Flatbrain spreadsheets: Mindtool outside the box?
Bibliographic record
Abstract
Abstract Managing the pedagogical aspects of the ‘computational turn’ that is occurring within the Humanities in general and the disciplines associated with cognitive science and neuroscience in particular, first implies facing the challenge of introducing students to computation. This paper presents what has proven to be an efficient approach to bringing undergraduate Humanities students to reach insight into the nature of computation and its bearing on reflecting upon the mind in general, and the brain in particular. It is set within the context of a course on the topic of sensory perception featuring a laboratory component aimed at guiding students to develop neuronal networking skills. In this course, students are asked to design, test and discuss the neurophysiological, psychological and philosophical implications of the neuronal blueprints of a virtual creature’s brain which they are challenged to ‘wire’ themselves in such a way as to allow it to ‘see the world’ within which they choose to place it. The insight on which we are reporting here is simply that a basic competence in using a spreadsheet application is all that is required to allow implementing and testing of virtual brains made of basic formal neurones, bringing the miracle of computer simulation within the reach of even the most computer‐shy undergraduates. Once introduced to basic neuronal networks (two 90‐minute laboratory sessions), two laboratory sessions are sufficient to bring groups of up to some 50 undergraduates to manipulate the basic spreadsheet operations successfully and understand how virtual brains consisting of basic formal neurones can be implemented in terms of these basic spreadsheet operations. It is the ‘flattening’ to which the virtual (formal neuronal) brains are thus subjected, as they are turned into spreadsheets that led to coining the concept of a ‘flatbrain spreadsheet’. The students are then challenged to develop and implement their very own virtual creature’s flatbrain spreadsheets, and gently tutored into noticing the key problems out of which arise the great debates in cognitive science about such issues as consciousness, qualia, categorisation, induction, computational explanation and the like. Empirical evidence gathered over the course of the last 6 years strongly suggests that the construction of flatbrain spreadsheets by students does make a difference in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.084 | 0.023 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".