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By Lee Rumpf. Human Cognitive Emulation. Abstract.
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By Lee Rumpf Human Cognitive Emulation
Abstract This project attempts to accurately model human responses to stimuli. Using a survey format, this research hopes to produce a unique response to a stimuli based on information gained about the user. Analyzed solely on its own, the ramification of this project can perhaps draw broad conclusions about groups of people and how they respond. When combined with other techniques of emulating human though patterns, computer programs can come closer to representing accurately human responses.
Scope of Project/Expected Results • Users placed into like groups • Closest match with group will predict response • Smaller groups = more accurate results
Similar Projects • Sandia National Labs is doing a much more complex version of this project with many different approaches adding up to a complete psychological profile. • Dr. Ann Speed is one of the lead psychologists at Sandia working on Cognitive Emulation.
Approach • Python • text-based • evolutionary programming
Construction def main(): answers = open('answers.txt', 'r') array = [] array = answers.read().split("\n") prompt = raw_input("new or name: ") if prompt == "new": prompt = raw_input("name: ") outfile = open(prompt, 'w') outfile.write(prompt+"\n") else: outfile = open(prompt, 'w') outfile.write(prompt+"\n") test = ["france","balls","home","salad","poodle","oar","classic rock","mary","door","math"] count = 0 hard = dict(zip(test, [None]*len(test))) for a in test: count = count + 1 for k in range(len(array)-1): temp = array[k].split(',') ans = temp[count] hard[a][k] = [ans] answers.close() The program is written in Python and as of now designed to survey the user for a response to the stimuli. Current work includes creating a visual output to both represent and quantify results.
France: Balls: Home: Salad: Poodle: Oar: Classic Rock: Mary: Door: Math: Future outputs Group: Individual: France: Balls: Home: Salad: Poodle: Oar: Classic Rock: Mary: Door: Math: