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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed

Do Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed | Social Foraging | Scoop.it

Interactions between individuals and the structure of their environment play a crucial role in shaping self-organized collective behaviors. Recent studies have shown that ants crossing asymmetrical bifurcations in a network of galleries tend to follow the branch that deviates the least from their incoming direction. At the collective level, the combination of this tendency and the pheromone-based recruitment results in a greater likelihood of selecting the shortest path between the colony's nest and a food source in a network containing asymmetrical bifurcations. It was not clear however what the origin of this behavioral bias is. Here we propose that it results from a simple interaction between the behavior of the ants and the geometry of the network, and that it does not require the ability to measure the angle of the bifurcation. We tested this hypothesis using groups of ant-like robots whose perceptual and cognitive abilities can be fully specified. We programmed them only to lay down and follow light trails, avoid obstacles and move according to a correlated random walk, but not to use more sophisticated orientation methods. We recorded the behavior of the robots in networks of galleries presenting either only symmetrical bifurcations or a combination of symmetrical and asymmetrical bifurcations. Individual robots displayed the same pattern of branch choice as individual ants when crossing a bifurcation, suggesting that ants do not actually measure the geometry of the bifurcations when travelling along a pheromone trail. Finally at the collective level, the group of robots was more likely to select one of the possible shorter paths between two designated areas when moving in an asymmetrical network, as observed in ants. This study reveals the importance of the shape of trail networks for foraging in ants and emphasizes the underestimated role of the geometrical properties of transportation networks in general.

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How hard is it to 'de-anonymize' cellphone data?

How hard is it to 'de-anonymize' cellphone data? | Social Foraging | Scoop.it
The proliferation of sensor-studded cellphones could lead to a wealth of data with socially useful applications — in urban planning, epidemiology, operations research and emergency preparedness, among other things. Of course, before being released to researchers, the data would have to be stripped of identifying information. But how hard could it be to protect the identity of one unnamed cellphone user in a data set of hundreds of thousands or even millions?
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Something Other Than Adaptation Could Be Driving Evolution

Something Other Than Adaptation Could Be Driving Evolution | Social Foraging | Scoop.it

What explains the incredible variety of life on Earth? It seems obvious. Evolution, of course! But perhaps not the evolution most people grew up with.

 

Some ecologists say the theory needs an update. They’ve proposed a new dynamic driving the emergence of new species, one that doesn’t involve adaptations or survival of the fittest.

 

Give evolution enough time and space, they say, and new species can just happen. Speciation might not only be an evolutionary consequence of fitness differences and natural selection, but a property intrinsic to evolution, just as all matter has gravity.

 

“Our work shows that evolution wants to be diverse,” said Yaneer Bar-Yam, president of the New England Complex Systems Institute. “It’s enough for organisms to be spread out in space and time.”

 

In a March 13 Proceedings of the National Academy of Sciences paper, Bar-Yam and his co-authors, Brazilian ecologists Ayana Martins at the University of Sao Paulo and Marcus Aguiar at the University of Campinas, modeled the evolution of greenish warblers living around the Tibetan plateau.

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So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves

So It Begins: Darpa Sets Out to Make Computers That Can Teach Themselves | Social Foraging | Scoop.it

The Pentagon’s blue-sky research agency is readying a nearly four-year project to boost artificial intelligence systems by building machines that can teach themselves — while making it easier for ordinary schlubs like us to build them, too.

 

When Darpa talks about artificial intelligence, it’s not talking about modeling computers after the human brain. That path fell out of favor among computer scientists years ago as a means of creating artificial intelligence; we’d have to understand our own brains first before building a working artificial version of one. But the agency thinks we can build machines that learn and evolve, using algorithms — “probabilistic programming” — to parse through vast amounts of data and select the best of it. After that, the machine learns to repeat the process and do it better.

 

But building such machines remains really, really hard: The agency calls it “Herculean.” There are scarce development tools, which means “even a team of specially-trained machine learning experts makes only painfully slow progress.” So on April 10, Darpa is inviting scientists to a Virginia conference to brainstorm. What will follow are 46 months of development, along with annual “Summer Schools,” bringing in the scientists together with “potential customers” from the private sector and the government.

