Category Archives: ToRead

Phil 12.12.17

7:00 – 3:30 ASRC MKT

  • Need to make sure that an amplified agent also has amplified influence in calculating velocity – Fixed
  • Towards the end of this video is an interview with Ian Couzin talking about how mass communication is disrupting our ability to flock ‘correctly’ due to the decoupling of distance and information
  • Write up fire stampede. Backups everywhere, one hole, antennas burn so the AI keeps trust in A* but loses awareness as the antennas burn: “The Los Angeles Police Department asked drivers to avoid navigation apps, which are steering users onto more open routes — in this case, streets in the neighborhoods that are on fire.” [LA Times] Also this slow motion version of the same thing: For the Good of Society — and Traffic! — Delete Your Map App
  • First self-driving car ‘race’ ends in a crash at the Buenos Aires Formula E ePrix; two cars enter, one car survives
  • Taking a closer look at Oscillator Models and Collective Motion (178 Citations) and Consensus and Cooperation in Networked Multi-Agent Systems (6,291 Citations)
  • Consensus and Cooperation in Networked Multi-Agent Systems
    • Reza Olfati-SaberAlex Fax, and Richard M. Murray
    • We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators, flocking, formation control, fast consensus in small world networks, Markov processes and gossip-based algorithms, load balancing in networks, rendezvous in space, distributed sensor fusion in sensor networks, and belief propagation. We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms (Abstract)
    • In networks of agents (or dynamic systems), “consensus” means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents. A “consensus algorithm” (or protocol) is an interaction rule that specifies the information exchange between an agent and all of its (nearest) neighbors on the network (pp 215)
      • In my work, this is agreement on heading and velocity
    • Graph Laplacians are an important point of focus of this paper. It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algorithms. (pp 216)
    • More recently, there has been a tremendous surge of interest among researchers from various disciplines of engineering and science in problems related to multi-agent networked systems with close ties to consensus problems. This includes subjects such as consensus [26]–[32], collective behavior of flocks and swarms [19], [33]–[37], sensor fusion [38]–[40], random networks [41], [42], synchronization of coupled oscillators [42]–[46], algebraic connectivity of complex networks [47]–[49], asynchronous distributed algorithms [30], [50], formation control for multi-robot systems [51]–[59], optimization-based cooperative control [60]–[63], dynamic graphs [64]–[67], complexity of coordinated tasks [68]–[71], and consensus-based belief propagation in Bayesian networks [72], [73]. (pp 216)
      • That is a dense lit review. How did they order it thematically?
    • A byproduct of this framework is to demonstrate that seemingly different consensus algorithms in the literature [10], [12]–[15] are closely related. (pp 216)
    • To understand the role of cooperation in performing coordinated tasks, we need to distinguish between unconstrained and constrained consensus problems. An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same. In contrast, in distributed computation of a function f(z), the state of all agents has to asymptotically become equal to f(z), meaning that the consensus problem is constrained. We refer to this constrained consensus problem as the f-consensus problem. (pp 217)
      • Normal exploring/flocking/stampeding is unconstrained. Herding adds constraint, though it’s dynamic. The variables that have to be manipulated in the case of constraint to result in the same amount of consensus are probably what’s interesting here. Examples could be how ‘loud’ does the herder have to be? Also, how ‘primed’ does the population have to be to accept herding?
    • …cooperation can be informally interpreted as “giving consent to providing one’s state and following a common protocol that serves the group objective.” (pp 217)
    • Formal analysis of the behavior of systems that involve more than one type of agent is more complicated, particularly, in presence of adversarial agents in noncooperative games [79], [80]. (pp 217)
    • The reason matrix theory [81] is so widely used in analysis of consensus algorithms [10], [12], [13], [14], [15], [64] is primarily due to the structure of P in (4) and its connection to graphs. (pp 218)
    • The role of consensus algorithms in particle based flocking is for an agent to achieve velocity matching with respect to its neighbors. In [19], it is demonstrated that flocks are networks of dynamic systems with a dynamic topology. This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent, i.e., the topology of flocks is a state dependent graph. The notion of state-dependent graphs was introduced by Mesbahi [64] in a context that is independent of flocking. (pp 218)
      • They leave out heading alignment here. Deliberate? Or is heading alignment just another variant on velocity
