SE367 Project Development of Structure in Artificial Language Vidur Kumar Y8560.

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Presentation transcript:

SE367 Project Development of Structure in Artificial Language Vidur Kumar Y8560

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.

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

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

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

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

Sample of Data  Fidelity increases [distance decreases] across generations  Final analysis pending completion of Data…

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  S EMANTIC - MAPPING – does not significantly affect stabilization of complex artificial languages (random signal systems) Thank You