Open peer commentary on the article “Creativity in Solving Short Tasks for Learning Computational Thinking” by Valentina Dagienė, Gerald Futschek & Gabrielė Stupurienė. Abstract: Computational thinking (CT) skills are nowadays strongly advocated for educational institutions at all levels. CT refers broadly to skills of thinking about the world from a computational perspective, however, not necessarily referring to programming skills in particular. There is still a lack of consensus about what CT means, and how CT should be taught. This open peer commentary briefly discusses some ongoing trends of CT in response to the target article, which reports development, field testing and piloting of an extensive set of new learning materials for teaching CT. Recent calls for interdisciplinary technology education, creativity and open-ended problem solving in CT are highlighted.

Context: The integration of data measured in first- and third-person frameworks is a challenge that becomes more prominent as we attempt to refine the ties between the dimensions we assume to be objective and our experience itself. As a result, cognitive science has been a target for criticism from the epistemological and methodological point of view, which has resulted in the emergence of new approaches. Neurophenomenology has been proposed as a means to address these limitations. The methodological application of this discipline, even in its mildest form, enriches the methodology typically used in cognitive sciences. Problem: Nowadays psychological studies are difficult to replicate. As a way to achieve replication of results published in a previous study in order to develop a methodological adaptation suitable for electroencephalographic (EEG) measurements in a subsequent experiment, first-person accounts from the participants in our pilot study were included in the experiment construction. This study’s objective is to show the benefit of including a mild-neurophenomenology-inspired approach in the adaptation from an original paradigm, which requires, foremost, the ability to replicate the original results. Method: Interviews with open and semi-structured questions were carried out at the end of an Approach-Avoidance Task (AAT. The first-person reports, together with the behavioral outcomes of each pilot, were taken into account for the development of the next piloting phase until replication of the original results was achieved, and the final experimental design was elaborated. Results: A sequence of four pilots, where the integration of third- and first-person information derived from subjects’ behavior and reported experiences while carrying them out rendered the behavioral replication we sought to achieve, providing support for a first-person enriched cognitive science paradigm. Implications: Including first-person accounts systematically during the development and performance of classic cognitive paradigms ensures that those paradigms are measuring what they claim to measure. This is the next logical step to improve replication rates, to refine the explanation of the results and avoid confounding third-person data interpretation. Constructivist content: Including first-person experiences and acknowledging the active role that participants’ experiences regarding the paradigm had in the modeling of its final version is in concordance with a constructivist standing.

Key words: Mild neurophenomenology-inspired approach, piloting, first-person enriched experimental paradigm, phenomenological validity, replication.

Open peer commentary on the article “Modeling Subjects’ Experience While Modeling the Experimental Design: A Mild-Neurophenomenology-Inspired Approach in the Piloting Phase” by Constanza Baquedano & Catalina Fabar. Upshot: The authors show in their pilots how open it is to participants not to obey the instructions during an experiment. Their findings leave us to choose between two options: either we (a) accept that subjective confounds are inevitable and stronger than we think, but in this case, why should we continue trying to measure subjective experience?; or (b) strive at designing better experiments in order to control for these fluctuations. I will argue for option (b) and propose an alternative model to go beyond the first- and third-person data gap, namely “predictive processing.”

Open peer commentary on the article “Modeling Subjects’ Experience While Modeling the Experimental Design: A Mild-Neurophenomenology-Inspired Approach in the Piloting Phase” by Constanza Baquedano & Catalina Fabar. Upshot: Baquedano and Fabar’s provoking article highlights several difficulties of neurophenomenology, and brings into light the necessity of further clarification of its basic concepts such as human experience, first-person perspective, phenomenological validation, explanation, adequate measurement and so on. Particularly, it becomes more and more clear that the “explanatory gap” cannot be liquidated by means of explanation procedures alone, for the unavoidable variety of modes and forms of (mutual and individual) human understanding cannot be exhaustively reduced to just any one of these modes.

Open peer commentary on the article “Modeling Subjects’ Experience While Modeling the Experimental Design: A Mild-Neurophenomenology-Inspired Approach in the Piloting Phase” by Constanza Baquedano & Catalina Fabar. Upshot: Demonstrating the relevance of collecting first-person data and of establishing reciprocal constraints between this these data and behavioral data to overcome the issue of behavioral data replication is an interesting result. However, this result, as such, falls short of offering any theoretical reorientation of the neurophenomenological project, strictly understood.