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Dynamics of Collective Decision Making of Honeybees in Complex Temperature Fields

Dynamics of Collective Decision Making of Honeybees in Complex Temperature Fields | Social Foraging | Scoop.it

Endothermic heat production is a crucial evolutionary adaptation that is, amongst others, responsible for the great success of honeybees. Endothermy ensures the survival of the colonies in harsh environments and is involved in the maintenance of the brood nest temperature, which is fundamental for the breeding and further development of healthy individuals and thus the foraging and reproduction success of this species. Freshly emerged honeybees are not yet able to produce heat endothermically and thus developed behavioural patterns that result in the location of these young bees within the warm brood nest where they further develop and perform tasks for the colony. Previous studies showed that groups of young ectothermic honeybees exposed to a temperature gradient collectively aggregate at the optimal place with their preferred temperature of 36°C but most single bees do not locate themselves at the optimum.

 

In this work we further investigate the behavioural patterns that lead to this collective thermotaxis. We tested single and groups of young bees concerning their ability to discriminate a local from a global temperature optimum and, for groups of bees, analysed the speed of the decision making process as well as density dependent effects by varying group sizes. We found that the majority of tested single bees do not locate themselves at the optimum whereas sufficiently large groups of bees are able to collectively discriminate a suboptimal temperature spot and aggregate at 36°C. Larger groups decide faster than smaller ones, but in larger groups a higher percentage of bees may switch to the sub-optimum due to crowding effects. We show that the collective thermotaxis is a simple but well evolved, scalable and robust social behaviour that enables the collective of bees to perform complex tasks despite the limited abilities of each individual.

 

Paper: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076250

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Twenty Years of Machine Learning at Microsoft

People may not realize it: Microsoft has more than twenty years of experience in creating machine learning systems and applying them to real problems. This experience is much longer than the recent buzz around Big Data and Deep Learning. It certainly gives us a good perspective on a variety of technologies and what it takes to actually deploy ML in production.

 

The story of ML at Microsoft started in 1992. We started working with Bayesian Networks, language modeling, and speech recognition. By 1993, Eric Horvitz, David Heckerman, and Jack Breese started the Decision Theory Group in Research and XD Huang started the Speech Recognition Group. In the 90s, we found that many problems, such as text categorization and email prioritization, were solvable through a combination of linear classification and Bayes networks. That work produced the first content-based spam detector and a number of other prototypes and products.

 

As we were working on solving specific problems for Microsoft products, we also wanted to get our tools directly into the hands of our customers. Making usable tools requires more than just clever algorithms: we need to consider the end-to-end user experience. We added predictive analytics to the Commerce Server product in order to provide recommendation service to our customers. We shipped the SQL Server Data Mining product in 2005, which allowed customers to build analytics on top of our SQL Server product.

 

As our algorithms became more sophisticated, we started solving tougher problems in fields related to ML, such as information retrieval, computer vision, and speech recognition. We blended the best ideas from ML and from these fields to make substantial forward progress. As I mentioned in my previous post, there are a number of such examples. Jamie Shotton, Antonio Criminisi, and others used decision forests to perform pixel-wise classification, both for human pose estimation and for medical imaging. Li Deng, Frank Seide, Dong Yu, and colleagues applied deep learning to speech recognition.

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Choices Can Become Overwhelming, So Make It Easier for Customers

Choices Can Become Overwhelming, So Make It Easier for Customers | Social Foraging | Scoop.it
Use these strategies to increase click-through rates and purchases when presenting choices.

 

Imagine the following scenario: All your friends have long been involved in relationships, and you are tired of being the third wheel at every social gathering. After a few failed matchmaking attempts, you decide to try a dating website. Soon you discover a new and exciting world that had previously been unknown to you. Suddenly you have many suitors and your dating possibilities become endless.

 

This experience makes you feel truly attractive and desired and, without even noticing, you become addicted. But make no mistake, you are not addicted to love or dating -- you are addicted to the idea of having many possibilities available to you.

 

The above scenario exemplifies a basic human trait: People love to have many options, even if they only exist in theory. When asked, who wouldn’t prefer to choose from a list of five different items over a list of only two?

 

Intuitively, people feel that the more options they have, the greater their chances are of finding the choice that will perfectly satisfy their needs. But this intuitive assumption turns out to be an illusion -- the more options we have, the less likely we are to make a decision at all.

