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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Meet The Mice Whose Brains Are Part Human

Meet The Mice Whose Brains Are Part Human | Social Foraging | Scoop.it
By injecting human cells into mouse pups, scientists have created mice whose brains are part human. These hybrid mice grew up to be smarter than their peers, performing much better in tests for memory and cognition. Although this may sound like the plot of a terrible Sci-Fi film, researchers hope to glean a lot of valuable information from these experiments. For example, by studying brain diseases in whole organisms rather than in cells in a dish, researchers should gain a better understanding of how the conditions develop and progress.

For the study, which has been published in The Journal of Neuroscience, researchers from the University of Rochester Medical Center started off by isolating immature glial cells from donated human fetuses. Glial cells are one of the two main cell types that build the nervous system, the other being neurons. Glia perform a variety of roles in the nervous system, such as providing support and protection for neurons, but unlike nerve cells they do not participate directly in electrical signaling, which is a form of communication used to transmit information between cells.
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Complexity from precipitation reactions

Complexity from precipitation reactions | Social Foraging | Scoop.it
A key challenge for modern molecular sciences is to bridge the gap between the nanoscopic world and macroscopic devices. The underlying question is whether one can control chemical reactions to produce directly macroscopic complexity, hierarchical order, and ultimately entirely new types of materials. If viewed as a form of complicated chemistry, biology unambiguously answers this question with a resounding “yes” and, furthermore, demonstrates the powerful potential of this approach. However, this fundamental reassurance does not provide significant help in developing nonbiological model systems and leaves us with seemingly insurmountable hurdles. The PNAS article by Haudin et al. (1) is an important step to overcome some of these hurdles, as it provides just such an experimental model.
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Show and Tell: A Neural Image Caption Generator

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU score improvements on Flickr30k, from 55 to 66, and on SBU, from 19 to 27.
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Europe’s next privacy war is with websites silently tracking users

Europe’s next privacy war is with websites silently tracking users | Social Foraging | Scoop.it
The pan-European data regulator group Article 29 has issued new opinion on how websites and advertisers can track users and the permissions they require.

The new opinion dictates that “device fingerprinting” – a process of silently collecting information about a user – requires the same level of consent as cookies that are used to track users across the internet.

“Parties who wish to process device fingerprints which are generated through the gaining of access to, or the storing of, information on the user’s terminal device must first obtain the valid consent of the user (unless an exemption applies),” the Article 29 Working Party wrote.

It means that some websites, including Google, Facebook and Microsoft, that have used alternative technical processes to try to bypass the need for a “cookie policy notice” will have to show a notification after all.

“The Article 29 Working Party has made it clear that companies cannot bypass consent by using covert methods to track users through their devices,” said Jim Killock, executive director of the Open Rights Group. “Building profiles to deliver personalised content and adverts clearly falls under e-privacy and data protection law.”
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Implementing a Distributed Deep Learning Network over Spark

Implementing a Distributed Deep Learning Network over Spark | Social Foraging | Scoop.it
Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. Our intention is to parallelize the training phase of the network and consequently reduce training time. We have built the first prototype of our distributed deep learning network over Spark, which has emerged as a de-facto standard for realizing machine learning at scale.

 

Introduction

 

Geoffrey Hinton presented the paradigm for fast learning in a deep

belief network [Hinton 2006]. This paper, with the advent of GPUs and widespread availability of computing power, led to the breakthrough in this field. Consequently, every big software technology company is working on deep learning and every other startup is using it. A number of applications are being realized over it, including in various fields such as credit card fraud detection (see for example Deep Learning Analytics from Fico), multi-modal information processing etc. This excludes areas such as speech recognition and image processing, which have been already transformed by the application of deep learning [Deng 2013].

 

The team at Google lead by Jeffrey Dean came up with the first implementation of distributed deep learning [Dean 2012]. Architecturally, it was a pseudo-central realization, with a centralized parameter server being a single source of parameter values across the distributed system. Oxdata has recently released its H20 software which also comprises a deep learning network in addition to several other machine learning algorithms. They have also made the H20 software to work over Spark, as evident from this blog on Sparkling Water. To the extent we have explored, only the Microsoft project Adam comes close to a fully distributed realization of a deep learning network .

