Physical skills and language skills are both fundamental intelligent abilities of human being. In this paper, we focus our attention to such sophisticated physical skills as playing sports and playing instruments and introduce research activities aiming at elucidating and verbalizing them. This research area has been launched recently. We introduce approaches from physical modeling, measurements and data analysis, cognitive science and human interface. We also discuss such issues as skill acquisition and its support systems. Furthermore, we (...) consider a fundamental issue of individual differences occurring in every application of skill elucidation. Finally we introduce several attempts of skill elucidation in the fields of dancing, manufacturing, playing string instruments, sports science and medical care. (shrink)

Children’s cognitive control and knowledge at school entry predict growth rates in analogical reasoning skill over time; however, the mechanisms by which these factors interact and impact learning are unclear. We propose that inhibitory control (IC) is critical for developing both the relational representations necessary to reason and the ability to use these representations in complex problem solving. We evaluate this hypothesis using computational simulations in a model of analogical thinking, Discovery of Relations by Analogy/Learning and Inference with Schemas and (...) Analogy (DORA/LISA; Doumas et al., 2008 ). Longitudinal data from children who solved geometric analogy problems repeatedly over 6 months show three distinct learning trajectories though all gained somewhat: analogical reasoners throughout, non-analogical reasoners throughout, and transitional – those who start non-analogical and grew to be analogical. Varying the base level of top-down lateral inhibition in DORA affected the model’s ability to learn relational representations, which, in conjunction with inhibition levels used in LISA during reasoning, simulated accuracy rates and error types seen in the three different learning trajectories. These simulations suggest that IC may not only impact reasoning ability but may also shape the ability to acquire relational knowledge given reasoning opportunities. (shrink)

The necessity, method and content of childhood religious education are discussed from various perspectives in our country and around the world. The ‘Gift to the Child’ education method, which is applied in England where different religions live together, is a secular religious education approach with psychological, pedagogical and theological foundations. According to this method, which was developed with different methods from adult religious education and considering educational-scientific inferences, children can learn many information and skills based on religions and can transform (...) this into positive gains in their lives. The ‘Gift to the Child’ method processes concepts such as Allah, prophet, revelation and prayer with values such as responsibility, empathy, tolerance and love. It is distinguished from other religious education practices by aiming to highlight the child’s sense of holiness.This study examines the effect of the ‘Call to Prayer’ section, which is included in the ‘Gift to the Child’ method, one of the childhood religious education approaches, on children’s religious development in the Religious Culture and Moral Knowledge course. The applicability of the ‘Gift to the Child’ method, which aims to obtain educational-scientific inferences by comparing children with religious content, is discussed in terms of the educational process, teacher-student-material dimension and religious education.In examining the applicability of this approach, which evaluates religious development theory and development theories together and can be evaluated in terms of its achievements; After conducting a literature review from relevant sources, the data of the study were collected with the semi-structured interview technique, which is one of the qualitative research methods. The data of the study were collected through teacher interviews conducted with the cross-sectional research method. Semi-structured interviews were conducted with 10 teachers working in secondary schools of official educational institutions affiliated to the Ministry of National Education. During the course, the teachers allowed the students to express their opinions on the subject and did not intervene in their questions and answers. As a result of the findings obtained from the data, it was observed that the use of achievements and materials prepared according to the ‘Gift to the Child’ method in religious education positively affected the religious development of the students. (shrink)

With the continuous progress and penetration of automated data collection technology, enterprises and organizations are facing the problem of information overload. The demand for expertise in data mining and analysis is increasing. Self-efficacy is a pivotal construct that is significantly related to willingness and ability to perform a particular task. Thus, the objective of this study is to develop an instrument for assessing self-efficacy in data mining and analysis. An initial measurement list was developed based on the (...) skills and abilities about executing data mining and analysis, and expert recommendations. A useful sample of 103 university students completed the online survey questionnaire. A 19-item four-factor model was extracted by exploratory factor analysis. Using the partial least squares-structural equation modeling technique, the model was cross-examined. The instrument showed satisfactory reliability and validity. The proposed instrument will be of value to researchers and practitioners in evaluating an individual’s abilities and readiness in executing data mining and analysis. (shrink)

More than three billion users are currently on one of Meta’s online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized. In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N (...) = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward “social networks use disorder” can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design. (shrink)

This study investigated the concept of individual differences (IDs) in the use of metacognitive strategies (planning, problem-solving, monitoring, and evaluating) and its relationship with task demand and learner performance within Kormos’ Bilingual Speech Production Model from the lens of Chinese English-as-foreign-language (EFL) learners in the context of integrated L2 speaking assessment. To measure metacognitive strategies, we administered an inventory on 134 Chinese EFL learners after they completed four integrated L2 speaking assessment tasks. Descriptive analysis and multiple linear regression were (...) adopted for data analysis, and results show that: (a) IDs displayed variance in Chinese EFL learners’ metacognitive strategy use; (b) among the four metacognitive strategies under investigation, problem-solving was reported to be used the most frequently in sharp contrast to monitoring, which had the lowest frequency; (c) metacognitive strategies worked interactively, responding to task demands involved in the four integrated L2 speaking assessment tasks; and (d) Chinese EFL learners’ use of metacognitive strategies, in individual and interactive working modes, had no relationship with their speaking performance. These results are expected to present some insights into the role of IDs in metacognitive strategy use during L2 speech production under assessment conditions, which will add robust evidence to the existing literature on L2 speaking, in particular on metacognitive strategy use in L2 speaking assessment. In the meantime, the findings will provide some empirical validation support for Kormos’ model, which will further provide some implications for L2 speaking instruction and L2 assessment. (shrink)

