Collective Information Processing Research Articles

What Makes Us Smart?

How did humans become clever enough to live in nearly every major ecosystem on earth, create vaccines against deadly plagues, explore the oceans depths, and routinely traverse the globe at 30,000 feet in aluminum tubes while nibbling on roasted almonds? Drawing on recent developments in our understanding of human evolution, we consider what makes us distinctively smarter than other animals. Contrary to conventional wisdom, human brilliance emerges not from our innate brainpower or raw computational capacities, but from the sharing of information in communities and networks over generations. We review how larger, more diverse, and more optimally interconnected networks of minds give rise to faster innovation and how the cognitive products of this cumulative cultural evolutionary process feedback to make us individually "smarter"-in the sense of being better at meeting the challenges and problems posed by our societies and socioecologies. Here, we consider not only how cultural evolution supplies us with "thinking tools" (like counting systems and fractions) but also how it has shaped our ontologies (e.g., do germs and witches exist?) and epistemologies, including our notions of what constitutes a "good reason" or "good evidence" (e.g., are dreams a source of evidence?). Building on this, we consider how cultural evolution has organized and distributed cultural knowledge and cognitive tasks among subpopulations, effectively shifting both thinking and production to the level of the community, population, or network, resulting in collective information processing and group decisions. Cultural evolution can turn mindless mobs into wise crowds by facilitating and constraining cognition through a wide variety of epistemic institutions-political, legal, and scientific. These institutions process information and aid better decision-making by suppressing or encouraging the use of different cultural epistemologies and ontologies.

Subcritical escape waves in schooling fish.

Theoretical physics predicts optimal information processing in living systems near transitions (or pseudo-critical points) in their collective dynamics. However, focusing on potential benefits of proximity to a critical point, such as maximal sensitivity to perturbations and fast dissemination of information, commonly disregards possible costs of criticality in the noisy, dynamic environmental contexts of biological systems. Here, we find that startle cascades in fish schools are subcritical (not maximally responsive to environmental cues) and that distance to criticality decreases when perceived risk increases. Considering individuals’ costs related to two detection error types, associated to both true and false alarms, we argue that being subcritical, and modulating distance to criticality, can be understood as managing a trade-off between sensitivity and robustness according to the riskiness and noisiness of the environment. Our work emphasizes the need for an individual-based and context-dependent perspective on criticality and collective information processing and motivates future questions about the evolutionary forces that brought about a particular trade-off.

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions.

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

Collective predator evasion: Putting the criticality hypothesis to the test.

According to the criticality hypothesis, collective biological systems should operate in a special parameter region, close to so-called critical points, where the collective behavior undergoes a qualitative change between different dynamical regimes. Critical systems exhibit unique properties, which may benefit collective information processing such as maximal responsiveness to external stimuli. Besides neuronal and gene-regulatory networks, recent empirical data suggests that also animal collectives may be examples of self-organized critical systems. However, open questions about self-organization mechanisms in animal groups remain: Evolutionary adaptation towards a group-level optimum (group-level selection), implicitly assumed in the “criticality hypothesis”, appears in general not reasonable for fission-fusion groups composed of non-related individuals. Furthermore, previous theoretical work relies on non-spatial models, which ignore potentially important self-organization and spatial sorting effects. Using a generic, spatially-explicit model of schooling prey being attacked by a predator, we show first that schools operating at criticality perform best. However, this is not due to optimal response of the prey to the predator, as suggested by the “criticality hypothesis”, but rather due to the spatial structure of the prey school at criticality. Secondly, by investigating individual-level evolution, we show that strong spatial self-sorting effects at the critical point lead to strong selection gradients, and make it an evolutionary unstable state. Our results demonstrate the decisive role of spatio-temporal phenomena in collective behavior, and that individual-level selection is in general not a viable mechanism for self-tuning of unrelated animal groups towards criticality.

Collective information processing in human phase separation.

In our digital societies, individuals massively interact through digital interfaces whose impact on collective dynamics can be important. In particular, the combination of social media filters and recommender systems can lead to the emergence of polarized and fragmented groups. In some social contexts, such segregation processes of human groups have been shown to share similarities with phase separation phenomena in physics. Here, we study the impact of information filtering on collective segregation behaviour of human groups. We report a series of experiments where groups of 22 subjects have to perform a collective segregation task that mimics the tendency of individuals to bond with other similar individuals. More precisely, the participants are each assigned a colour (red or blue) unknown to them, and have to regroup with other subjects sharing the same colour. To assist them, they are equipped with an artificial sensory device capable of detecting the majority colour in their 'environment' (defined as their k nearest neighbours, unbeknownst to them), for which we control the perception range, k = 1, 3, 5, 7, 9, 11, 13. We study the separation dynamics (emergence of unicolour groups) and the properties of the final state, and show that the value of k controls the quality of the segregation, although the subjects are totally unaware of the precise definition of the 'environment'. We also find that there is a perception range k = 7 above which the ability of the group to segregate does not improve. We introduce a model that precisely describes the random motion of a group of pedestrians in a confined space, and which faithfully reproduces and allows interpretation of the results of the segregation experiments. Finally, we discuss the strong and precise analogy between our experiment and the phase separation of two immiscible materials at very low temperature. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

The Bayesian superorganism: externalized memories facilitate distributed sampling.

A key challenge for any animal (or sampling technique) is to avoid wasting time by searching for resources (information) in places already found to be unprofitable. In biology, this challenge is particularly strong when the organism is a central place forager—returning to a nest between foraging bouts—because it is destined repeatedly to cover much the same ground. This problem will be particularly acute if many individuals forage from the same central place, as in social insects such as the ants. Foraging (sampling) performance may be greatly enhanced by coordinating movement trajectories such that each ant (walker) visits separate parts of the surrounding (unknown) space. We find experimental evidence for an externalized spatial memory in Temnothorax albipennis ants: chemical markers (either pheromones or cues such as cuticular hydrocarbon footprints) that are used by nest-mates to mark explored space. We show these markers could be used by the ants to scout the space surrounding their nest more efficiently through indirect coordination. We also develop a simple model of this marking behaviour that can be applied in the context of Markov chain Monte Carlo methods (Baddeley et al. 2019 J. R. Soc. Interface 16, 20190162 (doi:10.1098/rsif.2019.0162)). This substantially enhances the performance of standard methods like the Metropolis–Hastings algorithm in sampling from sparse probability distributions (such as those confronted by the ants) with only a little additional computational cost. Our Bayesian framework for superorganismal behaviour motivates the evolution of exploratory mechanisms such as trail marking in terms of enhanced collective information processing.

Flocking in complex environments-Attention trade-offs in collective information processing.

The ability of biological and artificial collectives to outperform solitary individuals in a wide variety of tasks depends crucially on the efficient processing of social and environmental information at the level of the collective. Here, we model collective behavior in complex environments with many potentially distracting cues. Counter-intuitively, large-scale coordination in such environments can be maximized by strongly limiting the cognitive capacity of individuals, where due to self-organized dynamics the collective self-isolates from disrupting information. We observe a fundamental trade-off between coordination and collective responsiveness to environmental cues. Our results offer important insights into possible evolutionary trade-offs in collective behavior in biology and suggests novel principles for design of artificial swarms exploiting attentional bottlenecks.

Thanks for your ideas: Gratitude and team creativity

Many ideas and products are borne out of collaborative efforts among members of teams and workgroups, and thus finding ways to improve team creativity is of significant interest. Adopting a collective information processing perspective, we argue that gratitude intervention for teams would serve as a powerful facilitator for information elaboration—whereby team members engage in more deliberate and thorough integration of others’ ideas—and, in turn, enhance team creativity. Study 1 found that teams in the gratitude condition increased information elaboration more than those in the neutral condition. Study 2 compared teams in gratitude emotion and teams in positive emotion in general. Teams in the gratitude condition generated highly creative ideas, due to more information elaboration. On the other hand, teams in the positive emotion condition expressed greater enthusiasm and confidence in their ideas and immediately accepted the ideas suggested, which led to an increase in the quantity of ideas. Our findings suggest that gratitude facilitates intellectual exchange in groups, which in turn enhances team creativity. We discuss our findings’ implications for team creativity and potential directions for future research.

Individual and collective encoding of risk in animal groups

The need to make fast decisions under risky and uncertain conditions is a widespread problem in the natural world. While there has been extensive work on how individual organisms dynamically modify their behavior to respond appropriately to changing environmental conditions (and how this is encoded in the brain), we know remarkably little about the corresponding aspects of collective information processing in animal groups. For example, many groups appear to show increased "sensitivity" in the presence of perceived threat, as evidenced by the increased frequency and magnitude of repeated cascading waves of behavioral change often observed in fish schools and bird flocks under such circumstances. How such context-dependent changes in collective sensitivity are mediated, however, is unknown. Here we address this question using schooling fish as a model system, focusing on 2 nonexclusive hypotheses: 1) that changes in collective responsiveness result from changes in how individuals respond to social cues (i.e., changes to the properties of the "nodes" in the social network), and 2) that they result from changes made to the structural connectivity of the network itself (i.e., the computation is encoded in the "edges" of the network). We find that despite the fact that perceived risk increases the probability for individuals to initiate an alarm, the context-dependent change in collective sensitivity predominantly results not from changes in how individuals respond to social cues, but instead from how individuals modify the spatial structure, and correspondingly the topology of the network of interactions, within the group. Risk is thus encoded as a collective property, emphasizing that in group-living species individual fitness can depend strongly on coupling between scales of behavioral organization.

