Emergence as the conversion of information: a unifying theory

Is reduction always a good scientific strategy? The existence of the special sciences above physics suggests not. Previous research has shown that dimensionality reduction (macroscales) can increase the dependency between elements of a system (a phenomenon called 'causal emergence'). Here, we provide an umbrella mathematical framework for emergence based on information conversion. We show evidence that coarse-graining can convert information from one 'type' to another. We demonstrate this using the well-understood mutual information measure applied to Boolean networks. Using partial information decomposition, the mutual information can be decomposed into redundant, unique and synergistic information atoms. Then by introducing a novel measure of the synergy bias of a given decomposition, we are able to show that the synergy component of a Boolean network's mutual information can increase at macroscales. This can occur even when there is no difference in the total mutual information between a macroscale and its underlying microscale, proving information conversion. We relate this broad framework to previous work, compare it to other theories, and argue it complexifies any notion of universal reduction in the sciences, since such reduction would likely lead to a loss of synergistic information in scientific models. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Evolution and emergence: higher order information structure in protein interactomes across the tree of life

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein-protein interactions, at different scales. We show the emergence of higher order 'macroscales' in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being 'certain' at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

A training course on interpersonal relationships using role play in a Master of Mental Health Care programme - The students' experiences

OBJECTIVES

To study how Master Students of Mental Health Care experienced role play as an educational method that strengthened their relational competence.

DESIGN

The study was qualitative with an exploratory descriptive design.

SETTINGS

Relational competence training course during the Master of Mental Health Care programme INFORMANTS: Master students in a Mental Health Care programme METHODS: Data from open-ended questions were analysed using qualitative content analysis based on Graneheim and Lundman.

RESULTS

The following three categories were identified: A deeper understanding of self and others, Different positions and situations provide comprehensive understanding and Engagement strengthens relational competence.

CONCLUSIONS

The study demonstrated that extensive use of role play, in which the students took on the roles of patient, healthcare professional and observer, combined with theoretical preparations and reflections seemed to elicit some of the humanistic values and attitudes central for strengthening relational competence.

The overfitted brain: Dreams evolved to assist generalization

Understanding of the evolved biological function of sleep has advanced considerably in the past decade. However, no equivalent understanding of dreams has emerged. Contemporary neuroscientific theories often view dreams as epiphenomena, and many of the proposals for their biological function are contradicted by the phenomenology of dreams themselves. Now, the recent advent of deep neural networks (DNNs) has finally provided the novel conceptual framework within which to understand the evolved function of dreams. Notably, all DNNs face the issue of overfitting as they learn, which is when performance on one dataset increases but the network's performance fails to generalize (often measured by the divergence of performance on training versus testing datasets). This ubiquitous problem in DNNs is often solved by modelers via "noise injections" in the form of noisy or corrupted inputs. The goal of this paper is to argue that the brain faces a similar challenge of overfitting and that nightly dreams evolved to combat the brain's overfitting during its daily learning. That is, dreams are a biological mechanism for increasing generalizability via the creation of corrupted sensory inputs from stochastic activity across the hierarchy of neural structures. Sleep loss, specifically dream loss, leads to an overfitted brain that can still memorize and learn but fails to generalize appropriately. Herein this "overfitted brain hypothesis" is explicitly developed and then compared and contrasted with existing contemporary neuroscientific theories of dreams. Existing evidence for the hypothesis is surveyed within both neuroscience and deep learning, and a set of testable predictions is put forward that can be pursued both in vivo and in silico.

Falsification and consciousness

The search for a scientific theory of consciousness should result in theories that are falsifiable. However, here we show that falsification is especially problematic for theories of consciousness. We formally describe the standard experimental setup for testing these theories. Based on a theory's application to some physical system, such as the brain, testing requires comparing a theory's predicted experience (given some internal observables of the system like brain imaging data) with an inferred experience (using report or behavior). If there is a mismatch between inference and prediction, a theory is falsified. We show that if inference and prediction are independent, it follows that any minimally informative theory of consciousness is automatically falsified. This is deeply problematic since the field's reliance on report or behavior to infer conscious experiences implies such independence, so this fragility affects many contemporary theories of consciousness. Furthermore, we show that if inference and prediction are strictly dependent, it follows that a theory is unfalsifiable. This affects theories which claim consciousness to be determined by report or behavior. Finally, we explore possible ways out of this dilemma.

Causal Geometry

Information geometry has offered a way to formally study the efficacy of scientific models by quantifying the impact of model parameters on the predicted effects. However, there has been little formal investigation of causation in this framework, despite causal models being a fundamental part of science and explanation. Here, we introduce causal geometry, which formalizes not only how outcomes are impacted by parameters, but also how the parameters of a model can be intervened upon. Therefore, we introduce a geometric version of “effective information”—a known measure of the informativeness of a causal relationship. We show that it is given by the matching between the space of effects and the space of interventions, in the form of their geometric congruence. Therefore, given a fixed intervention capability, an effective causal model is one that is well matched to those interventions. This is a consequence of “causal emergence,” wherein macroscopic causal relationships may carry more information than “fundamental” microscopic ones. We thus argue that a coarse-grained model may, paradoxically, be more informative than the microscopic one, especially when it better matches the scale of accessible interventions—as we illustrate on toy examples.