    • Consider a network of decision-making agents with dynamics ẋi = ui interested in reaching a consensus via local communication with their neighbors on a graph G = (V, E). By reaching a consensus, we mean asymptotically converging to a one-dimensional agreement space characterized by the following equation: x1 = x2 = … = x (pp 219)
    • A dynamic graph G(t) = (V, E(t)) is a graph in which the set of edges E(t) and the adjacency matrix A(t) are time-varying. Clearly, the set of neighbors Ni(t) of every agent in a dynamic graph is a time-varying set as well. Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19]. (pp 219)
    • GraphLaplacianGradientDescent(pp 220)
  • Not sure about this one. It just may be another set of algorithms to do flocking. Maybe some network implications? Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory. It is one of the papers that the Consensus and Cooperation paper above leans on heavily though…
  • The Emergence of Consensus: A Primer
    • The origin of population-scale coordination has puzzled philosophers and scientists for centuries. Recently, game theory, evolutionary approaches and complex systems science have provided quantitative insights on the mechanisms of social consensus. However, the literature is vast and scattered widely across fields, making it hard for the single researcher to navigate it. This short review aims to provide a compact overview of the main dimensions over which the debate has unfolded and to discuss some representative examples. It focuses on those situations in which consensus emerges ‘spontaneously’ in absence of centralised institutions and covers topic that include the macroscopic consequences of the different microscopic rules of behavioural contagion, the role of social networks, and the mechanisms that prevent the formation of a consensus or alter it after it has emerged. Special attention is devoted to the recent wave of experiments on the emergence of consensus in social systems.
  • Critical dynamics in population vaccinating behavior
    • Complex adaptive systems exhibit characteristic dynamics near tipping points such as critical slowing down (declining resilience to perturbations). We studied Twitter and Google search data about measles from California and the United States before and after the 2014–2015 Disneyland, California measles outbreak. We find critical slowing down starting a few years before the outbreak. However, population response to the outbreak causes resilience to increase afterward. A mathematical model of measles transmission and population vaccine sentiment predicts the same patterns. Crucially, critical slowing down begins long before a system actually reaches a tipping point. Thus, it may be possible to develop analytical tools to detect populations at heightened risk of a future episode of widespread vaccine refusal.
  • For Aaron’s Social Gradient Descent Agent research (lit review)
    • On distributed search in an uncertain environment (Something like Social Gradient Descent Agents)
      • The paper investigates the case where N agents solve a complex search problem by communicating to each other their relative successes in solving the task. The problem consists in identifying a set of unknown points distributed in an n–dimensional space. The interaction rule causes the agents to organize themselves so that, asymptotically, each agent converges to a different point. The emphasis of this paper is on analyzing the collective dynamics resulting from nonlinear interactions and, in particular, to prove convergence of the search process.
    • A New Clustering Algorithm Based Upon Flocking On Complex Network (Sizing and timing for flocking systems seems to be ok?)
      • We have proposed a model based upon flocking on a complex network, and then developed two clustering algorithms on the basis of it. In the algorithms, firstly a k-nearest neighbor (knn) graph as a weighted and directed graph is produced among all data points in a dataset each of which is regarded as an agent who can move in space, and then a time-varying complex network is created by adding long-range links for each data point. Furthermore, each data point is not only acted by its k nearest neighbors but also r long-range neighbors through fields established in space by them together, so it will take a step along the direction of the vector sum of all fields. It is more important that these long-range links provides some hidden information for each data point when it moves and at the same time accelerate its speed converging to a center. As they move in space according to the proposed model, data points that belong to the same class are located at a same position gradually, whereas those that belong to different classes are away from one another. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the rates of convergence of clustering algorithms are fast enough. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.
  • Done with the first draft of the white paper! And added the RFP section to the LMN productization version
  • Amazon Sage​Maker: Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments. (from the documentation)

