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Experimental evolution of prepared learning

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

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

 

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Dynamics of Social Interaction
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Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data

Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data | Social Foraging | Scoop.it
Traffic congestion varies spatially and temporally. The observation of the formation, propagation and dispersion of network traffic congestion can lead to insights about the network performance, the bottleneck dynamics etc. While many researchers use the traffic flow data to reconstruct the congestion profile, the data missing problem is bypassed. Current methods either omit the missing data or supplement the missing part by average etc. Great error may be introduced during these processes. Rather than simply discarding the missing data, this research regards the data missing event as a result of either the severe congestion which prevent the floating vehicle from entering the congested area, or a type of feature of the resulting traffic flow time series. Hence a new traffic flow operational index time series similarity measurement is expected to be established as a basis of identifying the dynamic network bottleneck. The method first measures the traffic flow operational similarity between pairs of neighboring links, and then the similarity results are used to cluster the spatial-temporal congestion. In order to get the similarity under missing data condition, the measurement is implemented in a two-stage manner: firstly the so called first order similarity is calculated given that the traffic flow variables are bounded both upside and downside; then the first order similarity is aggregated to generate the second order similarity as the output. We implement the method on part of the real-world road network; the results generated are not only consistent with empirical observation, but also provide useful insights.

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The Businesses That Platforms Are Actually Disrupting

Platforms are all the rage these days. Powered by online technologies, they are sweeping across the economic landscape, striking down companies large and small. Uber’s global assault on the taxi industry is well known. Many platforms, some household names and others laboring in obscurity, are doing the same in other sectors.

Surveying these changes, you might conclude that if your business isn’t a platform, you had better worry that one is coming your way. Everyone from automakers to plumbers should count their days as traditional businesses. And maybe you should jump on the platform bandwagon too. If it worked for Airbnb, why not you?

Based on our research into the wave of online platforms that have started in the last two decades, we don’t necessarily disagree. Traditional businesses should worry, and maybe they should think about platform strategies. But we think these conclusions are overwrought — and miss what’s really going on.
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The Computational Properties of a Simplified Cortical Column Model

The Computational Properties of a Simplified Cortical Column Model | Social Foraging | Scoop.it
The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.

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SantaFe Institute: Networks, AI, and social psychology for social change

SantaFe Institute: Networks, AI, and social psychology for social change | Social Foraging | Scoop.it
Influencing social change on a broad scale is a chronically difficult problem. But what if you could identify – and then target and train at exactly the right time – those members of a population most likely to have the greatest influence on their peers?
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Google AdWords New Machine Learning Based Smart Bidding

Google AdWords New Machine Learning Based Smart Bidding | Social Foraging | Scoop.it


Everything at Google is going the way of machine learning, not just the searc
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Advanced Algorithms for Local Routing Strategy on Complex Networks

Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets R c increases by over ten-fold and the average transmission time ⟨ T ⟩ decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks.
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The Store Of The Future: 72 Startups Transforming Bricks-And-Mortar Retail In One Infographic

The Store Of The Future: 72 Startups Transforming Bricks-And-Mortar Retail In One Infographic | Social Foraging | Scoop.it
Transforming bricks-and-mortar shopping is a high-stakes endeavor for retailers given Americans still do over 90% of our shopping in physical stores. In fact, one of the latest trends in retail is the launch of physical stores by online e-commerce companies, including Amazon, Warby Parker, and Birchbox.

Dozens of startups have taken on the challenge of helping retailers bridge the gap between digital and physical commerce through features ranging from shelf-stocking robots, to augmented reality displays, to Wi-Fi based beacons that collect data on shopper behavior.

Using CB Insights data, we identified startups enhancing the in-store experience with digital tools. The startups in our list have racked up partnerships with many big name brands — including Maybelline, Lancome, Kiehl’s, Cabela’s, Foot Locker, Home Depot, Express — and department stores, from Lord & Taylor to Target.
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Real-Time Anomaly Detection for Streaming Analytics

Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.
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Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks

Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks | Social Foraging | Scoop.it
Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.
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‘Differential Privacy’ Is About Collecting Your Data—But Not ​Your Data

‘Differential Privacy’ Is About Collecting Your Data—But Not ​Your Data | Social Foraging | Scoop.it
At WWDC, Apple name-checked the statistical science of learning as much as possible about a group while learning as little as possible about any individual in it.
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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences | Social Foraging | Scoop.it
Author Summary From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.
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Near-Real Time Sessionization with Spark Streaming and Apache Hadoop

Near-Real Time Sessionization with Spark Streaming and Apache Hadoop | Social Foraging | Scoop.it
This Spark Streaming use case is a great example of how near-real-time processing can be brought to Hadoop.

