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What's Your Social Media Genotype?

What's Your Social Media Genotype? | Social Foraging | Scoop.it
Your pattern of behaviour on Twitter can be defined by a simple “genotype” and used to predict your future behaviour, say network researchers

 

One of the curious features of human behaviour is that it is predictable in certain circumstances but not in others. Knowing the difference is a fantastically valuable skill.

 

That’s why social media researchers the world over are scrutinising social networks for clues they can use to predict people’s behaviour on scales that have never before been achieved. On this blog, we’ve looked at various attempts to show social media can be used to predict, with varying degrees of success, people’s buying habits, movie tastes and even their future stock market purchases.

 

Today, Petko Bogdanov at the University of California Santa Barbara and a few pals take a new, genetically-inspired approach to this task. They say every person has a fixed set of interests, called their social media genotype, which determines their pattern of behaviour on networks such as Twitter. What’s more, they say that these genotypes have been discovered, can be used to predict an individual’s future behaviour.

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Certain 'memories' pass between generations

Certain 'memories' pass between generations | Social Foraging | Scoop.it

Behavior can be affected by events in previous generations which have been passed on through a form of genetic memory, animal studies suggest. Experiments showed that a traumatic event could affect the DNA in sperm and alter the brains and behaviour of subsequent generations.

 

A Nature Neuroscience study shows mice trained to avoid a smell passed their aversion on to their "grandchildren". Experts said the results were important for phobia and anxiety research. The animals were trained to fear a smell similar to cherry blossom. The team at the Emory University School of Medicine, in the US, then looked at what was happening inside the sperm.


They showed a section of DNA responsible for sensitivity to the cherry blossom scent was made more active in the mice's sperm. Both the mice's offspring, and their offspring, were "extremely sensitive" to cherry blossom and would avoid the scent, despite never having experienced it in their lives. Changes in brain structure were also found.


"The experiences of a parent, even before conceiving, markedly influence both structure and function in the nervous system of subsequent generations," the report concluded. The findings provide evidence of "trans-generational epigenetic inheritance" - that the environment can affect an individual's genetics, which can in turn be passed on.


 

Prof Marcus Pembrey, from University College London, said the findings were "highly relevant to phobias, anxiety and post-traumatic stress disorders" and provided "compelling evidence" that a form of memory could be passed between generations. He commented: "It is high time public health researchers took human trans-generational responses seriously. "I suspect we will not understand the rise in neuropsychiatric disorders or obesity, diabetes and metabolic disruptions generally without taking a multigenerational approach."


Via Dr. Stefan Gruenwald
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Temporal network sparsity and the slowing down of spreading

Interactions in time-varying complex systems are often very heterogeneous at the topological level (who interacts with whom) and at the temporal level (when interactions occur and how often). While it is known that temporal heterogeneities often have strong effects on dynamical processes, e.g. the burstiness of contact sequences is associated with slower spreading dynamics, the picture is far from complete. In this paper, we show that temporal heterogeneities result in temporal sparsity} at the time scale of average inter-event times, and that temporal sparsity determines the amount of slowdown of Susceptible-Infectious (SI) spreading dynamics on temporal networks. This result is based on the analysis of several empirical temporal network data sets. An approximate solution for a simple network model confirms the association between temporal sparsity and slowdown of SI spreading dynamics. Since deterministic SI spreading always follows the fastest temporal paths, our results generalize -- paths are slower to traverse because of temporal sparsity, and therefore all dynamical processes are slower as well.

 

Temporal network sparsity and the slowing down of spreading
Juan Ignacio Perotti, Hang-Hyun Jo, Petter Holme, Jari Saramäki

http://arxiv.org/abs/1411.5553


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From participatory sense-making to language: there and back again