 

Called “Probabilistic Programming for Advanced Machine Learning,” or PPAML, scientists will be asked to figure out how to “enable new applications that are impossible to conceive of using today’s technology,” while making experts in the field “radically more effective,” according to a recent agency announcement. At the same time, Darpa wants to make the machines simpler and easier for non-experts to build machine-learning applications too.

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ping yan's curator insight, April 2, 2013 12:22 PM

isn't machine teaching themselves being the very concept of artificial intelligence? 

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'Networked minds' require fundamentally new kind of economics

'Networked minds' require fundamentally new kind of economics | Social Foraging | Scoop.it
In their computer simulations of human evolution, scientists have discovered the emergence of the “homo socialis” with “other-regarding” preferences.

Via Viktor Markowski
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Viktor Markowski's curator insight, March 25, 2013 3:49 PM

Economics has a beautiful body of theory. But does it describe real markets? Doubts have come up not only in the wake of the financial crisis, since financial crashes should not occur according to the then established theories. Since ages, economic theory is based on concepts such as efficient markets and the “homo economicus”, i.e. the assumption of competitively optimizing individuals and firms. It was believed that any behavior deviating from this would create disadvantages and, hence, be eliminated by natural selection. But experimental evidence from behavioral economics show that, on average, people behave more fairness-oriented and other-regarding than expected. A new theory by scientists from ETH Zurich now explains why. 

luiy's curator insight, March 25, 2013 5:33 PM

Evolution of “friendliness”


Prof. Dirk Helbing of ETH Zurich, who coordinated the study, adds: “Compared to conventional models for the evolution of social cooperation, we have distinguished between the actual behavior – cooperation or not – and an inherited character trait, describing the degree of other-regarding preferences, which we call the friendliness.” The actual behavior considers not only the own advantage (“payoff”), but also gives a weight to the payoff of the interaction partners depending on the individual friendliness. For the “homo economicus”, the weight is zero. The friendliness spreads from one generation to the next according to natural selection. This is merely based on the own payoff, but mutations happen.

For most parameter combinations, the model predicts the evolution of a payoff-maximizing “homo economicus” with selfish preferences, as assumed by a great share of the economic literature. Very surprisingly, however, biological selection may create a “homo socialis” with other-regarding preferences, namely if offsprings tend to stay close to their parents. In such a case, clusters of friendly people, who are “conditionally cooperative”, may evolve over time.

If an unconditionally cooperative individual is born by chance, it may be exploited by everyone and not leave any offspring. However, if born in a favorable, conditionally cooperative environment, it may trigger cascade-like transitions to cooperative behavior, such that other-regarding behavior pays off. Consequently, a “homo socialis” spreads.

 

 

Networked minds create a cooperative human species


“This has fundamental implications for the way, economic theories should look like,” underlines Professor Helbing. Most of today’s economic knowledge is for the “homo economicus”, but people wonder whether that theory really applies. A comparable body of work for the “homo socialis” still needs to be written.

While the “homo economicus” optimizes its utility independently, the “homo socialis” puts himself or herself into the shoes of others to consider their interests as well,” explains Grund, and Helbing adds: “This establishes something like “networked minds”. Everyone’s decisions depend on the preferences of others.” This becomes even more important in our networked world.

 

 

A participatory kind of economy


How will this change our economy? Today, many customers doubt that they get the best service by people who are driven by their own profits and bonuses. “Our theory predicts that the level of other-regarding preferences is distributed broadly, from selfish to altruistic. Academic education in economics has largely promoted the selfish type. Perhaps, our economic thinking needs to fundamentally change, and our economy should be run by different kinds of people,” suggests Grund. “The true capitalist has other-regarding preferences,” adds Helbing, “as the “homo socialis” earns much more payoff.” This is, because the “homo socialis” manages to overcome the downwards spiral that tends to drive the “homo economicus” towards tragedies of the commons. The breakdown of trust and cooperation in the financial markets back in 2008 might be seen as good example.

