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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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An Evolutionary Model of Cooperation, Fairness and Altruistic Punishment in Public Good Games

An Evolutionary Model of Cooperation, Fairness and Altruistic Punishment in Public Good Games | Social Foraging | Scoop.it

We identify and explain the mechanisms that account for the emergence of fairness preferences and altruistic punishment in voluntary contribution mechanisms by combining an evolutionary perspective together with an expected utility model. We aim at filling a gap between the literature on the theory of evolution applied to cooperation and punishment, and the empirical findings from experimental economics. The approach is motivated by previous findings on other-regarding behavior, the co-evolution of culture, genes and social norms, as well as bounded rationality. Our first result reveals the emergence of two distinct evolutionary regimes that force agents to converge either to a defection state or to a state of coordination, depending on the predominant set of self- or other-regarding preferences. Our second result indicates that subjects in laboratory experiments of public goods games with punishment coordinate and punish defectors as a result of an aversion against disadvantageous inequitable outcomes. Our third finding identifies disadvantageous inequity aversion as evolutionary dominant and stable in a heterogeneous population of agents endowed initially only with purely self-regarding preferences. We validate our model using previously obtained results from three independently conducted experiments of public goods games with punishment.

 

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An Algorithm for Network-Based Gene Prioritization That Encodes Knowledge Both in Nodes and in Links

An Algorithm for Network-Based Gene Prioritization That Encodes Knowledge Both in Nodes and in Links | Social Foraging | Scoop.it

Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods – which utilize a knowledge network derived from biological knowledge – have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.

Results

We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.

Conclusions

The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.

 

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Human Sensorimotor Communication: A Theory of Signaling in Online Social Interactions

Human Sensorimotor Communication: A Theory of Signaling in Online Social Interactions | Social Foraging | Scoop.it

Although the importance of communication is recognized in several disciplines, it is rarely studied in the context of online social interactions and joint actions. During online joint actions, language and gesture are often insufficient and humans typically use non-verbal, sensorimotor forms of communication to send coordination signals. For example, when playing volleyball, an athlete can exaggerate her movements to signal her intentions to her teammates (say, a pass to the right) or to feint an adversary. Similarly, a person who is transporting a table together with a co-actor can push the table in a certain direction to signal where and when he intends to place it. Other examples of “signaling” are over-articulating in noisy environments and over-emphasizing vowels in child-directed speech. In all these examples, humans intentionally modify their action kinematics to make their goals easier to disambiguate. At the moment no formal theory exists of these forms of sensorimotor communication and signaling. We present one such theory that describes signaling as a combination of a pragmatic and a communicative action, and explains how it simplifies coordination in online social interactions. We cast signaling within a “joint action optimization” framework in which co-actors optimize the success of their interaction and joint goals rather than only their part of the joint action. The decision of whether and how much to signal requires solving a trade-off between the costs of modifying one’s behavior and the benefits in terms of interaction success. Signaling is thus an intentional strategy that supports social interactions; it acts in concert with automatic mechanisms of resonance, prediction, and imitation, especially when the context makes actions and intentions ambiguous and difficult to read. Our theory suggests that communication dynamics should be studied within theories of coordination and interaction rather than only in terms of the maximization of information transmission.

 

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Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems | Social Foraging | Scoop.it

For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results.

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Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks | Social Foraging | Scoop.it

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models.

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Self-Replicating USBs Spread Software Faster than an Internet Connection

Self-Replicating USBs Spread Software Faster than an Internet Connection | Social Foraging | Scoop.it

Getting hold of software, even freeware, is a significant challenge in the developing world. Bandwidth is such a precious commodity in these places that even modest downloads are beyond the budget of most people. 

 

The map above reveals the problem. It shows the density of IPv4 addresses around the world, a useful proxy for the density of internet servers.  Clearly the internet is not yet evenly distributed.

And therein lies the problem. In most parts of the world, a free operating system that is several gigabytes in size will take too long and cost too much to download. Consequently, much of the best freeware simply hasn’t spread to those who would benefit from it most.

 

That looks set to change, at least in part, thanks to some neat work by Thierry Monteil at the Université Montpellier II in France. This guy has devised a cheap and simple way to transmit large software packages without using the internet and at rates that eclipse all but the best internet connections.

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Listening to the World's Oceans: Searching for Marine Mammals by Detecting and Classifying Terabytes of Bioacoustic Data...