The enactive approach to cognition distinctively emphasizes autonomy, adaptivity, agency, meaning, experience, and interaction. Taken together, these principles can provide the new sciences of language with a comprehensive philosophical framework: languaging as adaptive social sense-making. This is a refinement and advancement on Maturana’s idea of languaging as a manner of living. Overcoming limitations in Maturana’s initial formulation of languaging is one of three motivations for this paper. Another is to give a response to skeptics who challenge enactivism to connect “lower-level” sense-making with “higher-order” sophisticated moves like those commonly ascribed to language. Our primary goal is to contribute a positive story developed from the enactive account of social cognition, participatory sense-making. This concept is put into play in two different philosophical models, which respectively chronicle the logical and ontogenetic development of languaging as a particular form of social agency. Languaging emerges from the interplay of coordination and exploration inherent in the primordial tensions of participatory sense-making between individual and interactive norms; it is a practice that transcends the self-other boundary and enables agents to regulate self and other as well as interaction couplings. Linguistic sense-makers are those who negotiate interactive and internalized ways of meta-regulating the moment-to-moment activities of living and cognizing. Sense-makers in enlanguaged environments incorporate sensitivities, roles, and powers into their unique yet intelligible linguistic bodies. We dissolve the problematic dichotomies of high/low, online/offline, and linguistic/nonlinguistic cognition, and we provide new boundary criteria for specifying languaging as a prevalent kind of human social sense-making.

 

Cuffari, E. Di Paolo, E., De Jaegher, H. (2014) From participatory sense-making to language: There and back again, Phenomenology and the Cognitive Sciences, http://dx.doi.org/10.1007/s11097-014-9404-9


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Artificial intelligence is now creating its own magic tricks

Artificial intelligence is now creating its own magic tricks | Social Foraging | Scoop.it
You might not have to be a professional magician to come up with clever tricks in the near future. Researchers at Queen Mary University of London have developed artificial intelligence that can create magic tricks (specifically, those based on math) all on its own. Once their program learns the basics of creating magic jigsaws and "mind reading" stunts, it can generate many variants of these tricks by itself. This could be particularly handy if you like to impress your friends on a regular basis -- you could show them a new card trick every time without having to do much work.

The best part? You can try some of these computer-generated tricks yourself. The 12 Magicians of Osiris magic jigsaw is available as a web pack, and you can download the Android component for one card trick, Phoney, from Google Play. Neither will give you as much satisfaction as developing tricks from scratch, but they're proof that computers can do more with math than solve equations.
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Rethinking natural altruism: Simple reciprocal interactions trigger children’s benevolence

Rethinking natural altruism: Simple reciprocal interactions trigger children’s benevolence | Social Foraging | Scoop.it
A very simple reciprocal activity elicited high degrees of altruism in 1- and 2-y-old children, whereas friendly but nonreciprocal activity yielded little subsequent altruism. In a second study, reciprocity with one adult led 1- and 2-y-olds to provide help to a new person. These results question the current dominant claim that social experiences cannot account for early occurring altruistic behavior. A third study, with preschool-age children, showed that subtle reciprocal cues remain potent elicitors of altruism, whereas a fourth study with preschoolers showed that even a brief reciprocal experience fostered children’s expectation of altruism from others. Collectively, the studies suggest that simple reciprocal interactions are a potent trigger of altruism for young children, and that these interactions lead children to believe that their relationships are characterized by mutual care and commitment.
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Static network structure can stabilize human cooperation

Static network structure can stabilize human cooperation | Social Foraging | Scoop.it
The evolution of cooperation in network-structured populations has been a major focus of theoretical work in recent years. When players are embedded in fixed networks, cooperators are more likely to interact with, and benefit from, other cooperators. In theory, this clustering can foster cooperation on fixed networks under certain circumstances. Laboratory experiments with humans, however, have thus far found no evidence that fixed network structure actually promotes cooperation. Here, we provide such evidence and help to explain why others failed to find it. First, we show that static networks can lead to a stable high level of cooperation, outperforming well-mixed populations. We then systematically vary the benefit that cooperating provides to one’s neighbors relative to the cost required to cooperate (b/c), as well as the average number of neighbors in the network (k). When b/c > k, we observe high and stable levels of cooperation. Conversely, when b/c ≤ k or players are randomly shuffled, cooperation decays. Our results are consistent with a quantitative evolutionary game theoretic prediction for when cooperation should succeed on networks and, for the first time to our knowledge, provide an experimental demonstration of the power of static network structure for stabilizing human cooperation.
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Lost in the City: Revisiting Milgram's Experiment in the Age of Social Networks

