Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous (...) one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented. the robot’s performance and behavior repetoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored. (shrink)

Various threshold Boolean networks, a formalism used to model different types of biological networks, can produce similar dynamics, i.e. share same behaviors. Among them, some are complex, others not. By computing both structural and behavioral complexities, we show that most TBNs are structurally complex, even those having simple behaviors. For this purpose, we developed a new method to compute the structural complexity of a TBN based on estimates of the sizes of equivalence classes of the threshold Boolean functions (...) composing the TBN. (shrink)

Social entrepreneurship, individual activities with a social objective, is used in this study as a conceptual tool for empirically examining farmers’ participation in alternative food networks. This study verifies whether their participation is driven by the social entrepreneurship dimension to satisfy social and environmental needs. We develop a more inclusive view of how social entrepreneurship is present among farmers participating in AFNs by using a behavioural approach based on three main psychological constructs: attitude, objective, and behaviour. The empirical results show (...) that two types of farmers participate in AFNs. One type is closer to commercial entrepreneurs; the main attitudes and objectives affecting their behaviour are oriented toward profit maximization and farm progress. The second type is closer to social entrepreneurial activity; the main objectives affecting their behaviour are oriented towards satisfying social and environmental needs. The study’s results offer implications and suggest recommendations concerning social entrepreneurial practices and the motivations of the farmers who participate in AFNs. (shrink)

Social entrepreneurship, individual activities with a social objective, is used in this study as a conceptual tool for empirically examining farmers’ participation in alternative food networks. This study verifies whether their participation is driven by the social entrepreneurship dimension to satisfy social and environmental needs. We develop a more inclusive view of how social entrepreneurship is present among farmers participating in AFNs by using a behavioural approach based on three main psychological constructs: attitude, objective, and behaviour. The empirical results show (...) that two types of farmers participate in AFNs. One type is closer to commercial entrepreneurs; the main attitudes and objectives affecting their behaviour are oriented toward profit maximization and farm progress. The second type is closer to social entrepreneurial activity; the main objectives affecting their behaviour are oriented towards satisfying social and environmental needs. The study’s results offer implications and suggest recommendations concerning social entrepreneurial practices and the motivations of the farmers who participate in AFNs. (shrink)

Indirect speech acts—responding “I forgot to wear my watch today” to someone who asked for the time—are ubiquitous in daily conversation, but are understudied in current neurobiological models of language. To comprehend an indirect speech act like this one, listeners must not only decode the lexical-semantic content of the utterance, but also make a pragmatic, bridging inference. This inference allows listeners to derive the speaker’s true, intended meaning—in the above dialog, for example, that the speaker cannot provide the time. In (...) the present work, we address this major gap by asking non-aphasic patients with behavioral variant frontotemporal dementia and brain-damaged controls with amnestic mild cognitive impairment to judge simple question-answer dialogs of the form: “Do you want some cake for dessert?” “I’m on a very strict diet right now,” and relate the results to structural and diffusion MRI. Accuracy and reaction time results demonstrate that subjects with bvFTD, but not MCI, are selectively impaired in indirect relative to direct speech act comprehension, due in part to their social and executive limitations, and performance is related to caregivers’ judgment of communication efficacy. MRI imaging associates the observed impairment in bvFTD to cortical thinning not only in traditional language-associated regions, but also in fronto-parietal regions implicated in social and executive cerebral networks. Finally, diffusion tensor imaging analyses implicate white matter tracts in both dorsal and ventral projection streams, including superior longitudinal fasciculus, frontal aslant, and uncinate fasciculus. These results have strong implications for updated neurobiological models of language, and emphasize a core, language-mediated social disorder in patients with bvFTD. (shrink)

