Do modals identify better models? A comparison of signal detection and probabilistic models of inductive reasoning.

The nature of the relationship between deductive and inductive reasoning is a hotly debated topic. A key question is whether there is a single dimension of evidence underlying both deductive and inductive judgments. Following Rips (2001), Rotello and Heit (2009) and Heit and Rotello (2010) implemented one- and two-dimensional models grounded in signal detection theory to assess predictions for receiver operating characteristic data (ROCs), and concluded in favor of the two-dimensional model.

Putting replication in its place.

Direct replication is valuable but should not be elevated over other worthwhile research practices, including conceptual replication and checking of statistical assumptions. As noted by Rotello et al. (2015), replicating studies without checking the statistical assumptions can lead to increased confidence in incorrect conclusions.

Inductive reasoning 2.0.

Inductive reasoning entails using existing knowledge to make predictions about novel cases. The first part of this review summarizes key inductive phenomena and critically evaluates theories of induction. We highlight recent theoretical advances, with a special emphasis on the structured statistical approach, the importance of sampling assumptions in Bayesian models, and connectionist modeling.

Recognition memory for hue: Prototypical bias and the role of labeling.

How does the concurrent use of language affect perception and memory for exemplars? Labels cue more general category information than a specific exemplar. Applying labels can affect the resulting memory for an exemplar. Here 3 alternative hypotheses are proposed for the role of labeling an exemplar at encoding: (a) labels distort memory toward the label prototype, (b) labels guide the level of specificity needed in the current context, and (c) labels direct attention to the label's referent among all possible features within a visual scene.

Missing the Party: Political Categorization and Reasoning in the Absence of Party Label Cues.

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.

The Interdisciplinarity of Collaborations in Cognitive Science.

We introduce a new metric for interdisciplinarity, based on co-author publication history. A published article that has co-authors with quite different publication histories can be deemed relatively "interdisciplinary," in that the article reflects a convergence of previous research in distinct sets of publication outlets. In recent work, we have shown that this interdisciplinarity metric can predict citations.

A dynamic model of reasoning and memory.

Previous models of category-based induction have neglected how the process of induction unfolds over time. We conceive of induction as a dynamic process and provide the first fine-grained examination of the distribution of response times observed in inductive reasoning. We used these data to develop and empirically test the first major quantitative modeling scheme that simultaneously accounts for inductive decisions and their time course.

Metacognition and confidence: comparing math to other academic subjects.

Two studies addressed student metacognition in math, measuring confidence accuracy about math performance. Underconfidence would be expected in light of pervasive math anxiety. However, one might alternatively expect overconfidence based on previous results showing overconfidence in other subject domains.

Brain imaging, forward inference, and theories of reasoning.

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference.

When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions.

There is a replication crisis in science, to which psychological research has not been immune: Many effects have proven uncomfortably difficult to reproduce. Although the reliability of data is a serious concern, we argue that there is a deeper and more insidious problem in the field: the persistent and dramatic misinterpretation of empirical results that replicate easily and consistently. Using a series of four highly studied "textbook" examples from different research domains (eyewitness memory, deductive reasoning, social psychology, and child welfare), we show how simple unrecognized incompatibilities among dependent measures, analysis tools, and the properties of data can lead to fundamental interpretive errors.

Representational shifts made visible: movement away from the prototype in memory for hue.

In four experiments, a total of 205 participants studied individual color patches and were given an old-new recognition test after a brief retention interval (0.5 or 5.0 s).

Memory, reasoning, and categorization: parallels and common mechanisms.

Traditionally, memory, reasoning, and categorization have been treated as separate components of human cognition. We challenge this distinction, arguing that there is broad scope for crossover between the methods and theories developed for each task. The links between memory and reasoning are illustrated in a review of two lines of research.

Traditional difference-score analyses of reasoning are flawed.

Studies of the belief bias effect in syllogistic reasoning have relied on three traditional difference score measures: the logic index, belief index, and interaction index. Dube, Rotello, and Heit (2010, 2011) argued that the interaction index incorrectly assumes a linear receiver operating characteristic (ROC). Here, all three measures are addressed.

