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Social Physics: How Good Ideas Spread—The Lessons from a New Science: Alex Pentland

Social Physics: How Good Ideas Spread—The Lessons from a New Science

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For good or ill, big data and networks have taken over our lives and, unfortunately, they too often run amok. From the Arab Spring, mediated on Twitter and Facebook, to the NSA spying scandal, to the 2008 financial crash, big data and networks are causing wrenching changes but very rarely can we piece together why, how, or what do to about the problem.  Alex “Sandy” Pentland and his team have created a new data science that not only describes how networks of people behave but also creates actionable intelligence from that understanding.  Called “Social Physics,” it encapsulates social, analytical, computer, and managerial sciences into a synthesis that allows us to build more resilient and creative societies while at the same time providing greater protection for personal privacy and resistance to cyber attack.  Pentland’s new book, SOCIAL PHYSICS: How Good Ideas Spread—The Lessons from a New Science, is a landmark tour of this new science, offering revolutionary insights into the mysteries of collective intelligence and social influence.


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Liz Rykert's curator insight, February 10, 2014 7:24 PM

Adding this one to my reading list.

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Flexible Coding of Task Rules in Frontoparietal Cortex: An Adaptive System for Flexible Cognitive Control

Flexible Coding of Task Rules in Frontoparietal Cortex: An Adaptive System for Flexible Cognitive Control | Social Foraging | Scoop.it
How do our brains achieve the cognitive control that is required for flexible behavior? Several models of cognitive control propose a role for frontoparietal cortex in the structure and representation of task sets or rules. For behavior to be flexible, however, the system must also rapidly reorganize as mental focus changes. Here we used multivoxel pattern analysis of fMRI data to demonstrate adaptive reorganization of frontoparietal activity patterns following a change in the complexity of the task rules. When task rules were relatively simple, frontoparietal cortex did not hold detectable information about these rules. In contrast, when the rules were more complex, frontoparietal cortex showed clear and decodable rule discrimination. Our data demonstrate that frontoparietal activity adjusts to task complexity, with better discrimination of rules that are behaviorally more confusable. The change in coding was specific to the rule element of the task and was not mirrored in more specialized cortex (early visual cortex) where coding was independent of difficulty. In line with an adaptive view of frontoparietal function, the data suggest a system that rapidly reconfigures in accordance with the difficulty of a behavioral task. This system may provide a neural basis for the flexible control of human behavior.
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Big Data Analytics Are Leading to a “Shale 2.0” Revolution

Big Data Analytics Are Leading to a “Shale 2.0” Revolution | Social Foraging | Scoop.it
The number of active oil rigs in the United States continued to fall in May, as low prices pushed oil companies to temporarily shut down some of their production facilities. Since the end of May 2014, the U.S. rig count has fallen from 1,536 to 646, according to the energy analysis firm Platts—a 58 percent drop.

Low prices and plummeting rig counts have prompted a gusher of headlines claiming that the shale oil revolution, which by early this year boosted American oil production to nearly 10 million barrels a day, is grinding to a halt. The doomsayers, however, are missing a key parallel trend: lower prices are prompting unprecedented innovation in the oil fields, increasing production per well and slashing costs.
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AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power

AI Supercomputer Built by Tapping Data Warehouses for Their Idle Computing Power | Social Foraging | Scoop.it
Recent improvements in speech and image recognition have come as companies such as Google build bigger, more powerful systems of computers to run machine-learning software. Now a relative minnow, a private company called Sentient with only about 70 employees, says it can cheaply assemble even larger computing systems to power artificial-intelligence software.

The company’s approach may not be suited to all types of machine learning, a technology that has uses as varied as facial recognition and financial trading. Sentient has not published details, but says it has shown that it can put together enough computing power to produce significant results in some cases.

Sentient’s power comes from linking up hundreds of thousands of computers over the Internet to work together as if they were a single machine. The company won’t say exactly where all the machines it taps into are. But many are idle inside data centers, the warehouse-like facilities that power Internet services such as websites and mobile apps, says Babak Hodjat, cofounder and chief scientist at Sentient. The company pays a data-center operator to make use of its spare machines.
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Virtual Eyes Train Deep Learning Algorithm to Recognize Gaze Direction

Virtual Eyes Train Deep Learning Algorithm to Recognize Gaze Direction | Social Foraging | Scoop.it

Gaze estimation is a classic problem of machine vision, which can now be solved by one computer training another.

