On the empirical indistinguishability of knowledge structures

In recent years a number of articles have focused on the identifiability of the basic local independence model. The identifiability issue usually concerns two model parameter sets predicting an identical probability distribution on the response patterns. Both parameter sets are applied to the same knowledge structure. However, nothing is known about cases where different knowledge structures predict the same probability distribution. This situation is referred to as ʻempirical indistinguishabilityʼ between two structures and is the main subject of the present paper. Empirical indistinguishability is a stronger form of unidentifiability, which involves not only the parameters, but also the structural and combinatorial properties of the model. In particular, as far as knowledge structures are concerned, a consequence of empirical indistinguishability is that the existence of certain knowledge states cannot be empirically established. Most importantly, it is shown that model identifiability cannot guarantee that a certain knowledge structure is empirically distinguishable from others. The theoretical findings are exemplified in a number of different empirical scenarios.     

On the assessment of procedural knowledge: From problem spaces to knowledge spaces

By generalizing and completing the work initiated by Stefanutti and Albert (2003, Journal of Universal Computer Science, 9, 1455), this article provides the mathematical foundations of a theoretical approach whose primary goal is to construct a bridge between problem solving, as initially conceived by Newell and Simon (1972, Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.), and knowledge assessment (Doignon and Falmagne, 1985, International Journal of Man-Machine Studies, 23, 175; Doignon and Falmagne, 1999, Knowledge spaces. Berlin, Germany: Springer-Verlag.; Falmagne et al., 2013, Knowledge spaces: Applications in education. New York, NY: Springer-Verlag; Falmagne and Doignon, 2011, Learning spaces: Interdisciplinary applied mathematics. Berlin, Germany: Springer-Verlag.). It is shown that the collection of all possible knowledge states for a given problem space is a learning space. An algorithm for deriving a learning space from a problem space is illustrated. As an example, the algorithm is used to derive the learning space of a neuropsychological test whose problem space is well known: the Tower of London (TOL; Shallice, 1982, Philosophical Transactions of the Royal Society of London B: Biological Sciences, 298, 199). The derived learning space could then be used for adaptively assessing individual planning skills with the TOL.     

Sensitivity to Inflectional Morphemes in the Absence of Meaning: Evidence from a Novel Task

Journal of Psycholinguistic Research - A number of studies in different languages have shown that speakers may be sensitive to the presence of inflectional morphology in the absence of verb meaning...     

A class of k-modes algorithms for extracting knowledge structures from data

One of the most crucial issues in knowledge space theory is the construction of the so-called knowledge structures. In the present paper, a new data-driven procedure for large data sets is described, which overcomes some of the drawbacks of the already existing methods. The procedure, called k-states, is an incremental extension of the k-modes algorithm, which generates a sequence of locally optimal knowledge structures of increasing size, among which a “best” model is selected. The performance of k-states is compared to other two procedures in both a simulation study and an empirical application. In the former, k-states displays a better accuracy in reconstructing knowledge structures; in the latter, the structure extracted by k-states obtained a better fit.     

Distancing the present self from the past and the future: Psychological distance in anxiety and depression

Humans show a systematic tendency to perceive the future as psychologically closer than the past. Based on the clinical hypothesis that anxiety would be associated more with future threat life events, whereas depression with past loss events, here we explored whether people with anxiety- and depression-related personality traits perceive differently the psychological distance of temporal events. Results showed that the common tendency to perceive the future as psychologically closer than the past is exaggerated in individuals with anxiety-related personality traits, whereas this asymmetry drastically shrinks in individuals with depression-related personality traits. Beyond substantiating the hypothesis that the past and the future are differently faced by people with depression- and anxiety-related personality traits, the present findings suggest that temporal orientation of one’s self may be greatly altered in anxiety and depression.     

Borders,Tensegrity and Development in Dialogue

In this article we propose a development of the Dialogical Self Theory by introducing the notions of borders, cogenetic logic and tensegrity that we have elaborated during the last 5 years, in order to introduce a stronger developmental and dynamic perspective within the theory. We start from the discussion of some recent advancements of the model proposed by Hermans et al. (Integrative Psychological and Behavioural Science, 51(4), 2017), who refer to the metaphor of democratic society of the Self to understand the challenges and possible directions of adaptation that the persons can face in those border-crossing processes characterizing contemporary western societies. We conceptualized the Self as a dynamic semiotic system in constant evolutive tension, rather than a system in equilibrium adapting to the environmental changing conditions. Then, we propose to replace the concept of stability and continuity of the Self with the more fruitful idea of tensional integrity.     

The assessment of knowledge and learning in competence spaces: The gain–loss model for dependent skills

The gain–loss model (GaLoM) is a formal model for assessing knowledge and learning. In its original formulation, the GaLoM assumes independence among the skills. Such an assumption is not reasonable in several domains, in which some preliminary knowledge is the foundation for other knowledge. This paper presents an extension of the GaLoM to the case in which the skills are not independent, and the dependence relation among them is described by a well‐graded competence space. The probability of mastering skill s at the pretest is conditional on the presence of all skills on which s depends. The probabilities of gaining or losing skill s when moving from pretest to posttest are conditional on the mastery of s at the pretest, and on the presence at the posttest of all skills on which s depends. Two formulations of the model are presented, in which the learning path is allowed to change from pretest to posttest or not. A simulation study shows that models based on the true competence space obtain a better fit than models based on false competence spaces, and are also characterized by a higher assessment accuracy. An empirical application shows that models based on pedagogically sound assumptions about the dependencies among the skills obtain a better fit than models assuming independence among the skills.     

Personality Traits Predict Smartphone Usage

The present study investigates to what degree individual differences can predict frequency and duration of actual behaviour, manifested in mobile application (app) usage on smartphones. In particular, this work focuses on the identification of stable associations between personality on the factor and facet level, fluid intelligence, demography and app usage in 16 distinct categories. A total of 137 subjects (87 women and 50 men), with an average age of 24 (SD = 4.72), participated in a 90‐min psychometric lab session as well as in a subsequent 60‐day data logging study in the field. Our data suggest that personality traits predict mobile application usage in several specific categories such as communication, photography, gaming, transportation and entertainment. Extraversion, conscientiousness and agreeableness are better predictors of mobile application usage than basic demographic variables in several distinct categories. Furthermore, predictive performance is slightly higher for single factor—in comparison with facet‐level personality scores. Fluid intelligence and demographics additionally show stable associations with categorical app usage. In sum, this study demonstrates how individual differences can be effectively related to actual behaviour and how this can assist in understanding the behavioural underpinnings of personality. Copyright © 2017 European Association of Personality Psychology