Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Match-making economists win Nobel prize

Match-making economists win Nobel prize | Social Foraging | Scoop.it

Life-saving kidney exchanges and ways for schools to select students are just two practical applications of the market-matching theories for which US economists Alvin Roth and Lloyd Shapley won the Nobel prize for economics on Monday.

 

Pairing up employers with job seekers - for instance doctors and lawyers taking up their first appointments - are other examples of how Roth, 60, and Shapley, 89, have separately applied game theory to daily life.

 

The Royal Swedish Academy of Sciences, which awards the 8 million crown ($US1.2 million) prize, called their work an outstanding example of economic engineering.

 

Lloyd Shapley raises his arm after being notified of winning the 2012 economics Noble prize. Photo: Reuters

 

Roth and other economists who study market design are exploring how matching procedures can be improved to produce better results for all concerned.

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Stable image reconstruction using total variation minimization

This article presents near-optimal guarantees for accurate and robust image recovery from under-sampled noisy measurements using total variation minimization, and our results may be the first of this kind. In particular, we show that from O(slog(N)) nonadaptive linear measurements, an image can be reconstructed to within the best s-term approximation of its gradient up to a logarithmic factor, and this factor can be removed by taking slightly more measurements. Along the way, we prove a strengthened Sobolev inequality for functions lying in the null space of a suitably incoherent matrix.

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Google Puts Its Virtual Brain Technology to Work

Google Puts Its Virtual Brain Technology to Work | Social Foraging | Scoop.it

This summer Google set a new landmark in the field of artificial intelligence with software that learned how to recognize cats, people, and other things simply by watching YouTube videos (see "Self-Taught Software"). That technology, modeled on how brain cells operate, is now being put to work making Google's products smarter, with speech recognition being the first service to benefit.

 

Google's learning software is based on simulating groups of connected brain cells that communicate and influence one another. When such a neural network, as it's called, is exposed to data, the relationships between different neurons can change. That causes the network to develop the ability to react in certain ways to incoming data of a particular kind—and the network is said to have learned something.

 

Neural networks have been used for decades in areas where machine learning is applied, such as chess-playing software or face detection. Google's engineers have found ways to put more computing power behind the approach than was previously possible, creating neural networks that can learn without human assistance and are robust enough to be used commercially, not just as research demonstrations.

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Explaining the origins of word order using information theory

Explaining the origins of word order using information theory | Social Foraging | Scoop.it

The majority of languages — roughly 85 percent of them — can be sorted into two categories: those, like English, in which the basic sentence form is subject-verb-object (“the girl kicks the ball”), and those, like Japanese, in which the basic sentence form is subject-object-verb (“the girl the ball kicks”).

 

The reason for the difference has remained somewhat mysterious, but researchers from MIT’s Department of Brain and Cognitive Sciences now believe that they can account for it using concepts borrowed from information theory, the discipline, invented almost singlehandedly by longtime MIT professor Claude Shannon, that led to the digital revolution in communications. The researchers will present their hypothesis in an upcoming issue of the journal Psychological Science.

 

Shannon was largely concerned with faithful communication in the presence of “noise” — any external influence that can corrupt a message on its way from sender to receiver. Ted Gibson, a professor of cognitive sciences at MIT and corresponding author on the new paper, argues that human speech is an example of what Shannon called a “noisy channel.”

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Scientists discover mice can sing and mimic complex sounds

Scientists discover mice can sing and mimic complex sounds | Social Foraging | Scoop.it

Male mice can sing like humans, using high-pitched love songs to woo females and even spice up their tunes to overcome any competition, a new study has found.

 

Researchers found the mice have certain brain features, somewhat similar to humans and song-learning birds, which they may use to change their sounds.

 

"We are claiming that mice have limited versions of the brain and behaviour traits for vocal learning that are found in humans for learning speech and in birds for learning song," said Duke neurobiologist Erich Jarvis, who led the study.

 

The discovery contradicts scientists' 60-year-old assumption that mice do not have vocal learning traits at all.

 

"If we're not wrong, these findings will be a big boost to scientists studying diseases like autism and anxiety disorders," said Jarvis, a Howard Hughes Medical Institute investigator.

