Archive for cogsci

Psychonomics 2015

This weekend I was in Chicago for the Psychonomic Society and Society for Computers in Psychology meetings. Emily and I stayed Thursday through Saturday and experienced a record first snow of the season. I hope that our fellow conference-goers made it back safely as well.

Chicago is one of the best food towns we’ve ever been to: we cannot recommend Gino’s East deep-dish pizza and Santorini’s Greek restaurant enough.

Below are some conference observations and highlights.

Conference Impressions
As an abstract-only, non-proceedings conference, it is a great opportunity to showcase developing or under review work. For an idea of the breadth of the conference, please look at the abstract book. The talks were of varying quality, but the rapt attention of the audience and quality of questions were excellent. Next year it will be in Boston on November 17-20.

Distributed Cognition
One of the best talks was by Steven Sloman on “The Illusion of Explanatory Depth and the Community of Knowledge”:

Asking people to explain how something works reveals an illusion of explanatory depth: Typically, people know less about the causal mechanism they are describing than they think they do (Rozenblit & Keil, 2002). I report studies showing that explanation shatters people’s sense of understanding in politics. I also show that people’s sense of understanding increases when they are informed that someone else understands and that this effect is not attributable to task demands or understandability inferences. The evidence suggests that our sense of understanding resides in a community of knowledge: People fail to distinguish the knowledge inside their heads from the knowledge in other people’s heads.

The article detailing that explanation shatters political understanding is quite accessible. The further results about “a community of knowledge” are under review.

Prof. Sloman is the conference chair for the International Conference on Thinking on August 3-6, 2016 at Brown University. Submission deadline is March 31, 2016.

The Science of Narrative
Another excellent talk was by Mark Finlayson who studies “the science of narrative”. He developed “Analogical Story Merging” (ASM), which can replicate Vladmir Propp’s theory of the structure of folktale plots. This process is described in his dissertation, which is an excellent synthesis of literary theory and computer science.

Prof. Finlayson is hosting the 7th International Workshop on Computational Models of Narrative at Digital Humanities 2016 in Kraków, Poland on July 11-12. The call for papers is pending.


There were two talks in the Bilingualism track that were particularly interesting.  Conor McLennan and Sara Incera reported that mouse tracking behavior in bilinguals doing a word discrimination task shows the same sort of reaction delay as in expert discrimination tasks. This correlates with confidence in answers – experts may take longer but move directly to their answers. The results are published in Bilingualism.

Another talk looked at how multilingualism affects vocabulary size using a massive online experiment. While the task of identifying whether a word is known or not is riddled with false positives, the results were interesting in and of themselves. Mutlilinguals tended to have higher vocabularies across languages, and L2 learners tended to actually have a higher vocabulary than L1 native speakers within a language. The results are published in The Quarterly Journal of Experimental Psychology.

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Wisdom of the Few?

Wisdom of the Few? “Supertaggers” in Collaborative Tagging Systems

Jared Lorince, Sam Zorowitz, Jaimie Murdock, Peter M. Todd

A folksonomy is ostensibly an information structure built up by the “wisdom of the crowd”, but is the “crowd” really doing the work? Tagging is in fact a sharply skewed process in which a small minority of “supertagger” users generate an overwhelming majority of the annotations. Using data from three large-scale social tagging platforms, we explore (a) how to best quantify the imbalance in tagging behavior and formally define a supertagger, (b) how supertaggers differ from other users in their tagging patterns, and (c) if effects of motivation and expertise inform our understanding of what makes a supertagger. Our results indicate that such prolific users not only tag more than their counterparts, but in quantifiably different ways. These findings suggest that we should question the extent to which folkosonomies achieve crowdsourced classification via the “wisdom of the crowd”, especially for broad folksonomies like as opposed to narrow folksonomies like Flickr.

Preprint of article in review available at arXiv:1502.02777 [cs.SI]

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Last week I wrote and then gave two lectures on “Categorization” and “Practical Parallelism”. It was a ton of fun to prepare them, and actually giving them made me realize how much I miss teaching. Abstracts and slides follow.


Student Organization for Cognitive Science (SOCS)
November 15, 2011 @ 5:30pm

Abstract: Categorization is a fundamental problem in cognitive science that goes by a multitude of names: In artificial intelligence, categorization is known as clustering; in mathematics, the problem is partitioning. There are many applications in linguistics, vision, and memory research. In this talk, I will provide a brief overview of exemplar vs. prototype models in the cognitive sciences (Goldstone & Kersten 2003), followed by an introduction to three different general-purpose clustering algorithms: k-means (MacQueen 1967), qt-clust (Heyer et al 1999), and information-theoretic clustering (Gokcayso & Principe 2002). Open-source Python implementations of each algorithm will be provided.


Practical Parallelism

CS Club Tech Talk
November 17, 2011 @ 7pm

Abstract: In this talk, I will give a brief overview of several key parallelism concepts and practical tools for several languages. After this talk, attendees should have the resources to recognize and solve “painfully parallel problems”. Topics will include: threads vs. processes, Amdahl’s Law, shared vs. distributed memory, synchronization, locks, pipes, queues, process pools, futures, OpenMP, MapReduce, Hadoop, and GPU programming.


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