“Social media will promote a new kind of participatory economy, in which competition goes hand in hand with cooperation,” believes Helbing. Indeed, the digital economy’s paradigm of the “prosumer” states that the Internet, social platforms, 3D printers and other developments will enable the co-producing consumer. “It will be hard to tell who is consumer and who is producer”, says Christian Waloszek. “You might be both at the same time, and this creates a much more cooperative perspective.”

Onearth's curator insight, March 26, 2013 4:58 AM

After homo sapiens sapiens it's time for homo sapiens socialis

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Bacteria to become 'bio-batteries': Bacteria power 'bio-battery' breakthrough

Bacteria to become 'bio-batteries': Bacteria power 'bio-battery' breakthrough | Social Foraging | Scoop.it
Bacteria could soon be acting as microscopic "bio-batteries" thanks to a joint UK-US research effort.

 

The team of scientists has laid bare the power-generating mechanism used by well-known marine bacteria.

 

Before now it was not clear whether the bacteria generated an electrical charge themselves or used something else to do it.

 

Unpicking the process opens the door to using the bacteria as an in-situ robust power source.

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I Should but I Won’t: Why Young Children Endorse Norms of Fair Sharing but Do Not Follow Them

I Should but I Won’t: Why Young Children Endorse Norms of Fair Sharing but Do Not Follow Them | Social Foraging | Scoop.it

Young children endorse fairness norms related to sharing, but often act in contradiction to those norms when given a chance to share. This phenomenon has rarely been explored in the context of a single study. Using a novel approach, the research presented here offers clear evidence of this discrepancy and goes on to examine possible explanations for its diminution with age. In Study 1, 3–8-year-old children readily stated that they themselves should share equally, asserted that others should as well, and predicted that others had shared equally with them. Nevertheless, children failed to engage in equal sharing until ages 7–8. In Study 2, 7–8-year-olds correctly predicted that they would share equally, and 3–6-year-olds correctly predicted that they would favor themselves, ruling out a failure-of-willpower explanation for younger children's behavior. Similarly, a test of inhibitory control in Study 1 also failed to explain the shift with age toward adherence to the endorsed norm. The data suggest that, although 3-year-olds know the norm of equal sharing, the weight that children attach to this norm increases with age when sharing involves a cost to the self.

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The Co-Evolution of Fairness Preferences and Costly Punishment

The Co-Evolution of Fairness Preferences and Costly Punishment | Social Foraging | Scoop.it

We study the co-evolutionary emergence of fairness preferences in the form of other-regarding behavior and its effect on the origination of costly punishment behavior in public good games. Our approach closely combines empirical results from three experiments with an evolutionary simulation model. In this way, we try to fill a gap between the evolutionary theoretical literature on cooperation and punishment on the one hand and the empirical findings from experimental economics on the other hand. As a principal result, we show that the evolution among interacting agents inevitably favors a sense for fairness in the form of “disadvantageous inequity aversion”. The evolutionary dominance and stability of disadvantageous inequity aversion is demonstrated by enabling agents to co-evolve with different self- and other-regarding preferences in a competitive environment with limited resources. Disadvantageous inequity aversion leads to the emergence of costly (“altruistic”) punishment behavior and quantitatively explains the level of punishment observed in contemporary lab experiments performed on subjects with a western culture. Our findings corroborate, complement, and interlink the experimental and theoretical literature that has shown the importance of other-regarding behavior in various decision settings.

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A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages.

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A Computational Model for Collective Cellular Motion in Three Dimensions: General Framework and Case Study for Cell Pair Dynamics

A Computational Model for Collective Cellular Motion in Three Dimensions: General Framework and Case Study for Cell Pair Dynamics | Social Foraging | Scoop.it

