75 research outputs found
Decision making in uncertain times: what can cognitive and decision sciences say about or learn from economic crises?
B.M. was supported by a Visiting Scholar Award from the British Academy and Grant ME 3717/2 from the Deutsche Forschungsgemeinschaft (DFG) as part of the priority program âNew Frameworks of Rationalityâ (SPP 1516)
Andy Clark and his Critics
In this volume, a range of high-profile researchers in philosophy of mind, philosophy of cognitive science, and empirical cognitive science, critically engage with Clark's work across the themes of: Extended, Embodied, Embedded, Enactive, and Affective Minds; Natural Born Cyborgs; and Perception, Action, and Prediction. Daniel Dennett provides a foreword on the significance of Clark's work, and Clark replies to each section of the book, thus advancing current literature with original contributions that will form the basis for new discussions, debates and directions in the discipline
Relevance differently affects the truth, acceptability, and probability evaluations of âandâ, âbutâ, âthereforeâ, and âifâthenâ
In this study we investigate the influence of reason-relation readings of indicative conditionals and âandâ/âbutâ/âthereforeâ sentences on various cognitive assessments. According to the Frege-Grice tradition, a dissociation is expected. Specifically, differences in the reason-relation reading of these sentences should affect participantsâ evaluations of their acceptability but not of their truth value. In two experiments we tested this assumption by introducing a relevance manipulation into the truth-table task as well as in other tasks assessing the participantsâ acceptability and probability evaluations. Across the two experiments a strong dissociation was found. The reason-relation reading of all four sentences strongly affected their probability and acceptability evaluations, but hardly affected their respective truth evaluations. Implications of this result for recent work on indicative conditionals are discussed
Reach and speed of judgment propagation in the laboratory
In recent years, a large body of research has demonstrated that judgments and
behaviors can propagate from person to person. Phenomena as diverse as
political mobilization, health practices, altruism, and emotional states
exhibit similar dynamics of social contagion. The precise mechanisms of
judgment propagation are not well understood, however, because it is difficult
to control for confounding factors such as homophily or dynamic network
structures. We introduce a novel experimental design that renders possible the
stringent study of judgment propagation. In this design, experimental chains of
individuals can revise their initial judgment in a visual perception task after
observing a predecessor's judgment. The positioning of a very good performer at
the top of a chain created a performance gap, which triggered waves of judgment
propagation down the chain. We evaluated the dynamics of judgment propagation
experimentally. Despite strong social influence within pairs of individuals,
the reach of judgment propagation across a chain rarely exceeded a social
distance of three to four degrees of separation. Furthermore, computer
simulations showed that the speed of judgment propagation decayed exponentially
with the social distance from the source. We show that information distortion
and the overweighting of other people's errors are two individual-level
mechanisms hindering judgment propagation at the scale of the chain. Our
results contribute to the understanding of social contagion processes, and our
experimental method offers numerous new opportunities to study judgment
propagation in the laboratory
Putting Inferentialism and the Suppositional Theory of Conditionals to the Test
This dissertation is devoted to empirically contrasting the Suppositional Theory of conditionals, which holds that indicative conditionals serve the purpose of engaging in hypothetical thought, and Inferentialism, which holds that indicative conditionals express reason relations. Throughout a series of experiments, probabilistic and truth-conditional variants of Inferentialism are investigated using new stimulus materials, which manipulate previously overlooked relevance conditions. These studies are some of the first published studies to directly investigate the central claims of Inferentialism empirically.
In contrast, the Suppositional Theory of conditionals has an impressive track record through more than a decade of intensive testing. The evidence for the Suppositional Theory encompasses three sources. Firstly, direct investigations of the probability of indicative conditionals, which substantiate âthe Equationâ (P(if A, then C) = P(C|A)). Secondly, the pattern of results known as âthe defective truth tableâ effect, which corroborates the de Finetti truth table. And thirdly, indirect evidence from the uncertain and-to-if inference task.
Through four studies each of these sources of evidence are scrutinized anew under the application of novel stimulus materials that factorially combine all permutations of prior and relevance levels of two conjoined sentences. The results indicate that the Equation only holds under positive relevance (P(C|A) â P(C|ÂŹA) \u3e 0) for indicative conditionals. In the case of irrelevance (P(C|A) â P(C|ÂŹA) = 0), or negative relevance (P(C|A) â P(C|ÂŹA) \u3c 0), the strong relationship between P(if A, then C) and P(C|A) is disrupted. This finding suggests that participants tend to view natural language conditionals as defective under irrelevance and negative relevance (Chapter 2). Furthermore, most of the participants turn out only to be probabilistically coherent
above chance levels for the uncertain and-to-if inference in the positive relevance condition, when applying the Equation (Chapter 3). Finally, the results on the truth table task indicate that the de Finetti truth table is at most descriptive for about a third of the participants (Chapter 4).
Conversely, strong evidence for a probabilistic implementation of Inferentialism could be obtained from assessments of P(if A, then C) across relevance levels (Chapter 2) and the participantsâ performance on the uncertain-and-to-if inference task (Chapter 3). Yet the results from the truth table task suggest that these findings could not be extended to truth-conditional Inferentialism (Chapter 4). On the contrary, strong dissociations could be found between the presence of an effect of the reason relation reading on the probability and acceptability evaluations of indicative conditionals (and connate sentences), and the lack of an effect of the reason relation reading on the truth evaluation of the same sentences. A birdâs eye view on these surprising results is taken in the final chapter and it is discussed which perspectives these results open up for future research
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Human-level artificial intelligence must be a science
Human-level artificial intelligence (HAI) surely is a special research endeavor in more than one way: The very nature of intelligence is in the first place not entirely clear, there are no criteria commonly agreed upon necessary or sufficient for the ascription of intelligence other than similarity to human performance, there is a lack of clarity concerning how to properly investigate artificial intelligence and how to proceed after the very first steps of implementing an artificially intelligent system, etc. These and similar observations have led some researchers to claim that HAI might not be a science in the normal sense and would require a different approach. Taking a recently published paper by Cassimatis as starting point, I oppose this view, giving arguments why HAI should (and even has to) conform to normal scientific standards and methods, using the approach of psychometric artificial intelligence as one of the main foundations of my position
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