In attempting to solve a wide variety of tasks, people naturally seek to modify their external environment such that the physical space in which they work is more amenable or ‘congenial’ to achieving a desired outcome. Attempts to determine the effectiveness of certain artifacts or spatial reorganizations in aiding reasoners solve problems must be relativised to the difficulty of the task and the cognitive abilities of the reasoners. These factors were examined using a simple word production task with letter (...) tiles. Two sets of tiles that differed in terms of word production difficulty were selected. Participants were asked to produce as many words as they could within a finite time period for each letter set. In one group, participants were encouraged to rearrange or touch the tiles when producing words, and in the other group, participants could not interact with or point to the tiles. Participants were further split in a low and high verbal fluency group as a function of their score on the Thurstone word fluency test taken at the end of the experiment. In the high fluency group, letter rearrangement did not improve the participants’ ability to generate words. In contrast, in the low verbal fluency group, letter rearrangement significantly enhanced the ability to produce new words from both the hard and easy letter set. For these participants, the task was more taxing and the opportunity to restructure the letter set substantially elevated their performance. The advantages of a distributed cognition analysis of this and other reasoning tasks are discussed. (shrink)

Individual differences and polysemy have rich literatures in cognitive linguistics, but little is said about the prospect of individual differences in polysemy. This article reports an investigation that sought to establish whether people vary in the senses of a polysemous word that they find meaningful, and to develop a novel methodology to study polysemy. The methodology combined established tools: sentence-sorting tasks, a rarely used statistical model of inter-participant agreement, and network visualisation. Two hundred and five English-speaking participants completed (...) one of twelve sentence-sorting tasks on two occasions, separated by a delay of two months. Participants varied in how similarly they sorted the sentences as compared to other participants, and mean agreement across all 24 tasks did not meet an established threshold of acceptable agreement. Between the two test phases, inter-participant agreement varied to a significant but trivial degree. Networks generated for each dataset varied in the degree to which they captured all participants’ responses. This variation correlated with inter-participant agreement. The data collectively suggest that word senses may be subject to individual differences, as is the case in other linguistic phenomena. The methodology proved replicable and has a promise as a useful tool for studying polysemy. (shrink)

This study was aimed at predicting individual differences in text reading fluency. The basic proposal included two factors, i.e., the ability to decode letter strings (measured by discrete pseudo-word reading) and integration of the various sub-components involved in reading (measured by Rapid Automatized Naming, RAN). Subsequently, a third factor was added to the model, i.e., naming of discrete digits. In order to use homogeneous measures, all contributing variables considered the entire processing of the item, including pronunciation time. The model, (...) which was based on commonality analysis, was applied to data from a group of 43 typically developing readers (11- to 13-year-olds) and a group of 25 chronologically matched dyslexic children. In typically developing readers, both orthographic decoding and integration of reading sub-components contributed significantly to the overall prediction of text reading fluency. The model prediction was higher (from ca. 37% to 52% of the explained variance) when we included the naming of discrete digits variable, which had a suppressive effect on pseudo-word reading. In the dyslexic readers, the variance explained by the two-factor model was high (69%) and did not change when the third factor was added. The lack of a suppression effect was likely due to the prominent individual differences in poor orthographic decoding of the dyslexic children. Analyses on data from both groups of children were replicated by using patches of colours as stimuli (both in the RAN task and in the discrete naming task) obtaining similar results. We conclude that it is possible to predict much of the variance in text-reading fluency using basic processes, such as orthographic decoding and integration of reading sub-components, even without taking into consideration higher-order linguistic factors such as lexical, semantic and contextual abilities. The approach validity of using proximal vs distal causes to predict reading fluency is discussed. (shrink)

The current study evaluated the degree to which nature-based physical activity influenced two distinct types of psychological wellbeing: hedonic wellbeing and eudaimonic wellbeing. The type of motivation an individual experiences for physical activity, and the extent to which individuals have a sense of relatedness with nature, have been shown to influence the specific type of psychological wellbeing that is experienced as a result of NPA. However, the role of these two variables in the relationship between NPA and (...) psychological wellbeing has not been examined. Thus, this study assessed the potential mediating influence of motivational quality and nature relatedness on the relationships between NPA and hedonic and eudaimonic wellbeing, respectively. Participants completed an online survey assessing hedonic and eudaimonic wellbeing, NPA, intrinsic motivation, autonomous extrinsic motivation, and nature relatedness. Data were analysed using Partial Least Squares Structural Equation Modelling. Results showed that motivational quality and nature relatedness both fully mediated the relationships between NPA and hedonic and eudaimonic wellbeing. Specifically, intrinsic motivation positively mediated the relationship between NPA and hedonic wellbeing. Autonomous extrinsic motivation and nature relatedness positively mediated the relationship between NPA and eudaimonic wellbeing. These findings suggest that the associations between NPA and eudaimonic wellbeing and hedonic wellbeing, respectively, are driven by different mechanisms relating to an individual’s underlying motivation and sense of connection to nature. These findings suggest that promoting distinct types of wellbeing through NPA requires distinct approaches. Emphasising enjoyment, pleasure, and positive kinaesthetic experiences within NPA may be more conducive to hedonic wellbeing, while highlighting opportunities for connecting with nature or experiencing valued outcomes of NPA may be more conducive to eudaimonic wellbeing. (shrink)

Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process—such as cognitive processing speed, response caution, and motor execution speed—in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other (...) areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision‐making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies—that the heritability of cognitive ability is based on cognitive processing speed—may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement. (shrink)

Scalability, efficiency, and security had been a persistent problem over the years in data mining, several techniques had been proposed and implemented but none had been able to solve the problem of scalability, efficiency and security from cloud computing. In this research, we solve the problem scalability, efficiency and security in data mining over cloud computing by using a restful web services and combination of different technologies and tools, our model was trained by using different machine learning algorithm, (...) and finally using different validation and cross-validation methods to compare and validate our results. We build, train, test and deploy our model by restful API services which can be called through an endpoint, and with very high accuracy and huge success. (shrink)

