This article describes a new theory of propositional reasoning, that is, deductions depending on if, or, and, and not. The theory proposes that reasoning is a semantic process based on mental models. It assumes that people are able to maintain models of only a limited number of alternative states of affairs, and they accordingly use models representing as much information as possible in an implicit way. They represent a disjunctive proposition, such as "There is a circle or there is a triangle," by imagining initially 2 alternative possibilities: one in which there is a circle and the other in which there is a triangle. This representation can, if necessary, be fleshed out to yield an explicit representation of an exclusive or an inclusive disjunction. The theory elucidates all the robust phenomena of propositional reasoning. It also makes several novel predictions, which were corroborated by the results of 4 experiments.
The present study introduces dual task methodology to test opposing psychological processing predictions concerning the nature of implicatures in pragmatic theories. Implicatures routinely arise in human communication when hearers interpret utterances pragmatically and go beyond the logical meaning of the terms. The neo-Gricean view (e.g., Levinson, 2000) assumes that implicatures are generated automatically whereas relevance theory (Sperber & Wilson, 1986/1995) assumes that implicatures are effortful and not automatic. Participants were presented a sentence verification task with underinformative sentences that have the potential to produce scalar implicatures like Some oaks are trees. Depending on the nature of the interpretation of Some (logical or pragmatic) the sentence is judged true or false. Executive cognitive resources were experimentally burdened by the concurrent memorization of complex dot patterns during the interpretation process. Results showed that participants made more logical and fewer pragmatic interpretations under load. Findings provide direct support for the relevance theory view.
Conditional reasoning involves making inferences on the basis of an "if-then" relation and is considered one of the cornerstones of human reasoning. Research on conditional reasoning has been trying to identify the factors and processes that affect the performance on these "if-then" inference problems. In a standard conditional inference task, people are asked to assess arguments of the following four kinds: In standard propositional logic, MP and MT are considered valid inferences and DA and AC are regarded as fallacies.A growing body of evidence is showing that people's knowledge about the relation between the p (antecedent) and q (consequent) part of the conditional has a considerable effect on the reasoning process. In particular, the role of knowledge of alternative causes and disabling conditions has attracted interest (see Politzer, in press, for a review).An alternative cause (alternative) is a possible cause that can produce the effect mentioned in the conditional, whereas a disabling condition (disabler) prevents the effect from occurring despite the presence of the cause. Consider the following conditional:If the air conditioner is turned on, then you feel cool Possible alternative causes for this conditional are as follows:Taking off some clothes, the weather cools, swimming . . . The alternatives make it clear that it is not necessary to turn on the air conditioner in order to feel cool. Other causes are also possible. Possible disabling conditions are as follows:Air conditioner is broken, having fever, window open . . .If such disablers are present, turning on the air conditioner will not result in feeling cool. The disablers make it clear that it is not sufficient to turn on the air conditioner in order to feel cool. Additional conditionsmust be fulfilled. Rumain, Connell, and Braine (1983) showed that when a possible alternative was explicitly presented to participants, the AC and DA inferences were less endorsed. Byrne (1989) found a similar effect on MP and MT when a possible disabling condition was mentioned. In addition, using familiar relations (e.g., "If an animal has feathers, it is a bird") for which people have ready access to alternatives, Markovits (1986) showed that even without explicit Preparation of the manuscript was supported by grants from the Fund for Scientific Research-Flanders (FWO) This study tested and refined a framework that proposes a mechanism for retrieving alternative causes and disabling conditions (Cummins, 1995) during reasoning. Experiment 1 examined the relation between different factors affecting retrieval. The test revealed high correlations between the number of possible alternative causes or disabling conditions and their strength of association and plausibility. Experiment 2 explored the hypothesis that due to a more extended search process, conditional inferences would last longer when many alternative causes or disabling conditionswere available. Affirmation of the consequent (AC) and modus ponens (MP) latencies showed the hypothesized pattern. Denial o...
A large body of electrophysiological literature showed that metaphor comprehension elicits two different event-related brain potential responses, namely the so-called N400 and P600 components. Yet most of these studies test metaphor in isolation while in natural conversation metaphors do not come out of the blue but embedded in linguistic and extra-linguistic context. This study aimed at assessing the role of context in the metaphor comprehension process. We recorded EEG activity while participants were presented with metaphors and equivalent literal expressions in a minimal context (Experiment 1) and in a supportive context where the word expressing the ground between the metaphor's topic and vehicle was made explicit (Experiment 2). The N400 effect was visible only in minimal context, whereas the P600 was visible both in the absence and in the presence of contextual cues. These findings suggest that the N400 observed for metaphor is related to contextual aspects, possibly indexing contextual expectations on upcoming words that guide lexical access and retrieval, while the P600 seems to reflect truly pragmatic interpretative processes needed to make sense of a metaphor and derive the speaker's meaning, also in the presence of contextual cues. In sum, previous information in the linguistic context biases toward a metaphorical interpretation but does not suppress interpretative pragmatic mechanisms to establish the intended meaning.
Reasoning with conditionals involving causal content is known to be affected by retrieval of counterexamples from semantic memory. In this study we examined the characteristicsof this search process in everyday conditional reasoning. In Experiment 1 we manipulated the number (zero to four) of explicitly presented counterexamples (alternative causes or disabling conditions) for causal conditionals. In Experiment 2, using a generation pretest, we measured the number of counterexamples participants could retrieve for a set of causal conditionals. One month after the pretest, participants were presented a reasoning task with the same conditionals. The experiments indicated that acceptance of modus ponens linearly decreased with every additionally retrieved disabler, whereas affirmation of the consequent acceptance linearly decreased as a function of the number of retrieved alternatives.Results for denial of the antecedent and modus tollens were less clear. The findings show that the search process does not necessarily stop after retrieval of a single counterexample and that every additional counterexample has an impact on the inference acceptance.
M. Oaksford, N. Chater, and J. Larkin (2000) proffered a Bayesian model in which conditional inferences are a direct function of conditional probabilities. In the current article, the authors first considered this model regarding the processing of negatives in conditional reasoning. Its predictions were evaluated against a large-scale meta-analysis (W. J. Schroyens, W. Schaeken, & G. d'Ydewalle, 2001b). This evaluation shows that the model is flawed: The relative size of the negative effects does not match predictions. Next, the authors evaluated the model in relation to inferences about affirmative conditionals, again considering the results of a meta-analysis (W. J. Schroyens, W. Schaeken, & G. d'Ydewalle, 2001a). The conditional probability model is countered by the data reported in literature; a mental models based model produces a better fit. The authors conclude that a purely probabilistic model is deficient and incomplete and cannot do without algorithmic processing assumptions if it is to advance toward a descriptively adequate psychological theory.
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