4:00 – 5:00 Meeting with Aaron M. to discuss Academic RB wishlist.


Phil 12.1.17

7:00 – 4:30 ASRC MKT


  • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. This shows NNs filling in slots in semantic maps (which are actually semantic mattes, and not to be confused with earlier self-organizing semantic maps). How is this with other, more linear processes like sound and narrative?
  • Continuing Alignment in social interactions here.
  • People flock in computer mediated environments: Spontaneous flocking in human groups
  • Schooling as a strategy for taxis in a noisy environment
    • Daniel Grunbaum
    • Abstract
      • A common strategy to overcome this problem is taxis, a behaviour in which an animal performs a biased random walk by changing direction more rapidly when local conditions are getting worse.
        • Consider voters switching from Bush->Obama->Trump
      • Such an animal spends more time moving in right directions than wrong ones, and eventually gets to a favourable area. Taxis is ineffcient, however, when environmental gradients are weak or overlain by `noisy’ small-scale fluctuations. In this paper, I show that schooling behaviour can improve the ability of animals performing taxis to climb gradients, even under conditions when asocial taxis would be ineffective. Schooling is a social behaviour incorporating tendencies to remain close to and align with fellow members of a group. It enhances taxis because the alignment tendency produces tight angular distributions within groups, and dampens the stochastic effects of individual sampling errors. As a result, more school members orient up-gradient than in the comparable asocial case. However, overly strong schooling behaviour makes the school slow in responding to changing gradient directions. This trade-off suggests an optimal level of schooling behaviour for given spatio-temporal scales of environmental variations.
        • This has implications for everything from human social interaction to ANN design.
    • Notes
      • Because limiting resources typically have `patchy’ distributions in which concentrations may vary by orders of magnitude, success or failure in finding favourable areas often has an enormous impact on growth rates and reproductive success. To locate resource concentrations, many aquatic organisms display tactic behaviours, in which they orient with respect to local variations in chemical stimuli or other environmental properties. (pp 503)
      • Here, I propose that schooling behaviours improve the tactic capabilities of school members, and enable them to climb faint and noisy gradients which they would otherwise be unable to follow. (pp 504)
      • Schooling is thought to result from two principal behavioural components: (1) tendencies to move towards neighbours when isolated, and away from them when too close, so that the group retains a characteristic level of compactness; and (2) tendencies to align orientation with those of neighbours, so that nearby animals have similar directions of travel and the group as a whole exhibits a directional polarity. (pp 504)
        • My models indicate that attraction isn’t required, as long as there is a distance-graded awareness. In other words, you align most strongly with those agents that are closest.
      • I focus in this paper on schooling in aquatic animals, and particularly on phytoplankton as a distributed resource. However, although I do not examine them specifically, the modelling approaches and the basic results apply more generally to other environmental properties (such as temperature), to other causes of population movement (such as migration) and to other socially aggregating species which form polarized groups (such as flocks, herds and swarms). (pp 504)
      • Under these circumstances, the search of a nektonic filter-feeder for large-scale concentrations of phytoplankton is analogous to the behaviour of a bacterium performing chemotaxis. The essence of the analogy is that, while higher animals have much more sophisticated sensory and cognitive capacities, the scale at which they sample their environment is too small to identify accurately the true gradient. (pp 505)
        • And, I would contend for determining optimal social interactions in large groups.
      • Bacteria using chemotaxis usually do not directly sense the direction of the gradient. Instead, they perform random walks in which they change direction more often or by a greater amount if conditions are deteriorating than if they are improving (Keller and Segel, 1971; Alt, 1980; Tranquillo, 1990). Thus, on average, individuals spend more time moving in favourable directions than in unfavourable ones. (pp 505)
      • A bacterial analogy has been applied to a variety of behaviours in more complex organisms, such as spatially varying di€usion rates due to foraging behaviours or food-handling in copepods and larval ®sh (Davis et al., 1991), migration patterns in tuna (Mullen, 1989) and restricted area searching in ladybugs (Kareiva and Odell, 1987) and seabirds (Veit et al., 1993, 1995). The analogy provides for these higher animals a quantitative prediction of distribution patterns and abilities to locate resources at large space and time scales, based on measurable characteristics of small-scale movements. (pp 505)
