Mauricio Ramirez

Machine learning (ML) models make decisions for governments, companies, and individuals. Accordingly, there is the increasing concern of not having a rich explanatory and predictive account of the behaviour of these ML models relative to the users’ interests (goals) and (pre-)conceptions (ontologies). We argue that the recent research trends in finding better characterisations of what a ML model does are leading to the view of ML models as complex behavioural systems. A good explanation for a model should depend on how well it describes the behaviour of the model in simpler, more comprehensible, or more understandable terms according to a given context. Consequently, we claim that a more contextual abstraction is necessary (as is done in system theory and psychology), which is very much like building a subjective mind modelling problem. We bring some research evidence of how this partial and subjective modelling of machine learning models can take place, suggesting that more machine learning is the answer.

Background Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection of these fields of study in terms of variables such as global trends, methodologies, objects of study, or application of new technologies; however, these neuroscientific approaches are still often considered as disciplines detached from the arts and humanities. In this light, the question arises as to what evidence the history of neurotechnologies provides at the intersection of creativity and aesthetic experience. Methods We conducted a century-long bibliometric analysis of key parameters in multidisciplinary studies published in the Scopus database. Screening techniques based on the PRISMA method and advanced data analysis techniques were applied to 3612 documents metadata from the years 1922 to 2022. We made graphical representations of the results applying algorithmic and clusterization processes to keywords and authors relationships. Results From the analyses, we found a) a shift from a personality-focus quantitative analysis to a field-focus qualitative approach, considering topics such as art, perception, aesthetics and beauty; b) The locus of interest in fMRI-supported neuroanatomy has been shifting toward EEG technologies and models based on machine learning and deep learning in recent years; c) four main clusters were identified in the study approaches: humanistic, creative, neuroaesthetic and medical; d) the neuroaesthetics cluster is the most central and relevant, mediating between creativity and neuroscience; e) neuroaesthetics and neuroethics are two of the neologism that better characterizes the challenges that this convergence of studies will have in the next years. Conclusions Through a longitudinal analysis, we evidenced the great influence that neuroscience is having on the thematic direction of the arts and humanities. The perspective presented shows how this field is being consolidated and helps to define it as a new opportunity of great potential for future researchers.

Background Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection of these fields of study in terms of variables such as global trends, methodologies, objects of study, or application of new technologies; however, these neuroscientific approaches are still often considered as disciplines detached from the arts and humanities. In this light, the question arises as to what evidence the history of neurotechnologies provides at the intersection of creativity and aesthetic experience. Methods We conducted a century-long bibliometric analysis of key parameters in multidisciplinary studies published in the Scopus database. Screening techniques based on the PRISMA method and advanced data analysis techniques were applied to 3612 documents metadata from the years 1922 to 2022. We made graphical representations of the results applying algorithmic and clusterization processes to keywords and authors relationships. Results From the analyses, we found a) a shift from a personality-focus quantitative analysis to a field-focus qualitative approach, considering topics such as art, perception, aesthetics and beauty; b) The locus of interest in fMRI-supported neuroanatomy has been shifting toward EEG technologies and models based on machine learning and deep learning in recent years; c) four main clusters were identified in the study approaches: humanistic, creative, neuroaesthetic and medical; d) the neuroaesthetics cluster is the most central and relevant, mediating between creativity and neuroscience; e) neuroaesthetics and neuroethics are two of the neologism that better characterizes the challenges that this convergence of studies will have in the next years. Conclusions Through a longitudinal analysis, we evidenced the great influence that neuroscience is having on the thematic direction of the arts and humanities. The perspective presented shows how this field is being consolidated and helps to define it as a new opportunity of great potential for future researchers.

© 2015 Thessen et al.Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and two proposed example data models for linking phenotypes and environments using the extensible observation ontology and the Biological Collections Ontology ; these provide a starting point for the development of a data model linking phenotypes and environments.