Lost in the City: Revisiting Milgram's Experiment in the Age of Social Networks | Social Foraging | Scoop.it

As more and more users access social network services from smart devices with GPS receivers, the available amount of geo-tagged information makes repeating classical experiments possible on global scales and with unprecedented precision. Inspired by the original experiments of Milgram, we simulated message routing within a representative sub-graph of the network of Twitter users with about 6 million geo-located nodes and 122 million edges. We picked pairs of users from two distant metropolitan areas and tried to find a route between them using local geographic information only; our method was to forward messages to a friend living closest to the target. We found that the examined network is navigable on large scales, but navigability breaks down at the city scale and the network becomes unnavigable on intra-city distances. This means that messages usually arrived to the close proximity of the target in only 3–6 steps, but only in about 20% of the cases was it possible to find a route all the way to the recipient, in spite of the network being connected.

 

Szüle J, Kondor D, Dobos L, Csabai I, Vattay G (2014) Lost in the City: Revisiting Milgram's Experiment in the Age of Social Networks. PLoS ONE 9(11): e111973. http://dx.doi.org/10.1371/journal.pone.0111973


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Hyper-Brain Networks Support Romantic Kissing in Humans

Hyper-Brain Networks Support Romantic Kissing in Humans | Social Foraging | Scoop.it
Coordinated social interaction is associated with, and presumably dependent on, oscillatory couplings within and between brains, which, in turn, consist of an interplay across different frequencies. Here, we introduce a method of network construction based on the cross-frequency coupling (CFC) and examine whether coordinated social interaction is associated with CFC within and between brains. Specifically, we compare the electroencephalograms (EEG) of 15 heterosexual couples during romantic kissing to kissing one’s own hand, and to kissing one another while performing silent arithmetic. Using graph-theory methods, we identify theta–alpha hyper-brain networks, with alpha serving a cleaving or pacemaker function. Network strengths were higher and characteristic path lengths shorter when individuals were kissing each other than when they were kissing their own hand. In both partner-oriented kissing conditions, greater strength and shorter path length for 5-Hz oscillation nodes correlated reliably with greater partner-oriented kissing satisfaction. This correlation was especially strong for inter-brain connections in both partner-oriented kissing conditions but not during kissing one’s own hand. Kissing quality assessed after the kissing with silent arithmetic correlated reliably with intra-brain strength of 10-Hz oscillation nodes during both romantic kissing and kissing with silent arithmetic. We conclude that hyper-brain networks based on CFC may capture neural mechanisms that support interpersonally coordinated voluntary action and bonding behavior.

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Inner Workings: Breaking down bees' dances

Inner Workings: Breaking down bees' dances | Social Foraging | Scoop.it
Worker bees in the United Kingdom have taken a new job—collecting data for researchers at the University of Sussex. Margaret Couvillon and her colleagues have devised an environmental monitoring system that uses foraging honey bees and their “waggle” dances to measure land quality. The technique could help scientists assess the ecological effects of different land use schemes, they say.
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An evolutionary, ecosystem view of economies

An evolutionary, ecosystem view of economies | Social Foraging | Scoop.it
To understand market perturbations like crashes and bubbles, SFI Distinguished Professor Geoffrey West and three co-authors advocate a revised view that treats an economy like biologists would think about an ecosystem rife with evolutionary dynamics.
"Here, we emphasize the importance of an ecosystems perspective: it is precisely due to the web of interdependencies among all companies that the unrestrained growth of one, or a few, companies leads to systematic imbalance."
The growth of such imbalances, they say, is a result of evolutionary processes often leading to feedback loops. Drawing on their recent paper in Proceedings of the Royal Society A, for example, the authors suggest that two mechanisms "act as catalysts for the emergence of a crisis. The first is banks copying the business models of the most (short-term) successful bank, which leads to loss of both diversity and resilience. The second is investors such as fund managers increasing their appetite for risk by trying to outperform competitors."
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Twitter open sourced a recommendation algorithm for massive datasets