Collective Dynamics of Complex Systems Research Group Seminar Series November 20, 2013 Peter Dugan (Bioacoustics, Cornell University) "Listening to the World's…

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Sudden Progress on Prime Number Problem Has Mathematicians Buzzing

Sudden Progress on Prime Number Problem Has Mathematicians Buzzing | Social Foraging | Scoop.it

On May 13, an obscure mathematician — one whose talents had gone so unrecognized that he had worked at a Subway restaurant to make ends meet — garnered worldwide attention and accolades from the mathematics community for settling a long-standing open question about prime numbers, those numbers divisible by only one and themselves. Yitang Zhang, a lecturer at the University of New Hampshire, showed that even though primes get increasingly rare as you go further out along the number line, you will never stop finding pairs of primes separated by at most 70 million. His finding was the first time anyone had managed to put a finite bound on the gaps between prime numbers, representing a major leap toward proving the centuries-old twin primes conjecture, which posits that there are infinitely many pairs of primes separated by only two (such as 11 and 13).

 

In the months that followed, Zhang found himself caught up in a whirlwind of activity and excitement: He has lectured on his work at many of the nation’s preeminent universities, has received offers of jobs from top institutions in China and Taiwan and a visiting position at the Institute for Advanced Study in Princeton, N.J., and has been told that he will be promoted to full professor at the University of New Hampshire.

Meanwhile, Zhang’s work raised a question: Why 70 million? There is nothing magical about that number — it served Zhang’s purposes and simplified his proof. Other mathematicians quickly realized that it should be possible to push this separation bound quite a bit lower, although not all the way down to two.

 

By the end of May, mathematicians had uncovered simple tweaks to Zhang’s argument that brought the bound below 60 million. A May 30 blog post by Scott Morrison of the Australian National University in Canberra ignited a firestorm of activity, as mathematicians vied to improve on this number, setting one record after another. By June 4, Terence Tao of the University of California, Los Angeles, a winner of the Fields Medal, mathematics’ highest honor, had created a “Polymath project,” an open, online collaboration to improve the bound that attracted dozens of participants.

 

For weeks, the project moved forward at a breathless pace. “At times, the bound was going down every thirty minutes,” Tao recalled. By July 27, the team had succeeded in reducing the proven bound on prime gaps from 70 million to 4,680.

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Distribution of the Fittest Individuals and the Rate of Muller's Ratchet in a Model with Overlapping Generations

Distribution of the Fittest Individuals and the Rate of Muller's Ratchet in a Model with Overlapping Generations | Social Foraging | Scoop.it

Muller's ratchet is a paradigmatic model for the accumulation of deleterious mutations in a population of finite size. A click of the ratchet occurs when all individuals with the least number of deleterious mutations are lost irreversibly due to a stochastic fluctuation. In spite of the simplicity of the model, a quantitative understanding of the process remains an open challenge. In contrast to previous works, we here study a Moran model of the ratchet with overlapping generations. Employing an approximation which describes the fittest individuals as one class and the rest as a second class, we obtain closed analytical expressions of the ratchet rate in the rare clicking regime. As a click in this regime is caused by a rare, large fluctuation from a metastable state, we do not resort to a diffusion approximation but apply an approximation scheme which is especially well suited to describe extinction events from metastable states. This method also allows for a derivation of expressions for the quasi-stationary distribution of the fittest class. Additionally, we confirm numerically that the formulation with overlapping generations leads to the same results as the diffusion approximation and the corresponding Wright-Fisher model with non-overlapping generations.

 

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A Neurocomputational Model of the Mismatch Negativity

A Neurocomputational Model of the Mismatch Negativity | Social Foraging | Scoop.it

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering – a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN – in terms of latency and amplitude – and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and – more generally – illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

 

Paper: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003288

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Predictive Coding of Dynamical Variables in Balanced Spiking Networks

Predictive Coding of Dynamical Variables in Balanced Spiking Networks | Social Foraging | Scoop.it

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

 

Paper: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003258

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Cheaters Use Cognitive Tricks to Rationalize Infidelity

Cheaters Use Cognitive Tricks to Rationalize Infidelity | Social Foraging | Scoop.it

Most people believe that they are moral and good. They also believe cheating on a partner is wrong. So how do cheaters live with themselves after their infidelity? Understanding how they reconcile their indiscretions with their beliefs about themselves can help us figure out why “good people” cheat.