Cell migration in healthy and diseased systems is a combination of single and collective cell motion. While single cell motion has received considerable attention, our understanding of collective cell motion remains elusive. A new computational framework for the migration of groups of cells in three dimensions is presented, which focuses on the forces acting at the microscopic scale and the interactions between cells and their extracellular matrix (ECM) environment. Cell-cell adhesion, resistance due to the ECM and the factors regulating the propulsion of each cell through the matrix are considered. In particular, our approach emphasizes the role of receptors that mediate cell-cell and cell-matrix interactions, and examines how variation in their properties induces changes in cellular motion. As an important case study, we analyze two interacting cells. Our results show that the dynamics of cell pairs depends on the magnitude and the stochastic nature of the forces. Stronger intercellular stability is generally promoted by surface receptors that move. We also demonstrate that matrix resistance, cellular stiffness and intensity of adhesion contribute to migration behaviors in different ways, with memory effects present that can alter pair motility. If adhesion weakens with time, our findings show that cell pair break-up depends strongly on the way cells interact with the matrix. Finally, the motility for cells in a larger cluster (size 50 cells) is examined to illustrate the full capabilities of the model and to stress the role of cellular pairs in complex cellular structures. Overall, our framework shows how properties of cells and their environment influence the stability and motility of cellular assemblies. This is an important step in the advancement of the understanding of collective motility, and can contribute to knowledge of complex biological processes involving migration, aggregation and detachment of cells in healthy and diseased systems.

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Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement | Social Foraging | Scoop.it

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge.

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Attractor Metabolic Networks

Attractor Metabolic Networks | Social Foraging | Scoop.it

The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.

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Long-term evolutionary outcomes are constrained, surprisingly predictable

Long-term evolutionary outcomes are constrained, surprisingly predictable | Social Foraging | Scoop.it

A new study co-authored by SFI External Professor and Science Board co-chair Marcus Feldman demonstrates that not only is long-term evolution possible, but long-term evolutionary outcomes can be surprisingly predictable.

 

In recent years, some scientists have argued that natural selection occurs not just at the individual organism level, but also between lineages over the course of many generations.

 

The Stanford group set up a computer simulation in which 128 lineages of proteins continuously folded into new shapes, competing to bind with other molecules, called ligands, in each new configuration. The better each protein could attach itself to the ligands, the more ligands it would scoop up, and the higher its fitness – that is, its average number of "offspring" – would be. The simulation was run for 10,000 generations.

 

Although the chaos of 128 lineages – a total of more than 16,000 individual proteins – mutating over thousands of generations might seem unpredictable, and that it would be nearly impossible for the same thing to happen twice, it's actually the opposite, the researchers say.

 

"Even though things look complicated, the possible evolutionary trajectories are quite constrained," said lead author Michael Palmer, a computational biologist at Stanford. "There are only a few viable mutations at any point, which makes the dynamics predictable and repeatable, even over the long term."

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Making sense of big data

Making sense of big data | Social Foraging | Scoop.it

When Jameson Toole explores the streets of Boston and Cambridge, he sees problems waiting to be solved — and he believes some solutions are already in our pockets.

“Mobile phones are amazing data sensors,” says Toole, a native of Saratoga Springs, N.Y., and a second-year PhD student in the Engineering Systems Division at MIT. “Every time you use your cellphone, there is a little breadcrumb that’s stored that can be used in a lot of different ways to help improve human lives.”

Together with his adviser, Marta Gonzalez, the Gilbert W. Winslow Career Development Assistant Professor of Civil and Environmental Engineering, Toole is an advocate of big data: Specifically, he aims to harness the stockpile of information collected by our electronic devices. Whether it’s via an app or simply the data a phone company collects about its customers, Toole says, information stored in cellphones can be valuable in urban planning.

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Colin Camerer: Neuroscience, game theory, monkeys

When two people are trying to make a deal -- whether they’re competing or cooperating -- what’s really going on inside their brains? Behavioral economist Colin Camerer shows research that reveals just how little we’re able to predict what others are thinking. And he presents an unexpected study that shows chimpanzees might just be better at it than we are. (Filmed at TEDxCalTech.)

