Shared Protentions in Multi-Agent Active Inference.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time-consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior based on variational (approximate Bayesian) inference. Expanding upon Husserl's model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory, in particular, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitates the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

Mortality and mortality disparities among people with epilepsy in the United States, 2011-2021.

Studies on epilepsy mortality in the United States are limited. We used the National Vital Statistics System Multiple Cause of Death data to investigate mortality rates and trends during 2011-2021 for epilepsy (defined by the International Classification of Diseases, 10th Revision, codes G40.0-G40.9) as an underlying, contributing, or any cause of death (i.e., either an underlying or contributing cause) for U.S. residents. We also examined epilepsy as an underlying or contributing cause of death by selected sociodemographic characteristics to assess mortality rate changes and disparities in subpopulations. During 2011-2021, the overall age-standardized mortality rates for epilepsy as an underlying (39 % of all deaths) or contributing (61 % of all deaths) cause of death increased 83.6 % (from 2.9 per million to 6.4 per million population) as underlying cause and 144.1 % (from 3.3 per million to 11.0 per million population) as contributing cause (P < 0.001 for both based on annual percent changes). Compared to 2011-2015, in 2016-2020 mortality rates with epilepsy as an underlying or contributing cause of death were higher overall and in nearly all subgroups. Overall, mortality rates with epilepsy as an underlying or contributing cause of death were higher in older age groups, among males than females, among non-Hispanic Black or non-Hispanic American Indian/Alaska Native persons than non-Hispanic White persons, among those living in the West and Midwest than those living in the Northeast, and in nonmetro counties compared to urban regions. Results identify priority subgroups for intervention to reduce mortality in people with epilepsy and eliminate mortality disparity.

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.

Serial Bronchoalveolar Lavage Fluid Aspergillus Galactomannan and Treatment Response in Invasive Pulmonary Aspergillosis.

We studied patients diagnosed with aspergillosis based on positive bronchoalveolar lavage (BAL) Aspergillus galactomannan (GM) who had follow-up BAL sampling within 180 days. GM trend and clinical outcome were concordant in only 60% (30/50). While useful for the initial diagnosis, BAL GM trending does not always correlate with treatment response.

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

Objective: This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. Approach: We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes, by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train both subject-specific models using data from a single participant as well as multi-patient models exploiting data from multiple participants. Main Results: The subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. Significance: The proposed SwinTW decoder enables future speech neuroprostheses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests the exciting possibility of developing speech neuroprostheses for people with speech disability without relying on their own neural data for training, which is not always feasible.

Racial disparities in the utilization of invasive neuromodulation devices for the treatment of drug-resistant focal epilepsy.

Racial disparities affect multiple dimensions of epilepsy care including epilepsy surgery. This study aims to further explore these disparities by determining the utilization of invasive neuromodulation devices according to race and ethnicity in a multicenter study of patients living with focal drug-resistant epilepsy (DRE). We performed a post hoc analysis of the Human Epilepsy Project 2 (HEP2) data. HEP2 is a prospective study of patients living with focal DRE involving 10 sites distributed across the United States. There were no statistical differences in the racial distribution of the study population compared to the US population using census data except for patients reporting more than one race. Of 154 patients enrolled in HEP2, 55 (36%) underwent invasive neuromodulation for DRE management at some point in the course of their epilepsy. Of those, 36 (71%) were patients who identified as White. Patients were significantly less likely to have a device if they identified solely as Black/African American than if they did not (odds ratio = .21, 95% confidence interval = .05-.96, p = .03). Invasive neuromodulation for management of DRE is underutilized in the Black/African American population, indicating a new facet of racial disparities in epilepsy care.

Severe mental illness: cardiovascular risk assessment and management.

Patients with severe mental illness (SMI) including schizophrenia and bipolar disorder die on average 15-20 years earlier than the general population often due to sudden death that, in most cases, is caused by cardiovascular disease. This state-of-the-art review aims to address the complex association between SMI and cardiovascular risk, explore disparities in cardiovascular care pathways, describe how to adequately predict cardiovascular outcomes, and propose targeted interventions to improve cardiovascular health in patients with SMI. These patients have an adverse cardiovascular risk factor profile due to an interplay between biological factors such as chronic inflammation, patient factors such as excessive smoking, and healthcare system factors such as stigma and discrimination. Several disparities in cardiovascular care pathways have been demonstrated in patients with SMI, resulting in a 47% lower likelihood of undergoing invasive coronary procedures and substantially lower rates of prescribed standard secondary prevention medications compared with the general population. Although early cardiovascular risk prediction is important, conventional risk prediction models do not accurately predict long-term cardiovascular outcomes as cardiovascular disease and mortality are only partly driven by traditional risk factors in this patient group. As such, SMI-specific risk prediction models and clinical tools such as the electrocardiogram and echocardiogram are necessary when assessing and managing cardiovascular risk associated with SMI. In conclusion, there is a necessity for differentiated cardiovascular care in patients with SMI. By addressing factors involved in the excess cardiovascular risk, reconsidering risk stratification approaches, and implementing multidisciplinary care models, clinicians can take steps towards improving cardiovascular health and long-term outcomes in patients with SMI.