Examining the Causal Structures of Deep Neural Networks Using Information Theory

Deep Neural Networks (DNNs) are often examined at the level of their response to input, such as analyzing the mutual information between nodes and data sets. Yet DNNs can also be examined at the level of causation, exploring "what does what" within the layers of the network itself. Historically, analyzing the causal structure of DNNs has received less attention than understanding their responses to input. Yet definitionally, generalizability must be a function of a DNN's causal structure as it reflects how the DNN responds to unseen or even not-yet-defined future inputs. Here, we introduce a suite of metrics based on information theory to quantify and track changes in the causal structure of DNNs during training. Specifically, we introduce the effective information (EI) of a feedforward DNN, which is the mutual information between layer input and output following a maximum-entropy perturbation. The EI can be used to assess the degree of causal influence nodes and edges have over their downstream targets in each layer. We show that the EI can be further decomposed in order to examine the sensitivity of a layer (measured by how well edges transmit perturbations) and the degeneracy of a layer (measured by how edge overlap interferes with transmission), along with estimates of the amount of integrated information of a layer. Together, these properties define where each layer lies in the "causal plane", which can be used to visualize how layer connectivity becomes more sensitive or degenerate over time, and how integration changes during training, revealing how the layer-by-layer causal structure differentiates. These results may help in understanding the generalization capabilities of DNNs and provide foundational tools for making DNNs both more generalizable and more explainable.

Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control

The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question. In some cases, macro-scale models can minimize noise and increase the amount of information an experimenter or modeler has about "what does what." This result has numerous implications for evolution, pattern regulation, and biomedical strategies. Here, we provide an introduction to these quantitative techniques, and use them to show how informative macro-scales are common across biology. Our goal is to give biologists the tools to identify the maximally-informative scale at which to model, experiment on, predict, control, and understand complex biological systems.

Reduced Repertoire of Cortical Microstates and Neuronal Ensembles in Medically Induced Loss of Consciousness

Medically induced loss of consciousness (mLOC) during anesthesia is associated with a macroscale breakdown of brain connectivity, yet the neural microcircuit correlates of mLOC remain unknown. To explore this, we applied different analytical approaches (t-SNE/watershed segmentation, affinity propagation clustering, PCA, and LZW complexity) to two-photon calcium imaging of neocortical and hippocampal microcircuit activity and local field potential (LFP) measurements across different anesthetic depths in mice, and to micro-electrode array recordings in human subjects. We find that in both cases, mLOC disrupts population activity patterns by generating (1) fewer discriminable network microstates and (2) fewer neuronal ensembles. Our results indicate that local neuronal ensemble dynamics could causally contribute to the emergence of conscious states.

Gender and leadership for health literacy to combat the epidemic rise of noncommunicable diseases

Until recently, the noncommunicable diseases (NCDs) epidemic has been considered only a significant burden to men in high-income countries. However, latest figures indicate that half of all NCD-related deaths affect women, especially in low- and middle-income countries (LMICs), with global responses to the NCD epidemic overlooking the significance of women and girls in their approaches and programs. This case study highlights the burden of disease challenging South Africa that disproportionately affects women in the country and suggests that the country, along with other LMICs internationally, requires a shift in the gender-based leadership of health literacy and self-empowerment.

Presence of Anisakis simplex (Rudolphi, 1809 det. Krabbe, 1878) and Hysterothylacium aduncum (Rudolphi, 1802) (Nematoda; Anisakidae) in runts of farmed Atlantic salmon, Salmo salar L

One hundred farmed Atlantic salmon, Salmo salar L., were examined for the presence of nematodes by digestion of tissue in HCl-pepsin solution. All fish were sampled from one cage in a fish farm on the Norwegian south-west coast. Fifty harvest quality salmon, that is, salmon for human consumption (mean 5.4 kg, variation 3.0-7.6 kg), were sampled at the processing line while 50 salmon runts (mean 1.1 kg, variation 0.4-1.8 kg), discarded due to poor performance, were sampled from the discard bin after the grading station. Runts are individual fish with clear signs of poor performance over time and abnormal appearance and are thus not processed for human consumption. No nematodes were found in the musculature or viscera of the 50 harvest quality salmon. In total, 75 nematodes were found in 10 (20%) of the runts; 53 nematodes in the viscera and 22 in the musculature. Nematodes in the musculature were identified as Anisakis simplex (Rudolphi, 1809 det. Krabbe, 1878), while nematodes in the viscera were identified as A. simplex and Hysterothylacium aduncum (Rudolphi, 1802).

Ichthyobodo necator (Kinetoplastida)--a complex of sibling species

Ichthyobodo necator is a parasitic flagellate that attacks fishes, causing disease problems in freshwater worldwide. Findings of similar flagellates in strictly marine fishes have indicated that ichthyobodiosis may be caused by more than 1 flagellate species. We obtained partial small subunit rDNA (ssu rDNA) sequences of 14 Ichthyobodo isolates originating from fishes in Norway, Japan, Singapore, South Africa and Brazil, and identified 8 strains or species, including 2 species infecting cultured salmon in Norway. An Ichthyobodo species isolated from the skin of Atlantic salmon parr in freshwater is suggested to represent L. necator sensu stricto, while another species, showing particular affinity for the gills, infects salmon in both fresh- and seawater. Atlantic cod is infected with a marine Ichthyobodo species unrelated to those infecting salmonids; 2 cyprinids originating from different parts of the world had related Ichthyobodo strains/species, and 2 isolates from unrelated North and South American fishes were also closely related. The phylogenetic relationships of the Ichthyobodo isolates is described, and the implications of the molecular findings on past and future morphological studies of Ichthyobodo spp. are discussed.