Current theories of reasoning such as mental models or mental logic assume a universal cognitive mechanism that underlies human reasoning performance. However, there is evidence that this is not the case, for example, the work of Ford (1995), who found that some people adopted predominantly spatial and some verbal strategies in a syllogistic reasoning task. Using written and think-aloud protocols, the present study confirmed the existence of these individual differences. However, in sharp contrast to Ford, the present study found (...) few differences in reasoning performance between the two groups, in terms of accuracy or type of conclusion generated. Hence, reasoners present an outward appearance of ubiquity, despite underlying differences in reasoning processes. These findings have implications for theoretical accounts of reasoning, and for attempts to model reasoning data. Any comprehensive account needs to account for strategic differences and how these may develop in logically untrained individuals. (shrink)

The present work aims at developing a scale for the assessment of a construct that we called “physical–digital integration”, which refers to the tendency of some individuals not to perceive a clear differentiation between feelings and perceptions that pertain to the physical or digital environment. The construct is articulated in four facets: identity, social relationships, time–space perception, and sensory perception. Data from a sample of 369 participants were collected to evaluate factor structure (unidimensional model, bifactor model, correlated (...) four-factor model), internal consistency (Cronbach’s _α_, McDonald’s _ω_), and correlations of the physical–digital integration scale with other measures. Results showed that the scale is valid and internally consistent, and that both the total score and the scores at its four subscales are worthy of consideration. The physical–digital integration scores were found to be differently associated with digital and non-digital behaviors, individuals’ ability to read emotions in the facial expressions of others, and indicators of psychosocial functioning (anxiety, depression, and satisfaction with social relationships). The paper proposes a new measure whose scores are associated with several variables that may have relevant consequences at both individual and social levels. (shrink)

I contend that Kurzban et al.'s model is silent on three issues. First, the extent to which opportunity-cost computations are automatic or deliberative is unclear. Second, the role of individual differences in biasing opportunity-cost computations needs elucidating. Third, in the absence of tasks, task persistence will be indefinite, which seems unfeasible, so perhaps integration with a limited-resource account is necessary.

Purpose This paper aims to look at shifts in internet-related content and services economies, from audience labour economies to Web 2.0 user-generated content, and the emerging model of user computing power utilisation, powered by blockchain technologies. The authors look at and test three models of user computing power utilisation based on distributed computing two of which use cryptocurrency mining through distributed pool mining techniques, while the third is based on distributed computing of calculations for scientific research. The three models promise (...) benefits to their users, which the authors discuss throughout the paper, studying how they interplay with the three levels of the digital divide. Design/methodology/approach The goal of this article is twofold as follows: first to discuss how using the mining hype may reduce digital inequalities, and secondly to demonstrate how these services offer a new business model based on value rewarding in exchange for computational power, which would allow more online opportunities for people, and thus reduce digital inequalities. Finally, this contribution discusses and proposes a method for a fair revenue model for content and online service providers that uses user device computing resources or computational power, rather than their data and attention. The method is represented by a model that allows for consensual use of user computing resources in exchange for accessing content and using software tools and services, acting essentially as an alternative online business model. Findings Allowing users to convert their devices’ computational power into value, whether through access to services or content or receiving cryptocurrency and payments in return for providing services or content or direct computational powers, contributes to bridging digital divides, even at fairly small levels. Secondly, the advent of blockchain technologies is shifting power relations between end-users and content developers and service providers and is a necessity for the decentralisation of internet and internet services. Originality/value The article studies the effect of services that rely on distributed computing and mining on digital inequalities, by looking at three different case studies – Coinhive, Gridcoin and Cryptotab – that promise to provide value in return for using computing resources. The article discusses how these services may reduce digital inequalities by affecting the three levels of the digital divide, namely, access to information and communication technologies, skills and motivations in using ICTs and capacities in using ICTs to get concrete benefits. (shrink)

Complex skill learning depends on the joint contribution of multiple interacting systems: working memory (WM), declarative long-term memory (LTM) and reinforcement learning (RL). The present study aims to understand individual differences in the relative contributions of these systems during learning. We built four idiographic, ACT-R models of performance on the stimulus-response learning, Reinforcement Learning Working Memory task. The task consisted of short 3-image, and long 6-image, feedback-based learning blocks. A no-feedback test phase was administered after learning, with an interfering (...) task inserted between learning and test. Our four models included two single-mechanism RL and LTM models, and two integrated RL-LTM models: (a) RL-based meta-learning, which selects RL or LTM to learn based on recent success, and (b) a parameterized RL-LTM selection model at fixed proportions independent of learning success. Each model was the best fit for some proportion of our learners (LTM: 68.7%, RL: 4.8%, Meta-RL: 13.25%, bias-RL:13.25% of participants), suggesting fundamental differences in the way individuals deploy basic learning mechanisms, even for a simple stimulus-response task. Finally, long-term declarative memory seems to be the preferred learning strategy for this task regardless of block length (3- vs 6-image blocks), as determined by the large number of subjects whose learning characteristics were best captured by the LTM only model, and a preference for LTM over RL in both of our integrated-models, owing to the strength of our idiographic approach. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social (...) epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

The ability to reason and problem‐solve in novel situations, as measured by the Raven's Advanced Progressive Matrices (RAPM), is highly predictive of both cognitive task performance and real‐world outcomes. Here we provide evidence that RAPM performance depends on the ability to reallocate attention in response to self‐generated feedback about progress. We propose that such an ability is underpinned by the basal ganglia nuclei, which are critically tied to both reward processing and cognitive control. This hypothesis was implemented in a neurocomputational (...) model of the RAPM task, which was used to derive novel predictions at the behavioral and neural levels. These predictions were then verified in one neuroimaging and two behavioral experiments. Furthermore, an effective connectivity analysis of the neuroimaging data confirmed a role for the basal ganglia in modulating attention. Taken together, these results suggest that individual differences in a neural circuit related to reward processing underpin human fluid reasoning abilities. (shrink)