Peak frequency mapping of atypical atrial flutter.

INTRODUCTION: Peak frequency (PF) mapping is a novel method that may identify critical portions of myocardial substrate supporting reentry. The aim of this study was to describe and evaluate PF mapping combined with omnipolar voltage mapping in the identification of critical isthmuses of left atrial (LA) atypical flutters. METHODS AND RESULTS: LA omnipolar voltage and PF maps were generated in flutter using the Advisor HD-Grid catheter (Abbott) and EnSite Precision Mapping System (Abbott) in 12 patients. Normal voltage was defined as ≥0.5 mV, low-voltage as 0.1-0.5 mV, and scar as <0.1 mV. PF distributions were compared with ANOVA and post hoc Tukey analyses. The 1 cm radius from arrhythmia termination was compared to global myocardium with unpaired t-testing. The mean age was 65.8 ± 9.7 years and 50% of patients were female. Overall, 34 312 points were analyzed. Atypical flutters most frequently involved the mitral isthmus (58%) or anterior wall (25%). Mean PF varied significantly by myocardial voltage: normal (335.5 ± 115.0 Hz), low (274.6 ± 144.0 Hz), and scar (71.6 ± 140.5 Hz) (p < .0001 for all pairwise comparisons). All termination sites resided in low-voltage regions containing intermediate or high PF. Overall, mean voltage in the 1 cm radius from termination was significantly lower than the remaining myocardium (0.58 vs. 0.95 mV, p < .0001) and PF was significantly higher (326.4 vs. 245.1 Hz, p < .0001). CONCLUSION: Low-voltage, high-PF areas may be critical targets during catheter ablation of atypical atrial flutter.

Timing and location of speech errors induced by direct cortical stimulation.

Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g. planning versus motor execution). In this retrospective observational study, we analysed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 s. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 s), superior temporal gyrus (0.51 s) and middle temporal gyrus (0.54 s), followed by relatively long latencies in sensorimotor cortex (0.72 s) and especially long latencies in inferior frontal gyrus (0.95 s). Non-parametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.

Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns.

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. We hypothesize that language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language. To test this hypothesis, we densely record the neural activity patterns in the inferior frontal gyrus (IFG) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation for each word (i.e., a brain embedding) in each patient. Using stringent zero-shot mapping we demonstrate that brain embeddings in the IFG and the DLM contextual embedding space have common geometric patterns. The common geometric patterns allow us to predict the brain embedding in IFG of a given left-out word based solely on its geometrical relationship to other non-overlapping words in the podcast. Furthermore, we show that contextual embeddings capture the geometry of IFG embeddings better than static word embeddings. The continuous brain embedding space exposes a vector-based neural code for natural language processing in the human brain.

Relationship between sex, body size, and cardiac resynchronization therapy benefit: A patient-level meta-analysis of randomized controlled trials.

BACKGROUND: Women might benefit more than men from cardiac resynchronization therapy (CRT) and do so at shorter QRS durations. OBJECTIVE: This meta-analysis was performed to determine whether sex-based differences in CRT effects are better accounted for by height, body surface area (BSA), or left ventricular end-diastolic dimension (LVEDD). METHODS: We analyzed patient-level data from CRT trials (MIRACLE, MIRACLE ICD, MIRACLE ICD II, REVERSE, RAFT, COMPANION, and MADIT-CRT) using bayesian hierarchical Weibull regression models. Relationships between QRS duration and CRT effects were examined overall and in sex-stratified cohorts; additional analyses indexed QRS duration by height, BSA, or LVEDD. End points were heart failure hospitalization (HFH) or death and all-cause mortality. RESULTS: Compared with men (n = 5628), women (n = 1439) were shorter (1.62 [interquartile range, 1.57-1.65] m vs 1.75 [1.70-1.80] m; P < .001), with smaller BSAs (1.76 [1.62-1.90] m2 vs 2.02 [1.89-2.16] m2; P < .001). In adjusted sex-stratified analyses, the reduction in HFH or death was greater for women (hazard ratio, 0.54; credible interval, 0.42-0.70) than for men (hazard ratio, 0.77; credible interval, 0.66-0.89; Pinteraction = .009); results were similar for all-cause mortality even after adjustment for height, BSA, and LVEDD. Sex-specific differences were observed only in nonischemic cardiomyopathy. The effect of CRT on HFH or death was observed at a shorter QRS duration for women (126 ms) than for men (145 ms). Indexing QRS duration by height, BSA, or LVEDD attenuated sex-specific QRS duration thresholds for the effects of CRT on HFH or death but not on mortality. CONCLUSION: Although body size partially explains sex-specific QRS duration thresholds for CRT benefit, it is not associated with the magnitude of CRT benefit. Indexing QRS duration for body size might improve selection of patients for CRT, particularly with a "borderline" QRS duration. GOV REGISTRATION: NCT00271154, NCT00251251, NCT00267098, NCT00180271.