Tempos and modes of collectivity in the history of life.

Collective integration and processing of information have increased through the history of life, through both the formation of aggregates in which the entities may have very different properties and which jointly coarse-grained environmental variables (ranging from widely varying metabolism in microbial consortia to the ecological diversity of species on reefs) and through collectives of similar entities (such as cells within an organism or social groups). Such increases have been implicated in significant transitions in the history of life, including aspects of the origin of life, the generation of pangenomes among microbes and microbial communities such as stromatolites, multicellularity and social insects. This contribution provides a preliminary overview of the dominant modes of collective information processing in the history of life, their phylogenetic distribution and extent of convergence, and the effects of new modes for integrating and acting upon information on the tempo of evolutionary change.

Localized nonlinear excitations in diffusive memristor-based neuronal networks

We extend the existing ordinary differential equations modeling neural electrical activity to include the memory effect of electromagnetic induction through magnetic flux, used to describe time varying electromagnetic field. Through the multi-scale expansion in the semi-discrete approximation, we show that the neural network dynamical equations can be governed by the complex Ginzburg-Landau equation. The analytical and numerical envelop soliton of this equation are reported. The results obtained suggest the possibility of collective information processing and sharing in the nervous system, operating in both the spatial and temporal domains in the form of localized modulated waves. The effects of memristive synaptic electromagnetic induction coupling and perturbation on the modulated action potential dynamics examined. Large electromagnetic induction coupling strength may contribute to signal block as the amplitude of modulated waves are observed to decrease. This could help in the development of a chemical brain anaesthesia for some brain pathologies.

ScamCoins, S*** Posters, and the Search for the Next Bitcoin TM

Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naïvety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.

Effects of dynamic synapses, neuronal coupling, and time delay on firing of neuron

Neuronal firing plays a key role in the neuronal information transmission, and different neuronal firing patterns are reported, such as spiking, bursting. A number of neuron models are introduced to reproduce the firing patterns of single neuron or neuronal network. The key factors determining the firing pattern gain more and more attention in the study of neuron system, such as noise, network topology. Noise is able to induce sub-or super-threshold coherent neuronal firing easily, and a number of coherence resonances are reported in the noise induced firing. The network topology determines the synchronization of the firing patterns of the neuronal network, and the change of network topology may induce fruitful synchronization transitions. It is well known that synapses exhibit a high variability with a diverse origin during information transmission, such as the stochastic release of neurotransmitters, variations in chemical concentration through synapses, and spatial heterogeneity of synaptic response over dendrite tree. The collective effect of all of these factors might result in the notion of dynamic synapses. In reality, the neuronal network often involves time delay due to the ?nite signal propagation time in biological networks. Recently, neuronal networks with time delay have received considerable attention. Delay-sustained neuronal firing patterns may be relevant to neuronal networks for establishing a concept of collective information processing in the presence of delayed information transmission. According to the above-mentioned motivations, the firing dynamics of the single postsynapic neuron is investigated based on a simple postsynaptic neuron model by using numerical simulation and Fourier transform analysis. In this model, the postsynapic neuron receives dynamic synaptic currents from a population of presynaptic neurons. It is found that the firing rate resonance between the pre-and postsynaptic neuron determines the firing of the postsynaptic neuron. Stimulus currents in specific frequency range are easy to stimulate postsynaptic neuron firing. The random currents released from dynamic synapses determine the postsynaptic firing rate. Then the single postsynaptic neuron is extended to a neuronal network, in which 100 neurons connect to its 4 nearest neighbors regularly and receive delayed synaptic currents from connected neurons. All the neurons in the network receive the same dynamic synaptic currents from the presynaptic neurons. The results show that the synaptic coupling in the network is able to promote the neuron firing in the network, and time delay in the synaptic coupling could reinforce the promotion, but the mode of the promotion is not changed.

A face for all seasons: Searching for context-specific leadership traits and discovering a general preference for perceived health.