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Skin conductance based wearable detects seizures

Skin conductance based wearable detects seizures | Social Foraging | Scoop.it
Embrace by Empatica is a sleek, crowdfunded wearable that detects epileptic seizures.  It also tracks movement, sleep, and stress.  The technology is based on Rosalind Picard‘s 2008 study showing that seizures produce high levels of skin conductance.

Other seizure logging wearables focus on accelerometer tracked motion.  Empatica believes that Embrace more accurately measures seizures by combining accelerometer and gyroscope data with skin conductance measurements.  Electrodes on the inside of Embrace pass a tiny current through one’s skin to measure sweat gland stimulation.

The band vibrates if the algorithm detects a seizure, enabling the user to indicate a false alarm. If the user doesn’t respond, Embrace connect with his or her smartphone to alert designated contacts.
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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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Peter Rettig's curator insight, November 30, 12:10 PM

A very good reason to expose our young children to the sounds of different languages ...

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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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Carlos Rodrigues Cadre's curator insight, November 17, 4:28 PM

adicionar a sua visão ...

Diane Johnson's curator insight, November 18, 9:21 AM

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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Phenotypic Plasticity, the Baldwin Effect, and the Speeding up of Evolution: the Computational Roots of an Illusion

An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance and has been given several names before its final 'Baldwin effect' label. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a population. This gives the necessary time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s showed the Baldwin effect to happen in computer simulations, and claimed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. The Baldwin effect is once again in need of convincing theoretical foundations.
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The origin of risk aversion

The origin of risk aversion | Social Foraging | Scoop.it
Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of our model suggest that these implications are primitive and cut across species, physiology, and genetic origins.
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How Google "Translates" Pictures Into Words Using Vector Space Mathematics

How Google "Translates" Pictures Into Words Using Vector Space Mathematics | Social Foraging | Scoop.it
Translating one language into another has always been a difficult task. But in recent years, Google has transformed this process by developing machine translation algorithms that changing the nature of cross cultural communications through Google Translate.

Now that company is using the same machine learning technique to translate pictures into words. The result is a system that automatically generates picture captions that accurately describe the content of images. That’s something that will be useful for search engines, for automated publishing and for helping the visually impaired navigate the web and, indeed, the wider world.

The conventional approach to language translation is an iterative process that starts by translating words individually and then reordering the words and phrases to improve the translation. But in recent years, Google has worked out how to use its massive search database to translate text in an entirely different way.

The approach is essentially to count how often words appear next to, or close to, other words and then define them in an abstract vector space in relation to each other. This allows every word to be represented by a vector in this space and sentences to be represented by combinations of vectors.

Google goes on to make an important assumption. This is that specific words have the same relationship to each other regardless of the language. For example, the vector “king - man + woman = queen” should hold true in all languages.
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The Essence of Website Speed Optimization (part 2)

The Essence of Website Speed Optimization (part 2) | Social Foraging | Scoop.it
If you familiar with business and online marketing, there is a high chance that you would not encounter problems in generating leads, but if you are far from being an expert, then you might need some serious help. The truth is, even web masters commit a lot of mistakes. And one most neglected areas that need attention is the importance that web optimization plays in the success of any website. In her webinar, Theresa Biacollo of ConversionMax talks about how website optimization can help you gain an edge and we already covered part of in the first part of this article, here.
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Nanowire ink printed paper sensor reduces diagnostic cost

Nanowire ink printed paper sensor reduces diagnostic cost | Social Foraging | Scoop.it
University of Tennessee‘s Anming Hu, Southeast University‘s Rhou-Zhou Li and and colleagues are printing conductive ink on paper to create low cost, lightweight, foldable, paper based sensors. The devices, which can be made or used anywhere,  respond to touch or specific molecules, such as glucose. Current paper-based diagnostic and DNA tests require complicated,  expensive manufacturing.

A  pattern of silver nanowire ink is printed on paper in a few minutes and  hardened with the light of a camera flash. The resulting sensor responds to touch even when curved, folded and unfolded 15 times, and rolled and unrolled 5,000 times.
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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."


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Dr. Dea Conrad-Curry's curator insight, November 28, 11:41 AM

Interesting...and in my family, anecdotal evidence suggests....

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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


Via NESS, Complexity Digest
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