In the realistic world, various individuals have distinct personalities, preferences, and attitudes toward new information and behavior acceptance, called population heterogeneity. It is seldom taken into account and theoretically analyzed in information propagation on a weighted network. Therefore, we divide individuals into fashionable and conservative individuals according to their passion degree and willingness for novel behaviors acceptance. Then, we build two behavior adoption threshold models corresponding to fashionable and conservative individuals on the weighted network to explore the effect of (...) population heterogeneity on information propagation. Next, a partition theory based on edge weight and population heterogeneity is proposed to qualitatively analyze the information propagation mechanism. The theoretical analyses and simulation results show that fashionable individuals promote information propagation and behavior adoption. More importantly, the crossover phenomena of phase transition appear. When the fraction of fashionable individuals is relatively large, the increasing pattern of the final adoption size shows a second-order continuous phase transition. In comparison, the increasing pattern alters to first-order discontinuous phase transition with the decrease of the fraction of fashionable individuals. Moreover, reducing weight distribution heterogeneity promotes information propagation and slightly accelerates the change of the phase transition pattern from the first-order discontinuous to the second-order continuous. Besides, increasing the degree distribution heterogeneity accelerates the change of the phase transition pattern. Finally, our theoretical analyses coincide well with the simulation results. (shrink)

Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network we questioned whether resting-state functional connectivity within the DAN can predict individual performance in spatial attention tasks and whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI, they subsequently performed different tasks of spatial attention [including visual search ] and immediately afterwards received another rs-fMRI. Intra- and inter-hemispheric FC between core hubs (...) of the DAN, bilateral intraparietal sulcus and frontal eye field, was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect in VS. And the faster the VS, the stronger was the FC between right FEF and IPS after task engagement. To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation. (shrink)

How to improve the safety behaviors of construction workers has dogged the realm of construction project management. Previous studies mainly focused on the individual and/or organizational factors shaping safety behaviors, while there is a dearth of research focusing on the effect of individual-organizational nexus. Thus, this study employs social network analysis and multivariable regression analyses to explore the relationship between the characteristics of social networks of construction workers and their safety behaviors, considering the mediating role of safety (...) communication. The primary data were collected from ten Chinese construction projects. The results include the following three aspects. First, degree centrality, closeness centrality, and betweenness centrality all exert significant positive effects on safety participation. Closeness centrality yields a positive effect on safety compliance in formal networks. Degree centrality has a positive effect on both safety compliance and safety participation, whereas the other two centrality characteristics exhibit no significant effect in informal networks. Second, in formal networks, safety communication plays a partial mediation role between closeness centrality and safety compliance and a full mediation role between degree and closeness centralities and safety participation. Third, in informal networks, safety communication plays a full mediation role between degree centrality and safety compliance and a partial mediation role between degree centrality and safety participation. This study provides new insights for construction project management in achieving improved safety performance via shaping the social network characteristics. (shrink)

We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex activity according to real-time functional near-infrared spectroscopy neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity within the emotion regulation network and frontoparietal network, and rsFC (...) between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting (...) brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from (...) a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift). (shrink)

In this paper, I propose a novel approach to investigating the nature of well-being and a new theory about wellbeing. The approach is integrative and naturalistic. It holds that a theory of well-being should account for two different classes of evidence—our commonsense judgments about well-being and the science of well-being (i.e., positive psychology). The network theory holds that a person is in the state of well-being if she instantiates a homeostatically clustered network of feelings, emotions, attitudes, behaviors, traits, and (...) interactions with the world that tends to have a relatively high number of states that feel good, that lead to states that feel good, or that are valued by the agent or her culture. (shrink)

Research has shown the importance of engaging in networking behaviors for employees’ career success. Networking behaviors can be seen as a proactive way of creating access to career-related social resources and we argue that this type of proactive career behaviors might be particularly relevant for freelancers who cannot depend on an organizational career system supporting their further development, yet whose careers are characterized by high levels of uncertainty and unpredictability. To date, however, our understanding of (...) how freelancers, being a category of workers that are deprived of an organizational context of support for career development, can safeguard their employability, is limited. Therefore, this study addresses this gap and investigates whether freelancers’ networking behaviors are positively associated with career outcomes, through the mediating role of the need for relatedness fulfillment and employability-enhancing competencies. Hypotheses are tested via Structural Equation Modelling using a sample of 1,874 freelancers from Belgium, France, Germany, the Netherlands and the United Kingdom. The results generally support our hypotheses, providing evidence for a significant association between networking behaviors and need for relatedness fulfillment, and between networking behaviors and employability-enhancing competencies. Moreover, we found a significant association between need for relatedness fulfillment and employability-enhancing competencies, being the mediators in our research model and the outcomes of career satisfaction and future career opportunities. Implications for career development in the contemporary workplace are discussed, with particular attention for need for relatedness fulfillment, employability, and sustainable careers of freelance workers. (shrink)

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.