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Control Centrality and Hierarchical Structure in Complex Networks

Control Centrality and Hierarchical Structure in Complex Networks | Social Foraging | Scoop.it

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.

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Superman-strength bacteria produce 24-karat gold

Superman-strength bacteria produce 24-karat gold | Social Foraging | Scoop.it

At a time when the value of gold has reached an all-time high, Michigan State University researchers have discovered a bacterium's ability to withstand incredible amounts of toxicity is key to creating 24-karat gold."Microbial alchemy is what we're doing -- transforming gold from something that has no value into a solid, precious metal that's valuable," said Kazem Kashefi, assistant professor of microbiology and molecular genetics.

 

He and Adam Brown, associate professor of electronic art and intermedia, found the metal-tolerant bacteria Cupriavidus metallidurans can grow on massive concentrations of gold chloride -- or liquid gold, a toxic chemical compound found in nature.

 

In fact, the bacteria are at least 25 times stronger than previously reported among scientists, the researchers determined in their art installation, "The Great Work of the Metal Lover," which uses a combination of biotechnology, art and alchemy to turn liquid gold into 24-karat gold. The artwork contains a portable laboratory made of 24-karat gold-plated hardware, a glass bioreactor and the bacteria, a combination that produces gold in front of an audience.

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These electronics can biodegrade in your body

These electronics can biodegrade in your body | Social Foraging | Scoop.it

A new class of tiny electronic devices can dissolve completely in water—or bodily fluids.“We refer to this type of technology as transient electronics,” says John A. Rogers, professor of engineering at the University of Illinois, who led the multidisciplinary research team that demonstrated the new technology.

 

“From the earliest days of the electronics industry, a key design goal has been to build devices that last forever—with completely stable performance. But if you think about the opposite possibility—devices that are engineered to physically disappear in a controlled and programmed manner—then other, completely different kinds of application opportunities open up.”

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Twelve-Month-Olds' Understanding of Intention Transfer through Communication

Twelve-Month-Olds' Understanding of Intention Transfer through Communication | Social Foraging | Scoop.it

Do infants understand that intention can be transferred through communication? We answered this question by examining 12-month-olds' looking times in a violation-of-expectation paradigm with two human agents. In familiarization, the non-acting agent spoke, clapped her hands, read aloud a book, or remained silent before the acting agent grasped one (the target) of two objects. During test only the non-actor remained, grasping either the target or distractor. The infants looked longer in the distractor than target condition, suggesting violation of expectation, only if the non-actor had spoken or clapped in familiarization. Because the non-actor never had grasped any of the objects in familiarization, the infants' expectation on her behavior could have developed from the understanding that her intention was transferred to the actor, who executed it by grasping the target in familiarization, via speaking and clapping as acts of communication (but not reading aloud and remaining silent).

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Structural Design Principles of Complex Bird Songs: A Network-Based Approach

Structural Design Principles of Complex Bird Songs: A Network-Based Approach | Social Foraging | Scoop.it

Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks–transition relationships among different phrases and the related structural measures–are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a ‘small-world’ architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date.

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Early Warnings of Regime Shift When the Ecosystem Structure Is Unknown

Early Warnings of Regime Shift When the Ecosystem Structure Is Unknown | Social Foraging | Scoop.it

Abrupt changes in dynamics of an ecosystem can sometimes be detected using monitoring data. Using nonparametric methods that assume minimal knowledge of the underlying structure, we compute separate estimates of the drift (deterministic) and diffusion (stochastic) components of a general dynamical process, as well as an indicator of the conditional variance. Theory and simulations show that nonparametric conditional variance rises prior to critical transition. Nonparametric diffusion rises also, in cases where the true diffusion function involves a critical transition (sometimes called a noise-induced transition). Thus it is possible to discriminate noise-induced transitions from other kinds of critical transitions by comparing time series for the conditional variance and the diffusion function. Monte Carlo analysis shows that the indicators generally increase prior to the transition, but uncertainties of the indicators become large as the ecosystem approaches the transition point.

