Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society''s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.     

Discovering sparse control strategies in neural activity

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations—ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms—to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, “pivotal” neurons that account for most of the system’s sensitivity, suggesting a sparse mechanism of collective control.     

Quantifying Collective Attention from Tweet Stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.     

Short and Long Term Investor Synchronization Caused by Decoupling

The dynamics of collective decision making is not yet well understood. Its practical relevance however can be of utmost importance, as experienced by people who lost their fortunes in turbulent moments of financial markets. In this paper we show how spontaneous collective “moods” or “biases” emerge dynamically among human participants playing a trading game in a simple model of the stock market. Applying theory and computer simulations to the experimental data generated by humans, we are able to predict the onset of such moments before they actually happen.     

Quality along the Continuum: A Health Facility Assessment of Intrapartum and Postnatal Care in Ghana

Objective

To evaluate quality of routine and emergency intrapartum and postnatal care using a health facility assessment, and to estimate “effective coverage” of skilled attendance in Brong Ahafo, Ghana.

Methods

We conducted an assessment of all 86 health facilities in seven districts in Brong Ahafo. Using performance of key signal functions and the availability of relevant drugs, equipment and trained health professionals, we created composite quality categories in four dimensions: routine delivery care, emergency obstetric care (EmOC), emergency newborn care (EmNC) and non-medical quality. Linking the health facility assessment to surveillance data we estimated “effective coverage” of skilled attendance as the proportion of births in facilities of high quality.

Findings

Delivery care was offered in 64/86 facilities; only 3-13% fulfilled our requirements for the highest quality category in any dimension. Quality was lowest in the emergency care dimensions, with 63% and 58% of facilities categorized as “low” or “substandard” for EmOC and EmNC, respectively. This implies performing less than four EmOC or three EmNC signal functions, and/or employing less than two skilled health professionals, and/or that no health professionals were present during our visit. Routine delivery care was “low” or “substandard” in 39% of facilities, meaning 25/64 facilities performed less than six routine signal functions and/or had less than two skilled health professionals and/or less than one midwife. While 68% of births were in health facilities, only 18% were in facilities with “high” or “highest” quality in all dimensions.

Conclusion

Our comprehensive facility assessment showed that quality of routine and emergency intrapartum and postnatal care was generally low in the study region. While coverage with facility delivery was 68%, we estimated “effective coverage” of skilled attendance at 18%, thus revealing a large “quality gap.” Effective coverage could be a meaningful indicator of progress towards reducing maternal and newborn mortality.     

Searching for Collective Behavior in a Large Network of Sensory Neurons

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population''s capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.     

A model of naturalistic decision making in preference tests

Decisions as to whether to continue with an ongoing activity or to switch to an alternative are a constant in an animal’s natural world, and in particular underlie foraging behavior and performance in food preference tests. Stimuli experienced by the animal both impact the choice and are themselves impacted by the choice, in a dynamic back and forth. Here, we present model neural circuits, based on spiking neurons, in which the choice to switch away from ongoing behavior instantiates this back and forth, arising as a state transition in neural activity. We analyze two classes of circuit, which differ in whether state transitions result from a loss of hedonic input from the stimulus (an “entice to stay” model) or from aversive stimulus-input (a “repel to leave” model). In both classes of model, we find that the mean time spent sampling a stimulus decreases with increasing value of the alternative stimulus, a fact that we linked to the inclusion of depressing synapses in our model. The competitive interaction is much greater in “entice to stay” model networks, which has qualitative features of the marginal value theorem, and thereby provides a framework for optimal foraging behavior. We offer suggestions as to how our models could be discriminatively tested through the analysis of electrophysiological and behavioral data.     

Beyond the Movie Screen: An Antarctic Adventure

Movies depicting science-related issues often capture the attention of today's youth. As an instructional tool, movies can take us beyond the drama and action and thrilling scenes. In this article we share our experiences of using the movie Eight Below as a centerpiece for developing high school students’ understanding of basic chemistry concepts. In addition to the science concepts developed using the context of the movie, we engaged students in thinking about the ethical dilemmas embedded in the movie.     