Predicting Major Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion.

BACKGROUND: The National Cardiovascular Data Registry Left Atrial Appendage Occlusion Registry (LAAO) includes the vast majority of transcatheter LAAO procedures performed in the United States. The objective of this study was to develop a model predicting adverse events among patients undergoing LAAO with Watchman FLX. METHODS: Data from 41 001 LAAO procedures with Watchman FLX from July 2020 to September 2021 were used to develop and validate a model predicting in-hospital major adverse events. Randomly selected development (70%, n=28 530) and validation (30%, n=12 471) cohorts were analyzed with 1000 bootstrapped samples, using forward stepwise logistic regression to create the final model. A simplified bedside risk score was also developed using this model. RESULTS: Increased age, female sex, low preprocedure hemoglobin, no prior attempt at atrial fibrillation termination, and increased fall risk most strongly predicted in-hospital major adverse events and were included in the final model along with other clinically relevant variables. The median in-hospital risk-standardized adverse event rate was 1.50% (range, 1.03%-2.84%; interquartile range, 1.42%-1.64%). The model demonstrated moderate discrimination (development C-index, 0.67 [95% CI, 0.65-0.70] and validation C-index, 0.66 [95% CI, 0.62-0.70]) with good calibration. The simplified risk score was well calibrated with risk of in-hospital major adverse events ranging from 0.26% to 3.90% for a score of 0 to 8, respectively. CONCLUSIONS: A transcatheter LAAO risk model using National Cardiovascular Data Registry and LAAO Registry data can predict in-hospital major adverse events, demonstrated consistency across hospitals and can be used for quality improvement efforts. A simple bedside risk score was similarly predictive and may inform shared decision-making.

Speech-induced suppression and vocal feedback sensitivity in human cortex.

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.

Responsive Neurostimulation for People With Drug-Resistant Epilepsy and Autism Spectrum Disorder.

PURPOSE: Individuals with autism spectrum disorder (ASD) have comorbid epilepsy at much higher rates than the general population, and about 30% will be refractory to medication. Patients with drug-resistant epilepsy (DRE) should be referred for surgical evaluation, yet many with ASD and DRE are not resective surgical candidates. The aim of this study was to examine the response of this population to the responsive neurostimulator (RNS) System. METHODS: This multicenter study evaluated patients with ASD and DRE who underwent RNS System placement. Patients were included if they had the RNS System placed for 1 year or more. Seizure reduction and behavioral outcomes were reported. Descriptive statistics were used for analysis. RESULTS: Nineteen patients with ASD and DRE had the RNS System placed at 5 centers. Patients were between the ages of 11 and 29 (median 20) years. Fourteen patients were male, whereas five were female. The device was implanted from 1 to 5 years. Sixty-three percent of all patients experienced a >50% seizure reduction, with 21% of those patients being classified as super responders (seizure reduction >90%). For the super responders, two of the four patients had the device implanted for >2 years. The response rate was 70% for those in whom the device was implanted for >2 years. Improvements in behaviors as measured by the Clinical Global Impression Scale-Improvement scale were noted in 79%. No complications from the surgery were reported. CONCLUSIONS: Based on the authors' experience in this small cohort of patients, the RNS System seems to be a promising surgical option in people with ASD-DRE.

Timing and location of speech errors induced by direct cortical stimulation.

Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g., planning versus motor execution). In this retrospective observational study, we analyzed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 seconds. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 seconds), superior temporal gyrus (0.51 seconds), and middle temporal gyrus (0.54 seconds), followed by relatively long latencies in sensorimotor cortex (0.72 seconds) and especially long latencies in inferior frontal gyrus (0.95 seconds). Nonparametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.

Temporal dynamics of short-term neural adaptation across human visual cortex.

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventral- and lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences.

Federated inference and belief sharing.

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world-and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs-about what they see-among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme-that attends these optimisation processes-is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language-entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)-showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.