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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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The Impact of Mathematical Proficiency on the Number-Space Association

The Impact of Mathematical Proficiency on the Number-Space Association | Social Foraging | Scoop.it

A specific instance of the association between numerical and spatial representations is the SNARC (Spatial Numerical Association of Response Codes) effect. The SNARC effect describes the finding that during binary classification of numbers participants are faster to respond to small/large numbers with the left/right hand respectively. Even though it has been frequently replicated, important inter-individual variability has also been reported. Mathematical proficiency is an obvious candidate source for inter-individual variability in numerical judgments, but studies investigating its influence on the SNARC effect remain scarce. The present experiment included a total of 95 University students, divided into three groups differing significantly in their mathematical proficiency levels. Using group analyses, it appeared that the three groups differed significantly in the strength of their number-space associations in a parity judgment task. This result was further confirmed on an individual level, with higher levels in arithmetic leading to relatively weaker SNARC effects. To explain this negative relationship we propose accounts based on differences in access to qualitatively different numerical representations and also consider more domain general factors, with a focus on inhibition capacities.

 

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Visualizing numbers in the mind's eye: The role of visuo-spatial processes in numerical abilities

Visualizing numbers in the mind's eye: The role of visuo-spatial processes in numerical abilities | Social Foraging | Scoop.it

In the study of numerical and arithmetical abilities, there is compelling evidence demonstrating that number and space representations are connected to one another. Historically the first source of support came more than a century ago, when Galton's investigations on mental imagery suggested that the internal representation of numbers may evoke a stable, linear space. In the past few decades, empirical evidence lent further support to the hypothesis that numerical representation is spatially coded into a non-verbal ‘mental number line’, which in turn lead to considering this representation as the core of number meaning. Visuo-spatial processing is intuitively involved in various aspects of number processing and calculation: For instance, the meaning of a digit in a multi-digit number is coded following spatial information, given its association to its relative position within the number; similarly, to solve a complex written multiplication one has to know the correct location of the intermediate results. In this review behavioral, neuropsychological, and neuroimaging data concerning the close relationship between numerical abilities and visuo-spatial processes are considered.

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After Being Challenged by a Video Game Problem, Sleep Increases the Chance to Solve It

After Being Challenged by a Video Game Problem, Sleep Increases the Chance to Solve It | Social Foraging | Scoop.it

In the past years many studies have demonstrated the role of sleep on memory consolidation. It is known that sleeping after learning a declarative or non-declarative task, is better than remaining awake. Furthermore, there are reports of a possible role for dreams in consolidation of declarative memories. Other studies have reported the effect of naps on memory consolidation. With similar protocols, another set of studies indicated that sleep has a role in creativity and problem-solving. Here we hypothesised that sleep can increase the likelihood of solving problems. After struggling to solve a video game problem, subjects who took a nap (n = 14) were almost twice as likely to solve it when compared to the wake control group (n = 15). It is interesting to note that, in the nap group 9 out 14 subjects engaged in slow-wave sleep (SWS) and all solved the problem. Surprisingly, we did not find a significant involvement of Rapid Eye Movement (REM) sleep in this task. Slow-wave sleep is believed to be crucial for the transfer of memory-related information to the neocortex and implement intentions. Sleep can benefit problem-solving through the generalisation of newly encoded information and abstraction of the gist. In conclusion, our results indicate that sleep, even a nap, can potentiate the solution of problems that involve logical reasoning. Thus, sleep's function seems to go beyond memory consolidation to include managing of everyday-life events.