Experimental Evidence of Threat-Sensitive Collective Avoidance Responses in a Large Wild-Caught Herring School

Aggregation is commonly thought to improve animals'' security. Within aquatic ecosystems, group-living prey can learn about immediate threats using cues perceived directly from predators, or from collective behaviours, for example, by reacting to the escape behaviours of companions. Combining cues from different modalities may improve the accuracy of prey antipredatory decisions. In this study, we explored the sensory modalities that mediate collective antipredatory responses of herring (Clupea harengus) when in a large school (approximately 60 000 individuals). By conducting a simulated predator encounter experiment in a semi-controlled environment (a sea cage), we tested the hypothesis that the collective responses of herring are threat-sensitive. We investigated whether cues from potential threats obtained visually or from the perception of water displacement, used independently or in an additive way, affected the strength of the collective avoidance reactions. We modified the sensory nature of the simulated threat by exposing the herring to 4 predator models differing in shape and transparency. The collective vertical avoidance response was observed and quantified using active acoustics. The combination of sensory cues elicited the strongest avoidance reactions, suggesting that collective antipredator responses in herring are mediated by the sensory modalities involved during threat detection in an additive fashion. Thus, this study provides evidence for magnitude-graded threat responses in a large school of wild-caught herring which is consistent with the “threat-sensitive hypothesis”.     

Molecular and Mechanical Causes of Microtubule Catastrophe and Aging

Tubulin polymers, microtubules, can switch abruptly from the assembly to shortening. These infrequent transitions, termed “catastrophes”, affect numerous cellular processes but the underlying mechanisms are elusive. We approached this complex stochastic system using advanced coarse-grained molecular dynamics modeling of tubulin-tubulin interactions. Unlike in previous simplified models of dynamic microtubules, the catastrophes in this model arise owing to fluctuations in the composition and conformation of a growing microtubule tip, most notably in the number of protofilament curls. In our model, dynamic evolution of the stochastic microtubule tip configurations over a long timescale, known as the system’s “aging”, gives rise to the nonexponential distribution of microtubule lifetimes, consistent with experiment. We show that aging takes place in the absence of visible changes in the microtubule wall or tip, as this complex molecular-mechanical system evolves slowly and asymptotically toward the steady-state level of the catastrophe-promoting configurations. This new, to our knowledge, theoretical basis will assist detailed mechanistic investigations of the mechanisms of action of different microtubule-binding proteins and drugs, thereby enabling accurate control over the microtubule dynamics to treat various pathologies.     

What Would Jaws Do? The Tyranny of Film and the Relationship between Gaze and Higher-Level Narrative Film Comprehension

What is the relationship between film viewers’ eye movements and their film comprehension? Typical Hollywood movies induce strong attentional synchrony—most viewers look at the same things at the same time. Thus, we asked whether film viewers’ eye movements would differ based on their understanding—the mental model hypothesis—or whether any such differences would be overwhelmed by viewers’ attentional synchrony—the tyranny of film hypothesis. To investigate this question, we manipulated the presence/absence of prior film context and measured resulting differences in film comprehension and eye movements. Viewers watched a 12-second James Bond movie clip, ending just as a critical predictive inference should be drawn that Bond’s nemesis, “Jaws,” would fall from the sky onto a circus tent. The No-context condition saw only the 12-second clip, but the Context condition also saw the preceding 2.5 minutes of the movie before seeing the critical 12-second portion. Importantly, the Context condition viewers were more likely to draw the critical inference and were more likely to perceive coherence across the entire 6 shot sequence (as shown by event segmentation), indicating greater comprehension. Viewers’ eye movements showed strong attentional synchrony in both conditions as compared to a chance level baseline, but smaller differences between conditions. Specifically, the Context condition viewers showed slightly, but significantly, greater attentional synchrony and lower cognitive load (as shown by fixation probability) during the critical first circus tent shot. Thus, overall, the results were more consistent with the tyranny of film hypothesis than the mental model hypothesis. These results suggest the need for a theory that encompasses processes from the perception to the comprehension of film.     

