Reconciling Compensatory and Noncompensatory Strategies of Cue Weighting: A Causal Model Approach

When forming a judgment about any unknown item, people must draw inferences from information that is already known. This paper examines causal relationships between cues as a relevant factor influencing how people determine the amount of weight to place on each piece of available evidence. We propose that people draw from their beliefs about specific causal relationships between cues when determining how much weight to place on those cues, and that understanding this process can help reconcile differences between predictions of compensatory and lexicographic heuristic strategies. As causal relationships change, different cues become more or less important. Across three experiments, we find support for the use of causal models in determining cue weights, but leave open the possibility that they work in concert with other strategies as well. We conclude by discussing relative strengths and weaknesses of the causal model approach relative to existing models, and suggest areas for future research. Copyright © 2016 John Wiley & Sons, Ltd.     

Paranormal belief and errors of probabilistic reasoning: The role of constituent conditional relatedness in believers' susceptibility to the conjunction fallacy

The present study examines the extent to which stronger belief in either extrasensory perception, psychokinesis or life-after-death is associated with a proneness to making conjunction errors (CEs). One hundred and sixty members of the UK public read eight hypothetical scenarios and for each estimated the likelihood that two constituent events alone plus their conjunction would occur. The impact of paranormal belief plus constituents’ conditional relatedness type, estimates of the subjectively less likely and more likely constituents plus relevant interaction terms tested via three Generalized Linear Mixed Models. General qualification levels were controlled for. As expected, stronger PK beliefs and depiction of a positively conditionally related (verses conditionally unrelated) constituent pairs predicted higher CE generation. ESP and LAD beliefs had no impact with, surprisingly, higher estimates of the less likely constituent predicting fewer - not more - CEs. Theoretical implications, methodological issues and ideas for future research are discussed.     

The Automaticity of Affect for Political Leaders, Groups, and Issues: An Experimental Test of the Hot Cognition Hypothesis

We report the results of three experimental tests of the "hot cognition" hypothesis, which posits that all sociopolitical concepts that have been evaluated in the past are affectively charged and that this affective charge is automatically activated within milliseconds on mere exposure to the concept, appreciably faster than conscious appraisal of the object.
We find support for the automaticity of affect toward political leaders, groups, and issues; specifically:

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  Most Ss show significantly faster reaction times to affectively congruent political concepts and significantly slower response times to affectively incongruent concepts;

       

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.     

Reasoning about make-believe and hypothetical suppositions: Towards a theory of belief-contravening reasoning

The present study explores how suppositions which conflict with accepted beliefs are represented and reasoned about. Two studies test the predictions regarding the nature and developmental changes in children's ability to represent and reason about hypothetical or make-believe suppositions which violate their everyday knowledge and beliefs. In Study 1, 46 4th- and 5th-graders were introduced to a hand puppet, Freddy, who made claims inconsistent with generally accepted beliefs (e.g., “all dogs meow”) because he was pretending (Make-Believe Condition) or believed them (Hypothetical Condition). Participants were asked to think like Freddy and judge whether a conclusion (“There's a dog; does it meow?”) follows logically from the claim. In Study 2, 40 kindergarten (6-year-olds), 3rd–4th grade (10-year-olds), and college students were asked to represent belief contravening make-believe (pretend in a make-believe world that dogs meow) and hypothetical (imagine what the real world would be like if dogs meow) premises, evaluate conclusions of the premises (Rover is a dog, does Rover meow?) and make judgments about the attributes (growl, wag tail, purr, and eat mice) of the entity (a meowing dog) they created. The prediction that it would be easier to represent and reason from belief-contravening suppositions in the Make-Believe than Hypothetical conditions was confirmed in each study, although the two forms of reasoning were directly correlated (Study 2). The results were discussed in terms of the similarities (compartmentalization and integration) and differences (reconciliation) of processes involved in fancifully (make-believe) or seriously (hypothetical) representing and reasoning about belief-contravening suppositions.     

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.