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SE367 Project Development of Structure in Artificial Language

SE367 Project Development of Structure in Artificial Language

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SE367 Project Development of Structure in Artificial Language

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  1. SE367 ProjectDevelopment of Structure in Artificial Language Vidur Kumar Y8560

  2. Background • Study – Artificial (unfamiliar) signal transmission in iterative learning [whistling sounds] • Results – Structure develops across generations Q. Will semantic content improve/effect development? Tessa Verhoef, Simon Kirby & Carol Padden – “Cultural emergence of combinatorial structure in an artificial whistled language”. CogSci2011.

  3. Hypothesis • Semantic content – will allow for FASTER development of structure in artificial language transmission. Tools • “Symbols” instead of “sound” [ease of experimentation] • Semantic content via – GIFs

  4. Experimental design • Study 1 [3x (6-7 participants)]Non-semantic signal transmission • Methodology: • Input symbols to nth participant [random symbols for n = 1] [5-10sec] • Use output of nth participant as (n+1)th participant’s input • Analysis: • Error in transmission decreases with generations • Study 2 [3x (6-7 participants)]Semantic communication with artificial language • Methodology: • Train nth participant on random symbols against ‘seen’ GIFs [1 time] • Test of ‘unseen’ GIFs [different combination of colour& motion] • Use output of nth participant as (n+1)th participant’s training set • Analysis: • Error in transmission should decrease FASTER than in Study 1

  5. The Problem • Non-quantifiable fidelity • [Sample of Symbols used, and some results of Study 1 ]

  6. Re-evaluation of Experimental design • Quantifiable symbols • Every non-identical square = +1 diff. unit • Exact estimate of fidelity between generations • Restarted data collection…

  7. Sample of Data 5 • Fidelity increases [distance decreases] across generations • Final analysis pending completion of Data… 7 2 2 1 0

  8. Expected Results • Given current data – • No significant difference between Study 1 and Study 2 results • Perhaps a short-coming of : • Complexity of Artificial Language • Fewer number of generations in iterative learning • Variation in semantic content • Likely inference – • ease-of-transmission– more dominant in stabilizing complex artificial languages • Semantic-mapping – does not significantly affect stabilization of complex artificial languages (random signal systems) Thank You