Detection of Functional Modes in Protein Dynamics

Proteins frequently accomplish their biological function by collective atomic motions. Yet the identification of collective motions related to a specific protein function from, e.g., a molecular dynamics trajectory is often non-trivial. Here, we propose a novel technique termed “functional mode analysis” that aims to detect the collective motion that is directly related to a particular protein function. Based on an ensemble of structures, together with an arbitrary “functional quantity” that quantifies the functional state of the protein, the technique detects the collective motion that is maximally correlated to the functional quantity. The functional quantity could, e.g., correspond to a geometric, electrostatic, or chemical observable, or any other variable that is relevant to the function of the protein. In addition, the motion that displays the largest likelihood to induce a substantial change in the functional quantity is estimated from the given protein ensemble. Two different correlation measures are applied: first, the Pearson correlation coefficient that measures linear correlation only; and second, the mutual information that can assess any kind of interdependence. Detecting the maximally correlated motion allows one to derive a model for the functional state in terms of a single collective coordinate. The new approach is illustrated using a number of biomolecules, including a polyalanine-helix, T4 lysozyme, Trp-cage, and leucine-binding protein.     

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

We describe experimental and statistical steps for creating dopamine movies of the brain from dynamic PET data. The movies represent minute-to-minute fluctuations of dopamine induced by smoking a cigarette. The smoker is imaged during a natural smoking experience while other possible confounding effects (such as head motion, expectation, novelty, or aversion to smoking repeatedly) are minimized.We present the details of our unique analysis. Conventional methods for PET analysis estimate time-invariant kinetic model parameters which cannot capture short-term fluctuations in neurotransmitter release. Our analysis - yielding a dopamine movie - is based on our work with kinetic models and other decomposition techniques that allow for time-varying parameters ^1-7. This aspect of the analysis - temporal-variation - is key to our work. Because our model is also linear in parameters, it is practical, computationally, to apply at the voxel level. The analysis technique is comprised of five main steps: pre-processing, modeling, statistical comparison, masking and visualization. Preprocessing is applied to the PET data with a unique ''HYPR'' spatial filter ⁸ that reduces spatial noise but preserves critical temporal information. Modeling identifies the time-varying function that best describes the dopamine effect on ¹¹C-raclopride uptake. The statistical step compares the fit of our (lp-ntPET) model ⁷ to a conventional model ⁹. Masking restricts treatment to those voxels best described by the new model. Visualization maps the dopamine function at each voxel to a color scale and produces a dopamine movie. Interim results and sample dopamine movies of cigarette smoking are presented.     

Action Recognition Depends on Observer’s Level of Action Control and Social Personality Traits

Humans recognize both the movement (physical) goals and action (conceptual) goals of individuals with whom they are interacting. Here, we assessed whether spontaneous recognition of others’ goals depends on whether the observers control their own behavior at the movement or action level. We also examined the relationship between individual differences in empathy and ASD-like traits, and the processing of other individual’s movement and action goals that are known to be encoded in the “mirroring” and “mentalizing” brain networks. In order to address these questions, we used a computer-based card paradigm that made it possible to independently manipulate movement and action congruency of observed and executed actions. In separate blocks, participants were instructed to select either the right or left card (movement-control condition) or the higher or lower card (action-control condition), while we manipulated action- and movement-congruency of both actors’ goals. An action-congruency effect was present in all conditions and the size of this effect was significantly correlated with self-reported empathy and ASD-like traits. In contrast, movement-congruency effects were only present in the movement-control block and were strongly dependent on action-congruency. These results illustrate that spontaneous recognition of others’ behavior depends on the control scheme that is currently adopted by the observer. The findings suggest that deficits in action recognition are related to abnormal synthesis of perceived movements and prior conceptual knowledge that are associated with activations in the “mirroring” and “mentalizing” cortical networks.     

Carrot or Stick? Modelling How Landowner Behavioural Responses Can Cause Incentive-Based Forest Governance to Backfire

Mitigating the negative impacts of declining worldwide forest cover remains a significant socio-ecological challenge, due to the dominant role of human decision-making. Here we use a Markov chain model of land-use dynamics to examine the impact of governance on forest cover in a region. Each land parcel can be either forested or barren (deforested), and landowners decide whether to deforest their parcel according to perceived value (utility). We focus on three governance strategies: yearly incentive for conservation, one-time penalty for deforestation and one-time incentive for reforestation. The incentive and penalty are incorporated into the expected utility of forested land, which decreases the net gain of deforestation. By analyzing the equilibrium and stability of the landscape dynamics, we observe four possible outcomes: a stationary-forested landscape, a stationary-deforested landscape, an unstable landscape fluctuating near the equilibrium, and a cyclic-forested landscape induced by synchronized deforestation. We find that the two incentive-based strategies often result in highly fluctuating forest cover over decadal time scales or longer, and in a few cases, reforestation incentives actually decrease the average forest cover. In contrast, a penalty for deforestation results in the stable persistence of forest cover (generally >30%). The idea that larger conservation incentives will always yield higher and more stable forest cover is not supported in our findings. The decision to deforest is influenced by more than a simple, “rational” cost-benefit analysis: social learning and myopic, stochastic decision-making also have important effects. We conclude that design of incentive programs may need to account for potential counter-productive long-term effects due to behavioural feedbacks.     

