A theory of norms and normality is presented and applied to some phenomena of emotional responses, social judgment, and conversations about causes. Norms are assumed to be constructed ad hoc by recruiting specific representations. Category norms are derived by recruiting exemplars. Specific objects or events generate their own norms by retrieval of similar experiences stored in memory or by construction of counterfactual alternatives. The normality of a stimulus is evaluated by comparing it to the norms that it evokes after the fact, rather than to precomputed expectations. Norm theory is applied in analyses of the enhanced emotional response to events that have abnormal causes, of the generation of predictions and inferences from observations of behavior, and of the role of norms in causal questions and answers. This article is concerned with category norms that represent knowledge of concepts and with stimulus norms that govern comparative judgments and designate experiences as surprising. In the tradition of adaptation level theory (Appley, 1971; Helson, 1964), the concept of norm is applied to events that range in complexity from single visual displays to social interactions. We first propose a model of an activation process that produces norms, then explore the role of norms in social cognition. The central idea of the present treatment is that norms are computed after the event rather than in advance. We sketch a supplement to the generally accepted idea that events in the stream of experience are interpreted and evaluated by consulting precomputed schemas and frames of reference. The view developed here is that each stimulus selectively recruits its own alternatives (Garner, 1962, 1970) and is interpreted in a rich context of remembered and constructed representations of what it could have been, might have been, or should have been. Thus, each event brings its own frame of reference into being. We also explore the idea that knowledge of categories (e.g., "encounters with Jim") can be derived on-line by selectively evoking stored representations of discrete episodes and exemplars. The present model assumes that a number of representations can be recruited in parallel, by either a stimulus event or an

https://www3.canyons.edu/faculty/rafterm/0%200LLI%20LOVEandLOSS/Day%206%20Files/1986%20-%20Norm%20Theory%20-%20Comparing%20Reality%20to%20Its%20Alternatives.pdf

Norm theory: Comparing reality to its alternatives

A methodical gross examination should be undertaken in all births and the results incorporated in the patient’s record.

The Pathology of the Human Placenta

Because causal relations are neither observable nor deducible, they must be induced from observable events. The 2 dominant approaches to the psychology of causal induction—the covariation approach and the causal power approach—are each crippled by fundamental problems. This article proposes an integration of these approaches thai overcomes these problems. The proposal is that reasoners innately treat the relation between covariation (a function denned in terms of observable events) and causal power (an unobservable entity) as that between scientists' law or model and their theory explaining the model. This solution is formalized in the power PC theory, a causal power theory of the probabilistic contrast model (P. W. Cheng & L. R. Novick, 1990). The article reviews diverse old and new empirical tests discriminating this theory from previous models, none of which is justified by a theory. The results uniquely support the power PC theory.

From covariation to causation: A causal power theory.

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

Explanation in Artificial Intelligence: Insights from the Social Sciences

Concepts of cause and causal inference are largely self-taught from early experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multicausality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect than as a criterion-guided process for deciding whether an effect is present or not.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID849605_code468716.pdf?abstractid=849605&mirid=2

Causation and Causal Inference in Epidemiology

Hart and Honore's book is a thorough and scholarly study of the concept of causation in Anglo-American law. Closely reasoned, with full reference to the literature and a considerable treatment of Continental theories, it represents a serious attempt to restate the law in tort, contract and crime in an intelligible way. In future, any serious student of the subject will have to take note of it. Unfortunately for the authors they have backed a loser in supporting Re Polemis and criticising the foreseeability test; and since much of their argument depends upon their opinion on this question, their book must now be accounted largely out of date, if the decision of the Judicial Committee in The Wagon Mound receives the acceptance that seems probable. In respect of the general theory of causation, however, the discussion retains its intellectual interest; and the chapters on contract and crime, though relatively short, are largely unaffected by The Wagon Mound.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0008197300010205

Glanville Williams

Papers

Causation in the Law

What is a document

Textbook of criminal law

Criminal Law: The General Part

The sanctity of life and the criminal law