In contrast to Pietraszewski's account, latent structure learning neither requires conflict nor relies on observation of explicit coalitional behavior to support group inference. This alternative addresses how even non-conflict-based groups may be defined and is supported by experimental evidence in human behavior.

We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identified. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by (...) recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems. Keywords: latent variable models, causality, graphical models.. (shrink)

The hypothesis that perceptual mechanisms could have more representational and logical power than usually assumed is interesting and provocative, especially with regard to brain evolution. However, the importance of embodiment and grounding is exaggerated, and the implication that there is no highly abstract representation at all, and that human-like knowledge cannot be learned or represented without human bodies, is very doubtful. A machine-learning model, Latent Semantic Analysis (LSA) that closely mimics human word and passage meaning relations is offered (...) as a counterexample. (shrink)

ObjectivesUnder the COVID-19 prevention and control policy, online learning has been widely used. The current study aimed to identify latent profiles of self-regulated learning in the context of online mathematics learning during the recurrent outbreak of COVID-19, and examine the mechanisms underlying the relationship between self-regulated learning and online mathematics learning engagement among Chinese junior high school students using variable-and person-centered approaches.MethodsA sample of 428 Chinese junior high school students completed questionnaires on self-regulated (...) class='Hi'>learning, perceived academic control, and learning engagement. Mplus7.0 was used to analyze the latent classes of self-regulated learning. A mediation model was conducted using the software SPSS PROCESS macro.ResultsThree profiles of self-regulated learning were identified and named as low self-regulated learning, medium self-regulated learning, and high self-regulated learning. In the mediating analysis, results of the variable-centered approach showed that perceived academic control mediated the effects of self-regulated learning on learning engagement. For the person-centered approach, we selected the low self-regulated learning type as the reference profile, and the analysis revealed that compared with the reference profile, perceived academic control partially mediated the link between the medium self-regulated learning profile and learning engagement; perceived academic control partially mediated the relationship between the high self-regulated learning profile and learning engagement.ConclusionThis study showed the heterogeneity in the online mathematics self-regulated learning patterns of Chinese junior high school students during the COVID-19 pandemic, revealing the internal mechanisms of Chinese junior high school students’ online mathematics learning engagement using variable-and person-centered approaches. Furthermore, the findings of the study have important implications for promoting online mathematics learning engagement among junior high students during the pandemic. (shrink)

Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables is discussed. The problem of inferring the presence of latent variables, their relation to the observables, and the relation among themselves, is considered. A different approach for identifying causal structures, one that results in much simpler equivalence classes, is provided. It is found that the computational cost is much higher than the procedure implemented, but if datasets are individually of modest (...) dimensionality, it might be doable in practice. From the point of view of search algorithms for optimizing structure, much of the machinery of combinatorial optimization could optimize the penalized composite likelihood score by enforcing constraints such that the independence models over different subsets of variables agree on the overlapping sets. (shrink)

The zone of latent solutions hypothesis provides an alternative approach to explaining cultural patterns in primates and many other animals. According to the ZLS hypothesis, non-human great ape cultures consist largely or solely of latent solutions. The current competing hypothesis for ape culture argues instead that at least some of their behavioural or artefact forms are copied through specific social learning mechanisms and that their forms may depend on copying. In contrast, the ape ZLS hypothesis does not (...) require these forms to be copied. Instead, it suggests that several social learning mechanisms help determine the frequency of these behaviours and artefacts within connected individuals. The ZLS hypothesis thus suggests that increases and stabilisations of a particular behaviour’s or artefact’s frequency can derive from socially-mediated form reinnovations. Therefore, and while genes and ecology play important roles as well, according to the ape ZLS hypothesis, apes typically acquire the forms of their behaviours and artefacts individually, but are usually socially induced to do so. The ZLS approach is often criticized—perhaps also because it challenges the current null hypothesis, which instead assumes a requirement of form-copying social learning mechanisms to explain many ape behavioural forms. However, as the ZLS hypothesis is a new approach, with less accumulated literature compared to the current null hypothesis, some confusion is to be expected. Here, we clarify the ZLS approach—also in relation to other competing hypotheses—and address misconceptions and objections. We believe that these clarifications will provide researchers with a coherent theoretical approach and an experimental methodology to examine the necessity of form-copying variants of social learning in apes, humans and other species. (shrink)