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative models play in (...) Predictive Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined,structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation. (shrink)

From the standpoint of the theory of medicine, a formulation is given of three types of reasoning used by physicians. The first is deduction from probability models (as in prognosis or genetic counseling for Mendelian disorders). It is a branch of mathematics that leads to predictive statements about outcomes of individual events in terms of known formal assumptions and parameters. The second type is inference (as in interpreting clinical trials). In it the arguments from replications of the same process (...) (data) lead to conclusions about the parameters of a system, without calling into question either the probabilistic model or the criteria of evidence. The third is illation (as in the elucidation of symptoms in a patient). It is a process whereby, in the light of the total evidence and the conclusions from the other types of reasoning, one may modify, expand, simplify or demolish a conceptual framework proposed for deductions, and modify the nature of the evidence sought, the criteriology, the axioms, and the surmised complexity of the scientific theory. (The process of diagnosis as applied to a patient may in extreme cases lead to the discovery of an entirely new disease with its own, quite new, set of diagnostic criteria. This course cannot be accommodated inside either of the other two types of reasoning.) Illation has something of the character of Kuhn's scientific revolution in physics; but it differs in that it is the nature, not the degree or frequency of change that distinguishes it from Kuhn's normal science. (shrink)

While some aspects of pragmatic competence are known to decline with age, for metaphor skills the evidence is inconclusive, possibly due to heterogeneity in the assessment tools. Furthermore, the previous literature on age-related changes in pragmatic skills has rarely considered the role of Theory of Mind (ToM), which is described as one of the main factors affecting metaphor across theoretical and experimental studies in children and clinical populations. This study aimed at elucidating age-related differences in metaphor understanding and the interplay (...) between metaphor skills and ToM in middle-aged and older adults with a fine-grained approach. Participants (n = 201, age range 54–93) completed tasks assessing ToM and metaphor understanding. On the one side, we used the Physical and Mental Metaphors task, to distinguish between different types of metaphor (physical, such as “Lifeguards are lizards,” meaning that they lie in the sun, vs. mental, such as “Adolescents are pendulums,” meaning that they are emotionally unstable) and different aspects of metaphor understanding, namely, accuracy in finding a link between topic and vehicle versus type of interpretation (from physical to psychological). On the other side, we analyzed two aspects of ToM skills: the accuracy in mental state understanding and the intentionality, defined as the degree of mental state attribution, assessed with the Strange Stories and the Animated Social Triangles task, respectively. Structural equation models indicated a decline in metaphor skills with advancing age. Furthermore, we found that ToM is involved in metaphor understanding in a specific fashion. While higher ToM accuracy explained better metaphor accuracy, higher ToM intentionality explained better performance in the interpretation of mental, but not physical, metaphors. These findings suggest that age-related differences in pragmatics extend to metaphor skills and that ToM plays a role in metaphor comprehension in older age, with a division of labor where the ability to understand what others think is key to spotting a metaphorical link, but the greater tendency to attribute mental states is what specifically helps when it comes to grasping the psychological nuances of a metaphor. (shrink)

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm (...) in which models are treated as stochastic processes in experiments with participant‐to‐participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT‐R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof‐of‐concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data. (shrink)

This paper argues that, for a prospective philosophical analysis of models and simulations to be successful, it must accommodate an account of mathematically rigorous results. Such rigorous results are best thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results often provide new indirect ways of assessing the success of computer simulations of individual models. This is most obvious in cases where rigorous results map different models on (...) to one another. Not only does this allow for the transfer of warrant across different models, it also puts constraints on the extent to which performance in specific empirical contexts may be regarded as the main touchstone of success in scientific modelling. Rigorous results and relations can thus come to be seen as giving cohesion and stability to actual practices of scientific modelling. (shrink)

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves (...) must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation (...) system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (shrink)

In recent times psychologists have shown a growing interest in ethological methods of data collection. At the same time ethologists are showing a growing interest in the methods of data processing as developed in personality psychology. These methods of data processing appear to be most useful to ethological research when investigating differences between individuals. Using factor analysis of aggressive behaviour as an example, it is argued that an ethological approach which focusses on individual differences may add (...) substantial information to the models derived from a (classical) ethological approach which only concerns general pictures of species. The advantages of the proposed research procedures are discussed, especially in relation to behavioural genetic and ontogenetic research. (shrink)

The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of (...) basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making. (shrink)

A methodological model of origin and settlement of theory-choice situations (previously tried on the theories of Einstein and Lorentz in electrodynamics) is applied to modern Theory of Gravity. The process of origin and growth of empirically-equivalent relativistic theories of gravitation is theoretically reproduced. It is argued that all of them are proposed within the two rival research programmes – (1) metric (A. Einstein et al.) and (2) nonmetric (H. Poincare et al.). Each programme aims at elimination of the cross-contradiction between (...) the special theory of relativity and Newton’s theory of gravitation. New arguments in favor of Einstein’s programme are given. Nevertheless, this does not imply the necessity to rule out all the nonmetric theories, since Einstein’s and Poincare’s programmes are alternative only as different tools of the cross-contradiction elimination. In the other respects these programmes are complementary: description, explanation and prediction of gravitational experimental data entails the usage of the languages of nonmetric theories as well as of metric ones. The part of the present investigation elucidating the necessity of nonmetric theories is an implementation of the ideas of A.Z. Petrov, the founder of Kazan University Relativity Department. Late Alexei Zinovievich had frequently punctuated that the notion of Riemann space-time continuum common for all metric theories obfuscates all the gravitational notions considerably and hampers the analogies with other physical theories at hand. Since the ambiguity is a hallmark of all the general relativism notions, approach to their definitions “should be determined not by analogies and contingent facts, but by general considerations linked the physical measurements theory… No matter how far the events lie out of the frames of classical physical explanations, all the experimental data should be described by classical notions” (Petrov, 1965,pp. 59,66). Key words: Kip S. Thorne, A.P. Lightman, Stepin, theory of gravity . (shrink)