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Understanding Patchy Landscape Dynamics: Towards a Landscape Language

Understanding Patchy Landscape Dynamics: Towards a Landscape Language | Social Foraging | Scoop.it

Patchy landscapes driven by human decisions and/or natural forces are still a challenge to be understood and modelled. No attempt has been made up to now to describe them by a coherent framework and to formalize landscape changing rules. Overcoming this lacuna was our first objective here, and this was largely based on the notion of Rewriting Systems, also called Formal Grammars. We used complicated scenarios of agricultural dynamics to model landscapes and to write their corresponding driving rule equations. Our second objective was to illustrate the relevance of this landscape language concept for landscape modelling through various grassland managements, with the final aim to assess their respective impacts on biological conservation. For this purpose, we made the assumptions that a higher grassland appearance frequency and higher land cover connectivity are favourable to species conservation. Ecological results revealed that dairy and beef livestock production systems are more favourable to wild species than is hog farming, although in different ways. Methodological results allowed us to efficiently model and formalize these landscape dynamics. This study demonstrates the applicability of the Rewriting System framework to the modelling of agricultural landscapes and, hopefully, to other patchy landscapes. The newly defined grammar is able to explain changes that are neither necessarily local nor Markovian, and opens a way to analytical modelling of landscape dynamics.

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The Social Brain: Transcriptome Assembly and Characterization of the Hippocampus from a Social Subterranean Rodent, the Colonial Tuco-Tuco (Ctenomys sociabilis)

The Social Brain: Transcriptome Assembly and Characterization of the Hippocampus from a Social Subterranean Rodent, the Colonial Tuco-Tuco (Ctenomys sociabilis) | Social Foraging | Scoop.it

Elucidating the genetic mechanisms that underlie complex adaptive phenotypes is a central problem in evolutionary biology. For behavioral biologists, the ability to link variation in gene expression to the occurrence of specific behavioral traits has long been a largely unobtainable goal. Social interactions with conspecifics represent a fundamental component of the behavior of most animal species. Although several studies of mammals have attempted to uncover the genetic bases for social relationships using a candidate gene approach, none have attempted more comprehensive, transcriptome-based analyses using high throughout sequencing. As a first step toward improved understanding of the genetic underpinnings of mammalian sociality, we generated a reference transcriptome for the colonial tuco-tuco (Ctenomys sociabilis), a social species of subterranean rodent that is endemic to southwestern Argentina. Specifically, we analyzed over 500 million Illumina sequencing reads derived from the hippocampi of 10 colonial tuco-tucos housed in captivity under a variety of social conditions. The resulting reference transcriptome provides a critical tool for future studies aimed at exploring relationships between social environment and gene expression in this non-model species of social mammal.

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Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips

The advent of the Internet, with sophisticated algorithmic search engines, has made accessing information as easy as lifting a finger. No longer do we have to make costly efforts to find the things we want. We can “Google” the old classmate, find articles online, or look up the actor who was on the tip of our tongue. The results of four studies suggest that when faced with difficult questions, people are primed to think about computers and that when people expect to have future access to information, they have lower rates of recall of the information itself and enhanced recall instead for where to access it. The Internet has become a primary form of external or transactive memory, where information is stored collectively outside ourselves.

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EarthRisk Technologies Announces First Fully Operational "Genetic Algorithm" for Long-Range Commercial Weather Forecasts

EarthRisk Technologies, a pioneer in the research, analysis and visualization of extreme temperature and weather pattern risks, released today TempRisk 4.0, an important update of the company's flagship platform used for detecting both hot and cold weather patternsup to 40 days before they occur.The core improvement for TempRisk 4.0 is the introduction of the new comprehensive empirical forecast model which optimizes forecast signals through the usage of a genetic algorithm. This algorithm works by producing educated guesses to the solution of a problem, keeping good solutions and removing bad ones, effectively creating a "survival of the fittest" method of optimization.

 

Genetic algorithms are increasingly used to plan efficient routes and scheduling for travel planners, traffic routers and shipping companies. Automotive design companies use genetic algorithms to determine materials and shapes for faster, lighter, fuel-efficient vehicles. And the telecommunications industry developed genetic algorithms to optimize placement of routing cell towers for best coverage.