 

Eye contact is one of the most powerful forms of nonverbal communication. If avatars and robots are ever to exploit it, computer scientists will need to better monitor, understand, and reproduce this behavior.

But eye tracking is easier said than done. Perhaps the most promising approach is to train a machine-learning algorithm to recognize gaze direction by studying a large database of images of eyes in which the gaze direction is already known.

The problem here is that large databases of this kind do not exist. And they are hard to create: imagine photographing a person looking in a wide range of directions, using all kinds of different camera angles under many different lighting conditions. And then doing it again for another person with a different eye shape and face and so on. Such a project would be vastly time-consuming and expensive.

Today, Erroll Wood at the University of Cambridge in the U.K. and a few pals say they have solved this problem by creating a huge database of just the kind of images of eyes that a machine learning algorithm requires. That has allowed them to train a machine to recognize gaze direction more accurately than has ever been achieved before.

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The Role of the Organization Structure in the Diffusion of Innovations

The Role of the Organization Structure in the Diffusion of Innovations | Social Foraging | Scoop.it
Diffusion and adoption of innovations is a topic of increasing interest in economics, market research, and sociology. In this paper we investigate, through an agent based model, the dynamics of adoption of innovative proposals in different kinds of structures. We show that community structure plays an important role on the innovation diffusion, so that proposals are more likely to be accepted in homogeneous organizations. In addition, we show that the learning process of innovative technologies enhances their diffusion, thus resulting in an important ingredient when heterogeneous networks are considered. We also show that social pressure blocks the adoption process whatever the structure of the organization. These results may help to understand how different factors influence the diffusion and acceptance of innovative proposals in different communities and organizations.
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Percolation on Networks with Conditional Dependence Group

Percolation on Networks with Conditional Dependence Group | Social Foraging | Scoop.it
Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well.
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Measuring the signal-to-noise ratio of a neuron

Measuring the signal-to-noise ratio of a neuron | Social Foraging | Scoop.it
The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ2 random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are −10 dB to −3 dB for guinea pig auditory cortex neurons, −18 dB to −7 dB for rat thalamic neurons, −28 dB to −14 dB for monkey hippocampal neurons, and −29 dB to −20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron’s spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.
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Ambiguity and nonidentifiability in the statistical analysis of neural codes

Ambiguity and nonidentifiability in the statistical analysis of neural codes | Social Foraging | Scoop.it
Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.
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The Effect of Incentives and Meta-incentives on the Evolution of Cooperation

The Effect of Incentives and Meta-incentives on the Evolution of Cooperation | Social Foraging | Scoop.it
Although positive incentives for cooperators and/or negative incentives for free-riders in social dilemmas play an important role in maintaining cooperation, there is still the outstanding issue of who should pay the cost of incentives. The second-order free-rider problem, in which players who do not provide the incentives dominate in a game, is a well-known academic challenge. In order to meet this challenge, we devise and analyze a meta-incentive game that integrates positive incentives (rewards) and negative incentives (punishments) with second-order incentives, which are incentives for other players’ incentives. The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or non-cooperative behavior also tend to provide incentives to their incentive behaviors. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We find that the second-order free-rider problem is completely resolved without any third-order or higher (meta) incentive under the assumption. To do so, a second-order costly incentive, which is given individually (peer-to-peer) after playing donation games, is needed. The paper concludes that (1) second-order incentives for first-order reward are necessary for cooperative regimes, (2) a system without first-order rewards cannot maintain a cooperative regime, (3) a system with first-order rewards and no incentives for rewards is the worst because it never reaches cooperation, and (4) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the cost-effect ratio of incentives is sufficiently large. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives.
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Understanding Brains: Details, Intuition, and Big Data

Understanding Brains: Details, Intuition, and Big Data | Social Foraging | Scoop.it
Understanding how the brain works requires a delicate balance between the appreciation of the importance of a multitude of biological details and the ability to see beyond those details to general principles. As technological innovations vastly increase the amount of data we collect, the importance of intuition into how to analyze and treat these data may, paradoxically, become more important.
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Insect-Inspired Navigation Algorithm for an Aerial Agent Using Satellite Imagery