International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis and (...) built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n= 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies. (shrink)

Telecommunications is one of the most dynamic sectors in the market, where the customer base is an important pawn in receive safe revenues, so is important to focus attention is paid to maintaining them with an active status. Migrating customers from one network to another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, customer billing issues, etc. Determining an effective predictive model helps detect early warning signals when churn (...) occurs and assigns to each customer a score called “churn score” that indicates the likelihood that the individual might migrate to another network over a predefined time period. To this extent, the present paper uses more than 10k customers sample of a telecommunication company and tries to analyse the churn behavior. The aim of the paper is both to test the efficiency and performance of the most commonly used data mining techniques to predict the churn behavior and to underline the main indicators that can be used when conducting such analyses. Knowing the magnitude of the churn phenomenon, the company can prevent the instability that is going to occur by applying a series of measure in order to increase the retention of the current customers. (shrink)

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide‐ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab‐based experiments, new efforts have been underway toward greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game‐based tool that simultaneously assesses a broad suite of cognitive abilities while providing (...) an engaging narrative. Skill Lab consists of six mini‐games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10,725), we conducted a comprehensive validation in the wild of a game‐based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users’ in‐game behavior. Follow‐up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game‐based measures are five times faster to complete than the equivalent task‐based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross‐sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in‐the‐wild systematic assessment of cognitive abilities as a promising first step toward population‐scale benchmarking and individualized mental health diagnostics. (shrink)

ObjectivesA fitness club may be an important arena to promote regular exercise. However, authors have reported low attendance rates the first months after individuals sign up for membership. It is therefore important to understand the reasons for poor exercise adherence. In this project, we aimed to investigate different psychosocial factors that might increase the likelihood of reporting regular exercise the first year of a fitness club membership, including self-efficacy, motives, social support, life satisfaction, and customer satisfaction.MethodsNew members classified as novice (...) exercisers from 25 multipurpose gyms were followed for 1 year. Data were collected by an electronic survey including background and health factors, self-efficacy, social support, life satisfaction, motives, customer satisfaction, and exercise attendance, and was answered at start-up and after three, six, and 12 months. It is well established in the literature that ≥2 exercise sessions/week improve physical fitness in novice exercisers. Hence, we divided the participants into regular exercise attendance and non-regular exercise attendance in the analysis.ResultsA mixed-effects logistic regression model revealed that the strongest predictor for reporting regular exercise attendance was higher levels of the motive “enjoyment”, followed by self-efficacy “sticking to it” and social support from friends and family.ConclusionIn novice exercisers, regular exercise at three, six, and 12 months was associated with higher scores of the motive “enjoyment,” self-efficacy, and social support compared with non-regular exercise. Our results show that the majority of new fitness club members do not achieve regular exercise behavior. (shrink)

To explore the role of the social adaptability of different groups in human capital in an uncertain environment, this paper empirically tests the relationship between social adaptation and human capital by using a panel quantile regression model with the life satisfaction of 35 cities in China as an indicator. By selecting Nelder Mead and adaptive MCMC estimation methods, this paper finds that the higher the level of social adaptation is, the more obvious the promotion effect on the level of human (...) capital; for eastern cities, social adaptation can promote the improvement of the human capital level; for central or western cities, the relationship between social adaptation and human capital is not obvious and depends on the method selected for estimation. To enhance the social adaptability of individuals, the government leadership should actively create conditions, carry out targeted social adaptation training and active adaptation skills exchange for different social groups in order to strengthen social adaptability and enhance human capital. At the same time, policymakers need to consider the differences between the eastern, central and western regions and choose the most suitable human capital improvement action framework for the region to manage environmental uncertainty and make regional human capital sustainable competitive in the era of uncertainty. (shrink)

Effective human–robot teaming increasingly requires humans to work with intelligent, autonomous machines. However, novel features of intelligent autonomous systems such as social agency and incomprehensibility may influence the human’s trust in the machine. The human operator’s mental model for machine functioning is critical for trust. People may consider an intelligent machine partner as either an advanced tool or as a human-like teammate. This article reports a study that explored the role of individual differences in the mental model in a (...) simulated environment. Multiple dispositional factors that may influence the dominant mental model were assessed. These included the Robot Threat Assessment, which measures the person’s propensity to apply tool and teammate models in security contexts. Participants were paired with an intelligent robot tasked with making threat assessments in an urban setting. A transparency manipulation was used to influence the dominant mental model. For half of the participants, threat assessment was described as physics-based ; the remainder received transparency information that described psychological cues. We expected that the physics-based transparency messages would guide the participant toward treating the robot as an advanced machine, while psychological messaging would encourage perceptions of the robot as acting like a human partner. We also manipulated situational danger cues present in the simulated environment. Participants rated their trust in the robot’s decision as well as threat and anxiety, for each of 24 urban scenes. They also completed the RoTA and additional individual-difference measures. Findings showed that trust assessments reflected the degree of congruence between the robot’s decision and situational danger cues, consistent with participants acting as Bayesian decision makers. Several scales, including the RoTA, were more predictive of trust when the robot was making psychology-based decisions, implying that trust reflected individual differences in the mental model of the robot as a teammate. These findings suggest scope for designing training that uncovers and mitigates the individual’s biases toward intelligent machines. (shrink)

Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models (...) and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand. (shrink)

