Proportional reasoning,framing effects,and affirmative action: Is six of one really half a dozen of another in university admissions?

People often reason proportionally, perceiving fixed outcomes as larger or smaller depending upon the reference condition. Thus, for policies affecting individuals, presenting data as percentages rather than frequencies can alter perceived effects on high versus low base rate group members, even though identical numbers of individuals in each group are affected. Such numerical framing effects were explored through a case analysis of public debates over race-conscious selection policies and through experimental manipulations employing a race-conscious university admissions scenario. Undergraduates (N = 193) received data reporting the expected impact on black and white student enrollment resulting from a university shift to race-neutral admissions. Compared to those encountering percentages or proportions, participants receiving identical information expressed as frequencies revealed a predicted greater preference for race-neutral or “race blind” admissions. Structural equation analysis supported a model in which perceived impact and fairness mediated the relationship between format and endorsement of race-neutral admissions.     

Experience-grounded semantics: a theory for intelligent systems

An experience-grounded semantics is introduced for an intelligent reasoning system, which is adaptive, and works with insufficient knowledge and resources. According to this semantics, truth and meaning are defined with respect to the experience of the system – the truth value of a statement indicates the amount of available evidence, and the meaning of a term indicates its experienced relations with other terms. The major difference between experience-grounded semantics and model-theoretic semantics is that the former does not assume the sufficiency of knowledge and resources. This approach provides new ideas to the solution of some important problems in cognitive science.     

De‐escalation after repeated negative feedback: emergent expectations of failure

Research on willingness to make marginal investments (e.g., the escalation and sunk cost literatures) has often focused on project completion decisions, such as the “radar‐blank plane.” This paper discusses a fundamentally different type of marginal investment decision, that of couples deciding whether to continue infertility treatment in the face of repeated failures. Two experiments based on this context show that when people face multiple independent chances to achieve a valued goal but are unsure about chances of success, premature quitting or “de‐escalation” is the norm. Repeated negative feedback appears to induce individuals to see each successive failure as more and more diagnostic. As a result, even a short series of failed attempts evokes beliefs that future attempts will also fail. These emergent expectations of failure, generated by causal attribution processes, associative learning, and/or discounting of ambiguous information, appear very compelling and induce people to forgo profitable marginal investments. Copyright © 2004 John Wiley & Sons, Ltd.     

Reasoning through doing. Epistemic mediators in scientific discovery

The recent epistemological and cognitive studies concentrate on the concept of abduction, as a means to originate and refine new ideas. Traditional cognitive science and computational accounts concerning abduction aim at illustration discovery and creativity processes in terms of theoretical and “internal” aspects, by means of computational simulations and/or abstract cognitive models. I will illustrate in this paper that some typical internal abductive processes are involved in scientific reasoning and discovery (for example through radical innovations). Nevertheless, especially concrete manipulations of the external world constitute a fundamental passage in science: by a process of manipulative abduction it is possible to build prostheses (epistemic mediators) for human minds, by interacting with external objects and representations in a constructive way. In this manner it is possible to create implicit knowledge through doing and to produce various opportunity to find, for example, anomalies and fruitful new risky perspectives. This kind of embodied and unexpressed knowledge holds a key role in the subsequent processes of scientific comprehension and discovery.     

Abductive inference in defeasible reasoning: a model for research programmes

Abduction is regarded as the inference process that goes from observations to explanations within a more general context or theoretical framework. There are good characterizations of abduction of surprising observations in monotonic theories. Also, in normal logic programs there are a tight relation among SLDNF and the abduction of negative literals. However, a problem that faces abduction is the explanation of anomalous observations, i.e., observations that are contradictory with respect to the current theory. For this reason, in this work we will consider the problem of embedding abduction of surprising and anomalous observations in defeasible (nonmonotonic) theories. We discuss some issues arising the pragmatic acceptance of abductive inferences in defeasible theories, and how to accommodate anomalous observations and characterize all the possible outcomes that a defeasible theory may face when confronted with new evidence. We explore the use of partial structures approach as a semantic foundation for our system. Finally, we discuss an application of our system as a formal device for representing the methodology of scientific research programmes. In this representation, a programme is regarded as a defeasible theory that draws predictions. When confronted with surprising or anomalous observations, the programme protects itself by means of heuristic procedures, which are represented in our system as abductive inference procedures.     

Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.     

Probabilistic reasoning in prediction and diagnosis: Effects of problem type,response mode,and individual differences

In prediction, subset relations require that the probability of conjoined events is never higher than that of constituent events. However, people's judgments regularly violate this principle, producing conjunction errors. In diagnosis, the probability of a hypothesis normatively is often higher for conjoined cues. An online survey used a within‐subjects design to explore the degree to which participants (n = 347) differentiated diagnosis and prediction using matched scenarios and both choice and estimation responses. Conjunctions were judged more probable than a constituent in diagnosis (76%) more often than prediction (64%) and in choice (84%) more often than direct estimation (57%), with no interaction of type of task and response mode. Correlation, regression, and path analyses were used to determine the relationships among individual difference variables and the diagnosis and prediction tasks. Among the correlation findings was that time spent on the task predicted higher conjunction probabilities in diagnosis but not prediction and that class inclusion errors predicted increased conjunction errors in choice but not estimation. Need for cognition and numeracy were only minimally related to reasoning about conjunctions. Results are consistent with the idea that people may misapply diagnostic reasoning to the prediction task and consequently commit the conjunction error. Copyright © 2010 John Wiley & Sons, Ltd.     

Moral development, executive functioning, peak experiences and brain patterns in professional and amateur classical musicians: interpreted in light of a Unified Theory of Performance

This study compared professional and amateur classical musicians matched for age, gender, and education on reaction times during the Stroop color-word test, brainwaves during an auditory ERP task and during paired reaction-time tasks, responses on the Gibbs Sociomoral Reflection questionnaire, and self-reported frequencies of peak experiences. Professional musicians were characterized by: (1) lower color-word interference effects (Stroop task), (2) faster categorization of rare expected stimuli (P3b), and a trend for faster processing of rare unexpected stimuli (P3a), (3) higher scores on the Sociomoral Reflection questionnaire, and (4) more frequent peak experiences during rest, tasks, and sleep. Both groups had high values on the Brain Integration Scale. These findings are interpreted in light of a Unified Theory of Performance, which posits that effectiveness in any area is influenced by one’s level of mind-brain development—emotional, cognitive, moral, ego and cortical development—with higher mind-brain development supporting greater effectiveness in any domain.     

An analytic tableau calculus for a temporalised belief logic

A tableau is a refutation-based decision procedure for a related logic, and is among the most popular proof procedures for modal logics. In this paper, we present a labelled tableau calculus for a temporalised belief logic called TML⁺, which is obtained by adding a linear-time temporal logic onto a belief logic by the temporalisation method of Finger and Gabbay. We first establish the soundness and the completeness of the labelled tableau calculus based on the soundness and completeness results of its constituent logics. We then sketch a resolution-type proof procedure that complements the tableau calculus and also propose a model checking algorithm for TML⁺ based on the recent results for model checking procedures for temporalised logics. TML⁺ is suitable for formalising trust and agent beliefs and reasoning about their evolution for agent-based systems. Based on the logic TML⁺, the proposed labelled tableau calculus could be used for analysis, design and verification of agent-based systems operating in dynamic environments.     

贝叶斯推理中的“自击中率参照抑制”

本文旨在研究贝叶斯推理中基础比率、击中率起作用的机制。通过设置肝炎情境、中彩情境的贝叶斯推理问题,同时操作基础比率和击中率的水平,考察449名大学生被试的概率估计情况。结果表明,总体上,贝叶斯推理中的基础比率和击中率均起作用,表现为后验概率估计值随基础比率水平的提高而增加,随击中率水平的提高而增加。然而,在低基础比率的中彩情境中,后验概率估计值先随击中率的增加而增加;之后却随击中率水平的继续提高而显著降低,出现自击中率参照抑制。这似乎说明,击中率在贝叶斯推理中并非一致地起作用,而可能与推理者的知识经验对击中率加工的调节有关。