The role of robotic modelling in cognitive science

From the perspective of cognitive robotics, this paper presents a modern interpretation of Newell’s (1973) reasoning and suggestions for why and how cognitive psychologists should develop models of cognitive phenomena. We argue that the shortcomings of current cognitive modelling approaches are due in significant part to a lack of exactly the kind of integration required for the development of embodied autonomous robotics. Moreover we suggest that considerations of embodiment, situatedness, and autonomy, intrinsic to cognitive robotics, provide an appropriate basis for the integration and theoretic cumulation that Newell argued was necessary for psychology to mature. From this perspective we analyse the role of embodiment and modes of situatedness in terms of integration, cognition, emotion, and autonomy. Four complementary perspectives on embodied and situated cognitive science are considered in terms of their potential to contribute to cognitive robotics, cognitive science, and psychological theorizing: minimal cognition and organization, enactive perception and sensorimotor contingency, homeostasis and emotion, and social embedding. In combination these perspectives provide a framework for cognitive robotics, not only wholly compatible with the original aims of cognitive modelling, but as a more appropriate methodology than those currently in common use within psychology.     

A multilevel multitrait‐multimethod analysis of self‐ and peer‐reported daily affective experiences

We examined the psychometric properties of an experience‐sampling measure of affect (PANAS) using data from self‐ and peer reports. A multivariate multilevel model was used to assess the reliability of the latent PANAS scales at the within‐ and between‐person level. Findings suggest satisfying internal consistencies for self‐ and peer reports of affective experiences at both levels of analysis. Convergent and discriminant validity of the two affect scales were examined by means of a multilevel multitrait‐multimethod approach (MLM‐MTMM) indicating distinct findings at the within‐ and between‐person level. These findings provide further insights into the structural relations between the two PANAS scales: Whereas positive and negative affect were unrelated at the between‐person level; they were negatively correlated at the within‐person level. Copyright © 2010 John Wiley & Sons, Ltd.     

Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels

This paper compares the multilevel modelling (MLM) approach and the person‐specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i.e., an order equal to or higher than the maximum order of individual AR patterns in the sample) is fitted and successfully converges. If a lower‐order multilevel model is chosen for heterogeneous samples, the higher‐order lagged effects are misrepresented, resulting in bias at the population level and larger prediction errors at the individual level. In these cases, the PS approach is preferable, given sufficient measurement occasions (T ≥ 50). In addition, sample size and distribution of individual level AR coefficients do not have a large impact on the results. Implications of these findings on model selection and research design are discussed.     

An imprecise goal programming approach for modeling design team's preferences in quality function deployment planning process

The Quality Function Deployment (QFD) is an approach that facilitates designing product by analyzing and projecting the Customer's Needs (CNs) in the Engineering Characteristics (ECs) of a product. The aim of QFD planning process is to determine the target levels for ECs of a product that achieve high level of overall customers' satisfaction. However, integrating design team's preferences in this preliminary stage of product design could make the design more realistic and could also avoid unfeasibility in posterior phases of the product development processes. Moreover, this process is performed within an imprecise environment, and more than one factor must be taken into account in determining targets levels of ECs; especially, the limited resources and increased market competition. This paper presents an imprecise goal programming approach to determine the best aspiration levels of ECs in QFD planning process. Based on benchmarking data of ECs, the concept of satisfaction functions will be utilized to integrate explicitly the design team's preferences and incorporate the competitive analysis of target market into the modelling and solution processes. In addition, the relationships linking CNs and ECs and the ECs to each other are integrated by functional relationships. The proposed approach will be illustrated through an example of product development of an emulsification dynamite packing machine. Copyright © 2011 John Wiley & Sons, Ltd.     

Influences of information processing and disengagement in infants' looking behaviour

The present study considers the joint influences of information processing and disengagement in looking behaviour within a habituation paradigm. Six‐month‐old infants were habituated, during which their heart rate (HR) was measured. A parametric model of habituation yielded for each infant parameter estimates of their habituation performance. These parameters were interpreted as assessing information processing and disengagement. Corresponding measures were obtained from the HR data. The HR measures and habituation model parameter estimates were significantly correlated, as predicted. In addition, an attention getter, presented prior to each habituation trial, influenced indicators of information processing, but not of disengagement. Results confirmed the advantages of a modelling approach. In addition, and more importantly, findings led to the conclusion that both information processing as well as disengagement are involved in infants' looking behaviour in visual habituation. Copyright © 2009 John Wiley & Sons, Ltd.     

Memory span and general intelligence: A latent-variable approach

There are several studies showing that working memory and intelligence are strongly related. However, working memory tasks require simultaneous processing and storage, so the causes of their relationship with intelligence are currently a matter of discussion. The present study examined the simultaneous relationships among short-term memory (STM), working memory (WM), and general intelligence (g). Two hundred and eight participants performed six verbal, quantitative, and spatial STM tasks, six verbal, quantitative, and spatial WM tasks, and eight tests measuring fluid, crystallized, spatial, and quantitative intelligence. Especial care is taken to avoid misrepresenting the relations among the constructs being studied because of specific task variance. Structural equation modelling (SEM) results revealed that (a) WM and g are (almost) isomorphic constructs, (b) the isomorphism vanishes when the storage component of WM is partialed out, and (c) STM and WM (with its storage component partialed out) predict g.     

Family and work predictors of parenting role stress among two‐earner families of children with disabilities

Family resources (i.e. household income and spouse support), parenting challenges (i.e. number of children, difficulty finding reliable child care, and child characteristics), work rewards (i.e. work interest) and work demands (i.e. hours and work overload) were tested as predictors of parenting role stress among mothers and fathers in two‐earner families of five‐year old children with disabilities. The two‐level hierarchical model was adapted to assess mothers and fathers as nested within married couples. Both common and unique predictors of maternal and paternal parenting role stress were found. Having fewer children in the family predicted less stress for both parents. Household income and an interaction between child behaviour problems and work interest were significant predictors of maternal parenting role stress. In contrast, greater difficulty in finding reliable child care predicted higher levels of parenting role stress for fathers but not mothers. The policy and research implications of these findings are discussed. Copyright © 2005 John Wiley & Sons, Ltd.     

Doing cognitive neuroscience: a third way

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.     

Understanding Gang Joining from a Cross Classified Multi-Level Perspective

The present study seeks to address the gap in our understanding of the role of community- and school-level factors on individual-level gang joining. We assess our research question by bringing together data from a statewide survey of more than 35,000 school youth who were enrolled in more than 250 schools and resided in more than 300 communities. We first use multilevel modeling techniques to examine the independent relationship of community and school on the likelihood of individual youth gang joining. Second, while controlling for individual-level risk and protective factors, we examine the simultaneous relationship of community- and school-level conditions on gang joining. Overall, the results of the present study suggest that both community- and school-level characteristics play a role in gang joining. The results of the cross-classified model suggests that community structural characteristics may serve as protection against gang joining. Individual-level risk and protective factors remain important for understanding gang joining. A variety of factors are related to gang joining. Though individual-level factors are important, community- and school-level factors are meaningful in the understanding of gang joining.