 

"By using genetic algorithms with our empirical forecast model, TempRisk is now able to present more accurate forecasts by finding best possible combinations of precursor weather patterns and subsequent extreme temperature events," says EarthRisk CEO John Plavan. "It significantly increases forecast skill and reduces false alarms by intelligently identifying and removing redundant synoptic precursor signals. We also redesigned the TempRisk Almanac, TempRisk Indices, Dashboard, and Forecast Scorecard to give our commercial clients a more powerful and user friendly forecasting tool."

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MIT team builds most complex synthetic biology circuit yet

MIT team builds most complex synthetic biology circuit yet | Social Foraging | Scoop.it

Using genes as interchangeable parts, synthetic biologists design cellular circuits that can perform new functions, such as sensing environmental conditions. However, the complexity that can be achieved in such circuits has been limited by a critical bottleneck: the difficulty in assembling genetic components that don’t interfere with each other.

 

Unlike electronic circuits on a silicon chip, biological circuits inside a cell cannot be physically isolated from one another. “The cell is sort of a burrito. It has everything mixed together,” says Christopher Voigt, an associate professor of biological engineering at MIT.

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MIT's CSAIL launches new center to tackle the future of wireless and mobile technologies

MIT's CSAIL launches new center to tackle the future of wireless and mobile technologies | Social Foraging | Scoop.it

The MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) today inaugurated a new interdisciplinary center dedicated to developing the next generation of wireless networks and mobile devices. Headquartered at CSAIL and known as Wireless@MIT, the center will be a focal point for wireless research at MIT and will address some of the most important challenges facing the wireless and mobile-computing fields.

 

Wireless@MIT will involve more than 50 MIT faculty members, research staff and graduate students across different labs and academic departments, and will work with seven founding industry affiliates: Amazon, Cisco, Intel, MediaTek, Microsoft Research, STMicroelectronics and Telefonica.

 

“There are already over five billion mobile phones in the world today; add to this all the tablets, laptops, medical devices and wireless sensors, and the numbers are staggering,” says Hari Balakrishnan, the Fujitsu Professor in Electrical Engineering and Computer Science, who will serve as co-director of the center. “The goal of our center is to push the frontiers of wireless research to their full potential, and to ensure that the industry that grows up around these new devices is able to work in innovative and productive ways.”

 

The center’s work will focus on three key areas, the most important of which is the spectrum crisis: the exhaustion of radio spectrum caused by the explosive popularity of wireless systems. Wireless@MIT researchers aim to develop new techniques for overcoming this problem.

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Of Mice, Birds, and Men: The Mouse Ultrasonic Song System Has Some Features Similar to Humans and Song-Learning Birds

Of Mice, Birds, and Men: The Mouse Ultrasonic Song System Has Some Features Similar to Humans and Song-Learning Birds | Social Foraging | Scoop.it

Humans and song-learning birds communicate acoustically using learned vocalizations. The characteristic features of this social communication behavior include vocal control by forebrain motor areas, a direct cortical projection to brainstem vocal motor neurons, and dependence on auditory feedback to develop and maintain learned vocalizations. These features have so far not been found in closely related primate and avian species that do not learn vocalizations. Male mice produce courtship ultrasonic vocalizations with acoustic features similar to songs of song-learning birds.

 

However, it is assumed that mice lack a forebrain system for vocal modification and that their ultrasonic vocalizations are innate. Here we investigated the mouse song system and discovered that it includes a motor cortex region active during singing, that projects directly to brainstem vocal motor neurons and is necessary for keeping song more stereotyped and on pitch. We also discovered that male mice depend on auditory feedback to maintain some ultrasonic song features, and that sub-strains with differences in their songs can match each other's pitch when cross-housed under competitive social conditions. We conclude that male mice have some limited vocal modification abilities with at least some neuroanatomical features thought to be unique to humans and song-learning birds. To explain our findings, we propose a continuum hypothesis of vocal learning.

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A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control

A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control | Social Foraging | Scoop.it

Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.

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Researchers create virtual Android network to simulate attacks

Researchers create virtual Android network to simulate attacks | Social Foraging | Scoop.it

What happens to users and their phones in the event of a mobile malware epidemic or a terrorist attack? Researchers at a California-based lab are hoping to answer those very questions using simulations.

