Laterality effects in the spinning dancer illusion: The viewing‐from‐above bias is only part of the story

The ‘silhouette illusion’, representing the silhouette of a female dancer pirouetting about her vertical axis, is a bistable stimulus created by Japanese web designer Nobuyuki Kayahara. Although the dancer can be perceived as spinning either clockwise or counterclockwise, the clockwise rotation is usually preferred. Troje and McAdam (i‐Perception, 2010, 1, 143) showed that this clockwise bias can be attributed to the tendency to assume a viewpoint from above rather than from below, given that the dancer is portrayed from a vantage point that is not perpendicular to the axis of rotation. Here, we tested whether another possible factor contributing to the observers’ interpretation of this bistable stimulus might be the tendency to perceive movements of the right rather than the left foot. We confirmed both the viewing‐from‐above bias and our hypothesis. The bias to perceive movements of the right leg might be a generalization to lower limbs of a perceptual frequency effect already observed for upper limbs. Such a perceptual and attentional bias towards the right hand/foot could account for the greater ability to predict the outcome of sport actions when observing right‐ rather than left‐limbed movements, and thus the left‐handers' and left‐footers' advantage observed in a variety of interactive sports.     

An iterative procedure for extracting skill maps from data

The methodologies for the construction of a knowledge structure mainly refer to the query to experts, the skill maps, and the data-driven approaches. This last method is of growing interest in recent literature. In this paper, an iterative procedure for building a skill map from a set of data is introduced. This procedure is based on the minimization of the distance between the knowledge structure delineated by a given skill map and the data. The accuracy of the proposed method is tested through a number of simulation studies where the amount of noise in the data is manipulated as well as the kind of structure to be reconstructed. Results show that the procedure is accurate and that its performance tends to be sufficiently stable even with high error rates. The procedure is compared to two already-existing methodologies to derive knowledge structures from a set of data. The use of the corrected Akaike Information Criterion (AICc) as a stopping criterion of the iterative reconstruction procedure is tested against the app criterion introduced by Schrepp. Moreover, two empirical applications on clinical data are reported, and their results show the applicability of the procedure.     

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...     

Proof,Meaning and Paradox: Some Remarks

Topoi - In the present paper, the Fregean conception of proof-theoretic semantics that I developed elsewhere will be revised so as to better reflect the different roles played by open and closed...     

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.