The two faces of typicality in category-based induction

Penguins, Birds, and Pilot Knowledge: Can an Overlooked Attribute of Human Cognition Explain Our Most Puzzling Aircraft Accidents?

OBJECTIVE

We extend the theory of conceptual categories to flight safety events, to understand variations in pilot event knowledge.

BACKGROUND

Experienced, highly trained pilots sometimes fail to recognize events, resulting in procedures not being followed, damaging safety. Recognition is supported by typical, representative members of a concept. Variations in typicality ("gradients") could explain variations in pilot knowledge, and hence recognition. The role of simulations and everyday flight operations in the acquisition of useful, flexible concepts is poorly understood. We illustrate uses of the theory in understanding the industry-wide problem of nontypical events.

METHOD

One hundred and eighteen airline pilots responded to scenario descriptions, rating them for typicality and indicating the source of their knowledge about each scenario.

RESULTS

Significant variations in typicality in flight safety event concepts were found, along with key gradients that may influence pilot behavior. Some concepts were linked to knowledge gained in simulator encounters, while others were linked to real flight experience.

CONCLUSION

Explicit training of safety event concepts may be an important adjunct to what pilots may variably glean from simulator or operational flying experiences, and may result in more flexible recognition and improved response.

APPLICATION

Regulators, manufacturers, and training providers can apply these principles to develop new approaches to pilot training that better prepare pilots for event diversity.

Transfer of category learning to impoverished contexts

Learning often happens in ideal conditions, but then must be applied in less-than-ideal conditions - such as when a learner studies clearly illustrated examples of rocks in a book but then must identify them in a muddy field. Here we examine whether the benefits of interleaving (vs. blocking) study schedules, as well as the use of feature descriptions, supports the transfer of category learning in new, impoverished contexts. Specifically, keeping the study conditions constant, we evaluated learners' ability to classify new exemplars in the same neutral context versus in impoverished contexts in which certain stimulus features are occluded. Over two experiments, we demonstrate that performance in new, impoverished contexts during test is greater for participants who received an interleaved (vs. blocked) study schedule, both for novel and for studied exemplars. Additionally, we show that this benefit extends to both a short (3-min) or long (48-h) test delay. The presence of feature descriptions during learning had no impact on transfer. Together, these results extend the growing literature investigating how changes in context during category learning or test impacts performance and provide support for the use of interleaving to promote the far transfer of category knowledge to impoverished contexts.

Gender essentialism and the mental representation of transgender women and men: A multimethod investigation of stereotype content

The growing visibility of transgender women and men in the US challenges a dominant cultural model of gender in which dichotomous sex assigned at birth gives rise to dichotomous gender identity in adulthood. How are these groups - verbally marked as atypical relative to their cisgender counterparts - stereotyped? Moreover, how do gender essentialist beliefs predict the content of such stereotypes? Across four studies with diverse methods of stereotype measurement, we assessed characteristics that cisgender people associate with transgender women and men, comparing these to their stereotypes of cisgender women and men. In our final study, we directly assessed how cisgender people mentally position transgender groups relative to cisgender groups. Across these studies, transgender categories were characterized in less positive ways than cisgender ones, and there was as a lower level of consensus about transgender than cisgender stereotypes. On average, transgender groups were de-gendered relative to cisgender groups, such that transgender women and men were not strongly differentiated on traditionally-gendered stereotype dimensions. Finally, we showed that participants higher in gender essentialism (relative to participants lower in gender essentialism) evaluated cisgender groups more positively and were more likely to stereotype transgender groups based on their sex assigned at birth.

Communicative efficiency and differential case marking: a reverse-engineering approach

The use of differential case marking of A and P has been explained in terms of efficiency (economy) and markedness. The present study tests predictions based on these accounts, using conditional probabilities of a particular feature given the syntactic role (cue availability), and conditional probabilities of a particular syntactic role given the feature in question (cue reliability). Cue availability serves as a measure of markedness, whereas cue reliability is central for the efficiency account. Similar to reverse engineering, we determine which of the probabilistic measures could have been responsible for the recurrent cross-linguistic patterns described in the literature. The probabilities are estimated from spontaneous informal dialogues in English and Russian (Indo-European), Lao (Tai-Kadai), N||ng (Tuu) and Ruuli (Bantu). The analyses, which involve a series of mixed-effects Poisson models, clearly demonstrate that cue reliability matches the observed cross-linguistic patterns better than cue availability. Thus, the results support the efficiency account of differential marking.

Is Man the Measure of All Things? A Social Cognitive Account of Androcentrism

Androcentrism refers to the propensity to center society around men and men's needs, priorities, and values and to relegate women to the periphery. Androcentrism also positions men as the gender-neutral standard while marking women as gender-specific. Examples of androcentrism include the use of male terms (e.g., he), images, and research participants to represent everyone. Androcentrism has been shown to have serious consequences. For example, women's health has been adversely affected by over-generalized medical research based solely on male participants. Nonetheless, relatively little is known about androcentrism's proximate psychological causes. In the present review, we propose a social cognitive perspective arguing that both social power and categorization processes are integral to understanding androcentrism. We present and evaluate three possible pathways to androcentrism deriving from (a) men being more frequently instantiated than women, (b) masculinity being more "ideal" than femininity, and/or (c) masculinity being more common than femininity.

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. A number of new research directions in this field are identified including comparisons of inductive and deductive reasoning, the identification of common core processes in induction and memory tasks and induction involving category uncertainty. The implications of induction research for areas as diverse as complex decision-making and fear generalization are discussed. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Learning.

