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Short algorithm, long-range consequences

Short algorithm, long-range consequences | Social Foraging | Scoop.it

In the last decade, theoretical computer science has seen remarkable progress on the problem of solving graph Laplacians — the esoteric name for a calculation with hordes of familiar applications in scheduling, image processing, online product recommendation, network analysis, and scientific computing, to name just a few. Only in 2004 did researchers first propose an algorithm that solved graph Laplacians in “nearly linear time,” meaning that the algorithm’s running time didn’t increase exponentially with the size of the problem.

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Dynamics of Social Interaction
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Neural correlates of dueling affective reactions to win–win choices

Neural correlates of dueling affective reactions to win–win choices | Social Foraging | Scoop.it

Win–win choices cause anxiety, often more so than decisions lacking the opportunity for a highly desired outcome. These anxious feelings can paradoxically co-occur with positive feelings, raising important implications for individual decision styles and general well-being. Across three studies, people chose between products that varied in personal value. Participants reported feeling most positive and most anxious when choosing between similarly high-valued products. Behavioral and neural results suggested that this paradoxical experience resulted from parallel evaluations of the expected outcome (inducing positive affect) versus the cost of choosing a response (inducing anxiety). Positive feelings were reduced when there was no high-value option, and anxiety was reduced when only one option was highly valued. Dissociable regions within the striatum and the medial prefrontal cortex (mPFC) tracked these dueling affective reactions during choice. Ventral regions, associated with stimulus valuation, tracked positive feelings and the value of the best item. Dorsal regions, associated with response valuation, tracked anxiety. In addition to tracking anxiety, the dorsal mPFC was associated with conflict during the current choice, and activity levels across individual items predicted whether that choice would later be reversed during an unexpected reevaluation phase. By revealing how win–win decisions elicit responses in dissociable brain systems, these results help resolve the paradox of win–win choices. They also provide insight into behaviors that are associated with these two forms of affect, such as why we are pulled toward good options but may still decide to delay or avoid choosing among them.

 
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Robots with muscles: inspired by nature

Robots with muscles: inspired by nature | Social Foraging | Scoop.it

Myorobotics at the Technical University of Munich, takes us on a fascinating journey on how an adorable humanoid robot with muscles, called Roboy, is born in 9 months, and sheds light on the future of robotics, and what kind of future it might bring us. Being fascinated by the complexity and beauty of everything, Rafael Hostettler always had a hard time to choose. That’s why he has an MSc. in Computational Science from ETH Zurich, where he learnt to simulate just about everything on computers, so he didn’t have to make a decision. Now he’s building robots that imitate the building principles of the human musculoskeletal system and travels the world with Roboy. The 3D printed robot boy that plays in a theatre, goes to school and captivates the audience with his fascinating stories. 


Via Dr. Stefan Gruenwald
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Quantifying the semantics of search behavior before stock market moves

Quantifying the semantics of search behavior before stock market moves | Social Foraging | Scoop.it

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

 
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How To Spot A Social Bot On Twitter

How To Spot A Social Bot On Twitter | Social Foraging | Scoop.it

Social bots are sending a significant amount of information through the Twittersphere. Now there’s a tool to help identify them.

 

Back in 2011, a team from Texas A&M University carried out a cyber sting to trap nonhuman Twitter users that were polluting the Twittersphere with spam. Their approach was to set up “honeypot” accounts which posted nonsensical content that no human user would ever be interested in. Any account that retweeted this content, or friended the owner, must surely be a nonhuman user known as a social bot.

 
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From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model

From Spontaneous Motor Activity to Coordinated Behaviour: A Developmental Model | Social Foraging | Scoop.it

In mammals, the developmental path that links the primary behaviours observed during foetal stages to the full fledged behaviours observed in adults is still beyond our understanding. Often theories of motor control try to deal with the process of incremental learning in an abstract and modular way without establishing any correspondence with the mammalian developmental stages. In this paper, we propose a computational model that links three distinct behaviours which appear at three different stages of development. In order of appearance, these behaviours are: spontaneous motor activity (SMA), reflexes, and coordinated behaviours, such as locomotion. The goal of our model is to address in silico four hypotheses that are currently hard to verify in vivo: First, the hypothesis that spinal reflex circuits can be self-organized from the sensor and motor activity induced by SMA. Second, the hypothesis that supraspinal systems can modulate reflex circuits to achieve coordinated behaviour. Third, the hypothesis that, since SMA is observed in an organism throughout its entire lifetime, it provides a mechanism suitable to maintain the reflex circuits aligned with the musculoskeletal system, and thus adapt to changes in body morphology. And fourth, the hypothesis that by changing the modulation of the reflex circuits over time, one can switch between different coordinated behaviours. Our model is tested in a simulated musculoskeletal leg actuated by six muscles arranged in a number of different ways. Hopping is used as a case study of coordinated behaviour. Our results show that reflex circuits can be self-organized from SMA, and that, once these circuits are in place, they can be modulated to achieve coordinated behaviour. In addition, our results show that our model can naturally adapt to different morphological changes and perform behavioural transitions.