      • I do not consider more sophisticated (and possibly more effective) social tactic algorithms, in which explicit information about the environment at remote points is actively or passively transmitted between individuals, or in which individual algorithms (such as slowing down when in relatively high concentrations) cause the group to function as a single sensing unit (Kils, 1986, described in Pitcher and Parrish, 1993). (pp 506)
        • This is something that could be easily added to the model. There could be a multiplier for each data cell that acts as a velocity scalar of the flock. That should have significant effects! This could also be applied to gradient descent. The flock of Gradient Descent Agents (GDAs) could have a higher speed across the fitness landscape, but slow and change direction when a better value is found by one of the GDAs. It occurs to me that this would work with a step function, as long as the baseline of the flock is sufficiently broad.
      • When the noise predominates (d <= 1), the angular distribution of individuals is nearly uniform, and the up-gradient velocity is near zero. In a range of intermediate values of d(0.3 <= d <= 3), there is measurable but slow movement up-gradient. The question I will address in the next two sections is: Can individuals in this intermediate signal-to-noise range with slow gradient-climbing rates improve their tactic ability by adopting a social behaviour (i.e. schooling)? (pp 508)
      • The key attributes of these models are: (1) a decreasing probability of detection or responsiveness to neighbours at large separation distances; (2) a social response that includes some sort of switch from attractive to repulsive interactions with neighbours, mediated by either separation distance or local density of animals*; and (3) a tendency to align with neighbours (Inagaki et al., 1976; Matuda and Sannomiya, 1980, 1985; Aoki, 1982; Huth and Wissel, 1990, 1992; Warburton and Lazarus, 1991; Grunbaum, 1994). (pp 508)
        • Though not true of belief behavior (multiple individuals can share the same belief), for a Gradient Descent Agent (GDA), the idea of attraction/repulsion may be important.
      • If the number of neighbours is within an acceptable range, then the individual does not respond to them. On the other hand, if the number is outside that range, the individual turns by a small amount, Δθ3, to the left or right according to whether it has too many or too few of them and which side has more neighbours. In addition, at each time step, each individual randomly chooses one of its visible neighbours and turns by a small amount, Δθ4, towards that neighbour’s heading. (pp 508)
      • The results of simulations based on these rules show that schooling individuals, on average, move more directly in an up-gradient direction than asocial searchers with the same tactic parameters. Figure 4 shows the distribution of individuals in simulations of asocial and social taxis in a periodic domain (i.e. animals crossing the right boundary re-enter the left boundary, etc.). (pp 509)
      • Gradient Schooling
      • As predicted by Equation (5), asocial taxis results in a broad distribution of orientations, with a peak in the up-gradient (positive x-axis) direction but with a large fraction of individuals moving the wrong way at any given time (Fig. 5a,b). By comparison, schooling individuals tend to align with one another, forming a group with a tightened angular distribution. There is stochasticity in the average velocity of both asocial and social searchers (Fig. 5c). On average, however, schooling individuals move up-gradient faster and more directly than asocial ones. These simulation results demonstrate that it is theoretically possible to devise tactic search strategies utilizing social behaviours that are superior to asocial algorithms. That is, one of the advantages of schooling is that, potentially, it allows more successful search strategies under `noisy’ environmental conditions, where variations on the micro-scales at which animals sense their environment obscure the macro-scale gradients between ecologically favourable and unfavourable regions. (pp 510)
      • School-size effects must depend to some extent on the tactic and schooling algorithms, and the choices of parameters. However, underlying social taxis are the statistics of pooling outcomes of independent decisions, so the numerical dependence on school size may operate in a similar manner for many comparable behavioural schemes. For example, it seems reasonable to expect that, in many alternative schooling and tactic algorithms, decisions made collectively by less than 10 individuals would show some improvement over the asocial case but also retain much of the variability. Similarly, in most scenarios, group statistics probably vary only slowly with group size once it reaches sizes of 50-100. (pp 514)
      • when group size becomes large, the behaviour of model schools changes in character. With numerous individuals, stochasticity in the behaviour of each member has a relatively weaker effect on group motion. The behaviour of the group as a whole becomes more consistent and predictable, for longer time periods. (pp 514)
        • I think that this should be true in belief spaces as well. It may be difficult to track one person’s trajectory, but a group in aggregate, particularly a polarized group may be very detectable.
