An improved methodology for quantifying causality in complex ecological systems

Solvang, Hiroko Kato; Subbey, Sam

doi:10.1371/journal.pone.0208078

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…The correlations between the surprise induced by the DLM and the neural signals corroborate the observation that DLMs can be used to predict surprise related ERPs 55 . The correlational nature of the result, however, calls for future research that better establishes a causal link between the entropy and surprise of the models and the neural response [56][57][58] .…”

Section: Pre-onset Predictions Are Coupled With Post-onset Surprise Signalsmentioning

confidence: 99%

Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines

Goldstein

Zada

Buchnik

et al. 2020

Preprint

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Departing from rule-based linguistic models, advances in deep learning resulted in a new type of autoregressive deep language models (DLMs). These models are trained using a self-supervised next word prediction task. We provide empirical evidence for the connection between autoregressive DLMs and the human language faculty using spoken narrative and electrocorticographic recordings. Behaviorally, we demonstrate that humans have a remarkable capacity for word prediction in natural contexts, and that, given a sufficient context window, DLMs can attain human-level prediction performance. Leveraging on DLM embeddings we demonstrate that the brain constantly and spontaneously predicts the identity of the next word in natural speech, hundreds of milliseconds before they are perceived. Finally, we demonstrate that contextual embeddings derived from autoregressive DLMs capture neural representations of the unique, context-specific meaning of words in the narrative. Our findings suggest that DLMs provides a novel biologically feasible computational framework for studying the neural basis of language.

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Section: Pre-onset Predictions Are Coupled With Post-onset Surprise Signalsmentioning

confidence: 99%

Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines

Goldstein

Zada

Buchnik

et al. 2020

Preprint

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“…The application of ecological networks in ecological research can be traced back to the late 1970s when Robert May introduced the concept of food webs, which represent trophic interactions between species in an ecosystem [4]. Since then, the study of ecological networks has evolved and expanded to include other types of ecological interactions [5][6][7].…”

Section: Causal Network In Ecology: Insides or Illusions?mentioning

confidence: 99%

On formal limitations of causal ecological networks

Damos

2024

Phil. Trans. R. Soc. B

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Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel’s incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue ‘Connected interactions: enriching food web research by spatial and social interactions’.

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“…Causal inference analyses the situation of the outcome variable when the cause is changed (Pearl 2009). Analytical techniques for causal inference have been developed in recent decades across different domains, for example, health, economy, ecology, and most prominently epidemiology (Aldrich 1995; Pearl 1988, 2000, 2009; Rubin 2005; Saddiki and Balzer 2018; Ohlsson and Kendler 2019; Solvang and Subbey 2019; Handa et al 2020; Nguyen and Gouno 2020; Zhao et al 2020), and are increasingly finding their ways into analyses with a spatial component (Kolak 2017; Kolak and Anselin 2020). In most current spatial analyses, the typical goal is identifying correlation between variables.…”

Section: Introductionmentioning

confidence: 99%

Spatial Causality: A Systematic Review on Spatial Causal Inference

Akbari

Winter

Tomko

2021

Geographical Analysis

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The growing interest in causal inference in recent years has led to new causal inference methodologies and their applications across disciplines and research domains. Yet, studies on spatial causal inference are still rare. Causal inference on spatial processes is faced with additional challenges, such as spatial dependency, spatial heterogeneity, and spatial effects. These challenges can lead to spurious results and subsequently, incorrect interpretations of the outcomes of causal analyses. Recognizing the growing importance of causal inference in the spatial domain, we conduct a systematic literature review on spatial causal inference based on a formal concept mapping. To identify how to assess and control for the adverse effects of spatial influences, we assess publications relevant to spatial causal inference based on criteria relating to application discipline, methods used, and techniques applied for managing issues related to spatial processes. We thus present a snapshot of state of the art in spatial causal inference and identify methodological gaps, weaknesses and challenges of current spatial inference studies, along with opportunities for future research.

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An improved methodology for quantifying causality in complex ecological systems

Cited by 8 publications

References 26 publications

Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines

Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines

On formal limitations of causal ecological networks

Spatial Causality: A Systematic Review on Spatial Causal Inference

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