How similar are recognition memory and inductive reasoning?

Conventionally, memory and reasoning are seen as different types of cognitive activities driven by different processes. In two experiments, we challenged this view by examining the relationship between recognition memory and inductive reasoning involving multiple forms of similarity. A common study set (members of a conjunctive category) was followed by a test set containing old and new category members, as well as items that matched the study set on only one dimension.

The relationship between memory and inductive reasoning: does it develop?

In 2 studies, the authors examined the development of the relationship between inductive reasoning and visual recognition memory. In both studies, 5- to 6-year-old children and adults were shown instances of a basic-level category (dogs) followed by a test set containing old and new category members that varied in their similarity to study items. Participants were given either recognition instructions (memorize study items and discriminate between old and new test items) or induction instructions (learn about a novel property shared by the study items and decide whether it generalizes to test items).

The opposite of republican: polarization and political categorization.

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 = -.

Predicting reasoning from memory.

In an effort to assess the relations between reasoning and memory, in 8 experiments, the authors examined how well responses on an inductive reasoning task are predicted from responses on a recognition memory task for the same picture stimuli. Across several experimental manipulations, such as varying study time, presentation frequency, and the presence of stimuli from other categories, there was a high correlation between reasoning and memory responses (average r = .87), and these manipulations showed similar effects on the 2 tasks.

Judgments of learning and improvement.

Can learners accurately judge the rate of their learning? Rates of learning may be informative when study time is allocated across materials, and students' judgments of their learning rate have been proposed as a possible metacognitive tool. Participants estimated how much they improved between presentations in multitrial learning situations in which n-gram paragraphs (in Experiments 1 and 2) or word pairs (Experiments 3 and 4) were learned . In the first experiment, participants rated improvement on a percentage scale, whereas on the second and third, judgments were given on a 0-6 scale.

The belief bias effect is aptly named: a reply to Klauer and Kellen (2011).

In "Assessing the Belief Bias Effect With ROCs: It's a Response Bias Effect," Dube, Rotello, and Heit (2010) examined the form of receiver operating characteristic (ROC) curves for reasoning and the effects of belief bias on measurement indices that differ in whether they imply a curved or linear ROC function. We concluded that the ROC data are in fact curved and that analyses using statistics that assume a linear ROC are likely to produce Type I errors. Importantly, we showed that the interaction between logic and belief that has inspired much of the theoretical work on belief bias is in fact an error stemming from inappropriate reliance on a contrast (hit rate-false alarm rate) that implies linear ROCs.

Bayesian models of cognition.

There has been a recent explosion in research applying Bayesian models to cognitive phenomena. This development has resulted from the realization that across a wide variety of tasks the fundamental problem the cognitive system confronts is coping with uncertainty. From visual scene recognition to on-line language comprehension, from categorizing stimuli to determining to what degree an argument is convincing, people must deal with the incompleteness of the information they possess to perform these tasks, many of which have important survival-related consequences.

Assessing the belief bias effect with ROCs: it's a response bias effect.

A belief bias effect in syllogistic reasoning (Evans, Barston, & Pollard, 1983) is observed when subjects accept more valid than invalid arguments and more believable than unbelievable conclusions and show greater overall accuracy in judging arguments with unbelievable conclusions. The effect is measured with a contrast of contrasts, comparing the acceptance rates for valid and invalid arguments with believable and unbelievable conclusions. We show that use of this measure entails the assumption of a threshold model, which predicts linear receiver operating characteristics (ROCs).

Relations between inductive reasoning and deductive reasoning.

One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise-conclusion similarity on evaluation of arguments.

Inductive reasoning.

Inductive reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empirical phenomena that involve taxonomic similarity are described.

Modeling the effects of argument length and validity on inductive and deductive reasoning.

In an effort to assess models of inductive reasoning and deductive reasoning, the authors, in 3 experiments, examined the effects of argument length and logical validity on evaluation of arguments. In Experiments 1a and 1b, participants were given either induction or deduction instructions for a common set of stimuli. Two distinct effects were observed: Induction judgments were more affected by argument length, and deduction judgments were more affected by validity.