 

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Confidence and the Stock Market: An Agent-Based Approach

Confidence and the Stock Market: An Agent-Based Approach | Social Foraging | Scoop.it

Using a behavioral finance approach we study the impact of behavioral bias. We construct an artificial market consisting of fundamentalists and chartists to model the decision-making process of various agents. The agents differ in their strategies for evaluating stock prices, and exhibit differing memory lengths and confidence levels. When we increase the heterogeneity of the strategies used by the agents, in particular the memory lengths, we observe excess volatility and kurtosis, in agreement with real market fluctuations—indicating that agents in real-world financial markets exhibit widely differing memory lengths. We incorporate the behavioral traits of adaptive confidence and observe a positive correlation between average confidence and return rate, indicating that market sentiment is an important driver in price fluctuations. The introduction of market confidence increases price volatility, reflecting the negative effect of irrationality in market behavior.

 

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Cascading Failures in Spatially-Embedded Random Networks

Cascading Failures in Spatially-Embedded Random Networks | Social Foraging | Scoop.it

Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084563

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Bigger Helpers in the Ant Cataglyphis bombycina: Increased Worker Polymorphism or Novel Soldier Caste?

Bigger Helpers in the Ant Cataglyphis bombycina: Increased Worker Polymorphism or Novel Soldier Caste? | Social Foraging | Scoop.it

The mechanisms by which development favors or constrains the evolution of new phenotypes are incompletely understood. Polyphenic species may benefit from developmental plasticity not only regarding ecological advantages, but also potential for evolutionary diversification. For instance, the repeated evolution of novel castes in ants may have been facilitated by the existence of alternative queen and worker castes and their respective developmental programs.

 

During animal ontogeny, organs follow distinct growth rules that produce an integrated phenotype. Two parameters are crucial: growth rate defines the speed at which each organ grows, and critical size specifies the overall size of the individual at which growth stops and development is complete [1]. By modifying growth rules (growth rate and critical size), new phenotypes can be produced. Phenotypic plasticity relies on such modifications to generate variation. Changing critical size without altering growth rates results in new phenotypes that are in the continuity of existing ones. Potential changes are thus limited. In contrast, modifying growth rates can lead to dramatically different phenotypes. Here we explore how growth rules are modified to generate new phenotypes by using one of the most polyphenic animal taxon as a model: ants.

 

In ants, phenotypic plasticity generates two morphologically distinct female castes: workers and queens. The growth rules of these adult phenotypes differ in both growth rates and critical size (when a larva reaches critical size, pupation is triggered) [1]. Queens are generally large, with a complex articulated winged thorax, and a specialized reproductive apparatus, whereas workers are smaller, with a simplified wingless thorax, and a reduced reproductive apparatus ( [2]; Fig. 1). These two castes are adapted for distinct functions, namely independent colony foundation and reproduction for queens, and foraging, brood care, nest building and nest defense for workers.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084929

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An Evidence-Based Combining Classifier for Brain Signal Analysis

An Evidence-Based Combining Classifier for Brain Signal Analysis | Social Foraging | Scoop.it

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084341

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Soap bubbles can predict path of typhoons

Soap bubbles can predict path of typhoons | Social Foraging | Scoop.it

Soap bubbles can be used to predict the strength of hurricanes and typhoons, according to scientists.

 

Physicists from the University of Bordeaux found that the flow of surface liquid around a soap bubble membrane acts in the same way as huge weather systems which form over the earth.

 

A detailed study of the rotation rates of the bubbles vortices - the spinning fluid on the surface - enabled the scientists to obtain a relationship that accurately describes the evolution of their hurricanes and typhoons, and come up with a simple model to predicts their path.

 

Predicting wind intensity or strength in tropical cyclones, typhoons and hurricanes is a key objective in meteorology: the lives of hundreds of thousands of people may depend on it.

 

However, despite recent progress, such forecasts remain difficult since they involve many factors related to the complexity of these giant vortices and their interaction with the environment.