 

Researchers at Sandia National Laboratories in California have developed a virtual Android-based network that can be used to simulate what could happen in the event of an attack on the devices.

 

The network, dubbed MegaDroid, consists of 300,000 virtual Android devices that receive spoofed GPS coordinates to simulate human movement throughout a town. Researchers have also combined the virtual network with Google Street View, so that they can watch, in real time, the virtual devices wandering around.

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Markets are efficient if and only if P = NP

I prove that if markets are weak-form efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can "program" the market to solve NP-complete problems. Since P probably does not equal NP, markets are probably not efficient. Specifically, markets become increasingly inefficient as the time series lengthens or becomes more frequent. An illustration by way of partitioning the excess returns to momentum strategies based on data availability confirms this prediction.

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Empathy Emerges Spontaneously in the Ultimatum Game: Small Groups and Networks

Empathy Emerges Spontaneously in the Ultimatum Game: Small Groups and Networks | Social Foraging | Scoop.it

The Ultimatum game, in which one subject proposes how to share a pot and the other has veto power on the proposal, in which case both lose everything, is a paradigmatic scenario to probe the degree of cooperation and altruism in human subjects. It has been shown that if individuals are empathic, i.e., they play the game having in mind how their opponent will react by offering an amount that they themselves would accept, then non-rational large offers well above the smallest possible ones are evolutionarily selected. We here show that empathy itself may be selected and need not be exogenously imposed provided that interactions take place only with a fraction of the total population, and that the role of proposer or responder is randomly changed from round to round. These empathic agents, that displace agents with independent (uncorrelated) offers and proposals, behave far from what is expected rationally, offering and accepting sizable fractions of the amount to be shared. Specific values for the typical offer depend on the details of the interacion network and on the existence of hubs, but they are almost always significantly larger than zero, indicating that the mechanism at work here is quite general and could explain the emergence of empathy in very many different contexts.

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Immigration, Local Dispersal Limitation, and the Repeatability of Community Composition under Neutral and Niche Dynamics

Immigration, Local Dispersal Limitation, and the Repeatability of Community Composition under Neutral and Niche Dynamics | Social Foraging | Scoop.it

Repeatability of community composition has been a critical aspect for community structure, which is closely associated with community stability, predictability, conservation biology and ecological restoration. It has been shown that both immigration and local dispersal limitation can affect the community composition in both neutral and niche model. Hence, we use a spatially explicit individual-based model to investigate the potential influence of immigration rate and strength of local dispersal limitation on repeatability in both neutral and niche models.

 

Similarity measures are used to quantify repeatability. We examine the repeatability of community composition among replicate communities (which means the same community repeats many times), and between niche and neutral replicate communities. We find the correlation between repeatability and immigration rate is positive in the neutral model and an inverted unimodal in the niche model. The correlation between repeatability and local dispersal distance is positive in the niche model and negative in the neutral model. High repeatability between niche communities and neutral communities is observed with high immigration rates or when high local dispersal distance appears in the niche model or low local dispersal distance in the neutral model. Our results show that repeatability of community composition is not only dependent on the types of community models (niche vs. neutrality) but also strongly determined by immigration rates and local dispersal limitation.

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Why The Current Facebook Engagement Rate Calculation Is Inaccurate

As the social media analytics field is pretty new, it’s really important to understand how the metrics are computed.

 

Let’s take for example, one of the main metrics in measuring one’s page performance : the “Engagement Rate”.

 

What is supposed to be measured with the Engagement Rate ?

 

“The Engagement Rate measures how well your Fans interact with your content”. That’s a common definition.

 

What’s that supposed to mean ? If you have a 0.02%

 

Engagement Rate, it means 0.02% of your fans have engaged

with your content, isn’t it ?

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Towards a Unified Understanding of Event-Related Changes in the EEG: The Firefly Model of Synchronization through Cross-Frequency Phase Modulation

Towards a Unified Understanding of Event-Related Changes in the EEG: The Firefly Model of Synchronization through Cross-Frequency Phase Modulation | Social Foraging | Scoop.it

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not.

 

Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

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