Borsboom, Cramer, and Kalis propose that the network approach blocks reductionism in psychopathology. We argue that the two main arguments, intentionality and multiple realizability of mental disorders, are not sufficient to establish that mental disorders are not brain disorders, and that the specific role of networks in these arguments is unclear.

The concept of emotion as defined by Rolls is based upon reinforcement mechanisms and their underlying neural networks. He shows how these networks process signals at many levels, through both separate and convergent pathways essential for adaptive action. While many behavioral issues related to emotion are omitted from his review, he succeeds admirably in summarizing both the “current state of the art” in single unit analyses and in pointing out how future research directions may be crafted.

Behaviors are quantitative traits determined through actions of multiple genes and subject to genome–environment interactions. Early studies concentrated on analyzing the effects of single genes on behaviors, often generating views of simplified linear genetic pathways. The genome era has generated a profound paradigm shift enabling us to identify all the genes that contribute to expression of a behavioral phenotype, to investigate how they are organized as functional ensembles and to begin to identify polymorphisms that contribute to phenotypic variation (...) and are targets for natural selection. Recent studies show that the genetic architecture of behavior is determined by dynamic and plastic modular networks of pleiotropic genes and that the behavioral phenotype manifests itself as an emergent property of such networks. Such networks are exquisitely sensitive to genetic background and sex effects. This review describes how Drosophila can serve as a model for uncovering fundamental principles of the genetic architecture of behavior. BioEssays 26:1299–1306, 2004. © 2004 Wiley Periodicals, Inc. (shrink)

The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to (...) arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy. (shrink)

Here, we present some experiments of non-cooperative games of network formation based on Bala and Goyal (Econometrica 68:1181–1229, 2000 ). We have looked at the one-way and the two-way flow models, each for high and low link costs. The models come up with both multiple equilibria and coordination problems. We conducted the experiments under various conditions which allowed for repeated interactions between subjects. We found that coordination on non-empty Strict Nash equilibria was not an easy task to achieve, even in (...) the one-way model where the Strict Nash equilibria are wheels. We found some evidence of convergence to equilibrium networks through learning dynamics, while we found no effect of salient labels to help coordination. The evidence on learning behavior provides support for subjects that were choosing strategies in line with various learning rules, principally Reinforcement and Fictitious Play. (shrink)

Exponential random graph modeling (ERGM) is used here to test hypotheses derived from human behavioral ecology about the adaptive nature of human food sharing. Respondents in all (n = 317) households in the fishing and sea-hunting village of Lamalera, Indonesia, were asked to name those households to whom they had more frequently given (and from whom they had more frequently received) food during the preceding sea-hunting season. The responses were used to construct a social network of between-household food-sharing relationships in (...) the village. The results show that kinship, proximity, and reciprocal sharing all strongly increase the probability of giving food to a household. The effects of kinship and distance are relatively independent of each other, although reciprocity is more common among residentially and genealogically close households. The results show support for reciprocal altruism as a motivation for food sharing, while kinship and distance appear to be important partner-choice criteria. (shrink)

The network approach to psychiatric phenomena has the potential to clarify and enhance psychiatric diagnosis and classification. However, its generally well-justified anti-essentialism views psychiatric disorders as invariably fuzzy and arbitrary, and overlooks the likelihood that the domain includes some latent categories. Network models misrepresent these categories, and fail to recognize that some comorbidity may represent valid co-occurrence of discrete conditions.