Studies of individuals under conditions of confinement or severe social and physical restrictions have consistently shown deleterious mental health effects but also high levels of adaptability when dealing with such conditions. Considering the role of physical activity and sport in psychological adaptation, this paper describes a longitudinal study to explore to what extent the imposed restrictions due to the outbreak of SARS-CoV-2 may have affected athletes’ mental health outcomes and how far the process of adaptation to confinement conditions (...) is differentially affected depending on whether the sports activity was practiced individually or in a group, and outdoors, indoors, or both. Two hundred and seventy-four athletes were assessed over 7 weeks using the GHQ-28 and an ad hoc survey exploring the practice of physical activity. A mixed-model fixed effects ANCOVA was used to analyze the effects of time, place, and company in which the sport was practiced, with an index of the amount of physical activity expended as a covariate. Results show a significant effect of time in three out of four of the GHQ-28 subscales, in all cases showing a consistent adaptation to conditions over time. Results also show that playing sport indoors, outdoors, or both, and practicing alone vs. with others differentially affect the somatic symptoms exhibited during confinement: Athletes who practiced sport with others showed higher levels of somatic symptoms at the beginning of the set of data but a quicker rate of adaptation. Differences arising from practicing sport alone or with others were more pronounced in the case of indoor sports, which could be related to the fact that physical activity that can be practiced during confinement is more similar to that practiced indoors alone. Implications relating to what sport psychologists and other health professionals may offer to athletes in stressful situations are discussed. (shrink)

Learning new movement patterns is a normal part of daily life, but of critical importance in both sport and rehabilitation. A major question is how different sensory signals are integrated together to give rise to motor adaptation and learning. More specifically, there is growing evidence that pain can give rise to alterations in the learning process. Despite a number of studies investigating the role of pain on the learning process, there is still no systematic review to summarize and critically assess (...) investigations regarding this topic in the literature. Here in this systematic review, we summarize and critically evaluate studies that examined the influence of experimental pain on motor learning. Seventeen studies that exclusively assessed the effect of experimental pain models on motor learning among healthy human individuals were included for this systematic review, carried out based on the preferred reporting items for systematic reviews and meta-analyses statement. The results of the review revealed there is no consensus regarding the effect of pain on the skill learning acquisition and retention. However, several studies demonstrated that participants who experienced pain continued to express a changed motor strategy to perform a motor task even 1 week after training under the pain condition. The results highlight a need for further studies in this area of research, and specifically to investigate whether pain has different effects on motor learning depending on the type of motor task. (shrink)

Following the studies of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018), this empirical study uses two scenario-based online experiments. The sample consists of 221 subjects from Germany, differing in both age and gender. The original studies are not replicated one-to-one. New scenarios are constructed as realistically as possible and focused on everyday work situations. They are based on the AI acceptance model of Scheuer (Grundlagen intelligenter KI-Assistenten und deren vertrauensvolle Nutzung. Springer, Wiesbaden, (...) 2020) and are extended by individual descriptive elements of AI systems in comparison to the original studies. The first online experiment examines decisions made by artificial intelligence with varying degrees of impact. In the high-impact scenario, applicants are automatically selected for a job and immediately received an employment contract. In the low-impact scenario, three applicants are automatically invited for another interview. In addition, the relationship between age and risk perception is investigated. The second online experiment tests subjects’ perceived trust in decisions made by artificial intelligence, either semi-automatically through the assistance of human experts or fully automatically in comparison. Two task types are distinguished. The task type that requires “human skills”—represented as a performance evaluation situation—and the task type that requires “mechanical skills”—represented as a work distribution situation. In addition, the extent of negative emotions in automated decisions is investigated. The results are related to the findings of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018). Implications for further research activities and practical relevance are discussed. (shrink)

ObjectiveThe study aimed to enhance the learning motivation of college physical education students and improve their learning outcomes. Based on the perspective of the self-determination theory, this study explores the influence of “Small Private Online Course + flipped classroom” teaching on the learning motivation of students majoring in physical education and profoundly analyzes the influencing factors and promotion paths of learning motivation using this model.Materials and methodsA total of four classes of physical education majors in a university (...) were selected and randomly divided into an experimental group and a control group. The experimental group received “SPOC + flipped classroom” teaching, the control group received traditional teaching. Before and after the 16-week intervention, learning motivation, teacher support perception, basic psychological need satisfaction, and academic emotions of the 64 students were measured, and the data were analyzed by repeated-measures analysis of variance and partial least square regression.Results The instructional intervention reduced non-regulation, external regulation, and introjected regulation, while increased identified regulation, intrinsic regulation, and self-determination levels in the students. The levels of non-regulation, external regulation, identified regulation, and self-determination were also significantly different from those of the control group. After the intervention, the scores of support for autonomy, support for competence, support for relatedness, and need for relatedness in the experimental group were significantly higher than those in the control group. Support for autonomy, support for competence, support for relatedness, need for competence and need for relatedness positively predicted the self-determination level, and intrinsic regulation and identified regulation negatively predicted non-regulation, external regulation, and introjected regulation.Conclusion“SPOC + flipped classroom” teaching has a positive impact on students’ learning motivation of basketball skills and promotes students’ motivation autonomy. The improvement of support for autonomy, support for competence, support for relatedness, need for competence, and need for relatedness may be related to the improvement of learning motivation of college students majoring in Physical Education. “SPOC + flipped classroom” teaching enables students to obtain more demand satisfaction by giving them more demand support, while demand support and demand satisfaction can promote the internalization of learning motivation so that students can maintain high autonomy motivation. (shrink)