Twitter open sourced a recommendation algorithm for massive datasets | Social Foraging | Scoop.it
Twitter recently open sourced an algorithm designed to ease the process of running recommendation engines at large scale. Called DIMSUM, the algorithm pre-processes pairs of possible matches so the other algorithms in the process don’t waste resources on poor choices.
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Humans have innate grasp of probability - Study of Indigenous Maya

Humans have innate grasp of probability - Study of Indigenous Maya | Social Foraging | Scoop.it
People overrate the chances of dying in a plane crash and guess incorrectly at the odds that a coin toss will yield 'heads' after a string of several 'tails'. Yet humans have an innate sense of chance, a study of indigenous Maya people suggests. Adults in Guatemala who have never learned a formal number system or a written language did as well as formally educated adults and children at estimating the probability of chance events1, the researchers found.
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A typology of street patterns

A typology of street patterns | Social Foraging | Scoop.it
We propose a quantitative method to classify cities according to their street pattern. We use the conditional probability distribution of shape factor of blocks with a given area and define what could constitute the ‘fingerprint’ of a city. Using a simple hierarchical clustering method, these fingerprints can then serve as a basis for a typology of cities. We apply this method to a set of 131 cities in the world, and at an intermediate level of the dendrogram, we observe four large families of cities characterized by different abundances of blocks of a certain area and shape. At a lower level of the classification, we find that most European cities and American cities in our sample fall in their own sub-category, highlighting quantitatively the differences between the typical layouts of cities in both regions. We also show with the example of New York and its different boroughs, that the fingerprint of a city can be seen as the sum of the ones characterizing the different neighbourhoods inside a city. This method provides a quantitative comparison of urban street patterns, which could be helpful for a better understanding of the causes and mechanisms behind their distinct shapes.
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Babies' brains adjust to listening to a language, even if they never learn it.

Babies' brains adjust to listening to a language, even if they never learn it. | Social Foraging | Scoop.it

Our brains start soaking in details from the languages around us from the moment we can hear them. One of the first things infants learn of their native languages is the system of consonants and vowels, as well as other speech sound characteristics, like pitch. In the first year of life, a baby’s ear tunes in to the particular set of sounds being spoken in its environment, and the brain starts developing the ability to tell subtle differences among them—a foundation that will make a difference in meaning down the line, allowing the child to learn words and grammar.


But what happens if that child gets shifted into a different culture after laying the foundations of its first native language? Does it forget everything about that first language, or are there some remnants that remain buried in the brain?


According to a recent PNAS paper, the effects of very early language learning are permanently etched into the brain, even if input from that language stops and it’s replaced by another language. To identify this lasting influence, the researchers used functional magnetic resonance imaging (fMRI) scans on children who had been adopted to see what neural patterns could be identified years after adoption.


Because not all linguistic features have easily identifiable effects on the brain, the researchers decided to focus on lexical tone. This is a feature found in some languages that allows a single arrangement of consonants and vowels to have different meanings that are distinguished by a change in pitch. For example, in Mandarin Chinese, the word “ma” with a rising tone means “hemp”—the same syllable with a falling tone means “scold.”


People who speak tone languages have differences in brain activity in a certain region of the brain’s left hemisphere. This region activates in response to pitch differences that are used to convey a difference in linguistic meaning; non-linguistic pitch is processed in the right hemisphere. Tone information is learned very early in life: infants learning Chinese languages (including Mandarin and Cantonese) show signs of recognizing tonal contrasts as early as four months.


Via Dr. Stefan Gruenwald
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Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data

With the aim to contribute to humanitarian response to disasters and violent events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic and discrete changes in behavior, measured with mobile phone data, will indicate extreme events. In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive database of emergency and non-emergency events in Rwanda. Our methodology successfully captures anomalous behavioral patterns associated with a broad range of events, from religious and official holidays to earthquakes, floods, violence against civilians and protests. Our results suggest that human behavioral responses to extreme events are complex and multi-dimensional, including extreme increases and decreases in both calling and movement behaviors. We also find significant temporal and spatial variance in responses to extreme events. Our behavioral anomaly detection system and extensive discussion of results are a significant contribution to the long-term project of creating an effective real-time event detection system with mobile phone data and we discuss the implications of our findings for future research to this end. 

