Hi Everyone,
We are excited to announce that the next CRAM session will take place this THURSDAY, February 13th, at 12-1PM in room 461 (2001 McGill College). We will have the pleasure of hearing from Dr. Brendan Johns, our department's latest faculty hire. See below for the title and abstract of the talk.
As usual, snacks will be provided. All are welcome.
Best Regards, The CRAM Team ---------------------- Title: The Continued Importance of Theory: Lessons from Big Data Approaches to Cognition
Abstract: The cognitive sciences have begun to collect, collate, and use large natural language corpora to understand human lexical behavior. That effort is facilitated by impressive new machine learning techniques and technologies. The exercise has advanced the field and produced insights that would have otherwise remained hidden. However, it is important to consider the tradition and practice of big data within the history of cognitive science and to consider the potential costs associated with an over enthusiastic and unreflective embrace of the new way. By tradition, cognitive science is an empirical science grounded in the experimental hypothetico-deductive method. In short, theories produce a priori predictions and those predictions are tested in controlled targeted experiments. To the extent that the data match the theoretical predictions, the theories are sound. When data mismatch the theory, the data prevail. In contrast, the standard practice in big data is abductive: explanations are provided for found data. As any logician would tell you, deduction and abduction are not equivalent. In light of the current scenario, that difference forces a meaningful but sometimes overlooked distinction in the purpose as well as explanatory role of classical versus big data methods for understanding human behavior. I will articulate the distinction and present examples using computational models of language processing.
Hi Everyone,
Friendly reminder that there will be a CRAM session today at 12-1PM in room 461. Dr Brendan Johns will be talking to us about big data approaches to cognitive science. See below for more details.
Snacks and coffee will be provided, BYOM (bring your own mug). All are welcome.
Best Regards, The CRAM Team
---------- Forwarded Message ----------- From:"Cognitive Research at McGill" cram@psych.mcgill.ca To:faculty@psych.mcgill.ca, postdoc@psych.mcgill.ca, grad@psych.mcgill.ca, coggroup@psych.mcgill.ca, info@crblm.ca Sent:Mon, 10 Feb 2020 14:50:54 -0500 Subject:CRAM THURSDAY FEB 13TH
Hi Everyone,
We are excited to announce that the next CRAM session will take place this THURSDAY, February 13th, at 12-1PM in room 461 (2001 McGill College). We will have the pleasure of hearing from Dr. Brendan Johns, our department's latest faculty hire. See below for the title and abstract of the talk.
As usual, snacks will be provided. All are welcome.
Best Regards, The CRAM Team ---------------------- Title: The Continued Importance of Theory: Lessons from Big Data Approaches to Cognition
Abstract: The cognitive sciences have begun to collect, collate, and use large natural language corpora to understand human lexical behavior. That effort is facilitated by impressive new machine learning techniques and technologies. The exercise has advanced the field and produced insights that would have otherwise remained hidden. However, it is important to consider the tradition and practice of big data within the history of cognitive science and to consider the potential costs associated with an over enthusiastic and unreflective embrace of the new way. By tradition, cognitive science is an empirical science grounded in the experimental hypothetico-deductive method. In short, theories produce a priori predictions and those predictions are tested in controlled targeted experiments. To the extent that the data match the theoretical predictions, the theories are sound. When data mismatch the theory, the data prevail. In contrast, the standard practice in big data is abductive: explanations are provided for found data. As any logician would tell you, deduction and abduction are not equivalent. In light of the current scenario, that difference forces a meaningful but sometimes overlooked distinction in the purpose as well as explanatory role of classical versus big data methods for understanding human behavior. I will articulate the distinction and present examples using computational models of language processing. ------- End of Forwarded Message -------
Hi Everyone,
We are sorry to announce that the CRAM talk originally scheduled for this Friday is cancelled/postponed to a later semester.
Sorry for any inconvenience caused.
Best Regards, The CRAM Team
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Cognitive Research at McGill