Process account of curiosity and interest: a reward-learning perspective

Previous studies suggested roles for curiosity and interest in knowledge acquisition and exploration, but there has been a long-standing debate about how to define these concepts and whether they are related or different. In this paper, we address the definition issue by arguing that there is inherent difficulty in defining curiosity and interest, because both curiosity and interest are naïve concepts, which are not supposed to have a priori scientific definitions. We present a reward-learning framework of autonomous knowledge acquisition and use this framework to illustrate the importance of process account as an alternative to advance our understanding of curiosity and interest without being troubled by their definitions. The framework centers on the role of rewarding experience associated with knowledge acquisition and learning and posits that the acquisition of new knowledge strengthens the value of further information. Critically, we argue that curiosity and interest are the concepts that they subjectively construe through this knowledge-acquisition process. Finally, we discuss the implications of the reward-learning framework for education and empirical research in educational psychology

Cooperative development of logical modelling standards and tools with CoLoMoTo.

The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments

Loregic: A Method to Characterize the Cooperative Logic of Regulatory Factors

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible twoinput- one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a generalpurpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcriptionfactor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy

CorBel - Core Belief-Skala

Die CorBel-Skala ist ein Instrument zur Selbsteinschätzung für die Erfassung der 97 Nuancen des CorBel-Modells, dem bisher differenziertesten Modell zu Glaubenssätzen. Die deutsche Originalversion enthält 291 Items, die die 97 Nuancen des CorBel-Modells mit 3 Items je Nuance reliabel erfassen. Daneben wurden zwei Kurzversionen (K1 mit 97, K2 mit 101 Items) sowie eine englische Fassung entwickelt. Reliabilität: Für alle 97 Nuancen konnte Homogenität im Sinne von Omega(total) bestätigt werden (Median bei .82). Validität: Für die Items kann aufgrund der hohen Spezifität der Nuancen inhaltliche Validität angenommen werden. Eindimensionalität im Sinne der faktoriellen Validität konnte für alle Nuancen bestätigt werden. Normen: Auf Basis der vorliegenden Daten wurden für die drei Versionen zwischen März bis April 2023 Normen in Form von Mittelwerten und Standardabweichungen berechnet.The CorBel Scale is a self-assessment instrument for capturing the 97 nuances of the CorBel model, the most differentiated model on beliefs to date. The original German version contains 291 items that reliably capture the 97 nuances of the CorBel model with 3 items per nuance. In addition, two short versions (K1 with 97, K2 with 101 items) and an English version were developed. Reliability: For all 97 nuances homogeneity in the sense of Omega(total) could be confirmed (median at .82). Validity: Content validity can be assumed for the items due to the high specificity of the nuances. One-dimensionality in the sense of factorial validity could be confirmed for all nuances. Norms: On the basis of the available data, norms were calculated as means and standard deviations for the three versions between March and April 2023.reviewedpublishedVersio

Verfahrensdokumentation für CorBel: Core Belief-Skala (2. überarbeitete Version)

Die CorBel-Skala ist ein Selbsteinschätzungsinstrument zur Erfassung der 97 Nuancen des CorBel-Modells, des bisher differenziertesten Modells zu Glaubenssätzen. Die deutsche Originalversion enthält 291 Items, die die 97 Nuancen des CorBel-Modells mit 3 Items pro Nuance zuverlässig erfassen. Darüber hinaus wurden drei Kurzversionen (CorBel-101, CorBel-60, CorBel-20) und eine englische Version entwickelt. Reliabilität: Für alle 97 Nuancen konnte Homogenität im Sinne von Omega(total) bestätigt werden (Median bei .82). Validität: Für die Items kann aufgrund der hohen Spezifität der Nuancen von einer Inhaltsvalidität ausgegangen werden. Eindimensionalität im Sinne der faktoriellen Validität konnte für alle Nuancen bestätigt werden.The CorBel Scale is a self-assessment instrument for capturing the 97 nuances of the CorBel model, the most differentiated model on beliefs to date. The original German version contains 291 items that reliably capture the 97 nuances of the CorBel model with 3 items per nuance. In addition, three short versions (CorBel-101, CorBel-60, CorBel-20) and an English version were developed. Reliability: For all 97 nuances homogeneity in the sense of Omega(total) could be confirmed (median at .82). Validity: Content validity can be assumed for the items due to the high specificity of the nuances. One-dimensionality in the sense of factorial validity could be confirmed for all nuances.reviewedpublishedVersio