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Evolution and stability of ring species

Neutral models, in which genetic change arises through random variation without fitness differences, have proven remarkably successful in describing observed patterns of biodiversity, despite the manifest role of selection in evolution. Here we investigate the effect of barriers on biodiversity by simulating the expansion of a population around a barrier to form a ring species, in which the two ends of the population are reproductively isolated despite ongoing gene flow around the ring. We compare the spatial and genetic properties of a neutral agent-based population model to the greenish warblers’ complex, a well-documented example of an actual ring species in nature. Our results match the distribution of subspecies, the principal components of genetic diversity, and the linear spatial–genetic correlation of the observed data, even though selection is expected to be important for traits of this species. We find that ring species are often unstable to speciation or mixing but can persist for extended times depending on species and landscape features. For the greenish warblers, our analysis implies that the expanded area near the point of secondary contact is important for extending the duration of the ring, and thus, for the opportunity to observe this ring species. Nevertheless it also suggests the ring will break up into multiple species in 10,000 to 50,000 y. These results imply that simulations can be used to accurately describe empirical data for complex spatial–genetic traits of an individual species.

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Unlocking the Value of Personal Data: From Collection to Usage

Our world is changing. It is complex, hyperconnected, and increasingly driven by insights derived from big data.1 And the rate of change shows no sign of slowing. Nor does the volume of data show any sign of shrinking. But, the economic and social value of big data does not come just from its quantity. It also comes from its quality – the ways in which individual bits of data can be interconnected to reveal new insights with the potential to transform business and society. Fully tapping that potential holds much promise, and much risk. By themselves, technology and data are neutral. It is their use that can both generate great value and create significant harm, sometimes simultaneously. This requires a rethink of traditional approaches to data governance, particularly a shift from focusing away from trying to control the data itself to focusing on the uses of data. It is up to the individuals and institutions of various societies to govern and decide how to unlock the value – both economic and social – and ensure suitable protections.


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Artifacts Shed Light on Social Networks of the Past

Artifacts Shed Light on Social Networks of the Past | Social Foraging | Scoop.it

The advent of social networking sites like Facebook and Twitter have made us all more connected, but long-distance social networks existed long before the Internet.

 

An article published this week in the Proceedings of the National Academy of Sciences sheds light on the transformation of social networks in the late pre-Hispanic American Southwest and shows that people of that period were able to maintain surprisingly long-distance relationships with nothing more than their feet to connect them.

 

Led by University of Arizona anthropologist Barbara Mills, the study is based on analysis of more than 800,000 painted ceramic and more than 4,800 obsidian artifacts dating from A.D. 1200-1450, uncovered from more than 700 sites in the western Southwest, in what is now Arizona and western New Mexico.

 

With funding from the National Science Foundation, Mills, director of the UA School of Anthropology, worked with collaborators at Archeology Southwest in Tucson to compile a database of more than 4.3 million ceramic artifacts and more than 4,800 obsidian artifacts, from which they drew for the study.

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Banks: From Bubbles & Nuclear Winters To Golden Eras

Banks: From Bubbles & Nuclear Winters To Golden Eras | Social Foraging | Scoop.it

On November 15, 1971, an advertisement appeared in Electronic News in Santa Clara, California for a new electronic device. It was called the 4004. It was the first commercially available microprocessor that could make calculations on a silicon chip. It cost sixty dollars.

 

Although practically no one realized it at the time, this was “the big bang of a new universe of all-pervasive computing and digital communications”. The chips were powerful and cheap. They opened innumerable technological and business possibilities. They would transform the way people lived and worked around the world.

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The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks | Social Foraging | Scoop.it

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today's PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.

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A “Bat” Is Easier to Learn than a “Tab”: Effects of Relative Phonotactic Frequency on Infant Word Learning

A “Bat” Is Easier to Learn than a “Tab”: Effects of Relative Phonotactic Frequency on Infant Word Learning | Social Foraging | Scoop.it

Many studies have shown that during the first year of life infants start learning the prosodic, phonetic and phonotactic properties of their native language. In parallel, infants start associating sound sequences with semantic representations. However, the question of how these two processes interact remains largely unknown. The current study explores whether (and when) the relative phonotactic probability of a sound sequence in the native language has an impact on infants’ word learning. We exploit the fact that Labial-Coronal (LC) words are more frequent than Coronal-Labial (CL) words in French, and that French-learning infants prefer LC over CL sequences at 10 months of age, to explore the possibility that LC structures might be learned more easily and thus at an earlier age than CL structures. Eye movements of French-learning 14- and 16-month-olds were recorded while they watched animated cartoons in a word learning task. The experiment involved four trials testing LC sequences and four trials testing CL sequences. Our data reveal that 16-month-olds were able to learn the LC and CL words, while14-month-olds were only able to learn the LC words, which are the words with the more frequent phonotactic pattern. The present results provide evidence that infants’ knowledge of their native language phonotactic patterns influences their word learning: Words with a frequent phonotactic structure could be acquired at an earlier age than those with a lower probability. Developmental changes are discussed and integrated with previous findings.