 
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The Server Needs To Die To Save The Internet

The Server Needs To Die To Save The Internet | Social Foraging | Scoop.it

 Do we have the Internet we deserve? There’s an argument to say that yes, we absolutely do. Given web users’ general reluctance to pay for content. We are of course, paying. Just not with cold hard cash, but with our privacy — as digital business models rely on gathering and selling intel on their users to make the money to pay (the investors who paid) for the free service.

 

Users are also increasingly paying with time and attention, as more ad content — and more adverts masquerading as, infiltrating and degrading content — thrusts its way in front of our eyeballs in ever more insidious ways. Whether it’s repurposing our friends’ photos and endorsements to socially engineer selling us stuff, or resorting to other background tracking and targeting tricks to divert our attention from whatever it was we were actually trying to do online.

The commercialization of the web is the ugly reality of the hidden cost of all the datacenters and servers required to power the Internet. And that commercialization is compounded by the power of the big digital platforms that dominate the web we have today: Google, Facebook, Amazon. Increasingly we’re forced to play by their rules if we want to participate in the digital space where most of our friends are.

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Properties of artificial networks evolved to contend with natural spectra

Properties of artificial networks evolved to contend with natural spectra | Social Foraging | Scoop.it

Understanding why spectra that are physically the same appear different in different contexts (color contrast), whereas spectra that are physically different appear similar (color constancy) presents a major challenge in vision research. Here, we show that the responses of biologically inspired neural networks evolved on the basis of accumulated experience with spectral stimuli automatically generate contrast and constancy. The results imply that these phenomena are signatures of a strategy that biological vision uses to circumvent the inverse optics problem as it pertains to light spectra, and that double-opponent neurons in early-level vision evolve to serve this purpose. This strategy provides a way of understanding the peculiar relationship between the objective world and subjective color experience, as well as rationalizing the relevant visual circuitry without invoking feature detection or image representation.

 
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Origin and Structure of Dynamic Cooperative Networks

Origin and Structure of Dynamic Cooperative Networks | Social Foraging | Scoop.it
Societies are built on social interactions among individuals. Cooperation represents the simplest form of a social interaction: one individual provides a benefit to another one at a cost to itself. Social networks represent a dynamical abstraction of social interactions in a society. The behaviour of an individual towards others and of others towards the individual shape the individual's neighbourhood and hence the local structure of the social network. Here we propose a simple theoretical framework to model dynamic social networks by focussing on each individual's actions instead of interactions between individuals. This eliminates the traditional dichotomy between the strategy of individuals and the structure of the population and easily complements empirical studies. As a consequence, altruists, egoists and fair types are naturally determined by the local social structures, while globally egalitarian networks or stratified structures arise. Cooperative interactions drive the emergence and shape the structure of social networks.
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Predicting fruit fly’s sensing rate with insect flight simulations

Predicting fruit fly’s sensing rate with insect flight simulations | Social Foraging | Scoop.it

Without sensory feedback, flies cannot fly. Exactly how various feedback controls work in insects is a complex puzzle to solve. What do insects measure to stabilize their flight? How often and how fast must insects adjust their wings to remain stable? To gain insights into algorithms used by insects to control their dynamic instability, we develop a simulation tool to study free flight. To stabilize flight, we construct a control algorithm that modulates wing motion based on discrete measurements of the body-pitch orientation. Our simulations give theoretical bounds on both the sensing rate and the delay time between sensing and actuation. Interpreting our findings together with experimental results on fruit flies’ reaction time and sensory motor reflexes, we conjecture that fruit flies sense their kinematic states every wing beat to stabilize their flight. We further propose a candidate for such a control involving the fly’s haltere and first basalar motor neuron. Although we focus on fruit flies as a case study, the framework for our simulation and discrete control algorithms is applicable to studies of both natural and man-made fliers.

