The interaction between social saliency and perceptual saliency

The ability to select visual targets in hierarchical stimuli can be affected by both perceptual saliency and social saliency. However, the functional relations between the effects are not understood. Here we examined whether these two factors interact or combine in an additive way. Participants first learnt to associate geometric shapes with three people (e.g., triangle–self, square–stranger). After learning the associations, participants were presented with compound stimuli (e.g., a global triangle formed by a set of local squares) and had to select a target at the global or local level. In Experiment 1 the task was to identify the person associated with the local or global shape. In Experiment 2 the task was simply to identify the shape. We manipulated perceptual saliency by blurring local elements to form perceptually global salient stimuli or by contrasting the colours of neighbouring local elements (red vs. white) to form perceptually local salient stimuli. In Experiment 1 (person discrimination) there was a strong effect of saliency on local targets (there were faster and more accurate responses to high than to low saliency targets) when social and perceptual saliency occurred at same level. However, both perceptual and social saliency effects were eliminated when the effect of saliency at one level competed with that at the other level. In Experiment 2 (shape discrimination), there were only effects of perceptual saliency. The data indicate that social saliency interacts with perceptual saliency when explicit social categorizations are made, consistent with both factors modulating a common process of visual selection.     

Retrieval of bindings between task-irrelevant stimuli and responses can facilitate behaviour under conditions of high response certainty

Repetition priming can be driven by the encoding and retrieval of stimulus–response (S–R) bindings. When a previously encoded S–R binding is retrieved, and is congruent with the response currently required, it can bias response-selection processes towards selecting the retrieved response, resulting in facilitation. Previous studies have used classification tasks at retrieval. Here, two (or more) response options are competing, and it is likely that any evidence (e.g., an S–R binding) in favour of one option will be utilized to effect a decision. Thus, S–R effects are likely to be seen when using such a task. It is unclear whether such effects can be seen under conditions of higher response certainty, when participants are explicitly cued to make a response. Across two experiments, evidence for a modulating influence of S–R bindings is seen despite using a response cueing method at retrieval to minimize response uncertainty and despite stimuli being task irrelevant. Finally, the results suggest that responses within these S–R bindings are coded at the level of left versus right hand, and not a more fine-grained within-hand thumb versus index finger. The results underline the resilience of S–R effects, suggesting that they are present even under conditions where no explicit object-oriented decision is required.     

Whose Evolution? Which Theology?

The importance of scientific conflicts for theology andphilosophy is difficult to judge. In many disputes of significance, prominent scientists can be found on both sides. Profound philosophical and religious implications are sometimes said to be implied by the new theory as well. This article examines the dispute over natural selection between Richard Dawkins and Stephen Jay Gould as a contemporary instance of such a conflict. While both claim that profound philosophical conclusions flow from their own alternativeaccount of evolution, I suggest that the implication is not as great as is claimed and that the alleged implications have as much to do with their own perceptions of theology as with the actual theories themselves. Nevertheless, evolutionary theory is not irrelevant for theology. Theologians should be aware of the possible implications of evolutionary theory and at the same time theextent and limits of such implications.     

An evaluation of multi‐criteria methods in integrated assessment of climate policy

Those who conduct integrated assessments (IAs) are aware of the need to explicitly consider multiple criteria and uncertainties when evaluating policies for preventing global warming. MCDM methods are potentially useful for understanding tradeoffs and evaluating risks associated with climate policy alternatives. A difficulty facing potential MCDM users is the wide range of different techniques that have been proposed, each with distinct advantages. Methods differ in terms of validity, ease of use, and appropriateness to the problem. Alternative methods also can yield strikingly different rankings of alternatives. A workshop was held in which climate change experts and policy makers evaluated the usefulness of MCDM for IA. Participants applied several methods in the context of a hypothetical greenhouse gas policy decision. Methods compared include value and utility functions, goal programming, ELECTRE, fuzzy sets, stochastic dominance, min max regret, and several weight selection methods. Ranges, rather than point estimates, were provided for some questions to incorporate imprecision regarding weights. Additionally, several visualization methods for both deterministic and uncertain cases were used and evaluated. Analysis of method results and participant feedback through questionnaires and discussion provide the basis for conclusions regarding the use of MCDM methods for climate change policy and IA analyses. Hypotheses are examined concerning predictive and convergent validity of methods, existence of splitting bias among experts, perceived ability of methods to aid decision‐making, and whether expressing imprecision can change ranking results. Because participants gained from viewing a problem from several perspectives and results from different methods often significantly differed, it appears worthwhile to apply several MCDM methods to increase user confidence and insight. The participants themselves recommended such multimethod approaches for policymaking. Yet they preferred the freedom of unaided decision‐making most of all, challenging the MCDM community to create transparent methods that permit maximum user control. Copyright © 2002 John Wiley & Sons, Ltd.     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

Why does impression management positively influence interview ratings? The mediating role of competence and warmth

Though interviews assess job applicants' skills and abilities, they can be influenced by extraneous factors, including impression management (IM) tactics. Interviewees’ self‐promotion and ingratiation IM tactics predict higher interview ratings; however, researchers have yet to determine why these tactics work. We assessed whether two fundamental dimensions of social perception, competence and warmth, mediate the relationship between IM tactics and interview ratings. We hypothesized that interviewee competence mediates the relationship between self‐promotion and interview ratings, and interviewee warmth mediates the relationship between ingratiation and interview ratings. Using real employment interviews, we found that competence mediates the relationship between self‐promotion and interview ratings, but warmth did not mediate the relationship between ingratiation and interview ratings in the way we expected.     

Children’s scale errors are a natural consequence of learning to associate objects with actions: A computational model

Young children sometimes attempt an action on an object, which is inappropriate because of the object size—they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual‐route theory of action selection, in which actions on objects are selected via a direct (nonsemantic or visual) route or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object–action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions.