Optional Thinking and Loop Disentanglement Strategies in Films and Videogames
Bibliographic record
Abstract
Optional thinking (OT) is the cognitive ability to generate and compare alternative hypotheses to explain events. Not deploying OT in real-life may lead to premature acceptance of inadequate hypotheses and can result in dire consequences.(see Ben Shaul), 2012 for the methodological framework, research objectives and hypotheses underlying this proposal.). The skill of OT requires flexible cognition, not unlike the one sought for within the Cognitive Flexibility Theory (Spiro, 1992). The opposite to OT is closed- mindedness whereby holding a certain proposition true impedes the consideration of alternatives. The concept of data-base narration (Manovich, 2000)), stemming from digital culture and media, whereby different narrative trajectories or “algorithms” can run over the same database, is concurrent with Interactive narration, particularly in contemporary videogames where each gamer intervention bifurcates the story into different developments. The concept of database narration with its attendant interactive narration indicates a new moment in the history of classical narrative complexification (preceded by the Soviet Revolutionary films of the 1920s (e.g., Djiga Vertov’s Man with a Movie Camera (1929); the 1960s narrative complexity, particularly that of French New Wave film directors like Alain Resnais’s Last Year at Marienbad (1961); and contemporary complexification as in Christopher Nolan’s Memento (2000). Nevertheless, the classical narrative scheme of “exposition, complication and resolution” is still resilient along with its close-minding consequences. This viewer close-minding results chiefly from the use of suspense strategies and the imparting of a deterministic sense that things could not have turned out in any other way despite the inherent basis of probability underlying all narration. This database narrative complexity permeates interactive narration, particularly in contemporary videogames where each gamer intervention bifurcates the story into different developments as in “open World Games such as the Venice sequence in Assassin’s Creed where the gamer navigates his/her avatar, making different choices that lead to different optional trajectories a across the labyrinthine city. This is particularly relevant in respect of the “loop “inhering in the film Run Lola Runwhereby, in principle, Lola could restart at the end of a run further runs as she crosses Berlin again and again, This loop is also evident in the practice of video-gaming where the gamer often loops in his/her recurring attempts to overcome a game obstacle. These recurring attempts by video gamers to break free of the loop encourages optional thinking in gamers, due to their need to develop optional strategies at each recurring attempt at overcoming a difficult obstacle. This optional thinking process is beneficial and aids the exit of the loop as evidenced in Run Lola Run’s final scream scene and in the loop brake achieved upon successful decoding of a videogame obstacle’s previous impermeability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".