We propose the notion of myopic-farsighted absorbing set to determine the networks that emerge in the long run when some players are myopic while others are farsighted. A set of networks is a myopic-farsighted absorbing set if (No External Deviation) there is no myopic-farsighted improving path from networks within the set to some networks outside the set, (External Stability) there is a myopic-farsighted improving path from any network outside the set to some network within the set, and (Minimality) there is (...) no proper subset satisfying No External Deviation and External Stability. Contrary to the notion of myopic-farsighted stable set, we show that a myopic-farsighted absorbing set always exists. We partially characterize the myopic-farsighted absorbing sets and we provide sufficient conditions for the equivalence between a myopic-farsighted absorbing set and a myopic-farsighted stable set. We also introduce and fully characterize the notion of proper myopic-farsighted absorbing set that refines the concept of myopic-farsighted absorbing set by selecting the more absorbing networks. Finally, we consider a threshold game that illustrates the role of the relative number of farsighted and myopic players for reaching efficiency. (shrink)

In this paper, I argue that recent sociological theory has become increasingly bifurcated into two mutually incompatible styles of theorizing that I label formalist and behavioral-realist. Formalism favors mathematization and proposes an instrumentalist ontology of abstract processes while behavioral-realist theory takes at its basis the "real" physical individual endowed with concrete biological, cognitive and neurophysiological capacities and constraints and attempts to derive the proper conceptualization of social behavior from that basis. Formalism tends to lead toward a conceptually independent sociology that (...) in principle requires only minimal reference to the empirical and ontological storehouse of other disciplines, while behavioral-realist theory leads to an interdisciplinary sociology that can be located within a hierarchy of behavioral sciences, leading to questions regarding the relationship between sociology and other disciplines as well as issues of transdisciplinary unification and possible interdisciplinary reduction. I explore the consequences of this split for the project of explanatory sociological theory within the context of how it has manifested itself in sociological network theory and social psychology. I close with a critique and assessment of formalist tendencies in sociological theorizing. (shrink)

Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that “neuroscience needs behavior.” As a promising path forward, we suggest complementing image recognition tasks with increasingly realistic and well-controlled task environments that engage real-world object recognition behavior.

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational (...) models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted. (shrink)

If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler (...) systems. Its capabilities are described based on simulations calling for increasing levels of functionality and are used to show how the model can progress from fundamental sequence learning and recall tasks to sophisticated behaviors such as an ability to solve simple mathematical expressions and a creative capacity for the formation and application of inductive rules. (shrink)

Page proposes a simple, localist, lateral inhibitory network for implementing a selection process that approximately conforms to the Luce choice rule. I describe another localist neural mechanism for selection in accordance with the Luce choice rule. The mechanism implements an independent race model. It consists of parallel, independent nerve fibers connected to a winner-take-all cluster, which records the winner of the race.

Due to natural phenomena like global warming and climate change, agricultural production is increasingly faced with threats that transcend farm boundaries. Management practices at the landscape or community level are often required to adequately respond to these new challenges (e.g., pest migration). Such decision-making at a community or beyond-farm level—i.e., practices that are jointly developed by farmers within a community—can be aided by computing and communications technology. In this study, we employ a translational research process to examine the social and (...) behavioral drivers of adoption of smart and connected farm networks among commodity crop farmers in the United States. We implement focus groups and questionnaires to bring to the fore views on the use of digital technologies in collaborative contexts. We find that participating farmers are concerned with several issues about the potential features of the network (e.g., the ability to ensure data validity while maintaining data privacy) and the nature of their interactions with the various stakeholders involved in the network management. The participatory approach we adopt helps provide insights into the process of developing technologies that are both actionable and trusted by potential end users. (shrink)

Borsboom et al. have written a trenchant critique of biological reductionism in psychopathology. After commenting on recent controversies concerning the network perspective, I discuss ways of integrating biology into the network enterprise.

Cramer et al. contrast two possible explanations for psychological symptoms: latent variables (i.e., specific cause) versus a network of causality between symptoms. There is a third explanation: The reason for comorbidity and the reported network structure of psychological symptoms is that the underlying biological cause is a psychoneuroimmunoendocrine information network which, when dysregulated, leads to several maladaptive psychological and somatic symptoms.