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How the #BRA vs #GER match played out on Twitter powered by CartoDB - Geotagged Tweets

How the #BRA vs #GER match played out on Twitter powered by CartoDB - Geotagged Tweets | Social Foraging | Scoop.it
Geotagged Tweets mentioning teams around the World Cup game
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Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice

Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice | Social Foraging | Scoop.it

Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models (SSMs) have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times (RTs) become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography (EEG) and a functional magnetic resonance imaging (fMRI) study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies (14–30 Hz), indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

 
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For Brands, A Mind Is A Terrible Thing To Waste - Consumer Neuroscience

For Brands, A Mind Is A Terrible Thing To Waste - Consumer Neuroscience | Social Foraging | Scoop.it

Marketing has steadily evolved toward more precise and scientific methods. Focus groups and surveys are bowing to crowdsourcing and social listening, while manual data collection is fading in favor of big data and sophisticated analytics.

 

But somewhere between all of the data collection, number-crunching, and magical algorithms lies another, less obvious, marketing tool: neuroscience.

 

Simply put, neuroscience detects how the brain and body respond to messages. The concept is based on the study of sensorimotor, cognitive, and affective response to stimuli. It uses equipment, electrodes and sensors, and biometrics that measure heart rate, skin response, or eye movement--to understand how a person reacts to images, audio, and other sensory information. So-called neuromarketing is already used by a number of companies, including CBS (CBS), Coca-Cola (KO), Frito-Lay (PEP), Google (GOOGL), Hyundai, Microsoft (MSFT), and PayPal (EBAY).

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Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation

Movement-Based Estimation and Visualization of Space Use in 3D for Wildlife Ecology and Conservation | Social Foraging | Scoop.it

Advances in digital biotelemetry technologies are enabling the collection of bigger and more accurate data on the movements of free-ranging wildlife in space and time. Although many biotelemetry devices record 3D location data with x, y, and z coordinates from tracked animals, the third z coordinate is typically not integrated into studies of animal spatial use. Disregarding the vertical component may seriously limit understanding of animal habitat use and niche separation. We present novel movement-based kernel density estimators and computer visualization tools for generating and exploring 3D home ranges based on location data. We use case studies of three wildlife species – giant panda, dugong, and California condor – to demonstrate the ecological insights and conservation management benefits provided by 3D home range estimation and visualization for terrestrial, aquatic, and avian wildlife research.

 
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Frontiers in Ecology Evolution and Complexity

Frontiers in Ecology Evolution and Complexity | Social Foraging | Scoop.it

Advances in molecular biology, remote sensing, systems biology, bioinformatics, non-linear science, the physics of complex systems and other fields have rendered a great amount of data that remain to be integrated into models and theories that are capable of accounting for the complexity of ecological systems and the evolutionary dynamics of life. It is thus necessary to provide a solid basis to discuss and reflect on these and other challenges both at the local and global scales. This volume aims to delineate an integrative and interdisciplinary view that suggests new avenues in research and teaching, critically discusses the scope of the diverse methods in the study of complex systems, and points at key open questions. Finally, this book will provide students and specialists with a collection of high quality open access essays that will contribute to integrate Ecology, Evolution and Complexity in the context of basic research and in the field of Sustainability Sciences

 

Frontiers in Ecology, Evolution and Complexity
Editors: Mariana Benítez, Octavio Miramontes and Alfonso Valiente-Banuet
Prologue by Stuart A. Kauffman
http://scifunam.fisica.unam.mx/mir/copit/TS0012EN/TS0012EN.html ;


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june holley's curator insight, July 9, 5:10 AM

Downloadable book.

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Fruit Flies on the International Space Station

Fruit Flies on the International Space Station | Social Foraging | Scoop.it

Fruit flies are bug eyed and spindly, they love rotten bananas, and, following orders from their pin-sized brains, they can lay hundreds of eggs every day.

 

We have a lot in common.

 

Genetically speaking, people and fruit flies are surprisingly alike, explains biologist Sharmila Bhattacharya of NASA's Ames Research Center. "About 77% of known human disease genes have a recognizable match in the genetic code of fruit flies, and 50% of fly protein sequences have mammalian analogues."

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FingerReader: MIT finger device reads to the blind in real time

FingerReader: MIT finger device reads to the blind in real time | Social Foraging | Scoop.it

Scientists at the Massachusetts Institute of Technology are developing an audio reading device to be worn on the index finger of people whose vision is impaired, giving them affordable and immediate access to printed words.