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Beyond Big Data: Identifying Important Information for Real World Challenges

Beyond Big Data: Identifying Important Information for Real World Challenges | Social Foraging | Scoop.it

Much of human inquiry today is focused on collecting massive quantities of data about complex systems, with the underlying assumption that more data leads to more insight into how to solve the challenges facing humanity. However, the questions we wish to address require identifying the impact of interventions on the behavior of a system, and to do this we must know which pieces of information are important and how they fit together. Here we describe why complex systems require different methods than simple systems and provide an overview of the corresponding paradigm shift in physics. We then connect the core ideas of the paradigm shift to information theory and describe how a parallel shift could take place in the study of complex biological and social systems. Finally, we provide a general framework for characterizing the importance of information. Framing scientific inquiry as an effort to objectively determine what is important and unimportant rather than collecting as much information as possible is a means for advancing our understanding and addressing many practical biological and social challenges.

 

Yaneer Bar-Yam and Maya Bialik, Beyond Big Data: Identifying important information for real world challenges

http://www.necsi.edu/projects/yaneer/information/


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A New Method to Measure Consciousness Proposed

A New Method to Measure Consciousness Proposed | Social Foraging | Scoop.it

Leonardo Da Vinci, in his Treatise on Painting (Trattato della Pittura), advises painters to pay particular attention to the motions of the mind, moti mentali. “The movement which is depicted must be appropriate to the mental state of the figure,” he advises; otherwise the figure will be considered twice dead: “dead because it is a depiction, and dead yet again in not exhibiting motion either of the mind or of the body.” Francesco Melzi, student and friend to Da Vinci, compiled the Treatise posthumously from fragmented notes left to him. The vivid portrayal of emotions in the paintings from Leonardo’s school shows that his students learned to read the moti mentali of their subjects in exquisite detail.

 

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Intrinsic Noise Induces Critical Behavior in Leaky Markovian Networks Leading to Avalanching

Intrinsic Noise Induces Critical Behavior in Leaky Markovian Networks Leading to Avalanching | Social Foraging | Scoop.it

The role intrinsic statistical fluctuations play in creating avalanches – patterns of complex bursting activity with scale-free properties – is examined in leaky Markovian networks. Using this broad class of models, we develop a probabilistic approach that employs a potential energy landscape perspective coupled with a macroscopic description based on statistical thermodynamics. We identify six important thermodynamic quantities essential for characterizing system behavior as a function of network size: the internal potential energy, entropy, free potential energy, internal pressure, pressure, and bulk modulus. In agreement with classical phase transitions, these quantities evolve smoothly as a function of the network size until a critical value is reached. At that value, a discontinuity in pressure is observed that leads to a spike in the bulk modulus demarcating loss of thermodynamic robustness. We attribute this novel result to a reallocation of the ground states (global minima) of the system's stationary potential energy landscape caused by a noise-induced deformation of its topographic surface. Further analysis demonstrates that appreciable levels of intrinsic noise can cause avalanching, a complex mode of operation that dominates system dynamics at near-critical or subcritical network sizes. Illustrative examples are provided using an epidemiological model of bacterial infection, where avalanching has not been characterized before, and a previously studied model of computational neuroscience, where avalanching was erroneously attributed to specific neural architectures. The general methods developed here can be used to study the emergence of avalanching (and other complex phenomena) in many biological, physical and man-made interaction networks.

 

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Persistence of social signatures in human communication

We combine cell phone data with survey responses to show that a person’s social signature, as we call the pattern of their interactions with different friends and family members, is remarkably robust. People focus a high proportion of their communication efforts on a small number of individuals, and this behavior persists even when there are changes in the identity of the individuals involved. Although social signatures vary between individuals, a given individual appears to retain a specific social signature over time. Our results are likely to reflect limitations in the ability of humans to maintain many emotionally close relationships, both because of limited time and because the emotional “capital” that individuals can allocate between family members and friends is finite.