Insect-Inspired Navigation Algorithm for an Aerial Agent Using Satellite Imagery | Social Foraging | Scoop.it
Humans have long marveled at the ability of animals to navigate swiftly, accurately, and across long distances. Many mechanisms have been proposed for how animals acquire, store, and retrace learned routes, yet many of these hypotheses appear incongruent with behavioral observations and the animals’ neural constraints. The “Navigation by Scene Familiarity Hypothesis” proposed originally for insect navigation offers an elegantly simple solution for retracing previously experienced routes without the need for complex neural architectures and memory retrieval mechanisms. This hypothesis proposes that an animal can return to a target location by simply moving toward the most familiar scene at any given point. Proof of concept simulations have used computer-generated ant’s-eye views of the world, but here we test the ability of scene familiarity algorithms to navigate training routes across satellite images extracted from Google Maps. We find that Google satellite images are so rich in visual information that familiarity algorithms can be used to retrace even tortuous routes with low-resolution sensors. We discuss the implications of these findings not only for animal navigation but also for the potential development of visual augmentation systems and robot guidance algorithms.
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Species fluctuations sustained by a cyclic succession at the edge of chaos

Species fluctuations sustained by a cyclic succession at the edge of chaos | Social Foraging | Scoop.it
Although mathematical models and laboratory experiments have shown that species interactions can generate chaos, field evidence of chaos in natural ecosystems is rare. We report on a pristine rocky intertidal community located in one of the world’s oldest marine reserves that has displayed a complex cyclic succession for more than 20 y. Bare rock was colonized by barnacles and crustose algae, they were overgrown by mussels, and the subsequent detachment of the mussels returned bare rock again. These processes generated irregular species fluctuations, such that the species coexisted over many generations without ever approaching a stable equilibrium state. Analysis of the species fluctuations revealed a dominant periodicity of about 2 y, a global Lyapunov exponent statistically indistinguishable from zero, and local Lyapunov exponents that alternated systematically between negative and positive values. This pattern indicates that the community moved back and forth between stabilizing and chaotic dynamics during the cyclic succession. The results are supported by a patch-occupancy model predicting similar patterns when the species interactions were exposed to seasonal variation. Our findings show that natural ecosystems can sustain continued changes in species abundances and that seasonal forcing may push these nonequilibrium dynamics to the edge of chaos.
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Cascading Walks Model for Human Mobility Patterns

Cascading Walks Model for Human Mobility Patterns | Social Foraging | Scoop.it
Abstract

Background

Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies and the prediction of human movements. However, an agent-based model that could reproduce most of empirical observations without priori is still lacking.

Methodology/Principal Findings

In this paper, considering the empirical findings on the correlations of move-lengths and staying time in human trips, we propose a simple model which is mainly based on the cascading processes to capture the human mobility patterns. In this model, each long-range movement activates series of shorter movements that are organized by the law of localized explorations and preferential returns in prescribed region.

Conclusions/Significance

Based on the numerical simulations and analytical studies, we show more than five statistical characters that are well consistent with the empirical observations, including several types of scaling anomalies and the ultraslow diffusion properties, implying the cascading processes associated with the localized exploration and preferential returns are indeed a key in the understanding of human mobility activities. Moreover, the model shows both of the diverse individual mobility and aggregated scaling displacements, bridging the micro and macro patterns in human mobility. In summary, our model successfully explains most of empirical findings and provides deeper understandings on the emergence of human mobility patterns.
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The Challenge of Understanding the Brain: Where We Stand in 2015

The Challenge of Understanding the Brain: Where We Stand in 2015 | Social Foraging | Scoop.it
Starting with the work of Cajal more than 100 years ago, neuroscience has sought to understand how the cells of the brain give rise to cognitive functions. How far has neuroscience progressed in this endeavor? This Perspective assesses progress in elucidating five basic brain processes: visual recognition, long-term memory, short-term memory, action selection, and motor control. Each of these processes entails several levels of analysis: the behavioral properties, the underlying computational algorithm, and the cellular/network mechanisms that implement that algorithm. At this juncture, while many questions remain unanswered, achievements in several areas of research have made it possible to relate specific properties of brain networks to cognitive functions. What has been learned reveals, at least in rough outline, how cognitive processes can be an emergent property of neurons and their connections.
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Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems

Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems | Social Foraging | Scoop.it
Language universals have long been attributed to an innate Universal Grammar. An alternative explanation states that linguistic universals emerged independently in every language in response to shared cognitive or perceptual biases. A computational model has recently shown how this could be the case, focusing on the paradigmatic example of the universal properties of colour naming patterns, and producing results in quantitative agreement with the experimental data. Here we investigate the role of an individual perceptual bias in the framework of the model. We study how, and to what extent, the structure of the bias influences the corresponding linguistic universal patterns. We show that the cultural history of a group of speakers introduces population-specific constraints that act against the pressure for uniformity arising from the individual bias, and we clarify the interplay between these two forces.
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A natural experiment of social network formation and dynamics

A natural experiment of social network formation and dynamics | Social Foraging | Scoop.it
Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.
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Deep Learning Catches On in New Industries, from Fashion to Finance

Deep Learning Catches On in New Industries, from Fashion to Finance | Social Foraging | Scoop.it
A machine-learning technique that has already given computers an eerie ability to recognize speech and categorize images is now creeping into industries ranging from computer security to stock trading. If the technique works in those areas, it could create new opportunities but also displace some workers.

Deep learning, as the technique is known, involves applying layers of calculations to data, such as sound or images, to recognize key features and similarities. It offers a powerful way for machines to recognize similarities that would normally be abstruse to a computer: the same face seen from different angles, for instance, or a word spoken in different accents (see “10 Breakthrough Technologies 2013: Deep Learning”). The mathematical principles that underlie deep learning are relatively simple, but when combined with huge quantities of training data and computer systems capable of powerful parallel computations, the technique has resulted in dramatic progress in recent years, especially in voice and image recognition.
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Hybrid Epidemics—A Case Study on Computer Worm Conficker

Hybrid Epidemics—A Case Study on Computer Worm Conficker | Social Foraging | Scoop.it
Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm’s spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm’s effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols.
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A Wandering Mind Does Not Stray Far from Home: The Value of Metacognition in Distant Search

A Wandering Mind Does Not Stray Far from Home: The Value of Metacognition in Distant Search | Social Foraging | Scoop.it
When faced with a problem, how do individuals search for potential solutions? In this article, we explore the cognitive processes that lead to local search (i.e., identifying options closest to existing solutions) and distant search (i.e., identifying options of a qualitatively different nature than existing solutions). We suggest that mind wandering is likely to lead to local search because it operates by spreading activation from initial ideas to closely associated ideas. This reduces the likelihood of accessing a qualitatively different solution. However, instead of getting lost in thought, individuals can also step back and monitor their thoughts from a detached perspective. Such mindful metacognition, we suggest, is likely to lead to distant search because it redistributes activation away from initial ideas to other, less strongly associated, ideas. This hypothesis was confirmed across two studies. Thus, getting lost in thoughts is helpful when one is on the right track and needs only a local search whereas stepping back from thoughts is helpful when one needs distant search to produce a change in perspective.
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Evolutionary Dynamics for Persistent Cooperation in Structured Populations

The emergence and maintenance of cooperative behavior is a fascinating topic in evolutionary biology and social science. The public goods game (PGG) is a paradigm for exploring cooperative behavior. In PGG, the total resulting payoff is divided equally among all participants. This feature still leads to the dominance of defection without substantially magnifying the public good by a multiplying factor. Much effort has been made to explain the evolution of cooperative strategies, including a recent model in which only a portion of the total benefit is shared by all the players through introducing a new strategy named persistent cooperation. A persistent cooperator is a contributor who is willing to pay a second cost to retrieve the remaining portion of the payoff contributed by themselves. In a previous study, this model was analyzed in the framework of well-mixed populations. This paper focuses on discussing the persistent cooperation in lattice-structured populations. The evolutionary dynamics of the structured populations consisting of three types of competing players (pure cooperators, defectors and persistent cooperators) are revealed by theoretical analysis and numerical simulations. In particular, the approximate expressions of fixation probabilities for strategies are derived on one-dimensional lattices. The phase diagrams of stationary states, the evolution of frequencies and spatial patterns for strategies are illustrated on both one-dimensional and square lattices by simulations. Our results are consistent with the general observation that, at least in most situations, a structured population facilitates the evolution of cooperation. Specifically, here we find that the existence of persistent cooperators greatly suppresses the spreading of defectors under more relaxed conditions in structured populations compared to that obtained in well-mixed population.
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You Asked: Are My Devices Messing With My Brain?

