donderdag 29 november 2012

The Problem With Math Is That It Makes People Seem Smart


 
 
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The Problem With Math Is That It Makes People Seem Smart

In 1996 physicist Alan Sokal succeeded in getting a unique article published in the journal Social Text. The article was titled "Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity," and it was unique because it was complete gibberish. Known as the Sokal Hoax, it was a crowning moment for pretense-haters everywhere.

While journal editors have managed to avoid similar public shamings of late, a Swedish researcher named Kimmo Eriksson decided to investigate whether academics are in fact impressed by things they don't understand, even if those things are nonsense. Specifically, Eriksson wanted to see how people with research experience judged abstracts containing a nonsense math equation (pdf):

Participants were presented with the abstracts from two published papers (one in evolutionary anthropology and one in sociology). Based on these abstracts, participants were asked to judge the quality of the research. Either one or the other of the two abstracts was manipulated through the inclusion of an extra sentence taken from a completely unrelated paper and presenting an equation that made no sense in the context. The abstract that included the meaningless mathematics tended to be judged of higher quality. However, this "nonsense math effect" was not found among participants with degrees in mathematics, science, technology or medicine.

Obviously it's distressing to see research-savvy people base their judgments on nonsense, but beyond that there are two ways to spin the deeper meaning of the findings. The positive spin is that because math is judged to add quality, people will be motivated to learn and use mathematics. The negative spin is that math improves judgments of quality because it suggests the mastery of a difficult skill that people want no part of. In this case math has been shut out to the point that an equation is strictly a signal rather than something to be understood and evaluated.

The potential to develop this kind of hands-off attitude toward math is one reason it's important not to let elementary school kids take on a "math is not for me" identity. It's fine if a kid decides not to become an engineer, but it can be problematic if you're so uncomfortable with math that you develop faulty heuristics to use when your have to deal with it.
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Eriksson, Kimmo (2012). The nonsense math effect Judgment and Decision Making, 7 (6), 746-749


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dinsdag 20 november 2012

An emerging consensus for open evaluation: 18 visions for the future of scie...

 
 

Sent to you by Jonas via Google Reader:

 
 

via pubmed: top authors by Kriegeskorte N, Walther A, Deca D on 11/20/12

An emerging consensus for open evaluation: 18 visions for the future of scientific publishing.

Front Comput Neurosci. 2012;6:94

Authors: Kriegeskorte N, Walther A, Deca D

PMID: 23162460 [PubMed - in process]


 
 

Things you can do from here:

 
 
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woensdag 7 november 2012

Cite from search results

Finally!

 
 

Naudojant „Google Reader" atsiųsta jums nuo Jonas:

 
 

per Google Scholar Blog autorius jconnor 12.10.17

I remember writing research papers as a student and being frustrated at the tedium of formatting citations according to the strictures of the Modern Language Association.  Today we're simplifying this process by adding the ability to copy-and-paste formatted citations from search results.  To copy a formatted citation, click on the "Cite" link below a search result and select from the available citation styles (currently MLA, APA, or Chicago):

You can also use one of the import links to import the citation into BibTeX or another bibliography manager.  We hope that simplifying the chore of citation formatting will let you focus on what you really want to work on: writing a great paper!

Posted by: James Connor, Software Engineer


 
 

Veiksmai, kuriuos dabar galite atlikti:

 
 
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maandag 5 november 2012

Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

HMAX, now with feedback... Sander
 
 
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Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

by Salvador Dura-Bernal, Thomas Wennekers, Susan L. Denham

Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for example, in other sensory modalities.

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