 

Persistence of social signatures in human communication

Jari Saramäki, E. A. Leicht, Eduardo López, Sam G. B. Roberts, Felix Reed-Tsochas, and Robin I. M. Dunbar

PNAS

http://dx.doi.org/10.1073/pnas.1308540110


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IEEE Big Data 2014

IEEE Big Data 2014 | Social Foraging | Scoop.it

As cloud computing turning computing and software into commodity services, everything as a service in other words, it leads to not only a technology revolution but also a business revolution. Insights and impacts of various types of services (infrastructure as a service, platform as a service, software as a service, business process as a service) have to be reexamined.
2014 IEEE International Congress on Big Data (BigData 2014) aims to provide an international forum that formally explores various business insights of all kinds of value-added "services." Big Data is a key enabler of exploring business insights and economics of services.
BigData 2014's major topics include but not limited to: Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.

 

3rd International Congress on Big Data
June 27 - July 2, 2014, Anchorage, Alaska, USA

http://www.ieeebigdata.org/2014/


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The Heterogeneous Nature of Number–Space Interactions

It is generally accepted that the mental representation of numerical magnitude consists of a spatial “mental number line” (MNL) with smaller quantities on the left and larger quantities on the right. However, the amount of dissociations between tasks that were believed to tap onto this representational medium is accumulating, questioning the universality of this model. The aim of the present study was to unravel the functional relationship between the different tasks and effects that are typically used as evidence for the MNL. For this purpose, a group of right brain damaged patients (with and without neglect) and healthy controls were subjected to physical line bisection, number interval bisection, parity judgment, and magnitude comparison. Using principal component analysis, different orthogonal components were extracted. We discuss how this component structure captures the dissociations reported in the literature and how it can be considered as a first step toward a new unitary framework for understanding the relation between numbers and space.

 

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Does Sympathy Motivate Prosocial Behaviour in Great Apes?

Does Sympathy Motivate Prosocial Behaviour in Great Apes? | Social Foraging | Scoop.it

Prosocial behaviours such as helping, comforting, or sharing are central to human social life. Because they emerge early in ontogeny, it has been proposed that humans are prosocial by nature and that from early on empathy and sympathy motivate such behaviours. The emerging question is whether humans share these abilities to feel with and for someone with our closest relatives, the great apes. Although several studies demonstrated that great apes help others, little is known about their underlying motivations. This study addresses this issue and investigates whether four species of great apes (Pongo pygmaeus, Gorilla gorilla, Pan troglodytes, Pan paniscus) help a conspecific more after observing the conspecific being harmed (a human experimenter steals the conspecific’s food) compared to a condition where no harming occurred. Results showed that in regard to the occurrence of prosocial behaviours, only orangutans, but not the African great apes, help others when help is needed, contrasting prior findings on chimpanzees. However, with the exception of one population of orangutans that helped significantly more after a conspecific was harmed than when no harm occurred, prosocial behaviour in great apes was not motivated by concern for others.

 

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Two Types of Well Followed Users in the Followership Networks of Twitter

Two Types of Well Followed Users in the Followership Networks of Twitter | Social Foraging | Scoop.it

In the Twitter blogosphere, the number of followers is probably the most basic and succinct quantity for measuring popularity of users. However, the number of followers can be manipulated in various ways; we can even buy follows. Therefore, alternative popularity measures for Twitter users on the basis of, for example, users' tweets and retweets, have been developed. In the present work, we take a purely network approach to this fundamental question. First, we find that two relatively distinct types of users possessing a large number of followers exist, in particular for Japanese, Russian, and Korean users among the seven language groups that we examined. A first type of user follows a small number of other users. A second type of user follows approximately the same number of other users as the number of follows that the user receives. Then, we compare local (i.e., egocentric) followership networks around the two types of users with many followers. We show that the second type, which is presumably uninfluential users despite its large number of followers, is characterized by high link reciprocity, a large number of friends (i.e., those whom a user follows) for the followers, followers' high link reciprocity, large clustering coefficient, large fraction of the second type of users among the followers, and a small PageRank. Our network-based results support that the number of followers used alone is a misleading measure of user's popularity. We propose that the number of friends, which is simple to measure, also helps us to assess the popularity of Twitter users.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084265

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Graph Independent Component Analysis Reveals Repertoires of Intrinsic Network Components in the Human Brain