      • An example of group response to changing gradient direction shows that there can be a cost to strong alignment tendency. In this example, the gradient is initially pointed in the negative y-direction (Fig. 9). After an initial period of 5 time units, during which the gradient orients perpendicularly to the x-axis, the gradient reverts to the usual x-direction orientation. The school must then adjust to its new surroundings by shifting to climb the new gradient. This example shows that alignment works against course adjustment: the stronger the tendency to align, the slower is the group’s reorientation to the new gradient direction. This is apparently due to a non-linear interaction between alignment and taxis: asymmetries in the angular distribution during the transition create a net alignment flux away from the gradient direction. Thus, individuals that pay too much attention to neighbours, and allow alignment to overwhelm their tactic tendencies, may travel rapidly and persistently in the wrong direction. (pp 516)
        • So, if alignment (and velocity matching) are strong enough, the conditions for a stampede (group behavior with negative outcomes – in this case, less food) emerge
      • The models also suggest that there is a trade-off in strengthening tendencies to align with neighbours: strong alignment produces tight angular distributions, but increases the time needed to adjust course when the direction of the gradient changes. A reasonable balance seems to be achieved when individuals take roughly the same time to coalesce into a polarized group as they do to orient to the gradient in asocial taxis. (pp 518)
        • There is something about the relationship between explore and exploit in this statement that I really need to think about.
      • Social taxis is potentially effective in animals whose resources vary substantially over large length scales and for whom movements over these scales are possible. (pp 518)
        • Surviving as a social animal requires staying in the group. Since belief can cover wide ranges (e.g. religion), does there need to be a mechanism where individuals can harmonize their beliefs? From Social Norms and Other Minds The Evolutionary Roots of Higher Cognition :  Field research on primate societies in the wild and in captivity clearly shows that the capacity for (at least) implicit appreciation of permission, prohibition, and obligation social norms is directly related to survival rates and reproductive success. Without at least a rudimentary capacity to recognize and respond appropriately to these structures, remaining within a social group characterized by a dominance hierarchy would be all but impossible.
      • Interestingly, krill have been reported to school until a food patch has been discovered, whereupon they disperse to feed, consistent with a searching function for schooling. The apparent effectiveness of schooling as a strategy for taxis suggests that these schooling animals may be better able to climb obscure large-scale gradients than they would were they asocial. Interactive effects of taxis and sociality may affect the evolutionary value of larger groups both directly, by improving foraging ability with group size, and indirectly, by constraining alignment rates. (pp 518)
      • An example where sociality directly affects foraging strategy is forage area copying, in which unsuccessful fish move to the vicinity of neighbours that are observed to be foraging successfully (Pitcher et al., 1982; Ranta and Kaitala, 1991; Pitcher and Parrish, 1993). Pitcher and House (1987) interpreted area copying in goldfish as the result of a two-stage decision process: (1) a decision to stay put or move depending on whether feeding rate is high or low; and (2) a decision to join neighbours or not based upon whether or not further solitary searching is successful. Similar group dynamics have been observed in foraging seabirds (Porter and Seally, 1982; Haney et al., 1992).
      • Synchrokinesis depends upon the school having a relatively large spatial extent: part of a migrating school encounters an especially favourable or unfavourable area. The response of that section of the school is propagated throughout the school by alignment and grouping behaviours, with the result that the school as a whole is more effective at route-finding than isolated individuals. Forage area copying and synchrokinesis are distinct from social taxis in that an individual discovers and reacts to an environmental feature or resource, and fellow group members exploit that discovery. In social taxis, no individual need ever have greater knowledge about the environment than any other — social taxis is essentially bound up in the statistics of pooling the outcomes of many unreliable decisions. Synchrokinesis and social taxis are complementary mechanisms and may be expected to co-occur in migrating and gradient-climbing schools. (pp 519)
      • For example, in the comparisons of taxis among groups of various sizes, the most successful individuals were in the asocial simulation, even though as a fraction of the entire population they were vanishingly small. (pp 519)
        • Explorers have the highest payoff for the highest risks
  • Continuing white paper. Done with intro, background, and phase 1
  • Intel-powered AI Helps Fight Fraud