 

Dissonance theory predicts that when individuals’ thoughts and behaviors are inconsistent, something has to give. Have you ever wondered why anyone would be a smoker these days, given what we know about the link between “cancer sticks” and cancer? A smoker knows that smoking causes cancer, but might rationalize it by saying “I don’t smoke very much” or “My grandma smoked two packs a day and lived to be 90 years old!” By coming up with these rationalizations, people are able to preserve the impression that their behaviors and attitudes are consistent.

 

Similarly, cheaters might minimize the significance of their infidelity as a way to cope with knowing they did something wrong. The authors of a new study published in the Journal of Social and Personal Relationships propose that cheaters feel bad about their indiscretions, but try to feel better by reframing their past infidelities as uncharacteristic or out-of-the-ordinary behavior.

 

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ESA Swarm satellites to chart Earth's magnetic field

ESA Swarm satellites to chart Earth's magnetic field | Social Foraging | Scoop.it

The European Space Agency will launch three satellites this week from Russia’s Plesetsk Cosmodrome to gather data about the Earth’s magnetic field over the next few years. The planet’s magnetic poles have been shifting more and more rapidly over the last couple of decades, possibly as part of their usual flip from north to south every few hundred thousand years. The so-called “Swarm” mission will tell us about that and myriad other factors affecting the magnetic field surrounding Earth.

 

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Speech Recognition in Natural Background Noise

Speech Recognition in Natural Background Noise | Social Foraging | Scoop.it

In the real world, human speech recognition nearly always involves listening in background noise. The impact of such noise on speech signals and on intelligibility performance increases with the separation of the listener from the speaker. The present behavioral experiment provides an overview of the effects of such acoustic disturbances on speech perception in conditions approaching ecologically valid contexts. We analysed the intelligibility loss in spoken word lists with increasing listener-to-speaker distance in a typical low-level natural background noise. The noise was combined with the simple spherical amplitude attenuation due to distance, basically changing the signal-to-noise ratio (SNR). Therefore, our study draws attention to some of the most basic environmental constraints that have pervaded spoken communication throughout human history. We evaluated the ability of native French participants to recognize French monosyllabic words (spoken at 65.3 dB(A), reference at 1 meter) at distances between 11 to 33 meters, which corresponded to the SNRs most revealing of the progressive effect of the selected natural noise (−8.8 dB to −18.4 dB). Our results showed that in such conditions, identity of vowels is mostly preserved, with the striking peculiarity of the absence of confusion in vowels. The results also confirmed the functional role of consonants during lexical identification. The extensive analysis of recognition scores, confusion patterns and associated acoustic cues revealed that sonorant, sibilant and burst properties were the most important parameters influencing phoneme recognition. . Altogether these analyses allowed us to extract a resistance scale from consonant recognition scores. We also identified specific perceptual consonant confusion groups depending of the place in the words (onset vs. coda). Finally our data suggested that listeners may access some acoustic cues of the CV transition, opening interesting perspectives for future studies.

 

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Simulating the Evolution of the Human Family: Cooperative Breeding Increases in Harsh Environments

Simulating the Evolution of the Human Family: Cooperative Breeding Increases in Harsh Environments | Social Foraging | Scoop.it

Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members' decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers. The model presented here is inevitably a simplified representation of a human population, but it provides a basis for future modeling work toward evolutionary explanations of human behavior that consider the influence of both genetic and cultural transmission of behavior.

 

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High-Frequency Stimulation of Excitable Cells and Networks

High-Frequency Stimulation of Excitable Cells and Networks | Social Foraging | Scoop.it

High-frequency (HF) stimulation has been shown to block conduction in excitable cells including neurons and cardiac myocytes. However, the precise mechanisms underlying conduction block are unclear. Using a multi-scale method, the influence of HF stimulation is investigated in the simplified FitzhHugh-Nagumo and biophysically-detailed Hodgkin-Huxley models. In both models, HF stimulation alters the amplitude and frequency of repetitive firing in response to a constant applied current and increases the threshold to evoke a single action potential in response to a brief applied current pulse. Further, the excitable cells cannot evoke a single action potential or fire repetitively above critical values for the HF stimulation amplitude. Analytical expressions for the critical values and thresholds are determined in the FitzHugh-Nagumo model. In the Hodgkin-Huxley model, it is shown that HF stimulation alters the dynamics of ionic current gating, shifting the steady-state activation, inactivation, and time constant curves, suggesting several possible mechanisms for conduction block. Finally, we demonstrate that HF stimulation of a network of neurons reduces the electrical activity firing rate, increases network synchronization, and for a sufficiently large HF stimulation, leads to complete electrical quiescence. In this study, we demonstrate a novel approach to investigate HF stimulation in biophysically-detailed ionic models of excitable cells, demonstrate possible mechanisms for HF stimulation conduction block in neurons, and provide insight into the influence of HF stimulation on neural networks.

