Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning

Cognitive Biases in Human Causal Learning

El objetivo de este trabajo fue la búsqueda de sesgos cognitivos en la inferencia de relaciones causales para descubrir qué procesos psicológicos modulan el aprendizaje causal. A partir del efecto de la frecuencia de juicio, este trabajo presenta investigación consecuente sobre competición entre claves (ensombrecimiento, bloqueo o súper-condicionamiento) para demostrar cómo la fuerza de las creencias previas y la evidencia sobre la covariación de cada causa contribuyen aditivamente en los juicios causales y en la toma de decisiones, siendo su fuerza relativa modulada por la fiabilidad otorgada a cada tipo de información. Nuevos datos muestran también la incapacidad para detectar relaciones causales incidentales preventivas, pero no generativas. Esta “ceguera inatencional” parece deberse a un fallo en la codificación o recuperación de la información. Todos estos datos revelan que una arquitectura cognitiva del aprendizaje causal debe basarse en tres niveles. El primer nivel sería responsable de la codificación de los eventos en cada ensayo. El segundo nivel computaría la nueva evidencia a partir de la información recibida del primer nivel. En el tercer nivel, el individuo debe interpretar e integrar toda esta información con su conocimiento causal previo. En suma, los modelos sobre juicios de causalidad y toma de decisiones normalmente se han centrado en el efecto exclusivo de las “creencias y conocimiento causal” o de la “experiencia directa y covariación” entre causas y efectos. Este trabajo demuestra que ambos tipos de información se requieren e interactúan cuando se trata de explicar la complejidad y flexibilidad que implica el aprendizaje y la inferencia de relaciones causales en humanos.The main aim of this work was to look for cognitive biases in human inference of causal relationships in order to emphasize the psychological processes that modulate causal learning. From the effect of the judgment frequency, this work presents subsequent research on cue competition (overshadowing, blocking, and super-conditioning effects) showing that the strength of prior beliefs and new evidence based upon covariation computation contributes additively to predict causal judgments, whereas the balance between the reliability of both, beliefs and covariation knowledge, modulates their relative weight. New findings also showed “inattentional blindness” for negative or preventative causal relationships but not for positive or generative ones, due to failure in codifying and retrieving the necessary information for its computation. Overall results unveil the need of three hierarchical levels of a whole architecture for human causal learning: the lower one, responsible for codifying the events during the task; the second one, computing the retrieved information; finally, the higher level, integrating this evidence with previous causal knowledge. In summary, whereas current theoretical frameworks on causal inference and decision-making usually focused either on causal beliefs or covariation information, the present work shows how both are required to be able to explain the complexity and flexibility involved in human causal learning

Causal Induction from Continuous Event Streams: Evidence for Delay-Induced Attribution Shifts

Contemporary theories of Human Causal Induction assume that causal knowledge is inferred from observable contingencies. While this assumption is well supported by empirical results, it fails to consider an important problem-solving aspect of causal induction in real time: In the absence of well structured learning trials, it is not clear whether the effect of interest occurred because of the cause under investigation, or on its own accord. Attributing the effect to either the cause of interest or alternative background causes is an important precursor to induction. We present a new paradigm based on the presentation of continuous event streams, and use it to test the Attribution-Shift Hypothesis (Shanks & Dickinson, 1987), according to which temporal delays sever the attributional link between cause and effect. Delays generally impaired attribution to the candidate, and increased attribution to the constant background of alternative causes. In line with earlier research (Buehner & May, 2002, 2003, 2004) prior knowledge and experience mediated this effect. Pre-exposure to a causally ineffective background context was found to facilitate the discovery of delayed causal relationships by reducing the tendency for attributional shifts to occur. However, longer exposure to a delayed causal relationship did not improve discovery. This complex pattern of results is problematic for associative learning theories, but supports the Attribution-Shift Hypothesi

