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.     

The semantic structure of emotion words across languages is consistent with componential appraisal models of emotion

Appraisal theories of emotion, and particularly the Component Process Model, claim that the different components of the emotion process (action tendencies, physiological reactions, expressions, and feeling experiences) are essentially driven by the results of cognitive appraisals and that the feeling component constitutes a central integration and representation of these processes. Given the complexity of the proposed architecture, comprehensive experimental tests of these predictions are difficult to perform and to date are lacking. Encouraged by the “lexical sedimentation” hypothesis, here we propose an indirect examination of the compatibility of the theoretical assumptions with the semantic structure of a set of major emotion words as measured in a cross-language and cross-cultural study. Specifically, we performed a secondary analysis of the large-scale data set with ratings of affective features covering all components of the emotion process for 24 emotion words in 27 countries, constituting profiles of emotion-specific appraisals, action tendencies, physiological reactions, expressions, and feeling experiences. The results of a series of hierarchical regression analyses to examine the prediction of the theoretical model are highly consistent with the claim that appraisal patterns determine the structure of the response components, which in turn predict central dimensions of the feeling component.     

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.     

Feature selection using Ant Colony Optimization (ACO) and Road Sign Detection and Recognition (RSDR) system

Road Sign Detection and Recognition (RSDR) is aimed to enable drivers maintain basic functionality with the aim of identifying and notifying driver through the existing restrictions so that the process is a success on the present widened road. Examples for RSDR include ‘traffic light ahead’ or ‘pedestrian crossing’ signs. An innovative RSDR system has been introduced which comprises of pre-processing, edge detection, feature extraction, features selection and Ensemble Fuzzy Support Vector Machine (EFSVM) classifier. Feature selection is carried out successfully by deployment of Ant Colony Optimization (ACO) algorithm to determine most prominent and definitive features. These features are then fed into the ensemble SVM to enable both road side traffic detection as well as recognition. Suggested system’s performance is analyzed and evaluated with respect to road signs having a capable recognition rate.     

语义和句法信息在副中央凹加工中的作用

读者能够从副中央凹中提取到什么类型的信息, 是当前阅读眼动研究领域关注的焦点问题.当前普遍认为低水平信息(如正字法信息)可以在副中央凹中得以加工, 但是高水平的信息(如语义和句法信息)能否从副中央凹中进行提取存在争议.本文总结了近年来高水平信息在副中央凹加工的研究进展, 包括拼音文字和非拼音文字(如中文)阅读过程中语义和句法预视效益的研究现状及影响因素, 当前眼动控制模型(如E-Z读者和SWIFT)对预视效益的解释和不足之处, 最后提出未来关于语义和句法预视加工的研究方向.     

Unique contribution of semantic representations to immediate memory: an individual-differences approach

Studies have reported dissociations between the contributions of semantic and phonological representations to immediate remembering, found through experimental manipulations as well as specific clinical cases. Also, some studies based on the analysis of individual differences reported that a semantic cued recall task has a variance unshared with the immediate memory tasks which tap the contribution of phonological representations. However, another study suggested that cued recall performance could reflect a wider ability to cluster information by category. Furthermore, no studies of individual differences have investigated the unique semantic contribution in the visual domain. Therefore, the present study used specific immediate recognition memory tasks that have been used to investigate the semantic contribution in the verbal and visual domains. The results demonstrate the unshared variance that reflects the semantic contribution in both the verbal and the visual domain, and some commonality in these variances across the domains.