 

Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data
Adrian Dobra, Nathalie E. Williams, Nathan Eagle

http://arxiv.org/abs/1411.6179


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Summit Europe: Artificial Intelligence Evolving From Disappointing to Disruptive

Summit Europe: Artificial Intelligence Evolving From Disappointing to Disruptive | Social Foraging | Scoop.it
Neil Jacobstein, Singularity University’s co-chair in AI and Robotics, has been thinking about artificial intelligence for a long time, and at a recent talk at Summit Europe, he wanted to get a few things straight. There’s AI, and then there’s AI.

Elon Musk recently tweeted this about Nick Bostrom’s book, Superintelligence: “We need to be super careful with AI. Potentially more dangerous than nukes.”

AI has long been a slippery term, its definition in near-constant flux. Ray Kurzweil has said AI is used to describe human capabilities just out of reach for computers—but when they master these skills, like playing chess, we no longer call it AI.

These days we use the term to describe machine learning algorithms, computer programs that autonomously learn by interacting with large sets of data. But we also use it to describe the theoretical superintelligent computers of the future.

According to Jacobstein, the former are already proving hugely useful in a range of fields—and aren’t necessarily dangerous—and the latter are still firmly out of reach.
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Cooperation and control in multiplayer social dilemmas

Cooperation and control in multiplayer social dilemmas | Social Foraging | Scoop.it
Direct reciprocity and conditional cooperation are important mechanisms to prevent free riding in social dilemmas. However, in large groups, these mechanisms may become ineffective because they require single individuals to have a substantial influence on their peers. However, the recent discovery of zero-determinant strategies in the iterated prisoner’s dilemma suggests that we may have underestimated the degree of control that a single player can exert. Here, we develop a theory for zero-determinant strategies for iterated multiplayer social dilemmas, with any number of involved players. We distinguish several particularly interesting subclasses of strategies: fair strategies ensure that the own payoff matches the average payoff of the group; extortionate strategies allow a player to perform above average; and generous strategies let a player perform below average. We use this theory to describe strategies that sustain cooperation, including generalized variants of Tit-for-Tat and Win-Stay Lose-Shift. Moreover, we explore two models that show how individuals can further enhance their strategic options by coordinating their play with others. Our results highlight the importance of individual control and coordination to succeed in large groups.
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Mapping the unconscious maintenance of a lost first language

Mapping the unconscious maintenance of a lost first language | Social Foraging | Scoop.it
Optimal periods during early development facilitate the formation of perceptual representations, laying the framework for future learning. A crucial question is whether such early representations are maintained in the brain over time without continued input. Using functional MRI, we show that internationally adopted (IA) children from China, exposed exclusively to French since adoption (mean age of adoption, 12.8 mo), maintained neural representations of their birth language despite functionally losing that language and having no conscious recollection of it. Their neural patterns during a Chinese lexical tone discrimination task matched those observed in Chinese/French bilinguals who have had continual exposure to Chinese since birth and differed from monolingual French speakers who had never been exposed to Chinese. They processed lexical tone as linguistically relevant, despite having no Chinese exposure for 12.6 y, on average, and no conscious recollection of that language. More specifically, IA participants recruited left superior temporal gyrus/planum temporale, matching the pattern observed in Chinese/French bilinguals. In contrast, French speakers who had never been exposed to Chinese did not recruit this region and instead activated right superior temporal gyrus. We show that neural representations are not overwritten and suggest a special status for language input obtained during the first year of development.
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Viral Misinformation: The Role of Homophily and Polarization

Viral Misinformation: The Role of Homophily and Polarization | Social Foraging | Scoop.it

The spreading of unsubstantiated rumors on online social networks (OSN) either unintentionally or intentionally (e.g., for political reasons or even trolling) can have serious consequences such as in the recent case of rumors about Ebola causing disruption to health-care workers. Here we show that indicators aimed at quantifying information consumption patterns might provide important insights about the virality of false claims. In particular, we address the driving forces behind the popularity of contents by analyzing a sample of 1.2M Facebook Italian users consuming different (and opposite) types of information (science and conspiracy news). We show that users' engagement across different contents correlates with the number of friends having similar consumption patterns (homophily), indicating the area in the social network where certain types of contents are more likely to spread. Then, we test diffusion patterns on an external sample of 4,709 intentional satirical false claims showing that neither the presence of hubs (structural properties) nor the most active users (influencers) are prevalent in viral phenomena. Instead, we found out that in an environment where misinformation is pervasive, users' aggregation around shared beliefs may make the usual exposure to conspiracy stories (polarization) a determinant for the virality of false information.

 

Viral Misinformation: The Role of Homophily and Polarization
Aris Anagnostopoulos, Alessandro Bessi, Guido Caldarelli, Michela Del Vicario, Fabio Petroni, Antonio Scala, Fabiana Zollo, Walter Quattrociocchi

http://arxiv.org/abs/1411.2893


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Removing the brake: How to increase brain activity and memory

Removing the brake: How to increase brain activity and memory | Social Foraging | Scoop.it

Is it possible to rapidly increase (or decrease) the amount of information the brain can store? A new international study led by the Research Institute of the McGill University Health Centre (RI-MUHC) suggests is may be. Their research has identified a molecule that improves brain function and memory recall is improved. Published in the latest issue of Cell Reports, the study has implications for neurodevelopmental and neurodegenerative diseases, such as autism spectral disorders and Alzheimer’s disease.

 

“Our findings show that the brain has a key protein called FXR1P (Fragile X Related Protein 1) that limits the production of molecules necessary for memory formation,” says RI-MUHC neuroscientist Keith Murai, the study’s senior author and Associate Professor in the Department of Neurology and Neurosurgery at McGill University. “When this brake-protein is suppressed, the brain is able to store more information.”

 

Murai and his colleagues used a mouse model to study how changes in brain cell connections produce new memories. When FXR1P was selectively removed from certain parts of the brain, new molecules were produced. They strengthened connections between brain cells, which correlated with improved memory and recall in the mice.

 

“The role of FXR1P was a surprising result,” says Dr. Murai. “Previous to our work, no-one had identified a role for this regulator in the brain. Our findings have provided fundamental knowledge about how the brain processes information. We’ve identified a new pathway that directly regulates how information is handled and this could have relevance for understanding and treating brain diseases.” 

 

“Future research in this area could be very interesting,” he adds. “If we can identify compounds that control the braking potential of FXR1P, we may be able to alter the amount of brain activity or plasticity. For example, in autism, one may want to decrease certain brain activity and in Alzheimer’s disease, we may want to enhance the activity. By manipulating FXR1P, we may eventually be able to adjust memory formation and retrieval, thus improving the quality of life of people suffering from brain diseases.” 

 


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NGSS includes opportunities for students to understand and apply learning about information processing in biological systems

Lucile Debethune's curator insight, November 21, 5:45 AM

Parmi les nombreuses proteines du cerveau, cette recherche se concentre sur la proteines FXR1P, qui agit comme un frein à la production de molécule nécessaire à la formation de molécules. Travailler sur cette protéine pourait être un élément clef dans le traitement du fonctionnement anormal du cerveau.

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Goethe on the Psychology of Color and Emotion

Goethe on the Psychology of Color and Emotion | Social Foraging | Scoop.it
Color is an essential part of how we experience the world, both biologically and culturally. One of the earliest formal explorations of color theory came from an unlikely source — the German poet, artist, and politician Johann Wolfgang von Goethe, who in 1810 published Theory of Colours (public library; public domain), his treatise on the nature, function, and psychology of colors. Though the work was dismissed by a large portion of the scientific community, it remained of intense interest to a cohort of prominent philosophers and physicists, including Arthur Schopenhauer, Kurt Gödel, and Ludwig Wittgenstein.
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Twitter Firehose Reveals How Weather Affects Mood

Twitter Firehose Reveals How Weather Affects Mood | Social Foraging | Scoop.it

We’re all influenced by the weather but psychologists have struggled to gather convincing data revealing the correlation. So researchers are turning to Twitter instead.