Phil 11.23.17

Nice – I can get my notes of the Kindle by plugging it into my computer. I never found that on the help pages.

More than a Million Pro-Repeal Net Neutrality Comments were Likely Faked

  • I used natural language processing techniques to analyze net neutrality comments submitted to the FCC from April-October 2017, and the results were disturbing.
  • BotPlusOrganicI think that this kind of long-tail distribution is going to be what herding looks like.

Speaker–listener neural coupling underlies successful communication

    • Greg J. Stephens
    • Lauren J. Silbert
    • Uri Hasson (HassonLab at Princeton)
    • Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker’s spatiotemporal brain activity to model listeners’ brain activity and found that the speaker’s activity is spatially and temporally coupled with the listener’s activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener’s brain activity mirrors the speaker’s activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker–listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.
      • This seems to be the root article for neural coupling. It seems to be an area of vigorous study, with lots of work coming out from the three authors.
      • The study design is also really good.
    • In this study we directly examine the spatial and temporal coupling between production and comprehension across brains during natural verbal communication. (pp 14425)
    • Using fMRI, we recorded the brain activity of a speaker telling an unrehearsed real-life story and the brain activity … (n = 11) of a listener listening to the recorded audio of the spoken story, thereby capturing the time-locked neural dynamics from both sides of the communication. Finally, we used a detailed questionnaire to assess the level of comprehension of each listener. (pp 14425)
    • …because communication unfolds over time, this coupling will exhibit important temporal structure. In particular, because the speaker’s production-based processes mostly precede the listener’s comprehension-based processes, the listener’s neural dynamics will mirror the speaker’s neural dynamics with some delay. Conversely, when listeners use their production system to emulate and predict the speaker’s utterances, we expect the opposite: the listener’s dynamics will precede the speaker’s dynamics. (pp 14425)
    • To analyze the direct interaction of production and comprehension mechanisms, we considered only spatially local models that measure the degree of speaker–listener coupling within the same Talairach location. (pp 14426)
    • we also observed significant speaker–listener coupling in a collection of extralinguistic areas known to be involved in the processing of semantic and social aspects of the story (19), including the precuneus, dorsolateral prefrontal cortex, orbitofrontal cortex, striatum, and medial prefrontal cortex. (pp 14426)
    • In agreement with previous work, the story evoked highly reliable activity inmany brain areas across all listeners (8, 11, 12) (Fig. 2B, yellow). We note that the agreement with previous work is far from assured: the story here was both personal and spontaneous, and was recorded in the noisy environment of the scanner. The similarity in the response patterns across all listeners underscores a strong tendency to process incoming verbal information in similar ways. A comparison between the speaker–listener and the listener–listenermaps reveals an extensive overlap (Fig. 2B, orange). These areas include many of the sensory related, classic linguistic-related and extralinguistic-related brain areas, demonstrating that many of the areas involved in speech comprehension (listener–listener coupling) are also aligned during communication (speaker–listener coupling). (pp 14426)
    • To test whether the extensive speaker–listener coupling emerges only when information is transferred across interlocutors, we blocked the communication between speaker and listener. We repeated the experiment while recording a Russian speaker telling a story in the scanner, and then played the story to non–Russian speaking listeners (n = 11). In this experimental setup, although the Russian speaker is trying to communicate information, the listeners are unable to extract the information from the incoming acoustic sounds. Using identical analysis methods and statistical thresholds, we found no significant coupling between the speaker and the listeners or among the listeners. At significantly lower thresholds we found that the non–Russian-speaking listener–listener coupling was confined to early auditory cortices. This indicates that the reliable activity in most areas, besides early auditory cortex, depends on a successful processing of the incoming information, and is not driven by the low-level acoustic aspects of the stimuli. (pp 14426)
    • Neural Coupling
      • In my model, the anticipation is modeled by the alignment and velocity, but others come to similar conclusions. It may be a way of dealing with noisy environments. Which would be another way of saying group dynamics with incomplete information.
    • Our analysis also identifies a subset of brain regions in which the activity in the listener’s brain precedes the activity in the speaker’s brain. The listener’s anticipatory responses were localized to areas known to be involved in predictions and value representation (pp 14428)
    • Such findings are in agreement with the theory of interactive linguistic alignment (1). According to this theory, production and comprehension become tightly aligned on many different levels during verbal communication, including the phonetic, phonological, lexical, syntactic, and semantic representations. Accordingly, we observed neural coupling during communication at many different processing levels, including low-level auditory areas (induced by the shared input), production-based areas (e.g., Broca’s area), comprehension based areas (e.g., Wernicke’s area and TPJ), and high-order extralinguistic areas (e.g., precuneus and mPFC) that can induce shared contextual model of the situation (34). Interestingly, some of these extralinguistic areas are known to be involved in processing social information crucial for successful communication, including, among others, the capacity to discern the beliefs, desires, and goals of others. (pp 14429)


Brain-to-Brain coupling: A mechanism for creating and sharing a social world

  • Cognition materializes in an interpersonal space. The emergence of complex behaviors requires the coordination of actions among individuals according to a shared set of rules. Despite the central role of other individuals in shaping our minds, most cognitive studies focus on processes that occur within a single individual. We call for a shift from a single-brain to a multi-brain frame of reference. We argue that in many cases the neural processes in one brain are coupled to the neural processes in another brain via the transmission of a signal through the environment. Brain-to-brain coupling constrains and simplifies the actions of each individual in a social network, leading to complex joint behaviors that could not have emerged in isolation

Phil 9.14.17

7:00 – 4:00 ASRC MKT

  • Reducing Dimensionality from Dimensionality Reduction Techniques
    • In this post I will do my best to demystify three dimensionality reduction techniques; PCA, t-SNE and Auto Encoders. My main motivation for doing so is that mostly these methods are treated as black boxes and therefore sometime are misused. Understanding them will give the reader the tools to decide which one to use, when and how.
      I’ll do so by going over the internals of each methods and code from scratch each method (excluding t-SNE) using TensorFlow. Why TensorFlow? Because it’s mostly used for deep learning, lets give it some other challenges 🙂
      Code for this post can be found in this notebook.
    • This seems important to read in preparation for the Normative Mapping effort.
  • Stanford  deep learning tutorial. This is where I got the links to PCA and Auto Encoders, above.
  • Ok, back to writing:
    • The Exploration-Exploitation Dilemma: A Multidisciplinary Framework
    • Got hung up explaining the relationship of the social horizon radius, so I’m going to change it to the exploit radius. Also changed the agent flocks to red and green: GPM
    • There is a bug, too – when I upped the CellAccumulator hypercube size from 10-20. The max row is not getting set