Group Membership Affects Spontaneous Mental Representation: Failure to Represent the Out-Group in a Joint Action Task

Predicting others’ actions is crucial to successful social interaction. Previous research on joint action, based on a reaction-time paradigm called the Joint Simon Task, suggests that successful joint action stems from the simultaneous representation of the self with the other. Performance on this task provides a read-out of the degree of intrusion from a partner that participants experience from acting jointly compared to acting alone, which in turn is a measure of the degree to which participants mentally represent their co-actors during the task. To investigate the role of perceived group membership in this type of joint action and its influence on the representation of others, we first subjected participants to a minimal group paradigm while manipulating differences in social competition. We then asked participants to do the Joint Simon Task in pairs with an in-group or out-group member. Only participants who acted with an “in-group” partner on the joint task showed altered reaction times compared to when acting alone, presumably a change caused by the simultaneous and automatic representation of their in-group partner. In contrast, participants who acted with an out-group partner were unaffected in their reactions when doing the joint task, showing no evidence of representation of their out-group partner. This effect was present in both the high-competition and low-competition conditions, indicating that the differential effects of group membership on representation during joint action were driven by perceived group membership and independent of the effects of social competition. We concluded that participants failed to represent out-group members as socially relevant agents not based on any personality or situational characteristics, but in reaction only to their status as “other”. In this way group membership appears to affect cognition on a very immediate and subconscious level.     

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that sequence information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores sequence and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.     

Inherent Dynamics of the Acid-Sensing Ion Channel 1 Correlates with the Gating Mechanism

The acid-sensing ion channel 1 (ASIC1) is a key receptor for extracellular protons. Although numerous structural and functional studies have been performed on this channel, the structural dynamics underlying the gating mechanism remains unknown. We used normal mode analysis, mutagenesis, and electrophysiological methods to explore the relationship between the inherent dynamics of ASIC1 and its gating mechanism. Here we show that a series of collective motions among the domains and subdomains of ASIC1 correlate with its acid-sensing function. The normal mode analysis result reveals that the intrinsic rotation of the extracellular domain and the collective motions between the thumb and finger induced by proton binding drive the receptor to experience a deformation from the extracellular domain to the transmembrane domain, triggering the channel pore to undergo “twist-to-open” motions. The movements in the transmembrane domain indicate that the likely position of the channel gate is around Leu440. These motion modes are compatible with a wide body of our complementary mutations and electrophysiological data. This study provides the dynamic fundamentals of ASIC1 gating.     

Stress and the Multiple-Role Woman: Taking a Closer Look at the “Superwoman”

In the academic literature there is debate as to whether women who engage in multiple social roles experience more or less stress than women in fewer roles. For the present research we examined the relationship between levels of engagement in seven distinct roles and perceived stress and life satisfaction in a small non-random sample of women in North America (N = 308). We did not find a significant correlation between role engagement and perceived stress, though we did find a small but significant positive correlation between role engagement and life satisfaction. Similarly, in a subset of the participants (N = 31), there was not a significant relationship between the level of role engagement and physiological stress as measured by hair or urinary cortisol levels. We found a significant negative correlation between perceived stress and life satisfaction, and role satisfaction. The results from multiple regression models did not identify the level of role engagement as a significant predictor of either perceived stress or life satisfaction. Role satisfaction in addition to several life style variables such as the frequency of sex and exercise were identified as significant predictors of both outcome variables. We also examined the popularized notion of the “superwoman”, which we defined as women who fell within the 4^th quartile of role engagement, or those engaged in the wife/mother/worker/homemaker role combination. Based on popular discourses surrounding the superwoman we expected that superwomen would exhibit higher levels of perceived stress. Our results revealed that superwomen do not experience a significantly higher level of perceived stress than non-superwomen. The results of our study therefore suggest that multiple role engagement in women, even at a relatively high level as experienced by “superwomen”, is not associated with significantly higher stress, or reduced life satisfaction.