 

Researchers have long known that the weather has a profound physiological impact. The human body reacts to sunlight by producing serotonin, a neurotransmitter that is strongly linked with feelings of well-being. Some people are more likely to be depressed during winter, a condition known as seasonal affective disorder or SAD.

But despite this evidence, psychologists have long failed to find a clear correlation between weather and mood. This area of study is littered with studies that show contradictory correlations and others that show no correlation at all.

Now Jiwei Li at Stanford University and a few pals aim to change that. These guys have mined geotagged tweets for indications of mood and then searched for correlations with the weather. They say that some moods are clearly correlated with certain types of weather or changes in the weather but sometimes in counterintuitive ways.

Li and co begin with a database of tweets geotagged to one of 32 major urban areas in the US, such as New York, Chicago, Los Angeles, San Franciso and so on. These had been filtered from a dataset of consisting of 10 per cent of all tweets posted in 2010 and 2011.

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Computing with silicon neurons: Scientists use artificial nerve cells to classify different types of data

Computing with silicon neurons: Scientists use artificial nerve cells to classify different types of data | Social Foraging | Scoop.it

Scientists from Berlin and Heidelberg use artificial nerve cells to classify different types of data. They can recognize handwritten numbers, or distinguish plant species based on their flowers.

 

A bakery assistant who takes the bread from the shelf just to give it to his boss who then hands it over to the customer? Rather unlikely. Instead, both work at the same time to sell the baked goods. Similarly, computer programs are more efficient if they process data in parallel rather than to calculate them one after the other. However, most programs that are applied still work in a serial manner.

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Video: Twitter usage reveals ebbs and flows of life in NYC

Next time you’re in New York City and wondering where the party’s at, or if you’re going to get stuck on 8th Avenue in traffic from the Rangers’ game, you might do well to consult an unlikely ally: Twitter. Scientists have analyzed 6 million geolocated, time-stamped tweets from New York City and the surrounding area and discovered a “heartbeat” that says a lot about how New Yorkers live. As seen above, the frequency and location of tweets swells and recedes rhythmically, with red representing higher than average use and blue representing lower than average. Unsurprisingly, the study, published today on the arXiv preprint server, found that the most prominent predictor of Twitter usage is our daily sleep cycle. But by analyzing the locations of the tweets, the team was also able to watch New Yorkers commute from the suburbs into Manhattan, gather at stadiums for sports events, head downtown during late-night hours, and more. The team also found that Twitter usage tended to increase in transportation hubs where users are presumably bored with nothing else to do. The scientists posit that analyzing large-scale social population dynamics could help us design safer and more efficient cities—a goal that anyone who has ever tried to squeeze into an F train in midtown during rush hour can certainly appreciate.
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Acceleration of evolutionary spread by long-range dispersal

Acceleration of evolutionary spread by long-range dispersal | Social Foraging | Scoop.it
The spreading of evolutionary novelties across populations is the central element of adaptation. Unless populations are well mixed (like bacteria in a shaken test tube), the spreading dynamics depend not only on fitness differences but also on the dispersal behavior of the species. Spreading at a constant speed is generally predicted when dispersal is sufficiently short ranged, specifically when the dispersal kernel falls off exponentially or faster. However, the case of long-range dispersal is unresolved: Although it is clear that even rare long-range jumps can lead to a drastic speedup—as air-traffic–mediated epidemics show—it has been difficult to quantify the ensuing stochastic dynamical process. However, such knowledge is indispensable for a predictive understanding of many spreading processes in natural populations. We present a simple iterative scaling approximation supported by simulations and rigorous bounds that accurately predicts evolutionary spread, which is determined by a trade-off between frequency and potential effectiveness of long-distance jumps. In contrast to the exponential laws predicted by deterministic “mean-field” approximations, we show that the asymptotic spatial growth is according to either a power law or a stretched exponential, depending on the tails of the dispersal kernel. More importantly, we provide a full time-dependent description of the convergence to the asymptotic behavior, which can be anomalously slow and is relevant even for long times. Our results also apply to spreading dynamics on networks with a spectrum of long-range links under certain conditions on the probabilities of long-distance travel: These are relevant for the spread of epidemics.
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