 

The so-called FingerReader, a prototype produced by a 3-D printer, fits like a ring on the user’s finger, equipped with a small camera that scans text. A synthesized voice reads words aloud, quickly translating books, restaurant menus and other needed materials for daily living, especially away from home or office.

 

Reading is as easy as pointing the finger at text. Special software tracks the finger movement, identifies words and processes the information. The device has vibration motors that alert readers when they stray from the script, said Roy Shilkrot, who is developing the device at the MIT Media Lab.

 

For Jerry Berrier, 62, who was born blind, the promise of the FingerReader is its portability and offer of real-time functionality at school, a doctor’s office and restaurants.

“When I go to the doctor’s office, there may be forms that I wanna read before I sign them,” Berrier said.

 

He said there are other optical character recognition devices on the market for those with vision impairments, but none that he knows of that will read in real time.


Via Dr. Stefan Gruenwald
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Scientists threaten to boycott €1.2bn Human Brain Project

Scientists threaten to boycott €1.2bn Human Brain Project | Social Foraging | Scoop.it

The world's largest project to unravel the mysteries of the human brain has been thrown into crisis with more than 100 leading researchers threatening to boycott the effort amid accusations of mismanagement and fears that it is doomed to failure.

 

The European commission launched the €1.2bn (£950m) Human Brain Project (HBP) last year with the ambitious goal of turning the latest knowledge in neuroscience into a supercomputer simulation of the human brain. More than 80 European and international research institutions signed up to the 10-year project.

 

But it proved controversial from the start. Many researchers refused to join on the grounds that it was far too premature to attempt a simulation of the entire human brain in a computer. Now some claim the project is taking the wrong approach, wastes money and risks a backlash against neuroscience if it fails to deliver.

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Exploring Function Prediction in Protein Interaction Networks via Clustering Methods

Exploring Function Prediction in Protein Interaction Networks via Clustering Methods | Social Foraging | Scoop.it

Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.

 

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Extra-Pair Mating and Evolution of Cooperative Neighbourhoods

Extra-Pair Mating and Evolution of Cooperative Neighbourhoods | Social Foraging | Scoop.it

A striking but unexplained pattern in biology is the promiscuous mating behaviour in socially monogamous species. Although females commonly solicit extra-pair copulations, the adaptive reason has remained elusive. We use evolutionary modelling of breeding ecology to show that females benefit because extra-pair paternity incentivizes males to shift focus from a single brood towards the entire neighbourhood, as they are likely to have offspring there. Male-male cooperation towards public goods and dear enemy effects of reduced territorial aggression evolve from selfish interests, and lead to safer and more productive neighbourhoods. The mechanism provides adaptive explanations for the common empirical observations that females engage in extra-pair copulations, that neighbours dominate as extra-pair sires, and that extra-pair mating correlates with predation mortality and breeding density. The models predict cooperative behaviours at breeding sites where males cooperate more towards public goods than females. Where maternity certainty makes females care for offspring at home, paternity uncertainty and a potential for offspring in several broods make males invest in communal benefits and public goods. The models further predict that benefits of extra-pair mating affect whole nests or neighbourhoods, and that cuckolding males are often cuckolded themselves. Derived from ecological mechanisms, these new perspectives point towards the evolution of sociality in birds, with relevance also for mammals and primates including humans.

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Mode - The 'GitHub' of Data Analytics

Mode - The 'GitHub' of Data Analytics | Social Foraging | Scoop.it

Analyze data faster and share results more easily. Mode combines powerful SQL and visualization editors with publishing and discovery tools.

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Google Tests Personal Data Market To Find Out How Much Your Personal Information Is Worth

Google Tests Personal Data Market To Find Out How Much Your Personal Information Is Worth | Social Foraging | Scoop.it

The personal information that your smart phone can collect about you is increasingly detailed. Apps can record your location, your level of exercise, the phone calls that you make and receive, the photographs that you take and who you share them with and so on. Various studies have shown that this data provides a detailed and comprehensive insight into an individual’s habits and lifestyle, information that advertisers and marketers dearly love to have.

 

Indeed, this information can surprisingly useful. The Google Now smartphone app uses information such as your location to provide details it thinks you might find useful, such as directions home or nearby restaurants.

 

But this service isn’t entirely altruistic. Google knows perfectly well that it can use this information to sell adverts and other services.