Phil 9.12.17

7:00 – 5:00 ASRC MKT

  • Meeting with Wayne yesterday after Fika. Get him a draft by the end of the week to discuss Monday?
  • More writing
  • Herding in humans (Ramsey M. Raafat, Nick Chater, and Chris Frith)
    • Herding is a form of convergent social behaviour that can be broadly defined as the alignment of the thoughts or behaviours of individuals in a group (herd) through local interaction and without centralized coordination. We suggest that herding has a broad application, from intellectual fashion to mob violence; and that understanding herding is particularly pertinent in an increasingly interconnected world. An integrated approach to herding is proposed, describing two key issues: mechanisms of transmission of thoughts or behaviour between agents, and patterns of connections between agents. We show how bringing together the diverse, often disconnected, theoretical and methodological approaches illuminates the applicability of herding to many domains of cognition and suggest that cognitive neuroscience offers a novel approach to its study.
  • Alignment in social interactions (M.Gallotti, M.T.Fairhurst, C.D.Frith)
    • According to the prevailing paradigm in social-cognitive neuroscience, the mental states of individuals become shared when they adapt to each other in the pursuit of a shared goal. We challenge this view by proposing an alternative approach to the cognitive foundations of social interactions. The central claim of this paper is that social cognition concerns the graded and dynamic process of alignment of individual minds, even in the absence of a shared goal. When individuals reciprocally exchange information about each other’s minds processes of alignment unfold over time and across space, creating a social interaction. Not all cases of joint action involve such reciprocal exchange of information. To understand the nature of social interactions, then, we propose that attention should be focused on the manner in which people align words and thoughts, bodily postures and movements, in order to take one another into account and to make full use of socially relevant information.
  • Herding and escaping responses of juvenile roundfish to square mesh window in a trawl cod end (This is the only case I can find of 3-D stampeding. Note the [required?] dimension reduction)
    • The movements of juvenile roundfish, mainly haddock Melanogrammus aeglefinus and whiting Merlangius merlangus, reacting to a square mesh window in the cod end of a bottom trawl were observed during fishing experiments in the North Sea. Two typical behavioral responses of roundfish are described as the herding response and the escaping response, which were analyzed from video recordings by time sequences of the movement parameters. It was found that most of the actively escaping fish approached the square mesh window at right angles by swimming straight ahead with very little change in direction, while most of the herded fish approached the net at obtuse angles and retreated by sharp turning. The herding and escaping responses showed significant difference when characterized by frequency distributions of swimming speed and angular velocity, and both responses showed large and irregular variations in swimming movement parameters like the panic erratic responses. It is concluded that an escaping or herding response to the square mesh window could be decided by an interaction between the predictable parameters that describe the stimuli of net and angular changes of fish response, such as approaching angle, turning angle and angular velocity.
  • Assessing the Effect of “Disputed” Warnings and Source Salience on Perceptions of Fake News Accuracy
    • What are effective techniques for combating belief in fake news? Tagging fake articles with “Disputed by 3rd party fact-checkers” warnings and making articles’ sources more salient by adding publisher logos are two approaches that have received large-scale rollouts on social media in recent months. Here we assess the effect of these interventions on perceptions of accuracy across seven experiments (total N=7,534). With respect to disputed warnings, we find that tagging articles as disputed did significantly reduce their perceived accuracy relative to a control without tags, but only modestly (d=.20, 3.7 percentage point decrease in headlines judged as accurate). Furthermore, we find a backfire effect – particularly among Trump supporters and those under 26 years of age – whereby untagged fake news stories are seen as more accurate than in the control. We also find a similar spillover effect for real news, whose perceived accuracy is increased by the presence of disputed tags on other headlines. With respect to source salience, we find no evidence that adding a banner with the logo of the headline’s publisher had any impact on accuracy judgments whatsoever. Together, these results suggest that the currently deployed approaches are not nearly enough to effectively undermine belief in fake news, and new (empirically supported) strategies are needed.
  • Some meetings on marketing. Looks like we’re trying to get on this panel. Wrote bioblurbs!
  • More writing. Reasonable progress.