You Asked: Are My Devices Messing With My Brain? | Social Foraging | Scoop.it
Yes—and you're probably suffering from phantom text syndrome, too.

First it was radio. Then it was television. Now doomsayers are offering scary predictions about the consequences of smartphones and all the other digital devices to which we’ve all grown so attached. So why should you pay any attention to the warnings this time?

Apart from portability, the big difference between something like a traditional TV and your tablet is the social component, says Dr. David Strayer, a professor of cognition and neural science at the University of Utah. “Through Twitter or Facebook or email, someone in your social network is contacting you in some way all the time,” Strayer says.
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Optimal Census by Quorum Sensing

Optimal Census by Quorum Sensing | Social Foraging | Scoop.it
Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.
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Inside the Wonderful World of Bee Cognition – How it All Began

Inside the Wonderful World of Bee Cognition – How it All Began | Social Foraging | Scoop.it
One of the first things I get asked when I tell people that I work on bee cognition (apart from ‘do you get stung a lot?’) is ‘bees have cognition?’. I usually assume that this question shouldn’t be taken literally otherwise it would mean that whoever was asking me this thought that there was a possibility that bees didn’t have cognition and I had just been making a terrible mistake for the past two years. Instead I guess this question actually means ‘please tell me more about the kind of cognitive abilities bees have, as I am very much surprised to hear that bees can do more than just mindlessly sting people’. So, here it is: a summary of some of the more remarkable things that bees can do with their little brains. In the first part of two articles on this topic, I introduce the history and basics of bee learning. In the second article, I go on to discuss the more advanced cognitive abilities of bees.
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gathersord's curator insight, April 28, 5:03 AM

nice

firesolid's curator insight, May 2, 1:45 AM

Its fascinating

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Temporal dynamics in fMRI resting-state activity

Temporal dynamics in fMRI resting-state activity | Social Foraging | Scoop.it
In a significant new study, Mitra et al. (1) demonstrate the existence of reproducible temporal patterns of spontaneous activity from human functional magnetic resonance imaging (fMRI) recordings. This finding and the novel methods used to demonstrate it bring the question of the role of temporally patterned activity into the domain of human cognition.

The Brain as a Dynamical Machine What the brain does is ultimately simple: it takes in sensory information, transforms it into an abstract code of spikes, and uses it to generate motor patterns. This spike code thus constitutes a mental representation of the world, which interacts with memories, expectations, motivations, and other internal states of the animal to generate a series of behaviors that are adaptive and intelligent, and maximize the survival of the individual and the spread of its genes.
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Can the Intellectual Processes in Science Also Be Simulated? The Anticipation and Visualization of Possible Future States

Socio-cognitive action reproduces and changes both social and cognitive structures. The analytical distinction between these dimensions of structure provides us with richer models of scientific development. In this study, I assume that (i) social structures organize expectations into belief structures that can be attributed to individuals and communities; (ii) expectations are specified in scholarly literature; and (iii) intellectually the sciences (disciplines, specialties) tend to self-organize as systems of rationalized expectations. Whereas social organizations remain localized, academic writings can circulate, and expectations can be stabilized and globalized using symbolically generalized codes of communication. The intellectual restructuring, however, remains latent as a second-order dynamics that can be accessed by participants only reflexively. Yet, the emerging "horizons of meaning" provide feedback to the historically developing organizations by constraining the possible future states as boundary conditions. I propose to model these possible future states using incursive and hyper-incursive equations from the computation of anticipatory systems. Simulations of these equations enable us to visualize the couplings among the historical--i.e., recursive--progression of social structures along trajectories, the evolutionary--i.e., hyper-incursive--development of systems of expectations at the regime level, and the incursive instantiations of expectations in actions, organizations, and texts.
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