Spark Streaming is one of the most interesting components within the Apache Spark stack. With Spark Streaming, you can create data pipelines that process streamed data using the same API that you use for processing batch-loaded data. Furthermore, Spark Steaming’s “micro-batching” approach provides decent resiliency should a job fail for some reason.

In this post, I will demonstrate and walk you through some common and advanced Spark Streaming functionality via the use case of doing near-real time sessionization of Website events, then load stats about that activity into Apache HBase, and then populate graphs in your preferred BI tool for analysis. (Sessionization refers to the capture of all clickstream activity within the timeframe of a single visitor’s Website session.) You can find the code for this demo here.

A system like this one can be super-useful for understanding visitor behavior (whether human or machine). With some additional work, it can also be designed to contain windowing patterns for detecting possible fraud in an asynchronous manner.
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Agent-based model of information spread in social networks

We propose evolution rules of the multiagent network and determine statistical patterns in life cycle of agents - information messages. The main discussed statistical pattern is connected with the number of likes and reposts for a message. This distribution corresponds to Weibull distribution according to modeling results. We examine proposed model using the data from Twitter, an online social networking service.
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Mary Meeker's 2016 internet trends report: The internet is slowing down, messaging is taking on the home screen, and voice search is big.

The internet is slowing down, messaging is taking on the home screen, and voice search is big.
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Decoding Spontaneous Emotional States in the Human Brain

Decoding Spontaneous Emotional States in the Human Brain | Social Foraging | Scoop.it
Pattern classification of human brain activity provides unique insight into the neural underpinnings of diverse mental states. These multivariate tools have recently been used within the field of affective neuroscience to classify distributed patterns of brain activation evoked during emotion induction procedures. Here we assess whether neural models developed to discriminate among distinct emotion categories exhibit predictive validity in the absence of exteroceptive emotional stimulation. In two experiments, we show that spontaneous fluctuations in human resting-state brain activity can be decoded into categories of experience delineating unique emotional states that exhibit spatiotemporal coherence, covary with individual differences in mood and personality traits, and predict on-line, self-reported feelings. These findings validate objective, brain-based models of emotion and show how emotional states dynamically emerge from the activity of separable neural systems.

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Stimulation-Based Control of Dynamic Brain Networks

Stimulation-Based Control of Dynamic Brain Networks | Social Foraging | Scoop.it
The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbations. To study the system wide impact of regional stimulation, we employ a data-driven computational model of nonlinear brain dynamics to systematically explore the effects of targeted stimulation. Validating predictions from network control theory, we uncover the relationship between regional controllability and the focal versus global impact of stimulation, and we relate these findings to differences in the underlying network architecture. Finally, by mapping brain regions to cognitive systems, we observe that the default mode system imparts large global change despite being highly constrained by structural connectivity. This work forms an important step towards the development of personalized stimulation protocols for medical treatment or performance enhancement.

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How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences

The weight with which a specific outcome feature contributes to preference quantifies a person’s ‘taste’ for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person’s choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person’s choice variability. Temporal discounting coefficients are an important example of taste–for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others’ choices. We examined discounting preferences in a novel, large community study of 14–24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person’s choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.

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The Sapir-Whorf Hypothesis and Probabilistic Inference: Evidence from the Domain of Color

The Sapir-Whorf hypothesis holds that our thoughts are shaped by our native language, and that speakers of different languages therefore think differently. This hypothesis is controversial in part because it appears to deny the possibility of a universal groundwork for human cognition, and in part because some findings taken to support it have not reliably replicated. We argue that considering this hypothesis through the lens of probabilistic inference has the potential to resolve both issues, at least with respect to certain prominent findings in the domain of color cognition. We explore a probabilistic model that is grounded in a presumed universal perceptual color space and in language-specific categories over that space. The model predicts that categories will most clearly affect color memory when perceptual information is uncertain. In line with earlier studies, we show that this model accounts for language-consistent biases in color reconstruction from memory in English speakers, modulated by uncertainty. We also show, to our knowledge for the first time, that such a model accounts for influential existing data on cross-language differences in color discrimination from memory, both within and across categories. We suggest that these ideas may help to clarify the debate over the Sapir-Whorf hypothesis.
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Modelling the Evolution of Social Structure

