@phdthesis{Geiger2007,
  author    = {Geiger, Sonja Maria},
  title     = {If there are exceptions, it is still a rule : a probabilistic understanding of conditionals},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-13113},
  school      = {Universit{\"a}t Potsdam},
  year      = {2007},
  abstract  = {Numerous recent publications on the psychological meaning of "if" have proposed a probabilistic interpretation of conditional sentences. According to the proponents of probabilistic approaches, sentences like "If the weather is nice, I will be at the beach tomorrow" (or "If p, then q" in the abstract version) express a high probability of the consequent (being at the beach), given the antecedent (nice weather). When people evaluate conditional sentences, they assumingly do so by deriving the conditional probability P(q|p) using a procedure called the Ramsey test. This is a contradicting view to the hitherto dominant Mental Model Theory (MMT, Johnson-Laird, 1983), that proposes conditional sentences refer to possibilities in the world that are represented in form of mental models. Whereas probabilistic approaches gained a lot of momentum in explaining the interpretation of conditionals, there is still no conclusive probabilistic account of conditional reasoning. This thesis investigates the potential of a comprehensive probabilistic account on conditionals that covers the interpretation of conditionals as well as conclusion drawn from these conditionals when used as a premise in an inference task. The first empirical chapter of this thesis, Chapter 2, implements a further investigation of the interpretation of conditionals. A plain version of the Ramsey test as proposed by Evans and Over (2004) was tested against a similarity sensitive version of the Ramsey test (Oberauer, 2006) in two experiments using variants of the probabilistic truth table task (Experiments 2.1 and 2.2). When it comes to decide whether an instance is relevant for the evaluation of a conditional, similarity seems to play a minor role. Once the decision about relevance is made, believability judgments of the conditional seem to be unaffected by the similarity manipulation and judgments are based on frequency of instances, in the way predicted by the plain Ramsey test. In Chapter 3 contradicting predictions of the probabilistic approaches on conditional reasoning of Verschueren et al (2005), Evans and Over (2004) and Oaksford \& Chater (2001) are tested against each other. Results from the probabilistic truth table task modified for inference tasks supports the account of Oaksford and Chater (Experiment 3.1). A learning version of the task and a design with every day conditionals yielded results unpredicted by any of the theories (Experiments 3.2-3.4). Based on these results, a new probabilistic 2-stage model of conditional reasoning is proposed. To preclude claims that the use of the probabilistic truth table task (or variants thereof) favors judgments reflecting conditional probabilities, Chapter 4 combines methodologies used by proponents of the MMT with the probabilistic truth table task. In three Experiments (4.1 -4.3) it could be shown for believability judgments of the conditional and inferences drawn from it, that causal information about counterexamples only prevails, when no frequencies of exceptional cases are present. Experiment 4.4 extends these findings to every day conditionals. A probabilistic estimation process based on frequency information is used to explain results on all tasks. The findings confirm with a probabilistic approach on conditionals and moreover constitute an explanatory challenge for the MMT. In conclusion of all the evidence gathered in this dissertation it seems justified to draw the picture of a comprehensive probabilistic view on conditionals quite optimistically. Probability estimates not only explain the believability people assign to a conditional sentence, they also explain to what extend people are willing to draw conclusions from those sentences.},
  language  = {en}
}
@phdthesis{Weidenfeld2003,
  author    = {Weidenfeld, Andrea},
  title     = {Interpretation of and reasoning with conditionals : probabilities, mental models, and causality},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5207},
  school      = {Universit{\"a}t Potsdam},
  year      = {2003},
  abstract  = {In everyday conversation \"if\" is one of the most frequently used conjunctions. This dissertation investigates what meaning an everyday conditional transmits and what inferences it licenses. It is suggested that the nature of the relation between the two propositions in a conditional might play a major role for both questions. Thus, in the experiments reported here conditional statements that describe a causal relationship (e.g., \"If you touch that wire, you will receive an electric shock\") were compared to arbitrary conditional statements in which there is no meaningful relation between the antecedent and the consequent proposition (e.g., \"If Napoleon is dead, then Bristol is in England\"). Initially, central assumptions from several approaches to the meaning and the reasoning from causal conditionals will be integrated into a common model. In the model the availability of exceptional situations that have the power to generate exceptions to the rule described in the conditional (e.g., the electricity is turned off), reduces the subjective conditional probability of the consequent, given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). This conditional probability determines people's degree of belief in the conditional, which in turn affects their willingness to accept valid inferences (e.g., \"Peter touches the wire, therefore he receives an electric shock\") in a reasoning task. Additionally to this indirect pathway, the model contains a direct pathway: Cognitive availability of exceptional situations directly reduces the readiness to accept valid conclusions. The first experimental series tested the integrated model for conditional statements embedded in pseudo-natural cover stories that either established a causal relation between the antecedent and the consequent event (causal conditionals) or did not connect the propositions in a meaningful way (arbitrary conditionals). The model was supported for the causal, but not for the arbitrary conditional statements. Furthermore, participants assigned lower degrees of belief to arbitrary than to causal conditionals. Is this effect due to the presence versus absence of a semantic link between antecedent and consequent in the conditionals? This question was one of the starting points for the second experimental series. Here, the credibility of the conditionals was manipulated by adding explicit frequency information about possible combinations of presence or absence of antecedent and consequent events to the problems (i.e., frequencies of cases of 1. true antecedent with true consequent, 2. true antecedent with false consequent, 3. false antecedent with true consequent, 4. false antecedent with false consequent). This paradigm allows testing different approaches to the meaning of conditionals (Experiment 4) as well as theories of conditional reasoning against each other (Experiment 5). The results of Experiment 4 supported mainly the conditional probability approach to the meaning of conditionals (Edgington, 1995) according to which the degree of belief a listener has in a conditional statement equals the conditional probability that the consequent is true given the antecedent (e.g., the probability of receiving an electric shock when touching the wire). Participants again assigned lower degrees of belief to the arbitrary than the causal conditionals, although the conditional probability of the consequent given the antecedent was held constant within every condition of explicit frequency information. This supports the hypothesis that the mere presence of a causal link enhances the believability of a conditional statement. In Experiment 5 participants solved conditional reasoning tasks from problems that contained explicit frequency information about possible relevant cases. The data favored the probabilistic approach to conditional reasoning advanced by Oaksford, Chater, and Larkin (2000). The two experimental series reported in this dissertation provide strong support for recent probabilistic theories: for the conditional probability approach to the meaning of conditionals by Edgington (1995) and the probabilistic approach to conditional reasoning by Oaksford et al. (2000). In the domain of conditional reasoning, there was additionally support for the modified mental model approaches by Markovits and Barrouillet (2002) and Schroyens and Schaeken (2003). Probabilistic and mental model approaches could be reconciled within a dual-process-model as suggested by Verschueren, Schaeken, and d\&\#39;Ydewalle (2003).},
  subject      = {Konditional},
  language  = {en}
}