Approaches to foster motor creativity differ according to whether creative movements are assumed to be enacted creative ideas, or solutions to emerging motor problems that arise from task and environmental constraints. The twofold aim of the current study was to investigate whether an enriched physical education intervention delivered with a joint constraints-led and cognitive stimulation approach fosters motor creativity, and the responsiveness to the intervention is moderated by baseline motor and cognitive skills and sex; the intervention may benefit motor (...) creativity through gains in motor coordination, executive function, and creative thinking. Ninety-five children, aged 6–9 years, participated in a 6-month group randomized trial with specialist-led enriched PE vs. generalist-led conventional PE. Before and after the intervention, Bertsch’s Test of Motor Creativity, Movement Assessment Battery for Children, Random Number Generation task and Torrance Test of Creative Thinking were administered. Linear mixed models were run accounting for the random effects of data clusters. Multiple mediation analysis was performed to assess whether motor coordination, executive function and creative thinking mediated any improvement of motor creativity. Results showed that specialist-led enriched PE, compared to generalist-led conventional practice, elicited a more pronounced improvement in all motor creativity dimensions independently of baseline levels of motor and cognitive skills and sex; and improved motor creativity was partially mediated by improved motor coordination and, as regards motor flexibility, also by improved inhibitory ability. In conclusion, enriching PE with tailored manipulations of constraints and variability may enhance the ability to create multiple and original task-pertinent movements both directly and through indirect paths. The results are discussed extending to motor creativity a theoretical framework that distinguishes different creativity modes. The intervention may have fostered the generation of creative movements directly through the exposure to variation in constraints, activating the sensorimotor ‘flow’ mode of creativity that bypasses higher-order cognition, but also indirectly through a systematic and conscious convergence on solutions, activating the ‘deliberate’ mode of creativity that relies on inhibition to reject common or task-inappropriate movement categories. (shrink)

In view of the aging and dejuvenation of the working population and the expected shortages in employees’ skills in the future, it is of utmost importance to focus on older workers’ employability in order to prolong their working life until, or even beyond, their official retirement age. The primary aim of the current study was to examine the relationship between older workers’ employability (self-)perceptions and their intention to continue working until their official retirement age. In addition, we studied the role (...) of potential antecedents of their perceived employability at three different levels: training and education in current expertise area as well as in an adjacent expertise area (individual level factor), learning value of the job (job level factor), and organizational career management practices (organizational level factor). Data were collected by means of e-questionnaires that were distributed among two groups of Dutch older (45-plus) white collar workers. The samples consisted of 223 employees of an insurance company, and 325 university workers, respectively. Our research model was tested separately in each sample using Structural Equation Modeling. We controlled for effects of respondents’ (self-)perceived health and (self-)perceived financial situation. Similar results were found for both samples. First, the relationship of perceived employability with the intention to continue working until one’s retirement age was positive, whereas the relationship between a perceived good financial situation with the intention to continue working until one’s retirement age was negative. Secondly, as regards the potential antecedents, results showed that the learning value of the job was positively related to perceived employability. In addition, an employee’s perception of good health is a relevant correlate of perceived employability. So, whereas perceived employability contributes to the intention to continue working until one’s retirement age, a good financial situation is a push factor to retire early. In order to promote the labor participation of older workers, this study indicates that organizations should focus on the learning possibilities that are inherent to one’s job rather than on providing additional training or career management. Further research is needed to test the generalizability of our results to other samples. (shrink)

There are hardly any instruments to measure teamwork behaviors from an individual approach. This applies both in interprofessional teams or not, and in teams involved in health, social care, and other areas. The Individual Behavior Analysis scale measures efficacious behavior in work teams. It is one of the few instruments proposed in the literature to measure personal skills necessary for teamwork. Only a previous exploratory analysis of the scale was informed in another study. This article analyzes its internal (...) structure using different confirmatory factor analyses and its internal consistency, with a sample of 815 employees working for Spanish social organizations in the geriatric field, both private and public. The results of the definitive version adapted to Spanish, referred to as Individual Behavior Analysis −25, indicate a good fit of the model to the data and good reliability. Factor analysis confirmed the existence of two factors: Communication skills and Acceptance, with good internal consistency coefficients. This scale is a useful instrument for assessing, based on the reviewed literature, two of the most important individual skills an efficacious team should have. (shrink)

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment (...) of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618. (shrink)

This paper aims to investigate the relationship between Italian teachers’ well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers’ well-being, we investigate several aspects related to the teachers’ working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes (...) attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers’ well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers’ positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers’ job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher’s well-being. Findings and implications are discussed. (shrink)

One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages. Video is an illustration of interactive media data since it contains text, pictures, meta-data, visual, sound, (...) and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data. It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far-reaching method to association rule extraction from video databases. (shrink)

”The last remnant of physical objectivity of space-time” is disclosed in the case of a continuous family of spatially non-compact models of general relativity. The physical individuation of point-events is furnished by the autonomous degrees of freedom of the gravitational field, which represent -as it were -the ontic part of the metric field. The physical role of the epistemic part is likewise clarified as embodying the unavoidable non-inertial aspects of GR. At the end the philosophical import of (...) the Hole Argument is substantially weakened and in fact the Argument itself dis-solved, while a specific four-dimensional holistic and structuralist view of space-time emerges, including elements common to the tradition of both substantivalism and relationism. The observables of our models undergo real temporal change: this gives new evidence to the fact that statements like the frozen-time character of evolution, as other ontological claims about GR, are model dependent. (shrink)

The outbreak of the coronavirus disease 2019 has resulted in widespread university lockdown. However, impacts of the university lockdown on the learning and academic development of university students have not been thoroughly investigated. The current study examined college students’ changes of learning outcomes during the COVID-19 lockdown period and clarified what might explain individual differences in students’ learning outcomes after they had learned from home for a whole semester when universities were physically closed due to the COVID-19 pandemic. (...) class='Hi'>Data were derived from a longitudinal study examining the development of college students including students’ academic achievement and critical thinking before and after the university lockdown. We observed significant decreases in critical thinking skills and dispositions from pre- to post-lockdown. Both perceived academic achievement and critical thinking exhibited greater variability after the lockdown. In addition, students’ readiness for online learning, especially their self-management skills, consistently predicted post-lockdown learning outcomes after controlling for pre-lockdown outcomes and family socioeconomic status. Those who have assumed more responsibilities at home, or who were more vulnerable to emotional distress during the pandemic, performed less well in post-lockdown learning outcomes. These findings call for better management of student learning and development when major changes are required in higher education practices for responding to the ongoing COVID-19 crisis as well as other potential situations. (shrink)

