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The multi-layered structure of empathy: from theoretical to neuroscientific perspectives

2023· other· en· W7083316977 on OpenAlexaboutno aff

Bibliographic record

VenueUnicam Scientific Publications (University of Camerino) · 2023
Typeother
Languageen
FieldComputer Science
TopicWireless Sensor Networks for Data Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsReductionismNothingPhenomenonField (mathematics)NarrativeOrder (exchange)Reflexive pronounHuman scienceArgument (complex analysis)

Abstract

fetched live from OpenAlex

Before beginning, three years ago, my PhD course at the University of Camerino, in the research group of Professor Roberto Ciccocioppo, I didn’t know anything about Neurosciences. In my mind, it was the field of reductionism, where every phenomenon is explained through the principle of “this is how it works”. But let me be clearer: before starting my PhD course, I had always been studying Philosophy. Of course, during my philosophical studies I had met, now and then, some neuroscientific contributions, but nothing that could give me solid basis for understanding the field. The breakthrough happened while attending a first level Master course in Narrative Medicine, Communication and Ethics of Care, right before applying for the PhD program. During those classes I could open myself to a more scientific approach and understand that the problem is not the reductionism. As Luca Grion stresses, indeed, 1 although an ontological reductionism is to be avoided in order not to miss the whole complexity and eccentricity of our experience, there is a healthy reductionism, 2 which is the methodological reductionism. To methodologically reduce the analysis to the mechanisms that inhabit the complexity is the duty and the vocation of the Science (and thus also Neurosciences). On the other hand, Philosophy, and Human Sciences in general, must take care of keeping together the complexity itself. Pavel Florenskij, the Russian philosopher I wrote about in my masters’ thesis, says that every discipline is a different language through which we approach reality. Given the high number of languages (that is of disciplines) we use to study the phenomena of reality, reality seems to us extremely fragmented. But it is only a perspectival error: reality is one, although methodologically reducible to many layers. Keeping this awareness, I began my PhD course with the intent of building my research activity on a multi-layered approach that could take into account different languages and establish a dialogue between them. So I started addressing and studying the topic of consciousness. It has been always fascinating to me, since without consciousness, I would not even be here, asking myself what it means to be conscious. As the months passed, I realized that what I loved about consciousness was exactly what made its study so complicated. I refer to the fact that the only way we can analyze consciousness, is through consciousness itself. This makes things really hard. So to escape this loop, I thought it would be better to dissect this complexity by focusing on a different phenomenon, however related to consciousness. And thus I met empathy, one of the most discussed topics in both philosophy and neuroscience. I decided, together with my supervisors, Professor Ciccocioppo and Professor Donatella Pagliacci from the University of Macerata, to elaborate a novel model to study empathy, building my research on a multi-disciplinary approach that keeps together both the theoretical and the experimental perspectives. In particular, I first focus my investigations on revisiting the traditional approaches on empathy, such as the aesthetic, the phenomenological and the anthropological ones. I then elaborate a theoretical paradigm to read empathy as a multi-layered phenomenon. It involves bodily, emotional and cognitive dimensions and leads to particular kind of experiential knowledge, through which the self can access, although in a non-original way, the emotional state of another self and also come to a better knowledge of itself. During the six months I spent in the research group of Georg Northoff, at the Royal Ottawa Mental Health Center, in Ottawa (Canada), I developed the idea of self and empathy being highly intertwined. I test this hypothesis by performing an ALE meta- analysis on studies about empathy and comparing the results with an already published analysis , to look for overlapping brain regions between the empathic process and the self-processing. After that I analyze, from a neuroscientific point of view, the phenomenon of synchronization, that has been found to be at the basis of different inter-personal phenomena, among which empathy. This focus made me understand better the natural roots of the phenomenon I was addressing and, in general, the fact that we are nature. For this reason, I worked on the elaboration of an animal model of empathic-like behaviors that could help in the study of the molecular and biochemical mechanisms that underlie empathy. So in the fourth Chapter of this work, I propose a rodent paradigm to observe and evaluate intra-specific and inter-specific behaviors in response to different emotive states. Lastly, I analyze the case study of the public perception of laboratory animal testing, to warn against the biases and prejudices that can come from relying too much on what an unbalanced empathic experience could suggest. These are the things that you will find while reading my work. The things that you won’t find are all the ones that lie outside and yet surround my work. Without the latter, the former would never come to life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.046
Scholarly communication0.0100.017
Open science0.0020.007
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.223
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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