 

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Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector | Social Foraging | Scoop.it

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep activity at healthy, low levels, plasticity must therefore incorporate homeostatic control mechanisms. We find in numerical simulations of recurrent networks with a realistic triplet-based spike-timing-dependent plasticity rule (triplet STDP) that homeostasis has to detect rate changes on a timescale of seconds to minutes to keep the activity stable. We confirm this result in a generic mean-field formulation of network activity and homeostatic plasticity. Our results strongly suggest the existence of a homeostatic regulatory mechanism that reacts to firing rate changes on the order of seconds to minutes.

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Brain Connectivity Study Reveals Striking Differences Between Men and Women

Brain Connectivity Study Reveals Striking Differences Between Men and Women | Social Foraging | Scoop.it

A new brain connectivity study from Penn Medicine published today in the Proceedings of National Academy of Sciencesfound striking differences in the neural wiring of men and women that’s lending credence to some commonly-held beliefs about their behavior.

 

In one of the largest studies looking at the “connectomes” of the sexes, Ragini Verma, PhD, an associate professor in the department of Radiology at the Perelman School of Medicine at the University of Pennsylvania, and colleagues found greater neural connectivity from front to back and within one hemisphere in males, suggesting their brains are structured to facilitate connectivity between perception and coordinated action. In contrast, in females, the wiring goes between the left and right hemispheres, suggesting that they facilitate communication between the analytical and intuition.

 

“These maps show us a stark difference--and complementarity--in the architecture of the human brain that helps provide a potential neural basis as to why men excel at certain tasks, and women at others,” said Verma.

 

For instance, on average, men are more likely better at learning and performing a single task at hand, like cycling or navigating directions, whereas women have superior memory and social cognition skills, making them more equipped for multitasking and creating solutions that work for a group. They have a mentalistic approach, so to speak.

 

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Amazon Is Experimenting With Autonomous Flying Delivery Drones

Amazon Is Experimenting With Autonomous Flying Delivery Drones | Social Foraging | Scoop.it

Between launching a charity-friendly buying program, announcing Sunday deliveries, and gearing up for the first wave of frenzied holiday shoppers, Amazon has been busy these past few weeks. But that didn't stop CEO Jeff Bezos from spending a decent chunk of time talking to Charlie Rose on 60 Minutes about something, well, new.

 

60 Minutes has been more than happy to tease the unveiling with a clip of Bezos leading Rose into a room to show him something that elicited an “Oh my God!” from the veteran TV journo. The exclamation seemed to stem from a place of pleasure rather than worry, but the segment just aired and the truth is out.

 

So what did Bezos have up his proverbial sleeves? Amazon Prime Air drones that could feasibly be used as autonomous delivery vehicles. To hear the chief executive tell it, those electric drones - or “octocopters” as he referred to them - could make for delivery times as low as 30 minutes. Naturally, the size of those drones means there's a strict upper limit to how much cargo they can carry, but Bezos says they can carry packages of up to five pounds for round trips as long as 10 miles. Thankfully for Amazon, that means nearly 86 percent of the items that it carries can be lashed onto one of its sky-bound couriers.

 

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StartupNectar-The Biommimicry Business Incubator

StartupNectar-The Biommimicry Business Incubator | Social Foraging | Scoop.it

"StartupNectar enables early-stage, biomimicry-based ventures to access resources and gain traction in the marketplace. The incubation model is informed by nature’s strategies for creating conditions conducive to life".