 
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Accurate decisions in an uncertain world: collective cognition increases true positives while decreasing false positives

Accurate decisions in an uncertain world: collective cognition increases true positives while decreasing false positives | Social Foraging | Scoop.it

In a wide range of contexts, including predator avoidance, medical decision-making and security screening, decision accuracy is fundamentally constrained by the trade-off between true and false positives. Increased true positives are possible only at the cost of increased false positives; conversely, decreased false positives are associated with decreased true positives. We use an integrated theoretical and experimental approach to show that a group of decision-makers can overcome this basic limitation. Using a mathematical model, we show that a simple quorum decision rule enables individuals in groups to simultaneously increase true positives and decrease false positives. The results from a predator-detection experiment that we performed with humans are in line with these predictions: (i) after observing the choices of the other group members, individuals both increase true positives and decrease false positives, (ii) this effect gets stronger as group size increases, (iii) individuals use a quorum threshold set between the average true- and false-positive rates of the other group members, and (iv) individuals adjust their quorum adaptively to the performance of the group. Our results have broad implications for our understanding of the ecology and evolution of group-living animals and lend themselves for applications in the human domain such as the design of improved screening methods in medical, forensic, security and business applications.

 
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A texture-processing model of the ‘visual sense of number’

A texture-processing model of the ‘visual sense of number’ | Social Foraging | Scoop.it

It has been suggested that numerosity is an elementary quality of perception, similar to colour. If so (and despite considerable investigation), its mechanism remains unknown. Here, we show that observers require on average a massive difference of approximately 40% to detect a change in the number of objects that vary irrelevantly in blur, contrast and spatial separation, and that some naive observers require even more than this. We suggest that relative numerosity is a type of texture discrimination and that a simple model computing the contrast energy at fine spatial scales in the image can perform at least as well as human observers. Like some human observers, this mechanism finds it harder to discriminate relative numerosity in two patterns with different degrees of blur, but it still outpaces the human. We propose energy discrimination as a benchmark model against which more complex models and new data can be tested.

 
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The Effects of Within- and Between-Group Competition on Trust and Trustworthiness among Acquaintances

The Effects of Within- and Between-Group Competition on Trust and Trustworthiness among Acquaintances | Social Foraging | Scoop.it

Several studies have indicated that between-group competition is a key stimulator of trust and trustworthiness. Another important but neglected type of competition may also affect trust and trustworthiness: within-group competition, especially competition among acquaintances. The present study investigated the effects of both within- and between-group competition on trust and trustworthiness, which were measured using an investment game played by acquaintances. We found that, compared to the participants' performance in the non-competition condition, when individuals were motivated to compete with their in-group members or the other groups for financial rewards, they demonstrated more trust. When individuals were motivated to compete with their in-group members, they exhibited lower trustworthiness than in non-competition and between-group competition. In addition, within-group competition decreased the trustor's payoff while both within- and between- group competition increased the trustee's payoff. Finally, we found that males trusted their group members more than females.

 
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Say Hello to the Robotic Personal Assistant of Your Dreams

Say Hello to the Robotic Personal Assistant of Your Dreams | Social Foraging | Scoop.it
Born from MIT's Media Lab, 'Jibo' can take photos, remind you of important dates and events and, more importantly, interact with you and learn about you.
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Hierarchical random walks in trace fossils and the origin of optimal search behavior

Hierarchical random walks in trace fossils and the origin of optimal search behavior | Social Foraging | Scoop.it

Efficient searching is crucial for timely location of food and other resources. Recent studies show that diverse living animals use a theoretically optimal scale-free random search for sparse resources known as a Lévy walk, but little is known of the origins and evolution of foraging behavior and the search strategies of extinct organisms. Here, using simulations of self-avoiding trace fossil trails, we show that randomly introduced strophotaxis (U-turns)—initiated by obstructions such as self-trail avoidance or innate cueing—leads to random looping patterns with clustering across increasing scales that is consistent with the presence of Lévy walks. This predicts that optimal Lévy searches may emerge from simple behaviors observed in fossil trails. We then analyzed fossilized trails of benthic marine organisms by using a novel path analysis technique and find the first evidence, to our knowledge, of Lévy-like search strategies in extinct animals. Our results show that simple search behaviors of extinct animals in heterogeneous environments give rise to hierarchically nested Brownian walk clusters that converge to optimal Lévy patterns. Primary productivity collapse and large-scale food scarcity characterizing mass extinctions evident in the fossil record may have triggered adaptation of optimal Lévy-like searches. The findings suggest that Lévy-like behavior has been used by foragers since at least the Eocene but may have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa.

 

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Introduction to Hypernetworks

Introduction to Hypernetworks | Social Foraging | Scoop.it

A new module on the Étoile Platform, by Jeffrey Johnson

 

Based on the course presented at the 4th Ph.D. summer School - conference on “Mathematical Modeling of Complex Systems”, Cultural Foundation “Kritiki Estia”, 14 – 25 July, 2014, Athens.