Modelling the Evolution of Social Structure | Social Foraging | Scoop.it
Although simple social structures are more common in animal societies, some taxa (mainly mammals) have complex, multi-level social systems, in which the levels reflect differential association. We develop a simulation model to explore the conditions under which multi-level social systems of this kind evolve. Our model focuses on the evolutionary trade-offs between foraging and social interaction, and explores the impact of alternative strategies for distributing social interaction, with fitness criteria for wellbeing, alliance formation, risk, stress and access to food resources that reward social strategies differentially. The results suggest that multi-level social structures characterised by a few strong relationships, more medium ties and large numbers of weak ties emerge only in a small part of the overall fitness landscape, namely where there are significant fitness benefits from wellbeing and alliance formation and there are high levels of social interaction. In contrast, ‘favour-the-few’ strategies are more competitive under a wide range of fitness conditions, including those producing homogeneous, single-level societies of the kind found in many birds and mammals. The simulations suggest that the development of complex, multi-level social structures of the kind found in many primates (including humans) depends on a capacity for high investment in social time, preferential social interaction strategies, high mortality risk and/or differential reproduction. These conditions are characteristic of only a few mammalian taxa.
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Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates | Social Foraging | Scoop.it
Functional MRI (fMRI) is 25 years old, yet surprisingly its most common statistical methods have not been validated using real data. Here, we used resting-state fMRI data from 499 healthy controls to conduct 3 million task group analyses. Using this null data with different experimental designs, we estimate the incidence of significant results. In theory, we should find 5% false positives (for a significance threshold of 5%), but instead we found that the most common software packages for fMRI analysis (SPM, FSL, AFNI) can result in false-positive rates of up to 70%. These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.

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Google buys French startup Moodstocks to boost machine learning muscle

Google buys French startup Moodstocks to boost machine learning muscle | Social Foraging | Scoop.it
Google announced today it has acquired machine learning startup Moodstocks in an effort to bolster its work around smartphone image and item recognition. Terms of the deal were not disclosed.
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Nielsen : Consumer Neuroscience Unveils Trailblazing Ad Testing Solution

Nielsen Consumer Neuroscience, the leader in measuring non-conscious responses to deliver consumer insights, today announced the launch of an advertising research solution that will set a new standard for marketers looking to elevate their advertising creative and optimize in-market performance.

Video Ad Explorer, which was shown to predict in-market consumer sales behavior in a ground-breaking study with CBS, integrates the most comprehensive suite of neuroscience technologies. It helps brands unlock consumer insights and unravel the complexities of advertising creative development with unprecedented predictive power.

While individual neuroscience measures provide some level of prediction to in-market sales, Video Ad Explorer employs unique analyses using a rich combination of neuroscience tools for the highest level of prediction on a global scale. With analysis and feedback on a second by second basis, the results and insights can help optimize ideas and turn good advertising into great advertising.

By evaluating creative with measures from electroencephalography (EEG), core biometrics (which includes skin conductance response and heart rate), facial coding, eye tracking and self-report, brands can access their unique, complementary insights into the complexity of the consumer brain. The integrated use of these tools improves the ability of ad creative to drive-in-market success.

"Over the years, brands have had to settle for incomplete tools and processes for understanding creative development, but Video Ad Explorer changes that," said Dr. Carl Marci, Chief Neuroscientist for Nielsen Consumer Neuroscience. "By integrating these tools, we're providing brand teams with a full picture of their consumers' thinking and emotional response that will create greater confidence and understanding about how their creative will perform."
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Jelel Ezzine's curator insight, June 17, 4:05 AM
non=conscious responses.
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Could a neuroscientist understand a microprocessor?

There is a popular belief in neuroscience that we are primarily data limited, that producing large, multimodal, and complex datasets will, enabled by data analysis algorithms, lead to fundamental insights into the way the brain processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. Here we take a simulated classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the processor. This suggests that current approaches in neuroscience may fall short of producing meaningful models of the brain.

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A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of  In Vivo  Activity | Social Foraging | Scoop.it
The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.
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Mixtape Application: Last.fm Data Characterization

This report analyses data collected from Last.fm and used to create a real-time recommendation system. We collected over 2M songs and 1M tags and 372K user's listening habits. We characterize users' profiles: age, playcount, friends, gender and country. We characterized song, artist and tag popularity, genres of songs. Additionally we evaluated the co-occurrence of songs in users' histories, which can be used to compute similarity between songs.
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