 

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6 Powerful Psychological Effects That Explain How Our Brains Tick

6 Powerful Psychological Effects That Explain How Our Brains Tick | Social Foraging | Scoop.it

Understanding the psychology behind the way we tick might help us to tick even better.

 

Many studies and much research has been invested into the how and why behind our everyday actions and interactions. The results are revealing. If you are looking for a way to supercharge your personal development, understanding the psychology behind our actions is an essential first step.

 

Fortunately, knowing is half the battle. When you realize all the many ways in which our minds create perceptions, weigh decisions, and subconsciously operate, you can see the psychological advantages start to take shape. It’s like a backstage pass to the way we work, and being backstage, you have an even greater understanding of what it takes to succeed.

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A Brain-Computer Interface for Speech

A Brain-Computer Interface for Speech | Social Foraging | Scoop.it
Recordings from the brain’s surface are giving scientists unprecedented views into how the brain controls speech.

 

Could a person who is paralyzed and unable to speak, like physicist Stephen Hawking, use a brain implant to carry on a conversation?

That’s the goal of an expanding research effort at U.S. universities, which over the last five years has proved that recording devices placed under the skull can capture brain activity associated with speaking.

 

While results are preliminary, Edward Chang, a neurosurgeon at the University of California, San Francisco, says he is working toward building a wireless brain-machine interface that could translate brain signals directly into audible speech using a voice synthesizer.

 

The effort to create a speech prosthetic builds on success at experiments in which paralyzed volunteers have used brain implants to manipulate robotic limbs using their thoughts (see “The Thought Experiment”). That technology works because scientists are able to roughly interpret the firing of neurons inside the brain’s motor cortex and map it to arm or leg movements.

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malek's comment, July 9, 12:49 PM
Brain Machine Interface is ready for a huge tidal wave
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How Motivation Triggers Speedy Decisions

How Motivation Triggers Speedy Decisions | Social Foraging | Scoop.it

Animals must react quickly to objects and events in the environment to survive, especially when their decisions could result in a reward or punishment. Based on this fact, scientists have assumed that the speed of decision-making during behavioral tasks is affected by motivational salience—the extent to which an object or event predicts important behavioral outcomes. Neurons in a brain region called the basal forebrain (BF) respond to motivationally salient stimuli, but the influence of these BF neurons on decision-making speed has been unclear.

 

In a study published this month in PLOS Biology, Irene Avila and Shih-Chieh Lin of the National Institute on Aging at the National Institutes of Health provide new insights into how motivational salience not only speeds up reaction times but also reduces variability in decision-making speed. Avila and Lin's findings suggest that the activity of BF neurons determines the speed of rats' decisions in response to motivationally salient stimuli, providing a possible neural explanation for the slower decision-making speeds seen in conditions ranging from depression to dementia.

 

To examine the relationship between motivational salience and decision-making speed, Avila and Lin trained rats to stick their nose through a port in a Plexiglas chamber and wait for a noise that signaled a reward. White noise indicated that the rats would receive a large reward of four drops of water, whereas a clicking sound signaled a small reward of only one drop of water. During some trials, no noise or reward was presented.

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On the Origins of Suboptimality in Human Probabilistic Inference (Decision Making)

On the Origins of Suboptimality in Human Probabilistic Inference (Decision Making) | Social Foraging | Scoop.it

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.

 
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A Unique Nest-Protection Strategy in a New Species of Spider Wasp

A Unique Nest-Protection Strategy in a New Species of Spider Wasp | Social Foraging | Scoop.it

Hymenoptera show a great variation in reproductive potential and nesting behavior, from thousands of eggs in sawflies to just a dozen in nest-provisioning wasps. Reduction in reproductive potential in evolutionary derived Hymenoptera is often facilitated by advanced behavioral mechanisms and nesting strategies. Here we describe a surprising nesting behavior that was previously unknown in the entire animal kingdom: the use of a vestibular cell filled with dead ants in a new spider wasp (Hymenoptera: Pompilidae) species collected with trap nests in South-East China. We scientifically describe the ‘Bone-house Wasp’ as Deuteragenia ossariumsp. nov., named after graveyard bone-houses or ossuaries. We show that D. ossarium nests are less vulnerable to natural enemies than nests of other sympatric trap-nesting wasps, suggesting an effective nest protection strategy, most likely by utilizing chemical cues emanating from the dead ants.