 

The modern world is complex beyond human understanding and control. The science of complex systems aims to find new ways of thinking about the many interconnected networks of interaction that defy traditional approaches. Thus far, research into networks has largely been restricted to pairwise relationships represented by links between two nodes.

This course marks a major extension of networks to multidimensional hypernetworks for modeling multi-element relationships, such as companies making up the stock market, the neighborhoods forming a city, people making up committees, divisions making up companies, computers making up the internet, men and machines making up armies, or robots working as teams. This course makes an important contribution to the science of complex systems by: (i) extending network theory to include dynamic relationships between many elements; (ii) providing a mathematical theory able to integrate multilevel dynamics in a coherent way; (iii) providing a new methodological approach to analyze complex systems; and (iv) illustrating the theory with practical examples in the design, management and control of complex systems taken from many areas of application.


Via Jorge Louçã, Complexity Digest
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Experimental evolution of prepared learning

Experimental evolution of prepared learning | Social Foraging | Scoop.it

Animals learn some things more easily than others. To explain this so-called prepared learning, investigators commonly appeal to the evolutionary history of stimulus–consequence relationships experienced by a population or species. We offer a simple model that formalizes this long-standing hypothesis. The key variable in our model is the statistical reliability of the association between stimulus, action, and consequence. We use experimental evolution to test this hypothesis in populations ofDrosophila. We systematically manipulated the reliability of two types of experience (the pairing of the aversive chemical quinine with color or with odor). Following 40 generations of evolution, data from learning assays support our basic prediction: Changes in learning abilities track the reliability of associations during a population’s selective history. In populations where, for example, quinine–color pairings were unreliable but quinine–odor pairings were reliable, we find increased sensitivity to learning the quinine–odor experience and reduced sensitivity to learning quinine–color. To the best of our knowledge this is the first experimental demonstration of the evolution of prepared learning.

 

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Collecting just the right data: When you can’t collect all the data you need, a new algorithm tells you which to target.

Collecting just the right data: When you can’t collect all the data you need, a new algorithm tells you which to target. | Social Foraging | Scoop.it

Much artificial-intelligence research addresses the problem of making predictions based on large data sets. An obvious example is the recommendation engines at retail sites like Amazon and Netflix.

 

But some types of data are harder to collect than online click histories —information about geological formations thousands of feet underground, for instance. And in other applications — such as trying to predict the path of a storm — there may just not be enough time to crunch all the available data.

 

Dan Levine, an MIT graduate student in aeronautics and astronautics, and his advisor, Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics, have developed a new technique that could help with both problems. For a range of common applications in which data is either difficult to collect or too time-consuming to process, the technique can identify the subset of data items that will yield the most reliable predictions. So geologists trying to assess the extent of underground petroleum deposits, or meteorologists trying to forecast the weather, can make do with just a few, targeted measurements, saving time and money.

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Collective Behaviour without Collective Order in Wild Swarms of Midges

Collective Behaviour without Collective Order in Wild Swarms of Midges | Social Foraging | Scoop.it

Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarm's density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems.

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Hands On With The Production Version Of The Myo Gesture Control Armband

Hands On With The Production Version Of The Myo Gesture Control Armband | Social Foraging | Scoop.it

Thalmic Labs is a Waterloo-based startup with a very ambitious goal – to change the way we interact with our everyday computing devices. To that end, they’ve developed the Myo armband, a gesture control device that fits around the meaty part of your forearm and detects slight muscle movements, arm rotations and even electrical impulses as you gesture, translating all that information into real-time input.

 

We were lucky enough to get one of the first hands-on demos of the new version of the Myo, which is set to begin to start shipping to developers shortly, and to pre-order customers this fall. Thalmic CEO and co-founder Stephen Lake also takes us through the process of building a hardware startup, and shipping that startup’s crucial first product. The hardware design is final, and though there are a few bugs still be worked out (you can see a couple in the video above), the Myo is just about ready for prime time.

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Using sociometers to quantify social interaction patterns

Using sociometers to quantify social interaction patterns | Social Foraging | Scoop.it
Research on human social interactions has traditionally relied on self-reports. Despite their widespread use, self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings outside research laboratories. We used such sensors, sociometers, to examine gender, talkativeness and interaction style in two different contexts. Here, we find that in the collaborative context, women were much more likely to be physically proximate to other women and were also significantly more talkative than men, especially in small groups. In contrast, there were no gender-based differences in the non-collaborative setting. Our results highlight the importance of objective measurement in the study of human behaviour, here enabling us to discern context specific, gender-based differences in interaction style.
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Detecting Communities Based on Network Topology

Detecting Communities Based on Network Topology | Social Foraging | Scoop.it

Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.