In 2009, Tennie et al. proposed the theory of the Zone of Latent Solutions, defined as the range of behaviors an individual of a species can invent independently, i.e., which it can acquire without any form of social learning. By definition, species limited to their ZLS are unable to innovate and/or transmit behavioral traits outside their ZLS, i.e., they lack traits which go beyond the level of the individual—traits resulting from a gradual cultural evolution over successive transmission events (...) [“cumulative culture”, Boyd and Richerson ]. However, this does not exclude an influence of social learning on the population frequency of these behaviors: social learning can facilitate the acquisition of latent solutions and thus speed up and consolidate their spread within a population. Cultures—defined as behaviors at least influenced by social learning—are therefore still possible. Here, we elaborate on the ZLS account and relate it to the theories of Vygotsky who studied the role of social learning in human culture. We argue that the ZLS is a missing phylogenetic “baseline” of Vygotsky’s Zone of Actual Development. Vygotsky’s neglect of a need for a human “baseline ZAD” may have been due to his interpretation of Köhler’s work on great ape behaviors: Köhler used his observations on individual chimpanzees to draw conclusions about the chimpanzee species as a whole, stating that chimpanzees can only copy what they could have invented themselves, thus coming close to the ZLS concept. Vygotsky—studying the range of behaviors individuals could achieve independently—seemingly did not see that Köhler was suggesting a species “baseline”, upon which Vygotsky’s idea of an individual’s ZAD could develop. As a result, Vygotsky also did not see the need for a ZLS for his own study subjects: humans. Yet, there is no reason to assume that humans lack a ZLS, and in fact we present evidence for a human ZLS for tool-use behaviors. (shrink)

Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...) belong to 3 species at a few developing phases. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results: found that CNN-driven lemon classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.48% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). (...) We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles. (shrink)

We routinely observe others’ choices and use them to guide our own. Whose choices influence us more, and why? Prior work has focused on the effect of perceived similarity between two individuals, such as the degree of overlap in past choices or explicitly recognizable group affiliations. In the real world, however, any dyadic relationship is part of a more complex social structure involving multiple social groups that are not directly observable. Here we suggest that human learners go beyond dyadic similarities (...) in choice behaviors or explicit group memberships; they infer the structure of social influence by grouping individuals based on choices, and they use these groups to decide whose choices to follow. We propose a computational model that formalizes this idea, and we test the model predictions in a series of behavioral experiments. In Experiment 1, we reproduce a well-established finding that people's choices are more likely to be influenced by someone whose past choices are more similar to their own past choices, as predicted by our model as well as dyadic similarity models. In Experiments 2–5, we test a set of unique predictions of our model by looking at cases where the degree of choice overlap between individuals is equated, but their choices indicate a latent group structure. We then apply our model to prior empirical results on infants’ understanding of others’ preferences, presenting an alternative account of developmental changes. Finally, we discuss how our model relates to classical findings in the social influence literature and the theoretical implications of our model. Taken together, our findings demonstrate that structure learning is a powerful framework for explaining the influence of social information on decision making in a variety of contexts. (shrink)

Effective feature representation is the key to success of machine learning applications. Recently, many feature learning models have been proposed. Among these models, the Gaussian process latent variable model for nonlinear feature learning has received much attention because of its superior performance. However, most of the existing GPLVMs are mainly designed for classification and regression tasks, thus cannot be used in data clustering task. To address this issue and extend the application scope, this paper proposes a (...) novel GPLVM for clustering. Specifically, by combining GPLVM with the subspace clustering method, our C-GPLVM can obtain more representative latent variable for clustering. Moreover, it can directly predict the new samples by introducing a back constraint in the model, thus being more suitable for big data learning tasks such as analysis of chaotic time series and so on. In the experiment, we compare it with the related GPLVMs and clustering algorithms. The experimental results show that the proposed model not only inherits the feature learning ability of GPLVM but also has superior clustering accuracy. (shrink)

Through an educational reading of Édouard Glissant and Peter Sloterdijk, this article draws out and develops latent pedagogical philosophies that bear distinct relationships to colonialism and struggles against and beyond colonialism. In particular, it identifies two related educational philosophies that propel colonization and, in turn, proposes a theory of errant learning that might undergird decolonization. It focuses on Glissant’s minor remarks about different conceptions of understanding in order to identify the grasping drive as the educational foundation of the (...) colonizing apparatus. After articulating the other form of understanding he offers, giving-on-and-with, I point to a potential contradiction in this division as it relates to his overall project. By freeing grasping from the grasping drive, I reconfigure the relationship between grasping and giving-on-and-with in a way that allows for certain kinds of enclosures. This introduces the question of pedagogical form, a question explored in Sloterdijk’s sphereological investigations. Reading the colonizing phase of globalization through Sloterdijk’s notion of lordly imagining—which I link with the grasping drive—the article draws out how different educational processes produce different kinds of spheres. Finally, it articulates errant learning as a pneumatic process of grasping and giving-on-and-with that values opacity over transparency to produce foam formations with attention to the history of inequality and injury through immune deprivation. (shrink)

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not (...) eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work. (shrink)