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Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance

Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance | Social Foraging | Scoop.it

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

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Communication and Common Interest

Communication and Common Interest | Social Foraging | Scoop.it

Explaining the maintenance of communicative behavior in the face of incentives to deceive, conceal information, or exaggerate is an important problem in behavioral biology. When the interests of agents diverge, some form of signal cost is often seen as essential to maintaining honesty. Here, novel computational methods are used to investigate the role of common interest between the sender and receiver of messages in maintaining cost-free informative signaling in a signaling game. Two measures of common interest are defined. These quantify the divergence between sender and receiver in their preference orderings over acts the receiver might perform in each state of the world. Sampling from a large space of signaling games finds that informative signaling is possible at equilibrium with zero common interest in both senses. Games of this kind are rare, however, and the proportion of games that include at least one equilibrium in which informative signals are used increases monotonically with common interest. Common interest as a predictor of informative signaling also interacts with the extent to which agents' preferences vary with the state of the world. Our findings provide a quantitative description of the relation between common interest and informative signaling, employing exact measures of common interest, information use, and contingency of payoff under environmental variation that may be applied to a wide range of models and empirical systems.

 

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Dynamical Adaptation in Photoreceptors

Dynamical Adaptation in Photoreceptors | Social Foraging | Scoop.it

Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing. Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response. We introduce a non-linear dynamical adaptation model of photoreceptors. It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data. Yet the model is rich enough to capture intricate phenomenology. First, we show that it reproduces the known phenomenology of light response and short-term adaptation. Second, we present new recordings and demonstrate that the model reproduces cone response with great precision. Third, we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs, various forms of flickering inputs, and natural inputs. In particular, we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale, over ~ 300 ms—i. e., over the time scale of the response itself—and we confirm this prediction with data. For natural inputs, this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant.

 

Paper: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003289

 

 

 

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Genetic Architecture Promotes the Evolution and Maintenance of Cooperation

Genetic Architecture Promotes the Evolution and Maintenance of Cooperation | Social Foraging | Scoop.it

When cooperation has a direct cost and an indirect benefit, a selfish behavior is more likely to be selected for than an altruistic one. Kin and group selection do provide evolutionary explanations for the stability of cooperation in nature, but we still lack the full understanding of the genomic mechanisms that can prevent cheater invasion. In our study we used Aevol, an agent-based, in silico genomic platform to evolve populations of digital organisms that compete, reproduce, and cooperate by secreting a public good for tens of thousands of generations. We found that cooperating individuals may share a phenotype, defined as the amount of public good produced, but have very different abilities to resist cheater invasion. To understand the underlying genetic differences between cooperator types, we performed bio-inspired genomics analyses of our digital organisms by recording and comparing the locations of metabolic and secretion genes, as well as the relevant promoters and terminators. Association between metabolic and secretion genes (promoter sharing, overlap via frame shift or sense-antisense encoding) was characteristic for populations with robust cooperation and was more likely to evolve when secretion was costly. In mutational analysis experiments, we demonstrated the potential evolutionary consequences of the genetic association by performing a large number of mutations and measuring their phenotypic and fitness effects. The non-cooperating mutants arising from the individuals with genetic association were more likely to have metabolic deleterious mutations that eventually lead to selection eliminating such mutants from the population due to the accompanying fitness decrease. Effectively, cooperation evolved to be protected and robust to mutations through entangled genetic architecture. Our results confirm the importance of second-order selection on evolutionary outcomes, uncover an important genetic mechanism for the evolution and maintenance of cooperation, and suggest promising methods for preventing gene loss in synthetically engineered organisms.

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Writing Can Help Injuries Heal Faster: Expressive writing may lead to faster recovery from injury

Writing Can Help Injuries Heal Faster:     Expressive writing may lead to faster recovery from injury | Social Foraging | Scoop.it

Expressive writing is known to help assuage psychological trauma and improve mood. Now studies suggest that such writing, characterized by descriptions of one's deepest thoughts and feelings, also benefits physical health.

 

Researchers in New Zealand investigated whether expressive writing could help older adults heal faster after a medically necessary biopsy. In the study, 49 healthy adults aged 64 to 97 years wrote about either upsetting events or daily activities for 20 minutes, three days in a row. After a time lag of two weeks, to make sure any initial negative feelings stirred up by recalling upsetting events had passed, all the subjects had a biopsy on the arm, and photographs over the next 21 days tracked its healing. On the 11th day, 76 percent of the group that did expressive writing had fully healed as compared with 42 percent of the control group.

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Priscila Borba's curator insight, November 23, 2013 12:09 PM

Interesting concept in light of how emotionally distressing biopsies can be for patients who undergo them. The psychological components of healing and the creative power of the mind should not be overlooked!