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Limiting Factors for Mapping Corpus-Based Semantic Representations to Brain Activity

Limiting Factors for Mapping Corpus-Based Semantic Representations to Brain Activity | Social Foraging | Scoop.it

To help understand how semantic information is represented in the human brain, a number of previous studies have explored how a linear mapping from corpus derived semantic representations to corresponding patterns of fMRI brain activations can be learned. They have demonstrated that such a mapping for concrete nouns is able to predict brain activations with accuracy levels significantly above chance, but the more recent elaborations have achieved relatively little performance improvement over the original study. In fact, the absolute accuracies of all these models are still currently rather limited, and it is not clear which aspects of the approach need improving in order to achieve performance levels that might lead to better accounts of human capabilities. This paper presents a systematic series of computational experiments designed to identify the limiting factors of the approach. Two distinct series of artificial brain activation vectors with varying levels of noise are introduced to characterize how the brain activation data restricts performance, and improved corpus based semantic vectors are developed to determine how the word set and model inputs affect the results. These experiments lead to the conclusion that the current state-of-the-art input semantic representations are already operating nearly perfectly (at least for non-ambiguous concrete nouns), and that it is primarily the quality of the fMRI data that is limiting what can be achieved with this approach. The results allow the study to end with empirically informed suggestions about the best directions for future research in this area.

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Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model | Social Foraging | Scoop.it

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.

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Drawing from Memory: Hand-Eye Coordination at Multiple Scales

Drawing from Memory: Hand-Eye Coordination at Multiple Scales | Social Foraging | Scoop.it

Eyes move to gather visual information for the purpose of guiding behavior. This guidance takes the form of perceptual-motor interactions on short timescales for behaviors like locomotion and hand-eye coordination. More complex behaviors require perceptual-motor interactions on longer timescales mediated by memory, such as navigation, or designing and building artifacts. In the present study, the task of sketching images of natural scenes from memory was used to examine and compare perceptual-motor interactions on shorter and longer timescales. Eye and pen trajectories were found to be coordinated in time on shorter timescales during drawing, and also on longer timescales spanning study and drawing periods. The latter type of coordination was found by developing a purely spatial analysis that yielded measures of similarity between images, eye trajectories, and pen trajectories. These results challenge the notion that coordination only unfolds on short timescales. Rather, the task of drawing from memory evokes perceptual-motor encodings of visual images that preserve coarse-grained spatial information over relatively long timescales as well.

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Here Today, Gone Tomorrow – Adaptation to Change in Memory-Guided Visual Search

Here Today, Gone Tomorrow – Adaptation to Change in Memory-Guided Visual Search | Social Foraging | Scoop.it

Visual search for a target object can be facilitated by the repeated presentation of an invariant configuration of nontargets (‘contextual cueing’). Here, we tested adaptation of learned contextual associations after a sudden, but permanent, relocation of the target. After an initial learning phase targets were relocated within their invariant contexts and repeatedly presented at new locations, before they returned to the initial locations. Contextual cueing for relocated targets was neither observed after numerous presentations nor after insertion of an overnight break. Further experiments investigated whether learning of additional, previously unseen context-target configurations is comparable to adaptation of existing contextual associations to change. In contrast to the lack of adaptation to changed target locations, contextual cueing developed for additional invariant configurations under identical training conditions. Moreover, across all experiments, presenting relocated targets or additional contexts did not interfere with contextual cueing of initially learned invariant configurations. Overall, the adaptation of contextual memory to changed target locations was severely constrained and unsuccessful in comparison to learning of an additional set of contexts, which suggests that contextual cueing facilitates search for only one repeated target location.

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