Some types of first-person narrations of mental processes that constitute phenomenological accounts and texts, such as internal monolog statements, epitomize the best expressions and representations of human consciousness available and therefore may be used to model phenomenological streams of consciousness. The type of autonomous monolog in which an author or narrator declares actual mental processes in a think aloud manner seems particularly suitable for modeling streams of consciousness. A narrative method to extract and depict conscious processes, operations, contents, and states from an acceptable phenomenological text would require three subsequent steps: operational criteria for producing and/or selecting a phenomenological text, a system for detecting text items that are indicative of conscious contents and processes, and a procedure for representing such items in formal dynamic system devices such as Petri nets. The requirements and restrictions of each of these steps are presented, analyzed, and applied to phenomenological texts in the following manner: (1) the relevance of introspective language and narrative analyses to consciousness research and the idea that specific narratives are of paramount interest for such investigation is justified; (2) some of the obstacles and constraints to attain plausible consciousness inferences from narrative texts and the methodological requirements to extract and depict items relevant to consciousness contents and operations from a suitable phenomenological text are examined; (3) a preliminary exercise of the proposed method is used to analyze and chart a classical interior monolog excerpted from James Joyce's Ulysses, a masterpiece of the stream-of-consciousness literary technique and, finally, (4) an inter-subjective evaluation for inter-observer agreement of mental attributions of another phenomenological text (an excerpt from the Intimate Journal of Miguel de Unamuno) is presented using some mathematical tools.
This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 March 2021.
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