IntroductionBurnout is a growing problem among young researchers, affecting individuals, organizations and society. Our study aims to identify burnout profiles and highlight the corresponding job demands and resources, resulting in recommendations to reduce burnout risk in the academic context.MethodsThis cross-sectional study collected data from young researchers at five Flemish universities through an online survey measuring burnout risk, work engagement, sleeping behavior, and the most prominent job demands and resources. We conducted Latent Profile Analysis to identify burnout profiles in young (...) researchers and subsequently compared these groups on job demands and resources patterns.ResultsFive burnout profiles were identified: High Burnout Risk, Cynical, Overextended, Low Burnout Risk, and No Burnout Risk. Each burnout profile was associated with a different pattern of job demands and resources. For instance, high levels of meaningfulness decreased the odds to being classified in the Cynical profile.ConclusionOur findings show that the Cynical profile corresponds to a relatively high number of young researchers, which may imply that they are particularly vulnerable to the cynicism dimension of burnout. Additionally, work-life interference and perceived publication pressure seemed the most significant predictors of burnout risk, while meaningfulness, social support from supervisor and learning opportunities played an important protective role. (shrink)

Expectation Maximisation (EM) and Latent Dirichlet Allocation (LDA) are two frequently used inference algorithms, for finding an appropriate mixture of latent variables, and for finding an allocation of topics for a collection of documents. A recent insight in probabilistic learning is that Jeffrey’s update rule gives a decrease of Kullback-Leibler divergence. Its logic is error correction. It is shown that this same rule and divergence decrease logic is at the heart of EM and LDA, ensuring that successive (...) iterations are decreasingly wrong. (shrink)

In our recent article, "Where Does Cumulative Culture Begin? A Plea for a Sociologically Informed Perspective" we commented on a fundamental notion in current approaches to cultural evolution, the “zones of latent solutions”, and proposed a modification of it, namely a social and dynamic interpretation of the latent solutions which were originally introduced within an individualistic framework and as static, genetically fixed entities. This modification seemed, and still seems, relevant to us and, in particular, more adequate for coping (...) with the archaeological record. Bandini et al. rejected our proposition and deemed it unnecessary. In their critique, they focused on: our reservations about an individualistic approach; our objections to the presumption of fully naive individuals; and our demand for an extended consideration of forms of social learning simpler than emulation and imitation. We will briefly reply to their critique in order to clarify some misunderstandings. However, the criticisms also show that we are at an impasse on certain crucial topics, such as the meaning of ZLS and the scope and nature of culture in general. Thus, we consider it necessary to make an additional effort to identify the conceptual roots which are at the very basis of the dissent with Bandini et al. (shrink)

We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects’ learning. One benchmark is (...) based on optimal performance as calculated by a dynamic programming procedure. The other is based on an adaptive computational agent that uses a reinforcement-learning method known as Q-learning to learn to perform the task. Our analyses suggest that subjects were successful learners. In particular, they learned to perform the perceptual matching task in a near-optimal manner (i.e., using a small number of manipulations) at the end of training. Subjects were able to achieve near-optimal performance because they learned, at least partially, the causal structure underlying the task. In addition, subjects’ performances were broadly consistent with those of model-based reinforcement-learning agents that built and used internal models of how their actions influenced the external environment. We hypothesize that people will achieve near-optimal performances on tasks requiring sequences of action—especially sensorimotor tasks with underlying latent causal structures—when they can detect the effects of their actions on the environment, and when they can represent and reason about these effects using an internal mental model. (shrink)

In the nation of Belize, and in particular the south side of Belize City, the main metropolitan area of the nation, significant economic disparities have led to child and adolescent exposure to high rates of violent crime, gang activity, unsafe neighborhoods, sexual, and physical violence. Problems associated with poor Social-Emotional Character Development are especially prevalent among boys. Consequently, valid culture-relevant measures are required that identify problematic behavior for policy-based intervention and evaluation of educational programs designed to ameliorate this problem. The (...) present study demonstrates the application of Exploratory Structural Equation Modeling to existing measures through the investigation of structural validity and generalizability of the Social-Emotional and Character Development Scale with a large sample of children from schools in Belize. Exploratory structural equation modeling results demonstrate the original factor correlations were reduced, providing less biased estimates than confirmatory factor analysis. Moreover, a multi-group Exploratory Structural Equation Modeling analysis illuminates significant differences between latent factor scores of males and females for most factors. Using this newer factor analytic procedure, original factors are reconceptualized to better situate the Social Emotional Character Development Scales into the larger body of Social-Emotional Learning competencies literature. (shrink)

The purpose of cognitive diagnostic modeling is to classify students' latent attribute profiles using their responses to the diagnostic assessment. In recent years, each diagnostic classification model makes different assumptions about the relationship between a student's response pattern and attribute profile. The previous research studies showed that the inappropriate DCMs and inaccurate Q-matrix impact diagnostic classification accuracy. Artificial Neural Networks have been proposed as a promising approach to convert a pattern of item responses into a diagnostic classification in some (...) research studies. However, the ANNs methods produced very unstable and unappreciated estimation unless a great deal of care was taken. In this research, we combined ANNs with two typical DCMs, the deterministic-input, noisy, “and” gate model and the deterministic-inputs, noisy, “or” gate model, within a semi-supervised learning framework to achieve a robust and accurate classification. In both simulated study and real data study, the experimental results showed that the proposed method could achieve appreciated performance across different test conditions, especially when the diagnostic quality of assessment was not high and the Q-matrix contained misspecified elements. This research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria. (shrink)