Fear of negative evaluation and attentional bias for facial expressions: An event-related study

Numerous studies have shown an exacerbation of attentional bias towards threat in anxiety states. However, the cognitive mechanisms responsible for these attentional biases remain largely unknown. Further, the authors outline the need to consider the nature of the attentional processes in operation (hypervigilance, avoidance, or disengagement). We adapted a dot-probe paradigm to record behavioral and electrophysiological responses in 26 participants reporting high or low fear of evaluation, a major component of social anxiety. Pairs of faces including a neutral and an emotional face (displaying anger, fear, disgust, or happiness) were presented during 200 ms and then replaced by a neutral target to discriminate. Results show that anxious participants were characterized by an increased P1 in response to pairs of faces, irrespective of the emotional expression included in the pair. They also showed an increased P2 in response to angry–neutral pairs selectively. Finally, in anxious participants, the P1 response to targets was enhanced when replacing emotional faces, whereas non-anxious subjects showed no difference between the two conditions. These results indicate an early hypervigilance to face stimuli in social anxiety, coupled with difficulty in disengaging from threat and sustained attention to emotional stimuli. They are discussed within the framework of current models of anxiety and psychopathology.     

Ethics in Research

Summary

This article presents an overview of the history of efforts to protect human subjects in research. It discusses the establishment of international, national, organizational, and institutional procedures designed to protect human participants. The article provides a detailed summary of the principal ethical codes for research and their origins. It includes discussions of the most frequently encountered ethical issues ranging from the initial decision to undertake the project, through the selection and application of the various research procedures, to the analysis and interpretation of the data.     

Body-size stigmatization by preschool girls: In a doll's world,it is good to be “Barbie”

A number of studies have measured body size stigmatization, that is, the assigning of negative characteristics to individuals who are considered fat, in samples of children as young as preschool-age. The results of such studies are fairly consistent, but may be criticized for the abstract nature of the line drawings typically used as test stimuli. In the present study the utility of using toy dolls to gauge young girls’ views toward different body shapes was assessed. Forty girls ages 3½–5½ were asked to assign various traits to one of three dolls (thin, average, and fat). As with previous work, responses fell out in a stereotypical pattern, with the positive characteristics attributed most often to the thin or average doll and all of the negative characteristics most often to the fat doll. The strengths and weaknesses of this doll paradigm in studies of body-size stigmatization by young children are discussed.     

Biased generalization of newly learned phonological alternations by 12-month-old infants

Previous work has suggested that learners are sensitive to phonetic similarity when learning phonological patterns (e.g.,  and ). We tested 12-month-old infants to see if their willingness to generalize newly learned phonological alternations depended on the phonetic similarity of the sounds involved. Infants were exposed to words in an artificial language whose distributions provided evidence for a phonological alternation between two relatively dissimilar sounds ([p ∼ v] or [t ∼ z]). Sounds at one place of articulation (labials or coronals) alternated whereas sounds at the other place of articulation were contrastive. At test, infants generalized the alternation learned during exposure to pairs of sounds that were more similar ([b ∼ v] or [d ∼ z]). Infants in a control group instead learned an alternation between similar sounds ([b ∼ v] or [d ∼ z]). When tested on dissimilar pairs of sounds ([p ∼ v] or [t ∼ z]), the control group did not generalize their learning to the novel sounds. The results are consistent with a learning bias favoring alternations between similar sounds over alternations between dissimilar sounds.     

Syntax and intentionality: An automatic link between language and theory-of-mind

Three studies provided evidence that syntax influences intentionality judgments. In Experiment 1, participants made either speeded or unspeeded intentionality judgments about ambiguously intentional subjects or objects. Participants were more likely to judge grammatical subjects as acting intentionally in the speeded relative to the reflective condition (thus showing an intentionality bias), but grammatical objects revealed the opposite pattern of results (thus showing an unintentionality bias). In Experiment 2, participants made an intentionality judgment about one of the two actors in a partially symmetric sentence (e.g., “John exchanged products with Susan”). The results revealed a tendency to treat the grammatical subject as acting more intentionally than the grammatical object. In Experiment 3 participants were encouraged to think about the events that such sentences typically refer to, and the tendency was significantly reduced. These results suggest a privileged relationship between language and central theory-of-mind concepts. More specifically, there may be two ways of determining intentionality judgments: (1) an automatic verbal bias to treat grammatical subjects (but not objects) as intentional (2) a deeper, more careful consideration of the events typically described by a sentence.     

Uncovering unknown unknowns: Towards a Baconian approach to management decision-making

Bayesian decision theory and inference have left a deep and indelible mark on the literature on management decision-making. There is however an important issue that the machinery of classical Bayesianism is ill equipped to deal with, that of “unknown unknowns” or, in the cases in which they are actualised, what are sometimes called “Black Swans”. This issue is closely related to the problems of constructing an appropriate state space under conditions of deficient foresight about what the future might hold, and our aim is to develop a theory and some of the practicalities of state space elaboration that addresses these problems. Building on ideas originally put forward by Bacon (1620), we show how our approach can be used to build and explore the state space, how it may reduce the extent to which organisations are blindsided by Black Swans, and how it ameliorates various well-known cognitive biases.     

The similarity heuristic

Decision makers often make snap judgments using fast‐and‐frugal decision rules called cognitive heuristics. Research into cognitive heuristics has been divided into two camps. One camp has emphasized the limitations and biases produced by the heuristics; another has focused on the accuracy of heuristics and their ecological validity. In this paper we investigate a heuristic proposed by the first camp, using the methods of the second. We investigate a subset of the representativeness heuristic we call the “similarity” heuristic, whereby decision makers who use it judge the likelihood that an instance is a member of one category rather than another by the degree to which it is similar to others in that category. We provide a mathematical model of the heuristic and test it experimentally in a trinomial environment. In this environment, the similarity heuristic turns out to be a reliable and accurate choice rule and both choice and response time data suggest it is also how choices are made. We conclude with a theoretical discussion of how our work fits in the broader “fast‐and‐frugal” heuristics program, and of the boundary conditions for the similarity heuristic. Copyright © 2009 John Wiley & Sons, Ltd.     

There are at least two kinds of probability matching: evidence from a secondary task

Probability matching is a suboptimal behavior that often plagues human decision-making in simple repeated choice tasks. Despite decades of research, recent studies cannot find agreement on what choice strategies lead to probability matching. We propose a solution, showing that two distinct local choice strategies—which make different demands on executive resources—both result in probability-matching behavior on a global level. By placing participants in a simple binary prediction task under dual- versus single-task conditions, we find that individuals with compromised executive resources are driven away from a one-trial-back strategy (utilized by participants with intact executive resources) and towards a strategy that integrates a longer window of past outcomes into the current prediction. Crucially, both groups of participants exhibited probability-matching behavior to the same extent at a global level of analysis. We suggest that these two forms of probability matching are byproducts of the operation of explicit versus implicit systems.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.