A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand data by discovering useful categories. However, research in data mining has not paid attention to the cognitive factors that make learned categories intelligible to human users. We show that one factor that influences the intelligibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind. Introduction Knowledge-discovery in databases is a field whose goal is to extract usable models from a collection of data. Such models are expected to be accurate and are further expected to be intelligible to experts in the field. For example, knowledge acquired through such methods on a medical database might be published in scientific journals or written down as procedures to be followed in a health maintenance organization. While it is important that such knowled...

The experience of willing an act arises from interpreting one's thought as the cause of the act. Conscious will is thus experienced as a function of the priority, consistency, and exclusivity of the thought about the action. The thought must occur before the action, be consistent with the action, and not be accompanied by other causes. An experiment illustrating the role of priority found that people can arrive at the mistaken belief that they have intentionally caused an action that in fact they were forced to perform when they are simply led to think about the action just before its occurrence. Conscious will is a pervasive human experience. We all have the sense that we do things, that we cause our acts, that we are agents. As William James (1890) observed, "the whole sting and excitement of our voluntary life... depends on our sense that in it things are really being decided from one moment to another, and that it is not the dull rattling off of a chain that was forged innumerable ages ago " (p. 453). And yet, the very notion of the will seems to contradict the core assumption of psychological science. After all, psychology examines how behavior is caused by mechanisms—the rattling off of genetic, unconscious, neural, cognitive, emotional, social, and yet other chains that lead, dully or not, to the things people do. If the things we do are caused by such mechanisms, how is it that we nonetheless experience willfully doing them? Our approach to this problem is to look for yet another chain—to examine the mechanisms that produce the experience of conscious will itself. In this article, we do this by exploring the possibility that the experience of will is a result of the same mental processes that people use in the perception of causality more generally. Quite simply, it may be that people experience conscious will when they interpret their own thought as the cause of their action. This idea means that people can experience conscious will quite independent of any actual causal connection between

The theory outlined in the present chapter adopts a cognitive approach to motivation. In the pages that follow we describe a research program premised on the notion that the cognitive treatment affords conceptual and methodological advantages enabling new insights into problems of motivated action, self-regulation and self-control. We begin by placing our work in the broader historical context of social psychological theorizing about motivation and cognition. We then present our theoretical notions and trace their implications for a variety of psychological issues including activity-experience, goal-commitment, choice, and substitution. The gist of the chapter that follows describes our empirical research concerning a broad range of phenomena informed by the goal-systemic analysis. Motivation Versus Cognition, or Motivation as Cognition Motivation versus cognition: the “separatist program. ” Social psychological theories have often treated motivation as separate from cognition, and have often approached it in a somewhat static manner. The separatism of the “motivation versus cognition ” approach was manifest in several major formulations and debates. Thus, for example, the dissonance versus self-perception debate (Bem, 1972) pitted against each other motivational (i.e., dissonance) versus cognitive (i.e., self-perception) explanations of attitude change phenomena. A similar subsequent controversy pertained to the question of whether a motivational explanation of biased causal attributions in terms of ego-defensive tendencies (cf. Kelley, 1972) is valid, given the alternative possibility of a purely cognitive explanation (Miller & Ross, 1975). The separatism of the “motivation versus cognition ” approach assigned distinct functions to motivational and cognitive variables. This is apparent in major social psychological notions of persuasion, judgment or impression formation. For instance, in the popular dual-mode theories of

Large databases are routinely being collected in science, business and medicines. A variety of techniques from statistics, signal processing, pattern recognition, machine learning, and neural networks have been proposed to understand the data by discovering useful categories. However, to date research in data mining has not paid attention to the cognitive factors that make learned categories comprehensible. We show that one factor that influences the comprehensibility of learned models is consistency with existing knowledge and describe a learning algorithm that creates concepts with this goal in mind. Introduction Knowledge-discovery in databases is a field whose goal is to extract usable knowledge from a collection of data. It draws upon methods in statistics, signal processing, pattern recognition, information theory, machine learning, and neural networks to produce models that provide insight into data. Such models are expected to be accurate and are further expected to be compre...