Graph Independent Component Analysis Reveals Repertoires of Intrinsic Network Components in the Human Brain | Social Foraging | Scoop.it

Does each cognitive task elicit a new cognitive network each time in the brain? Recent data suggest that pre-existing repertoires of a much smaller number of canonical network components are selectively and dynamically used to compute new cognitive tasks. To this end, we propose a novel method (graph-ICA) that seeks to extract these canonical network components from a limited number of resting state spontaneous networks. Graph-ICA decomposes a weighted mixture of source edge-sharing subnetworks with different weighted edges by applying an independent component analysis on cross-sectional brain networks represented as graphs. We evaluated the plausibility in our simulation study and identified 49 intrinsic subnetworks by applying it in the resting state fMRI data. Using the derived subnetwork repertories, we decomposed brain networks during specific tasks including motor activity, working memory exercises, and verb generation, and identified subnetworks associated with performance on these tasks. We also analyzed sex differences in utilization of subnetworks, which was useful in characterizing group networks. These results suggest that this method can effectively be utilized to identify task-specific as well as sex-specific functional subnetworks. Moreover, graph-ICA can provide more direct information on the edge weights among brain regions working together as a network, which cannot be directly obtained through voxel-level spatial ICA.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082873

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Gravity Influences Top-Down Signals in Visual Processing

Gravity Influences Top-Down Signals in Visual Processing | Social Foraging | Scoop.it

Visual perception is not only based on incoming visual signals but also on information about a multimodal reference frame that incorporates vestibulo-proprioceptive input and motor signals. In addition, top-down modulation of visual processing has previously been demonstrated during cognitive operations including selective attention and working memory tasks. In the absence of a stable gravitational reference, the updating of salient stimuli becomes crucial for successful visuo-spatial behavior by humans in weightlessness. Here we found that visually-evoked potentials triggered by the image of a tunnel just prior to an impending 3D movement in a virtual navigation task were altered in weightlessness aboard the International Space Station, while those evoked by a classical 2D-checkerboard were not. Specifically, the analysis of event-related spectral perturbations and inter-trial phase coherency of these EEG signals recorded in the frontal and occipital areas showed that phase-locking of theta-alpha oscillations was suppressed in weightlessness, but only for the 3D tunnel image. Moreover, analysis of the phase of the coherency demonstrated the existence on Earth of a directional flux in the EEG signals from the frontal to the occipital areas mediating a top-down modulation during the presentation of the image of the 3D tunnel. In weightlessness, this fronto-occipital, top-down control was transformed into a diverging flux from the central areas toward the frontal and occipital areas. These results demonstrate that gravity-related sensory inputs modulate primary visual areas depending on the affordances of the visual scene.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082371

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Optimized Strategy for the Control and Prevention of Newly Emerging Influenza Revealed by the Spread Dynamics Model

Optimized Strategy for the Control and Prevention of Newly Emerging Influenza Revealed by the Spread Dynamics Model | Social Foraging | Scoop.it

No matching vaccine is immediately available when a novel influenza strain breaks out. Several nonvaccine-related strategies must be employed to control an influenza epidemic, including antiviral treatment, patient isolation, and immigration detection. This paper presents the development and application of two regional dynamic models of influenza with Pontryagin’s Maximum Principle to determine the optimal control strategies for an epidemic and the corresponding minimum antiviral stockpiles. Antiviral treatment was found to be the most effective measure to control new influenza outbreaks. In the case of inadequate antiviral resources, the preferred approach was the centralized use of antiviral resources in the early stage of the epidemic. Immigration detection was the least cost-effective; however, when used in combination with the other measures, it may play a larger role. The reasonable mix of the three control measures could reduce the number of clinical cases substantially, to achieve the optimal control of new influenza.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084694

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Overlapping Structures in Sensory-Motor Mappings

