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Flatbrain spreadsheets: Mindtool outside the box?

2006· article· en· W2120665767 on OpenAlex
Claude Lamontagne, François Desjardins, Michèle Bénard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Educational Technology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCompetence (human resources)PerceptionComputer scienceContext (archaeology)BlueprintSet (abstract data type)Cognitive scienceViewpointsCognitionPsychologyHuman–computer interactionNeuroscience

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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