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Course Overview Q560: Experimental Methods in Cognitive Science Lecture 1.

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Presentation on theme: "Course Overview Q560: Experimental Methods in Cognitive Science Lecture 1."— Presentation transcript:

1 Course Overview Q560: Experimental Methods in Cognitive Science Lecture 1

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3 Research in Cognitive Science  What is different about research methods in Cognitive Science?

4 Research in Cognitive Science  What is different about research methods in Cognitive Science?  Focused on “science of intelligence”  We want to determine the mental processes and representations that drive intelligent behavior  But we have to infer this from observable behavior…adds noise to what we are studying  Compared to hard sciences (e.g., physics), we have much more variability, so our tools of inference have to be based on probabilistic reasoning and must deal with observation noise

5 Loftus and Palmer (1974) “ How fast were the cars going when they ____ each other? ” (hit, bumped, smashed) Reconstructive Memory:

6 Research in Cognitive Science  Hardware/Software Analogy  If neuroscience studies hardware (wetware), cognitive science studies software  Can’t touch software; must operate using hardware constraints  Our software comes prepackaged by nature/learning, so we have to reverse-engineer to determine analytically how it works  How? Experimentation and model building/testing  The need for statistics to summarize, predict, and evaluate causation "Prediction is very difficult, especially if it's about the future.” --Niels Bohr

7 Research in Cognitive Science  Example: NBC special on Turret’s treatment:  We need probabilistic models of when we “know” something  This requires good models of chance  Our inference models are also dependent on well-controlled experimental manipulation  We will take the opposite approach to Mr. Miyagi’s in the classic feature film The Karate Kid, starring Ralph Macchio

8 Research in Cognitive Science  Course URL: http://q560.weebly.com or compcog.comhttp://q560.weebly.comcompcog.com  Lecture slides and materials will be posted here  Check weekly for changes in schedule  Syllabus  Policies  Projects and Discussion Topics Design and analysis are inseparable components

9 Our first study: A shark researcher believes that he has created an effective shark deterrent. A underwater beacon gives off a sonic pulse that humans can’t hear, but that drives sharks away. To test his device, he places one at a beach in Boston. Over the next two months, he records the number of shark attacks at his beach. He then sends his assistant to a beach in Miami to counts the number of shark attacks there for two months. There were only 12 shark attacks on his Boston beach that had the beacon, compared with 38 attacks at the Miami beach. The researcher concludes that his device is effective, and starts to market it. Would you feel safer at a beach with his beacon? What other alternative explanations can you come up with to account for this result (fewer shark attacks at the test beach) ?

10 Alternative explanations for shark attack differences: 1.There were more shark attacks in Miami than Boston because there are more people in the water in Miami than Boston 2.There were more shark attacks in Miami than Boston because there are more fishing trawlers in Miami, which draws the sharks closer to shore. 3.There were more shark attacks in Miami than Boston because….

11 What is Big Data (to Psyc Science)?  Hollow buzzword? ●“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” Dan Ariely ●Big data makes theory obsolete? ●We want causal mechanisms: description, prediction, explanation  Opportunity to develop and refine theory? ●Rob Goldstone (2014): ↳ “ The very expertise with which we wield our tools to achieve laboratory control may have had the unwelcome effect of blinding us to the possibilities of discovering principles of behavior without conducting experiments” ●Bill Estes (1975): ↳ “ We may be accidentally positing mechanistic explanations that apply only to artificial situations. We may be falsely endorsing the wrong model as having generated the data” ●Big data makes theory essential


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