Overlapping Structures in Sensory-Motor Mappings | Social Foraging | Scoop.it

This paper examines a biologically-inspired representation technique designed for the support of sensory-motor learning in developmental robotics. An interesting feature of the many topographic neural sheets in the brain is that closely packed receptive fields must overlap in order to fully cover a spatial region. This raises interesting scientific questions with engineering implications: e.g. is overlap detrimental? does it have any benefits? This paper examines the effects and properties of overlap between elements arranged in arrays or maps. In particular we investigate how overlap affects the representation and transmission of spatial location information on and between topographic maps. Through a series of experiments we determine the conditions under which overlap offers advantages and identify useful ranges of overlap for building mappings in cognitive robotic systems. Our motivation is to understand the phenomena of overlap in order to provide guidance for application in sensory-motor learning robots.

 

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Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084240

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Content Strategy for an International SEO Campaign

Content Strategy for an International SEO Campaign | Social Foraging | Scoop.it

A content strategy is a critical piece of your marketing efforts. Here's how to take your SEO-friendly content strategy to an international audience by creating a true content resource for visitors in the location you're targeting.


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Wanda J. Barreto's curator insight, January 10, 2014 3:51 PM
Good point! Tags are very important. The same product could have a different name in other countries. Sometimes, the name could be offensive for others.
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A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior

One challenging aspect of the clinical assessment of brain-injured, unresponsive patients is the lack of an objective measure of consciousness that is independent of the subject’s ability to interact with the external environment. Theoretical considerations suggest that consciousness depends on the brain’s ability to support complex activity patterns that are, at once, distributed among interacting cortical areas (integrated) and differentiated in space and time (information-rich). We introduce and test a theory-driven index of the level of consciousness called the perturbational complexity index (PCI). PCI is calculated by (i) perturbing the cortex with transcranial magnetic stimulation (TMS) to engage distributed interactions in the brain (integration) and (ii) compressing the spatiotemporal pattern of these electrocortical responses to measure their algorithmic complexity (information). We test PCI on a large data set of TMS-evoked potentials recorded in healthy subjects during wakefulness, dreaming, nonrapid eye movement sleep, and different levels of sedation induced by anesthetic agents (midazolam, xenon, and propofol), as well as in patients who had emerged from coma (vegetative state, minimally conscious state, and locked-in syndrome). PCI reliably discriminated the level of consciousness in single individuals during wakefulness, sleep, and anesthesia, as well as in patients who had emerged from coma and recovered a minimal level of consciousness. PCI can potentially be used for objective determination of the level of consciousness at the bedside.

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Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks | Social Foraging | Scoop.it

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

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Interference and Shaping in Sensorimotor Adaptations with Rewards

Interference and Shaping in Sensorimotor Adaptations with Rewards | Social Foraging | Scoop.it

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subject's performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules.

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Guided Self-Organization: Inception (Emergence, Complexity and Computation): Mikhail Prokopenko

Is it possible to guide the process of self-organisation towards specific patterns and outcomes? Wouldn’t this be self-contradictory? After all, a self-organising process assumes a transition into a more organised form, or towards a more structured functionality, in the absence of centralised control. Then how can we place the guiding elements so that they do not override rich choices potentially discoverable by an uncontrolled process?
This book presents different approaches to resolving this paradox. In doing so, the presented studies address a broad range of phenomena, ranging from autopoietic systems to morphological computation, and from small-world networks to information cascades in swarms. A large variety of methods is employed, from spontaneous symmetry breaking to information dynamics to evolutionary algorithms, creating a rich spectrum reflecting this emerging field.
Demonstrating several foundational theories and frameworks, as well as innovative practical implementations, Guided Self-Organisation: Inception, will be an invaluable tool for advanced students and researchers in a multiplicity of fields across computer science, physics and biology, including information theory, robotics, dynamical systems, graph theory, artificial life, multi-agent systems, theory of computation and machine learning.


Via Complexity Digest
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António F Fonseca's curator insight, January 7, 2014 2:56 AM

A potpourri about self organization, maybe good ideas for new theories.