 
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Loss of criticality in the avalanche statistics of sandpiles with dissipative sites

To account for the dissipative mechanisms found in nature, non-conservative elements have been incorporated in the energy redistribution rules of sandpiles and similar models of hazard phenomena. In this work, we found that incorporating non-conservation in the form of spatially-distributed sink sites affect both the external driving and internal cascade mechanisms of the sandpile. Increasing sink densities result in the loss of critical behavior, as evidenced by the gradual evolution of the avalanche size distribution from power-law (correlated) to exponential (random). For low density cases, we found no optimal configuration that will minimize the risk of producing large avalanches. Our model is inspired by analogs in natural avalanche systems, where non-conservative elements have an inherent spatial distribution.

 

Loss of criticality in the avalanche statistics of sandpiles with dissipative sites

Antonino A. Paguirigan Jr., Christopher P. Monterola, Rene C. Batac

19 June 2014

http://dx.doi.org/10.1016/j.cnsns.2014.06.020


Via Complexity Digest
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Co-Following on Twitter

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. 


We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43 Twitter friends are considered. 


Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. 


In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between Apple and Puma and, less obviously, between Nike and Coca-Cola. Concerning a user's popularity we find a statistically significant connection between having a more "average" followership and having more followers than direct rivals. Interestingly, a \emph{larger} audience also seems to be linked to a \emph{less diverse} audience in terms of their co-following.

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A simple generative model of collective online behavior

A simple generative model of collective online behavior | Social Foraging | Scoop.it

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates—even when using purely observational data without experimental design—that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior.

 
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Enlarging the scope: grasping brain complexity

To further advance our understanding of the brain, new concepts and theories are needed. In particular, the ability of the brain to create information flows must be reconciled with its propensity for synchronization and mass action. The theoretical and empirical framework of Coordination Dynamics, a key aspect of which is metastability, are presented as a starting point to study the interplay of integrative and segregative tendencies that are expressed in space and time during the normal course of brain and behavioral function. Some recent shifts in perspective are emphasized, that may ultimately lead to a better understanding of brain complexity.

 

Front. Syst. Neurosci., 25 June 2014 | http://dx.doi.org/10.3389/fnsys.2014.00122

Enlarging the scope: grasping brain complexity
Emmanuelle Tognoli and J. A. Scott Kelso


Via Complexity Digest
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Watching TV and Food Intake: The Role of Content

Watching TV and Food Intake: The Role of Content | Social Foraging | Scoop.it

Obesity is a serious and growing health concern worldwide. Watching television (TV) represents a condition during which many habitually eat, irrespective of hunger level. However, as of yet, little is known about how the content of television programs being watched differentially impacts concurrent eating behavior. In this study, eighteen normal-weight female students participated in three counter-balanced experimental conditions, including a ‘Boring’ TV condition (art lecture), an ‘Engaging’ TV condition (Swedish TV comedy series), and a no TV control condition during which participants read (a text on insects living in Sweden). Throughout each condition participants had access to both high-calorie (M&Ms) and low-calorie (grapes) snacks. We found that, relative to the Engaging TV condition, Boring TV encouraged excessive eating (+52% g, P = 0.009). Additionally, the Engaging TV condition actually resulted in significantly less concurrent intake relative to the control ‘Text’ condition (−35% g, P = 0.05). This intake was driven almost entirely by the healthy snack, grapes; however, this interaction did not reach significance (P = 0.07). Finally, there was a significant correlation between how bored participants were across all conditions, and their concurrent food intake (beta = 0.317, P = 0.02). Intake as measured by kcals was similarly patterned but did not reach significance. These results suggest that, for women, different TV programs elicit different levels of concurrent food intake, and that the degree to which a program is engaging (or alternately, boring) is related to that intake. Additionally, they suggest that emotional content (e.g. boring vs. engaging) may be more associated than modality (e.g. TV vs. text) with concurrent intake.

 

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Yahoo releases massive Flickr dataset, and a supercomputer steps up to analyze it

Yahoo releases massive Flickr dataset, and a supercomputer steps up to analyze it | Social Foraging | Scoop.it
Yahoo has released a massive dataset for researchers to experiment on. The dataset includes URLs for nearly 100 million images and 700,000 videos, as well as their metadata. Soon, a larger supercomputer-processed dataset that includes audio and visual features will be available.
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