Phil 8.29.16

7:00 – 6:00 ASRC

  • Selective Use of News Cues: A Multiple-Motive Perspective on Information Selection in Social Media Environments – Quite close to the Explorer/Confirmer/Avoider study but using a custom(?) browsing interface that tracked the marking of news stories to read later. Subjects were primed for a task with motivations – accuracy, defense and impression. Added this to paragraph 2.9, where explorers are introduced.
  • Looked through Visual Complexity – Mapping Patterns of Information, and it doesn’t even mention navigation. Most information mapping efforts are actually graphing efforts. Added a paragraph in section 2.7
  • Added a TODO for groupthink/confirmation bias, etc.
  • Chat with Heath about AI.He’s looking to build a MUD agent and will probably wind up learning WEKA, etc. so a win, I think.
  • Working on getting the configurator to add string values.
  • Added to DocumentStatistics. Need to switch over to getSourceInfo() from getAddressStrings in the Configurator.
  • Meeting with Wayne about the proposal. One of the branches of conversation went into some research he did on library architecture. That’s been rattling around in my head.
    We tend to talk about interface design where the scale is implicitly for the individual. The environment where these systems function is often thought of as an ecosystem, with the Darwinian perspective that goes along with that. But I think that such a perspective leads to ‘Survival of the Frictionlesss’, where the easiest thing to use wins and damn the larger consequences.
    Reflecting on how the architecture and layout of libraries affected the information interactions of the patrons, I wonder whether we should be thinking about Information Space Architecture. Such a perspective means that the relationships between design at differing scales needs to be considered. In the real world, architecture can encompass everything from the chairs in a room to the landscaping around the building and how that building fits into the skyline.
    I think that regarding information spaces as a designed continuum from the very small to very large is what my dissertation is about at its core. I want a park designed for people, not a wilderness, red in tooth and claw.

Phil 8.26.16

7:00 – 4:00 ASRC

    • Adding more model feedback
    • Something more to think about WRT Group Polarization models? Collective Memory and Spatial Sorting in Animal Groups
    • Need to be able to associate an @attribute  key/value map with Labeled2Dmatrix rows so that we can compare different nominal values across a shared set of numeric columns. This may wind up being a derived class?
      • Working on adding an array of key/value maps;
      • Forgot to add the name to the @data section – oops!
      • text is added to ARFF out. Should I add it to the xlsx outputs as well?
    • Here’s the initial run against the random test data within the class (L2D.arff).
=== Run information ===

Scheme: weka.classifiers.bayes.NaiveBayes
Relation: testdata
Instances: 8
Attributes: 12
Test mode: split 66.0% train, remainder test

=== Classifier model (full training set) ===

Naive Bayes Classifier

Attribute p1 p2 p3 p4 s1 s2 s3 s4
(0.13) (0.13) (0.13) (0.13) (0.13) (0.13) (0.13) (0.13)
p4-sv1 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0
s2-sv1 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0
p2-sv1 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
s1-sv1 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 4.0 5.0 4.0 5.0 5.0 5.0 4.0 4.0

p2-sv2 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
s4-sv2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0
p1-sv2 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
s1-sv2 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 5.0 5.0 4.0 4.0 5.0 4.0 4.0 5.0

p2-sv3 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0
p1-sv3 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
s4-sv3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0
p3-sv3 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0
p4-sv3 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0
s2-sv3 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0
s1-sv3 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0
[total] 8.0 8.0 8.0 8.0 8.0 8.0 7.0 8.0

mean 1 0 0 0 1 1 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 0 1 0 0 1 0 1 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 0 0 1 0 1 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 0 0 0 1 1 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 1 1 1 1 1 0 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 1 0 0 0 0 1 0 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 0 1 0 0 0 0 1 0
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

mean 0 0 1 1 0 0 0 1
std. dev. 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667 0.1667
weight sum 1 1 1 1 1 1 1 1
precision 1 1 1 1 1 1 1 1

Time taken to build model: 0 seconds

=== Evaluation on test split ===

Time taken to test model on training split: 0 seconds

=== Summary ===

Correctly Classified Instances 0 0 %
Incorrectly Classified Instances 3 100 %
Kappa statistic 0
Mean absolute error 0.2499
Root mean squared error 0.4675
Relative absolute error 108.2972 %
Root relative squared error 133.419 %
Total Number of Instances 3

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.000 0.333 0.000 0.000 0.000 0.000 ? ? p1
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 p2
0.000 0.333 0.000 0.000 0.000 0.000 ? ? p3
0.000 0.000 0.000 0.000 0.000 0.000 ? ? p4
0.000 0.000 0.000 0.000 0.000 0.000 0.500 0.500 s1
0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000 s2
0.000 0.333 0.000 0.000 0.000 0.000 ? ? s3
0.000 0.000 0.000 0.000 0.000 0.000 ? ? s4
Weighted Avg. 0.000 0.000 0.000 0.000 0.000 0.000 0.500 0.611

=== Confusion Matrix ===

a b c d e f g h <-- classified as
0 0 0 0 0 0 0 0 | a = p1
0 0 0 0 0 0 1 0 | b = p2
0 0 0 0 0 0 0 0 | c = p3
0 0 0 0 0 0 0 0 | d = p4
0 0 1 0 0 0 0 0 | e = s1
1 0 0 0 0 0 0 0 | f = s2
0 0 0 0 0 0 0 0 | g = s3
0 0 0 0 0 0 0 0 | h = s4
  • Need to add text data from xml or from other(wrapper info? structured data? UI selections?) sources