Self-Directed Learning: A Cognitive and Computational Perspective

A widely advocated idea in education is that people learn better when the flow of experience is under their control (i.e., learning is self-directed). However, the reasons why volitional control might result in superior acquisition and the limits to such advantages remain poorly understood. In this article, we review the issue from both a cognitive and computational perspective. On the cognitive side, self-directed learning allows individuals to focus effort on useful information they do not yet possess, can expose information that is inaccessible via passive observation, and may enhance the encoding and retention of materials. On the computational side, the development of efficient "active learning" algorithms that can select their own training data is an emerging research topic in machine learning. This review argues that recent advances in these related fields may offer a fresh theoretical perspective on how people gather information to support their own learning.

Categorization = decision making + generalization

We rarely, if ever, repeatedly encounter exactly the same situation. This makes generalization crucial for real world decision making. We argue that categorization, the study of generalizable representations, is a type of decision making, and that categorization learning research would benefit from approaches developed to study the neuroscience of decision making. Similarly, methods developed to examine generalization and learning within the field of categorization may enhance decision making research. We first discuss perceptual information processing and integration, with an emphasis on accumulator models. We then examine learning the value of different decision making choices via experience, emphasizing reinforcement learning modeling approaches. Next we discuss how value is combined with other factors in decision making, emphasizing the effects of uncertainty. Finally, we describe how a final decision is selected via thresholding processes implemented by the basal ganglia and related regions. We also consider how memory related functions in the hippocampus may be integrated with decision making mechanisms and contribute to categorization.

Speeded induction under uncertainty: the influence of multiple categories and feature conjunctions

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, & Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.

Category vs. Object Knowledge in Category-based Induction

In one form of category-based induction, people make predictions about unknown properties of objects. There is a tension between predictions made based on the object's specific features (e.g., objects above a certain size tend not to fly) and those made by reference to category-level knowledge (e.g., birds fly). Seven experiments with artificial categories investigated these two sources of induction by looking at whether people used information about correlated features within categories, suggesting that they focused on feature-feature relations rather than summary categorical information. The results showed that people relied heavily on such correlations, even when there was no reason to think that the correlations exist in the population. The results suggested that people's use of this strategy is largely unreflective, rather than strategically chosen. These findings have important implications for models of category-based induction, which generally ignore feature-feature relations.

Uncertainty in category-based induction: when do people integrate across categories?

Two experiments investigated how people perform category-based induction for items that have uncertain categorization. Whereas normative considerations suggest that people should consider multiple relevant categories, much past research has argued that people focus on only the most likely category. A new method is introduced in which responses on individual trials can be classified as using single or multiple categories, an improvement on past methods that relied on null effects as evidence for single-category use. Experiment 1 found that people did use multiple categories when the most likely category gave an ambiguous induction but that few people did so when it gave an unambiguous induction. Experiment 2 suggested that the reluctance to use multiple categories arose from a cognitive shortcut, in which only one source of information is consulted. The experiments revealed significant individual differences, suggesting that use of multiple categories is one of a number of strategies that can be used rather than being the basis for most category-based induction.

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. We also examine phenomena involving more complex background knowledge about premises and conclusions of inductive arguments and the properties referenced. Earlier models are shown to give a good account of similarity-based phenomena but not knowledge-based phenomena. Recent models that aim to account for both similarity-based and knowledge-based phenomena are reviewed and evaluated. Among the most important new directions in induction research are a focus on induction with uncertain premise categories, the modeling of the relationship between inductive and deductive reasoning, and examination of the neural substrates of induction. A common theme in both the well-established and emerging lines of induction research is the need to develop well-articulated and empirically testable formal models of induction. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Induction with uncertain categories: When do people consider the category alternatives?

These three experiments examined how people make property inferences about exemplars whose category membership is uncertain. Participants were shown two categories and a novel exemplar with a feature that indicated that the exemplar was more likely to belong to one category (target) than to the other (nontarget). Participants then made categorization decisions and property inferences about the novel exemplar. In some conditions, property inferences could be made only by considering both target and nontarget categories. In other conditions, predictions could be based on both categories or on the target category alone. Consistent with previous studies (e.g., Murphy & Ross, 1994, 2005), we found that many people made predictions based only on consideration of the target category. However, the prevalence of such single-category reasoning was greatly reduced by highlighting the costs of neglecting nontarget alternatives and by asking for inferences before categorization decisions. The results suggest that previous work may have exaggerated the prevalence of single-category reasoning and that people may be more flexible in their use of multiple categories in property inference than has been previously recognized.

Graded motor responses in the time course of categorizing atypical exemplars

The time course of categorization was investigated in four experiments, which revealed graded competitive effects in a categorization task. Participants clicked one of two categories (e.g., mammal or fish) in response to atypical or typical exemplars (e.g., whale or cat) in the form of words (Experiments 1 and 2) or pictures (Experiments 3 and 4). Streaming x, y coordinates of mouse movement trajectories were recorded. Normalized mean trajectories revealed a graded competitive process: Atypical exemplars produced trajectories with greater curvature toward the competing category than did typical exemplars. The experiments contribute to recent examination of the time course of categorization and carry implications for theories of representation in cognitive science.

Influence of multiple categories on the prediction of unknown properties

Knowing an item's category helps us predict its unknown properties. Previous studies suggest that when asked to evaluate the probability of an unknown property, people tend to consider only an item's most likely category, ignoring alternative categories. In the present study, property prediction took the form of either a probability rating or a speeded binary-choice judgment. In keeping with past findings, the subjects ignored alternative categories in their probability ratings. However, their binary-choice judgments were influenced by alternative categories. This novel finding suggests that the way in which category knowledge is used in prediction depends critically on the form of the prediction.