Previous research indicates that followers tend to contingently match particular leader qualities to evolutionarily consistent situations requiring collective action (i.e., context-specific cognitive leadership prototypes) and information processing undergoes categorization which ranks certain qualities as first-order context-general and others as second-order context-specific. To further investigate this contingent categorization phenomenon we examined the “attractiveness halo”—a first-order facial cue which significantly biases leadership preferences. While controlling for facial attractiveness, we independently manipulated the underlying facial cues of health and intelligence and then primed participants with four distinct organizational dynamics requiring leadership (i.e., competition vs. cooperation between groups and exploratory change vs. stable exploitation). It was expected that the differing requirements of the four dynamics would contingently select for relatively healthier- or intelligent-looking leaders. We found perceived facial intelligence to be a second-order context-specific trait—for instance, in times requiring a leader to address between-group cooperation—whereas perceived health is significantly preferred across all contexts (i.e., a first-order trait). The results also indicate that facial health positively affects perceived masculinity while facial intelligence negatively affects perceived masculinity, which may partially explain leader choice in some of the environmental contexts. The limitations and a number of implications regarding leadership biases are discussed.

The Emergence of Team Cognition: Based on the Perspective of Collective Information Processing

The Emergence of Team Cognition: Based on the Perspective of Collective Information Processing

Building team adaptive capacity: The roles of sensegiving and team composition.

The current study draws on motivated information processing in groups theory to propose that leadership functions and composition characteristics provide teams with the epistemic and social motivation needed for collective information processing and strategy adaptation. Three-person teams performed a city management decision-making simulation (N=74 teams; 222 individuals). Teams first managed a simulated city that was newly formed and required growth strategies and were then abruptly switched to a second simulated city that was established and required revitalization strategies. Consistent with hypotheses, external sensegiving and team composition enabled distinct aspects of collective information processing. Sensegiving prompted the emergence of team strategy mental models (i.e., cognitive information processing); psychological collectivism facilitated information sharing (i.e., behavioral information processing); and cognitive ability provided the capacity for both the cognitive and behavioral aspects of collective information processing. In turn, team mental models and information sharing enabled reactive strategy adaptation.

A new approach to extract multiple distinct conformers and co-existing distinct electronic properties of a single molecule by point-contact method

Conventionally, it is assumed that a single molecule has only one absolute electronic property. Molecular electronics, molecular machines, intelligent drugs are some of the fields that require atomic scale control of molecular properties. In spite of remarkable achievements in the last two decades, absolute control of molecular properties has not been achieved. Here, experimental evidences argue against assigning a fixed property to a molecule. A molecule might structurally accommodate itself to the new environment and its electronic properties might change accordingly. By isolating electronic properties of all the conformers of a Rose Bengal molecule one by one, a map of its complete electronic properties is drawn here. The existing concept of one absolute electronic property of a molecule is true only for those molecules, which have only one stable conformer. For others, depending on the conformer, multiple distinct electronic properties may co-exist, leading to a variable output in an electronic characterization. Thus, we present a generalized method for characterizing/resolving collective electronic properties that emerge statistically. The method could be used for designing molecular switches for collective and evolutionary information processing.

Smallest artificial molecular neural-net for collective and emergent information processing

While exploring the random diffusion of 2 bit molecular switches (we define as molecular neuron) on an atomic flat Au (111) substrate, we have found that at least four molecules are required to construct a functional neural net. Surface electron density wave enables communication of one to many molecules at a time—a prerequisite for the parallel processing. Here we have shown that in a neural net of several molecules, some of them could dynamically store information as memory and consistently replicate the fundamental relationship that is found only in a collective and emergent computing system like our brain.

Collective information processing and pattern formation in swarms, flocks, and crowds.

The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals' capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution, we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individual's ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.

Optimisation of a honeybee-colony's energetics via social learning based on queuing delays

Natural selection shaped the foraging-related processes of honeybees in such a way that a colony can react to changing environmental conditions optimally. To investigate this complex dynamic social system, we developed a multi-agent model of the nectar flow inside and outside of a honeybee colony. In a honeybee colony, a temporal caste collects nectar in the environment. These foragers bring their harvest into the colony, where they unload their nectar loads to one or more storer bees. Our model predicts that a cohort of foragers, collecting nectar from a single nectar source, is able to detect changes in quality in other food sources they have never visited, via the nectar processing system of the colony. We identified two novel pathways of forager-to-forager communication. Foragers can gain information about changes in the nectar flow in the environment via changes in their mean waiting time for unloadings and the number of experienced multiple unloadings. This way two distinct groups of foragers that forage on different nectar sources and that never communicate directly can share information via a third cohort of worker bees. We show that this noisy and loosely knotted social network allows a colony to perform collective information processing, so that a single forager has all necessary information available to be able to ‘tune’ its social behaviour, like dancing or dance-following. This way the net nectar gain of the colony is increased.