Computation of context as a cognitive tool

In the field of cognitive science, as well as the area of Artificial Intelligence (AI), the role of context has been investigated in many forms, and for many purposes. It is clear in both areas that consideration of contextual information is important. However, the significance of context has not been emphasized in the Bayesian networks literature. We suggest that consideration of context is necessary for acquiring knowledge about a situation and for refining current representational models that are potentially erroneous due to hidden independencies in the data.In this thesis, we make several contributions towards the automation of contextual consideration by discovering useful contexts from probability distributions. We show how context-specific independencies in Bayesian networks and discovery algorithms, traditionally used for efficient probabilistic inference can contribute to the identification of contexts, and in turn can provide insight on otherwise puzzling situations. Also, consideration of context can help clarify otherwise counter intuitive puzzles, such as those that result in instances of Simpson's paradox. In the social sciences, the branch of attribution theory is context-sensitive. We suggest a method to distinguish between dispositional causes and situational factors by means of contextual models. Finally, we address the work of Cheng and Novick dealing with causal attribution by human adults. Their probabilistic contrast model makes use of contextual information, called focal sets, that must be determined by a human expert. We suggest a method for discovering complete focal sets from probabilistic distributions, without the human expert

A conversational model of causal explanation

Es wird ein Konversationsmodell der kausalen Erklärung vorgestellt, das existierende Modelle der kausalen Erklärung integriert und konsistent mit allgemeinen Modellen von Gesprächsanalysen ist. Fundamentale Fragen über die Faktoren der Erklärung wie Wahrscheinlichkeit und Relevanz werden diskutiert. Ferner werden Erklärungsmuster im Bereich des umgangssprachlichen Gesprächs behandelt. (psz)'A conversational model of causal explanation is outlined, which emphasises the role of counterfactual reasoning, contrast cases and conversational constraints in causal explanation. It is used to organise existing models of causal attributions, to integrate attribution research with other models of causal reasoning, and to study explanations in ordinary language.' (author's abstract

Too heavy to be popular? : lay theories about weight and social status in adolescence

The present research examines causal lay theories about the relation between weight and social status in adolescence. We hypothesize that these causal schemata are expected to manifest in social information processing in the presence of weight and social status cues. Participants include 80 ninth grade adolescents and 203 college adults. First, in probabilistic judgment scenarios (Part 1), we show that participants expect statistical covariation between weight and social status when inferring others’ social status. Next, in a weight social categorization task (Part 2), participants were more likely to use weight cues to categorize people when they were presented with cues that were inconsistent with their lay theories (e.g., weight causes certain social status). Lastly, in a weight attribution task (Part 3), young adults tended to make more negative, dispositional attribution to overweight targets relative to thin targets when interpreting ambiguously-caused negative social events. As an initial test of the developmental process model, the findings support our theoretical predictions that adolescents’ and young adults’ causal lay theories about weight and social status serve as mental models that guide social information processing in judgment of others’ social status.Psycholog

The Development of Spatial-Temporal, Probability, and Covariation Information to Infer Continuous Causal Processes

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

The development of scientific reasoning in preschoolers: hypothesis testing, evidence evaluation and argumentation from evidence

Although research on scientific reasoning has shown that young children have poor skills in epistemic activities such as evidence evaluation or experimentation; recent research demonstrated that they have powerful learning mechanisms in making causal predictions from evidence patterns or performing experiments to reveal causal relations that are not readily available to them. Although these abilities are informative concerning early epistemic activities, little is known about whether young children can reason scientifically. The ability to coordinate hypotheses and evidence; and having a metacognitive understanding of the hypothesis–evidence relation are the two foundational abilities for scientific reasoning. In three empirical studies, the present thesis investigated the development of these two abilities in 4- to 6-year-old preschoolers in three epistemic activities; namely, hypothesis testing, evidence evaluation, and argumentation from evidence. Study 1 showed that older preschoolers can differentiate between epistemic goals of hypothesis testing and practical goals of effect production, which suggest that the epistemic categories of hypotheses and evidence; and the ability to coordinate the two is already present in the late preschool years. Study 2 revealed that preschoolers can generate disconfirming evidence in order to refute false causal claims and they can reflect on the relation between beliefs and evidence. Study 3 showed that 5- and 6-year-olds can reflect on the relation between their knowledge states and confounded evidence. The findings of the three studies suggest that the foundational abilities for scientific reasoning, understanding the inferential relation of hypothesis and evidence and the reflective ability over this relation are present in preschoolers