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  • Journal article
    Rivers R, Evans T, 2020,

    How do we avoid imposing the present on the past when modelling spatial interactions?

    , Documenta Praehistorica, Vol: 47, Pages: 462-475, ISSN: 1318-6701

    Theoretical archaeological modelling for describing spatial interactions often adopts contemporary socioeconomic ideas whose C20th language gets translated into historical behaviour with the simplest of lexicons. This can lead to the impression that the past is like the present. Our intention in this paper is that, when this happens, to strip out as much of the contemporary context as we can, to bring modelling back to basic epistemic propositions. We suggest that although the underlying ontology may be specific to contemporary society the epistemology has much greater generality, leading to essentially the same conclusions without the carapace of intricate economics.

  • Journal article
    Rosas FE, Mediano PAM, Jensen HJ, Seth AK, Barrett AB, Carhart-Harris RL, Bor Det al., 2020,

    Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data

    , PLoS Computational Biology, Vol: 16, ISSN: 1553-734X

    The broad concept of emergence is instrumental in various of the most challenging open scientific questions—yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour—which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway’s Game of Life, Reynolds’ flocking model, and neural activity as measured by electrocorticography.

  • Journal article
    Andersen MM, Schjoedt U, Price H, Rosas FE, Scrivner C, Clasen Met al., 2020,

    Playing with fear: a field study in recreational horror

    , Psychological Science, Vol: 31, Pages: 1497-1510, ISSN: 0956-7976

    Haunted attractions are illustrative examples of recreational fear in which people voluntarily seek out frightening experiences in pursuit of enjoyment. We present findings from a field study at a haunted-house attraction where visitors between the ages of 12 and 57 years (N = 110) were equipped with heart rate monitors, video-recorded at peak scare points during the attraction, and asked to report on their experience. Our results show that enjoyment has an inverted-U-shaped relationship with fear across repeated self-reported measures. Moreover, results from physiological data demonstrate that the experience of being frightened is a linear function of large-scale heart rate fluctuations, whereas there is an inverted-U-shaped relationship between participant enjoyment and small-scale heart rate fluctuations. These results suggest that enjoyment is related to forms of arousal dynamics that are “just right.” These findings shed light on how fear and enjoyment can coexist in recreational horror.

  • Journal article
    Sahasranaman A, Jensen HJ, 2020,

    Poverty in the time of epidemic: A modelling perspective

    , PLoS One, Vol: 15, Pages: 1-16, ISSN: 1932-6203

    We create a network model to study the spread of an epidemic through physically proximate and accidental daily human contacts in a city, and simulate outcomes for two kinds of agents—poor and non-poor. Under non-intervention, peak caseload is maximised, but no differences are observed in infection rates across poor and non-poor. Introducing interventions to control spread, peak caseloads are reduced, but both cumulative infection rates and current infection rates are systematically higher for the poor than for non-poor, across all scenarios. Larger populations, higher fractions of poor, and longer durations of intervention are found to progressively worsen outcomes for the poor; and these are of particular concern for economically vulnerable populations in cities of the developing world. Addressing these challenges requires a deeper, more rigorous understanding of the relationships between structural poverty and epidemy, as well as effective utilization of extant community level infrastructure for primary care in developing cities. Finally, improving iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.

  • Journal article
    Rosas FE, Mediano PAM, Rassouli B, Barrett ABet al., 2020,

    An operational information decomposition via synergistic disclosure

    , Journal of Physics A: Mathematical and Theoretical, Vol: 53, Pages: 485001-485001, ISSN: 1751-8113

    Multivariate information decompositions hold promise to yield insight into complex systems, and stand out for their ability to identify synergistic phenomena. However, the adoption of these approaches has been hindered by there being multiple possible decompositions, and no precise guidance for preferring one over the others. At the heart of this disagreement lies the absence of a clear operational interpretation of what synergistic information is. Here we fill this gap by proposing a new information decomposition based on a novel operationalisation of informational synergy, which leverages recent developments in the literature of data privacy. Our decomposition is defined for any number of information sources, and its atoms can be calculated using elementary optimisation techniques. The decomposition provides a natural coarse-graining that scales gracefully with the system's size, and is applicable in a wide range of scenarios of practical interest.

  • Journal article
    Herzog R, Mediano PAM, Rosas FE, Carhart-Harris R, Perl YS, Tagliazucchi E, Cofre Ret al., 2020,

    A mechanistic model of the neural entropy increase elicited by psychedelic drugs

    , Scientific Reports, Vol: 10, ISSN: 2045-2322

    Psychedelic drugs, including lysergic acid diethylamide and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, mediating both acute alterations in consciousness and long-term effects. However, no clear mechanistic explanation for this entropy increase has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in some regions and decreased in others, suggesting a topographical reconfiguration mediated by 5HT2A-R activation. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brain's anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity.

  • Journal article
    Hilton B, Sood AP, Evans TS, 2019,

    Predictive limitations of spatial interaction models: a non-Gaussian analysis

    , Scientific Reports, ISSN: 2045-2322

    We present a method to compare spatial interaction models against data basedon well known statistical measures which are appropriate for such models anddata. We illustrate our approach using a widely used example: commuting data,specifically from the US Census 2000. We find that the radiation model performssignificantly worse than an appropriately chosen simple gravity model. Variousconclusions are made regarding the development and use of spatial interactionmodels, including: that spatial interaction models fit badly to data in anabsolute sense, that therefore the risk of over-fitting is small and addingadditional fitted parameters improves the predictive power of models, and thatappropriate choices of input data can improve model fit.

  • Journal article
    Cofré R, Herzog R, Mediano PAM, Piccinini J, Rosas FE, Sanz Perl Y, Tagliazucchi Eet al., 2020,

    Whole-brain models to explore altered states of consciousness from the bottom up

    , Brain Sciences, Vol: 10, ISSN: 2076-3425

    The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness.

  • Journal article
    Palmieri L, Jensen HJ, 2020,

    The forest fire model: the subtleties of criticality and scale invariance

    , Frontiers in Physics, Vol: 8, Pages: 1-8, ISSN: 2296-424X

    Amongst the numerous models introduced with SOC, the Forest Fire Model (FFM) is particularly attractive for its close relationship to stochastic spreading, which is central to the study of systems as diverse as epidemics, rumors, or indeed, fires. However, since its introduction, the nature of the model's scale invariance has been controversial, and the lack of scaling observed in many studies diminished its theoretical attractiveness. In this study, we analyse the behavior of the tree density, the average cluster size and the largest cluster and show that the model could be of high practical relevance for the activation dynamics seen in brain and rain studies. From this perspective, its peculiar scaling properties should be regarded as an asset rather than a limitation.

  • Journal article
    Chen J, Wang Z, Zhu T, Rosas FEet al., 2020,

    Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism

    , Complexity, Vol: 2020, Pages: 1-19, ISSN: 1076-2787

    The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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