As electronic data becomes widely available, the need for tools that help people gain insight from data has arisen. A variety of techniques from statistics, machine learning, and neural networks have been applied to databases in the hopes of mining knowledge from data. Multiple regression is one such method for modeling the relationship between a set of explanatory variables and a dependent variable by fitting a linear equation to observed data. Here, we investigate and discuss some factors that influence whether the resulting regression equation is a credible model of the data. Introduction Multiple linear regression (e.g., Draper and Smith, 1981) is a technique for finding a linear relationship between a set of explanatory variables (x ) and a dependent variable (y): y = b 0 + b 1 x 1 + b 2 x 2 + ... + b n x n . The coefficients, (b i ) provide some indication of the explanatory variables effect on the dependent variable. With the wide availability of personal computers and the inc...

Causal inferences about sparsely observed objects are often supported by causal schemata, or systems of abstract causal knowledge. We present a hierarchical Bayesian framework that learns simple causal schemata given only raw data as input. Given a set of objects and observations of causal events involving some of these objects, our framework simultaneously discovers the causal type of each object, the causal powers of these types, the characteristic features of these types, and the characteristic interactions between these types. Previous behavioral studies confirm that humans are able to discover causal schemata, and we show that our framework accounts for data collected by Lien and Cheng and Shanks and Darby.

How is information transmitted in a group? A multi-agent connectionist model is proposed that combines features of standard recurrent models to simulate the process of information uptake, integration and memorization within individual agents, with novel aspects that simulate the communication of beliefs and opinions between agents. A crucial aspect in belief updating based on information from other agents is the trust in the information provided, implemented as the consistency with the receiving agents’ existing beliefs. Trust leads to a selective propagation and thus filtering out of less reliable information, and implements Grice’s (1975) maxims of quality and quantity in communication. By studying these communicative aspects within the framework of standard models of information processing, the unique contribution of communicative mechanisms beyond intra-personal factors was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions.

Framing is a potentially useful paradigm for examining the strategic creation of public relations messages and audience responses. Based on a literature review across disciplines, this article identifies 7 distinct types of framing applicable to public relations. These involve the framing of situations, attributes, choices, actions, issues, responsibility, and news. Potential applications for public relations practice and research are discussed. Public relations can be examined from a variety of frameworks, including systems, critical, and rhetorical perspectives (Toth, 1992). The rhetorical approach focuses on how public relations is engaged in the construction of messages and meanings that are intended to influence key publics important to an organization. Rhetorical theory encompasses a wide range of approaches, including argumentation, advocacy and persuasion, corporate communication, dialectics and discourse, dramatism and storytelling, information, organizing, public opinion, and reputation management. Yet, none of these approaches represents a comprehensive foundation for fully understanding the processes or consequences of public relations. Another theoretically rich approach that offers the potential of subsuming and tying together many of these seemingly unrelated approaches involves framing theory. Framing has been used as a paradigm for understanding and investigating communication and related behavior in a wide range of disciplines (Rendahl, 1995). These include psychology, speech communication (especially discourse

Research suggests that counterfactuals (i.e., thoughts of how things might have been different) play an important role in determining the perceived cause of a target outcome. Results from 3 scenario studies indicate that counterfactual content overlapped primarily with thoughts of how an outcome might have been prevented (preventability ascriptions) rather than with thoughts of how it might have been caused (causal ascriptions). Counterfactuals and preventability ascriptions focused mainly on controllable antecedents, whereas causal ascriptions focused mainly on antecedents that covaried with the target outcome over a focal set of instances. Contrary to current theorizing, causal ascriptions were unrelated to counterfactual content (Study 3). Results indicate that the primary criterion used to recruit causal ascriptions (covariation) differs from that used to recruit counterfactuals (controllability).

In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning -- a linear and a noisy-OR model -- based on information contained in conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We conclude by considering the appropriateness of non-parametric approaches to counterfactual reasoning, and examining the prospects for other parametric approaches in the future.