Three experiments examined the influence of argument length on plausibility judgements, in a category-based induction task. The general results were that when arguments were logically invalid they were considered stronger when they were longer, but for logically valid arguments longer arguments were considered weaker. In Experiments 1a and 1b when participants were forewarned to avoid using length as a cue to judging plausibility, they still did so. Indeed, participants given the opposite instructions did not follow those instructions either. In Experiment (...) 2 arguments came from a reliable or unreliable speaker. This manipulation affected accuracy as well as response bias, but the effects of argument length for both reliable and unreliable speakers replicated Experiments 1a and 1b. The results were analysed using receiver operating characteristic (ROC) curves and modelled using multidimensional signal detection theory (SDT). Implications for models of category-based inductive reasoning, and theories of reasoning more generally, are discussed. (shrink)

Without inductive reasoning, we couldn't generalize from one instance to another, derive scientific hypotheses, or predict that the sun will rise again tomorrow morning. Despite the widespread nature of inductive reasoning, books on this topic are rare. Indeed, this is the first book on the psychology of inductive reasoning in twenty years. The chapters survey recent advances in the study of inductive reasoning and address questions about how it develops, the role of knowledge in induction, how best to model people's (...) reasoning, and how induction relates to other forms of thinking. Written by experts in philosophy, developmental science, cognitive psychology, and computational modeling, the contributions here will be of interest to a general cognitive science audience as well as to those with a more specialized interest in the study of thinking. (shrink)

Four experiments investigated how people judge the plausibility of category-based arguments, focusing on the diversity effect, in which arguments with diverse premise categories are considered particularly strong. In Experiment 1 we show that priming people as to the nature of the blank property determines whether sensitivity to diversity is observed. In Experiment 2 we find that people's hypotheses about the nature of the blank property predict judgements of argument strength. In Experiment 3 we examine the effect of our priming methodology (...) on people's tendency to bring knowledge about causality or similarity to bear when evaluating arguments, and in Experiment 4 we show that whether people's hypotheses about the nature of the blank property were causal predicted ratings of argument strength. Together these results suggest that diversity effects occur because diverse premises lead people to bring general features of the premise categories to mind. Although our findings are broadly consistent with Bayesian and Relevance-based approaches to category-based inductive reasoning, neither approach captures all of our findings. (shrink)

This research addressed theoretical approaches in political science arguing that the American electorate is either poorly informed or dependent on party label cues, by assessing performance on political judgment tasks when party label information is missing. The research materials were created from the results of a national opinion survey held during a national election. The experiments themselves were run on nationally representative samples of adults, identified from another national electoral survey. Participants saw profiles of simulated individuals, including information about demographics (...) and issue positions, but omitting party labels. In Experiment 1, participants successfully judged the likelihood of party membership based on the profiles. In Experiment 2, participants successfully voted based on their party interests. The results were mediated by participants' political knowledge. Conclusions are drawn with respect to theories from political science and issues in cognitive science regarding categorization and reasoning. (shrink)

Two experiments examined the typicality structure of contrasting political categories. In Experiment 1, two separate groups of participants rated the typicality of 15 individuals, including political figures and media personalities, with respect to the categories Democrat or Republican. The relation between the two sets of ratings was negative, linear, and extremely strong, r = −.9957. Essentially, one category was treated as a mirror image of the other. Experiment 2 replicated this result, showing some boundary conditions, and extending the result to (...) liberal and conservative categories. The same method was applied to two other pairs of contrasting categories, healthy and junk foods, and male and female jobs. For those categories, the relation between contrasting pairs was weaker and there was less of a direct trade-off between typicality in one category versus typicality in its opposite. The results are discussed in terms of implications for political decision making and reasoning, and conceptual representation. (shrink)

The proposal regarding rules and similarity is considered in terms of ability to provide insights regarding previous work on reasoning and categorization. For reasoning, the issue is the relation between this proposal and one-process as well as two-process accounts of deduction and induction. For categorization, the issue is how the proposal would simultaneously explain both similarity-to-rule and rule-to-similarity shifts.

It is timely to assess Bayesian models, but Bayesianism is not a religion. Bayesian modeling is typically used as a tool to explain human data. Bayesian models are sometimes equivalent to other models, but have the advantage of explicitly integrating prior hypotheses with new observations. Any lack of representational or neural assumptions may be an advantage rather than a disadvantage.

The great advantage of Tenenbaum and Griffiths's model is that it incorporates both specific and general prior knowledge into category learning. Two phenomena are presented as supporting the detailed assumptions of this model. However, one phenomenon, effects of diversity, does not seem to require these assumptions, and the other phenomenon, effects of sample size, is not representative of most reported results. [Tenenbaum & Griffiths].