three conditions. An alternative hypothesis is that thinking requires the construction and manipulation of the cognitive equivalent of scale models. A reading of this hypothesis is provided that satisfies condition (1) and which, even though it may not fully satisfy condition (2), turns out (in light of the frame problem) to be the only known way to satisfy condition (3).     

儿童类比推理的影响因素及干预促进

类比推理这一重要认知能力能够帮助儿童在未经历过的复杂情况下进行推断和学习。近年来研究者主要从行为研究、计算机模型和眼动技术的角度探究了儿童抑制控制和工作记忆在类比推理中的交互作用模式及类比推理策略对类比推理的影响。在此基础上,研究者围绕语言标签和物理表征两方面对儿童类比推理进行了干预研究。儿童类比推理的未来研究应着眼于改进研究方法、关注类比推理各加工阶段影响因素及加强儿童类比推理策略的干预应用研究。     

The computational value of debate in defeasible reasoning

Defeasible reasoning is concerned with the logics of non-deductive argument. As is described in the literature, the study of this type of reasoning is considerably more involved than the study of deductive argument, even so that, in realistic applications, there is often a lack of resources to perform an exhaustive analysis. It follows that, in a theory of defeasible reasoning, the order and direction in which arguments are developed, i.e. theprocedure, is important. The aim of this article is to show that debate is the most efficient procedure to argue in the presence of limited resources. To do so, there is first some general theory on defeasible argumentation, which is followed by an introduction to the problem of dialectical search. The problem of dialectical search is (or at least, should be) the essential issue in every theory on argumentation, and emerges at every occasion that involves adjudication on competing arguments. Starting with an example, it is explained that dialectical search can be best scheduled according to classical debating techniques, that work along well-tried methods. These methods (which include various forms of curtailment, interruption, and interpretation) have proven their value in keeping debating efforts within reasonable bounds. How they apply in a theory of formal argument, will be shown in this article.This research was made possible by SION, and is financed by NWO under contract number 612-316-019. Part of this research has been conducted at the Vrije Universiteit Amsterdam. This article contains fragments of Chapter 6 and Chapter 7 of the author's dissertation. Studies in Defeasible Argumentation (1993).     

The sound-board account of reasoning: A one-system alternative to dual-process theory

In order to explain the effects found in the heuristics and biases literature, dual-process theories of reasoning claim that human reasoning is of two kinds: Type-1 processing is fast, automatic, and associative, while Type-2 reasoning is slow, controlled, and rule based. If human reasoning is so divided, it would have important consequences for morality, epistemology, and philosophy of mind. Although dual-process theorists have typically argued for their position by way of an inference to the best explanation, they have generally failed to consider alternative hypotheses. Worse still, it is unclear how we might test dual-process theories. In this article, I offer a one-system theory, which I call the Sound-Board Account of Reasoning, according to which there is one reasoning system which is flexible, allowing the properties used to distinguished Type-1 and Type-2 reasoning to cross-cut one another. I empirically distinguish my theory from the two dominant versions of dual-process theory (parallel-competitive and default-interventionist dual-process theory) and argue that my theory’s predictions are confirmed over both of these versions of dual-process theory.