Normische Gesetzeshypothesen und die wissenschaftsphilosophische Bedeutung des nichtmonotonen Schliessens

Normic Laws and the Significance of Nonmonotonic Reasoning for Philosophy of Science. Normic laws have the form ‘if A then normally B’. They have been discovered in the explanation debate, but were considered as empirically vacuous (§1). I argue that the prototypical (or ideal) normality of normic laws implies statistical normality (§2), whence normic laws have empirical content. In §3–4 I explain why reasoning from normic laws is nonmonotonic, and why the understanding of the individual case is so important here. After sketching some foundations of nonmonotonic reasoning as developed by AI-researchers (§5), Iargue that normic laws are also the best way to understand ceteris paribus laws (§6). §7 deals with the difference between physical and non-physical disciplines and §9 with the difference between normicity and approximation. In §8 it is shown how nonmonotonic reasoning provides a new understanding of the protection of theories against falsification by auxiliary hypotheses. §10, finally, gives a system- and evolution-theoretical explanation of the deeper reason for the omnipresence of normic laws in practice and science, and forthe connection between ideal and statistical normality. This revised version was published online in August 2006 with corrections to the Cover Date.     

关键词对精加工推理的影响作用初探

本实验研究在文本中突出关键词对精加工推理的产生水平和产生阶段的影响。研究采用了让被试阅读后做测试题的方法,通过分析测试题的成绩得出的结果表明：在文章中突出关键词有助于产生近推理和内涵推理,且使这两类推理更有可能在阅读过程中产生。本研究补充了McDaniel和Donnelly 1996年的研究结果。     

A Statistical Interpretive Method for Neuropsychological Test Data

The accurate interpretation of large numbers of neuropsychological tests within a flexible battery approach is a difficult and sometimes controversial process. We present a statistically based method of interpretation (Rohling's Interpretive Method or RIM) and evaluation of neuropsychological data that allows for varying numbers of tests along a varying number of cognitive domains, yet remains psychometrically based. This method requires informed clinical judgment in that the level of confidence for tests, cognitive domains, and global indices are used as the backdrop for interpretive decisions. Specific procedures for use are presented in a systematic, detailed fashion to allow the interested reader to replicate the method. Two case examples are presented: a straightforward case of cerebrovascular insult and a more complicated case of mixed etiology. Examples include a variety of different neuropsychological tests commonly used in a flexible battery approach. A discussion of the practicality, ease of use, and potential limitations of this method are further presented.     

Forward inference in risky choice: Mapping gaze and decision processes

The study of cognitive processes is built on a close mapping between three components: overt gaze behavior, overt choice, and covert processes. To validate this overt–covert mapping in the domain of decision‐making, we collected eye‐movement data during decisions between risky gamble problems. Applying a forward inference paradigm, participants were instructed to use specific decision strategies to solve those gamble problems (maximizing expected values or applying different choice heuristics) during which gaze behavior was recorded. We revealed differences between overt behavior, as indicated by eye movements, and covert decision processes, instructed by the experimenter. However, our results show that the overt–covert mapping is for some eye‐movement measures not as close as expected by current decision theory, and hence question reverse inference as being prone to fallacies due to a violation of its prerequisite, that is, a close overt–covert mapping. We propose a framework to rehabilitate reverse inference.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Who caused it? Interpersonal causal inferences in young children

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.     

Assessment and choice: an operant simulation of foraging in patches

Pigeons were presented with an operant simulation of two prey patches using concurrent random-ratio schedules of reinforcement. An unstable patch offered a higher initial reinforcement probability, which then declined unpredictably to a zero reinforcement probability in each session. A stable patch offered a low but unvarying reinforcement probability. When the reinforcement probability declined to zero in a single step, the birds displayed shorter giving-up times in the unstable patch when the ratio between the initial reinforcement probabilities in the unstable and stable patches was greater and when the combined magnitude of the reinforcement probabilities in the two patches was greater. When the unstable patch declined in two steps, the birds behaved as if their giving-up times were influenced heavily by events encountered during the most recent step of the double-step change. This effect was observed, however, only when the reinforcement probability in that step was .04, not when it was .06. All of these data agree with the predictions of a capture-probability model based on a comparison of the estimated probability of receiving a reinforcer in the current patch with that in alternative patches.     

归因过程“背景效应假设”的初步实验研究

通过两个实验考证了归因过程的“背景效应假设”：实验1表明,当要求活动者和观察者采用同样的背景事件时．他们之间的归因差异消失了;实验2表明,当有其它的背景信息可供参考时,被试就不再完全按照Kelley所假定的一致性和区别性信息进行归因推断。