Why was Hensen unsuccesful in the quantification of ecological sampling? No aspect of plankton research itself seems to have hindered quantification; both collecting methods and taxonomy were sufficiently advanced. The reason is probably that at the time he began sampling, Hensen had to devise his own statistical methods for expressing the reproducibility and validity of samples. Hensen might have succeeded in this if he had overcome prevalent nineteenth-century attitudes toward randomness.The statistical literature of medicine and physics with which Hensen was (...) probably familiar gave methods for expressing reproducibility and for comparing differences between means of different sets of observations. For example, a student of Poisson writing on medical statistics advocated using Poisson's limit (standard error 2√2) to test the difference between two means56. Other authors suggested that differences between means were most meaningful if very large numbers of observations were used.57 In his laboratory subsampling, Hensen used the propable error as a limit about means. In this and other ways, he seems most indebted to the physicist Ernst Abbe for statistical methods.58 However, all the methodology available to Hensen had been developed for situations in which errors are a property of the measurement or sampling process, and not of the phenomena themselves. The available methods for measuring reproducibility were based on the assumption that differences from the average were small and that they tended to accumulate about the mean in a bell-shaped pattern. Hensen constantly reinvestigated the distribution of plankton numbers about the average using a different method each time. Westergaard points out that medical statisticians did not make such investigations with their biological data.59To a considerable extent, biological sampling problems forced development of theory because samples afforded the only information on a pattern in water or soil which could not be directly observed. The sampling methods of Laplace and the late nineteenth-century government statisticians contrasted strongly with Hensen's because, either through subjective knowledge of the population sampled or through censuses, they attempted to choose representative or typical samples.60 The high reproducibility and validity of representative sampling is attained by knowing more about a population than a biologist can ordinarily know. The uncertain reproducibility and validity of biological sampling spurred the development of formal sampling theory.A formal sampling theory developed only after change in the general intellectual attitude toward randomness, which was reflected in nineteenth-century statistics.61 The ninteenth-century attitude that randomness is not part of nature changed in the twentieth century to a view of randomness as a property of nature.62 The physicist's incorporation of randomness into physical models in response to this intellectual change late in the nineteenth-century is discussed by Bork.63 In biology, the change was initiated by the attention Darwin focused on morphological variation. The English biometricians — Francis Galton and W. F. R. Weldon, for example — were prominent in developing methods for the analysis of biological variation.64 Most pertinent to the development of sampling theory was Karl Pearson's use of frequency distributions as models of biological variation. In ecology, quantification was brought about by Ronald Fisher more than by anyone else; he incorporated randomness into sampling plans and built upon the methods developed earlier for analysis of individual variation. Fisher's use of random sampling allowed comparison between the sample collections and the collections expected from a model population of known patterning (calculated with a frequency distribution). This is a much more efficient method of determining the validity of a sample than Hensen's comparison of collections with a model uniform collection. Intellectual background and accumulated biological information caused Fisher to find variability where Hensen had seen uniformity.In summary, Victor Hensen became interested in fisheries research because of the economic importance of fishing to Germany. Hensen had considerable understanding of the prerequisites for valid sampling, but the value of his quantitative approach was limited by the general preconceptions shared by most nineteenth-century biologists. Through Hensen's efforts many other biologists were stimulated to undertake quantitative samples, even though the statistical methods for analyzing variation among populations developed only after methods for analyzing variation among individuals had been developed. *** DIRECT SUPPORT *** A8402011 00003. (shrink)

Theories related to cognitive sciences, Human-in-the-loop Cyber-physical systems, data analysis for decision-making, and computational ethics make clear the need to create transdisciplinary learning, research, and application strategies to bring coherence to the paradigm of a truly human-oriented technology. Autonomous objects assume more responsibilities for individual and collective phenomena, they have gradually filtered into routines and require the incorporation of ethical practice into the professions related to the development, modeling, and design of algorithms. To make this possible, it (...) is pertinent and urgent to bring them closer to the problems and approaches of the humanities. Increasingly transdisciplinary research must be part of the construction of systems that provide developers and scientists with the necessary bases to understand the ethical debate and therefore their commitment to society. This article considers two theories as articulating axes: Blumenberg’s, coming from the field of philosophy, for whom the process of technification and especially the implementation of mathematical models in their algorithmic form leads to an emptying of meaning and therefore makes programmers who implement their functions to be alien to the concerns that gave them origin; Daston’s, belonging to the field of the history of science and according to which the division of labor in the processes of technification of the calculation implies a kind of subordination in which those who implement the inventions of a small group of privileged mathematicians ignore the procedures that put them into operation. Given these two theories, the black box models prevalent in AI development, and the urgency of establishing explanatory frameworks for the development of computational ethics, this article exposes the need to give a voice to the cultural history of consciousness for promoting the discussions around the implementation of mathematical algorithms. The paper takes as a reference the different points of view that have emerged around the study of technological ethics, its applicability, management, and design. It criticizes the current state of studies from a humanistic perspective and explains how the historical perspective allows promoting the training of software engineers, developers and creators so that they assume intuitions and moral values in the development of their work. Specifically, it aims to expose how cultural history, applied to the study of consciousness and its phenomena, makes those involved in this technological revolution aware of the effect that they, through their algorithms, have on society in general and on human beings in particular. (shrink)