  
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Ivon Prefontaine's curator insight, July 26, 6:54 PM

Community is a more complex and organic organizing than teams. Teams are inherently hierarchical with predetermined goals. Communities are fluid and the goals are continuously being negotiated. Schools and classrooms are better served to be thought of as communities with overlapping qualities and permeable boundaries with other communities.

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Why do honeybees love hexagons? - Zack Patterson and Andy Peterson

Why do honeybees love hexagons? - Zack Patterson and Andy Peterson | Social Foraging | Scoop.it
Honeybees are some of nature’s finest mathematicians. Not only can they calculate angles and comprehend the roundness of the earth, these smart insects build and live in one of the most mathematically efficient architectural designs around: the beehive. Zack Patterson and Andy Peterson delve into the very smart geometry behind the honeybee’s home.
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Rationality in collective decision-making by ant colonies

Rationality in collective decision-making by ant colonies | Social Foraging | Scoop.it

Economic models of animal behaviour assume that decision-makers are rational, meaning that they assess options according to intrinsic fitness value and not by comparison with available alternatives. This expectation is frequently violated, but the significance of irrational behaviour remains controversial. One possibility is that irrationality arises from cognitive constraints that necessitate short cuts like comparative evaluation. If so, the study of whether and when irrationality occurs can illuminate cognitive mechanisms. We applied this logic in a novel setting: the collective decisions of insect societies. We tested for irrationality in colonies of Temnothorax ants choosing between two nest sites that varied in multiple attributes, such that neither site was clearly superior. In similar situations, individual animals show irrational changes in preference when a third relatively unattractive option is introduced. In contrast, we found no such effect in colonies. We suggest that immunity to irrationality in this case may result from the ants’ decentralized decision mechanism. A colony's choice does not depend on site comparison by individuals, but instead self-organizes from the interactions of multiple ants, most of which are aware of only a single site. This strategy may filter out comparative effects, preventing systematic errors that would otherwise arise from the cognitive limitations of individuals.

 
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Bridging the Mechanical and the Human Mind: Spontaneous Mimicry of a Physically Present Android

Bridging the Mechanical and the Human Mind: Spontaneous Mimicry of a Physically Present Android | Social Foraging | Scoop.it

The spontaneous mimicry of others' emotional facial expressions constitutes a rudimentary form of empathy and facilitates social understanding. Here, we show that human participants spontaneously match facial expressions of an android physically present in the room with them. This mimicry occurs even though these participants find the android unsettling and are fully aware that it lacks intentionality. Interestingly, a video of that same android elicits weaker mimicry reactions, occurring only in participants who find the android “humanlike.” These findings suggest that spontaneous mimicry depends on the salience of humanlike features highlighted by face-to-face contact, emphasizing the role of presence in human-robot interaction. Further, the findings suggest that mimicry of androids can dissociate from knowledge of artificiality and experienced emotional unease. These findings have implications for theoretical debates about the mechanisms of imitation. They also inform creation of future robots that effectively build rapport and engagement with their human users.

 
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The future lies in uncertainty

 

Statisticians have celebrated a lot recently. 2013 marked the 300th anniversary of Jacob Bernoulli's Ars Conjectandi, which used probability theory to explore the properties of statistics as more observations were taken. It was also the 250th anniversary of Thomas Bayes' essay on how humans can sequentially learn from experience, steadily updating their beliefs as more data become available (1). And it was the International Year of Statistics (2). Now that the bunting has been taken down, it is a good time to take stock of recent developments in statistical science and examine its role in the age of Big Data.
Much enthusiasm for statistics hangs on the ever-increasing availability of large data sets, particularly when something has to be ranked or classified. These situations arise, for example, when deciding which book to recommend, working out where your arm is when practicing golf swings in front of a games console, or (if you're a security agency) deciding whose private e-mail to read first. Purely data-based approaches, under the title of machine-learning, have been highly successful in speech recognition, real-time interpretation of moving images, and online translation.

 

The future lies in uncertainty
. D. J. Spiegelhalter

Science 18 July 2014:
Vol. 345 no. 6194 pp. 264-265
http://dx.doi.org/10.1126/science.1251122


Via Complexity Digest
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Complexity Digest's curator insight, July 18, 6:25 PM

“Predicting the past is very easy. Predicting the future is not so easy” -Ignacio Méndez