I finally had some spare time to document and post the mirror tracing and word-search tasks I developed for some replication work my students and I completed ​(Cusack, Vezenkova, Gottschalk, & Calin-Jageman, 2015)​.

Each task is (I think) pretty nifty, and I’ve had lots of emails about them over the past couple of years. I’ve finally posted both code bases to github along with working demos in Qualtrics and some rudimentary instructions. The code itself is not pretty–I was learning javascript and wrote most it during a conference I was attending in Amsterdam. Still, it works, and I’m sure it could come in handy.

The mirror-tracing task is just like it sounds–participants trace an image with their mouse or track pad but the mouse movements are mirrored, making it hard to stay in the line. You can vary task difficulty by changing line thickness. There is an expected weak negative correlation with age. The script can even posts the traced images back to your server, which is cool for making figures showing how groups differ with representative data.

• On Github:
  https://github.com/rcalinjageman/mirror_trace
• Try out the demo:
  https://dom.az1.qualtrics.com/jfe/form/SV_1MsS71ODB4uA7n7

The word-search task is also like it sounds. You can use pre-defined grids, or the script can generate a grid for you. I’ve used it to try priming for power (control vs. power-related words hidden in the grid) and to look at frustration (by having a grid that *doesn’t* have all the target letters…mean, I know).

• On Github:
  https://github.com/rcalinjageman/word_search
• Try out the demo:
  https://dom.az1.qualtrics.com/jfe/form/SV_didJZP14WPlvKPX

At the end of February I (Dr. Bob) visited a local elementary school as part of the Oak Park Educational Foundation’s Science Alliance Program.

I was matched up with Sue Tressalt’s Third Grade Class at Irving Elementary. For an activity, I brought along the neuroscience program’s collection of Finch Robots, a set of laptops, and the Cartoon Network simulator I have been developing (Calin-Jageman, 2017, 2018). I introduced kids to the basic rules of neural communication, and they explored Cartoon Network, learning how to make brains to get the Finch Robots to do what they wanted (e.g. avoid light, sing when touched, etc.). It was a great class, and a ton of fun.

I’m proud of Cartoon Network, and the fact that it can make exploring brain circuitry fun. It’s simple enough that the kids were able to dive right in (with some help), yet complex enough that really interesting behaviors and dynamics can be modelled.

As a kid, my most formative experience in science was learning logo, the programming language developed by Seymour Papert and colleagues at MIT. Logo was fun to use, and it made me need/want key programming concepts. I clearly remember sitting in the classroom writing a program to draw my name and being frustrated at having to re-write the commands to make a B at the end of my name when I had already typed them out for the B at the beginning of my name. The teacher came by and introduced me to functions, and I remember being so happy about the idea of a “to b” function, and I immediately grasped that I could write functions for every letter once and then be able to have the turtle type anything I wanted in no time at all.

Years later I read Mindstorms and it remains, to my mind, one of the most important books on pedagogy, teaching, and technology. Papert applied Piaget’s model of children as scientists (he had trained with Piaget). He believed that if you can make a microworld that is fun to explore, children will naturally need, discover, and understand deep concepts embedded in that world. That’s what I was experiencing back in 2nd grade–I desperately needed functions, and so the idea of them stuck with me in a way that they never would in an artificial “hello world” type of programming exercise. Having been a “logo kid” it was amazing to read Mindstorms and recognize Papert’s intentionality behind the experiences I had learning Logo.

Anyways, bringing Cartoon Network to an elementary school for a day gave me a great feeling of carrying on a tiny piece of Papert’s legacy. The insights kids develop in just an hour of playing with neural networks are amazing–the idea of a recurrent loop made immediate sense to them, and that also sets up the idea that both excitation and inhibition are important. And, like in Logo, the kids were excited to explore–to know that their experience was not dependent on getting the ‘right’ answer but on trying, observing, and trying again.

The day was fun and even better I received a whole stack of thank-you cards this week. Reading through them has kept a smile on my face all week. Here’s a sample.

This summer I (Bob) was asked to write a series of perspective pieces on statistical issues for the Journal of Undergraduate Neuroscience.

My first effort has just been published–it is a call for neuroscience education to shift away from p values, and an explanation of the basic principles of the New Statistics with an example drawn from neuroscience.

It turns out that the paper was published just before the annual meeting of the Society for Neuroscience, which I am currently attending.  It’s been very gratifying to see the paper is already sparking some discussion.

Here’s the key figure from the paper comparing/contrasting the NHST approach with the New Statistics approach with data from a paper in Nature Neuroscience.

This fall I (Bob) was invited to give a talk at Indiana University as part of a series on good science and statistical practice organized by the university’s Social Science Research Commons (which is like a core facility for getting advice on statistics and experimental design…what a cool thing for a university to have!).

I really enjoyed my visit (thanks Emily, Cami, and Patricia)–good conversation with fascinating people in a beautiful setting.  The series has a video archive, so my talk is now posted online as a video and as a powerpoint. Here’s the link–take a look if you want to know more about how to get started using Open Science practices and the New Statistics:  https://media.dlib.indiana.edu/media_objects/gt54kp23k

I (Bob) have a new replication paper out today, a collaboration with DU student Elle Lehmann (Lehmann & Calin-Jageman, 2017).  The OSF page for the paper with all the materials and data is here: https://osf.io/j3fyq/ (Calin-Jageman & Lehmann, 2015).

The paper replicates a set of previous findings showing that the color red dramatically increases romantic attraction for both women rating men (A. J. Elliot et al., 2010) and men rating women (A. Elliot & Niesta, 2008).  Elle and I conducted two replications: one in-person with a standard psychology participant pool, the other online with MTurk participants.  In each case we planned for an informative sample, used original materials, pre-registered our design and analysis plan, and used extensive exclusion criteria to ensure suitable participants (e.g. testing for color-blindness).  In both cases, we are sad to report that there was little-to-no effect of red on perceived attractiveness or desired sexual behavior.

There were a few weaknesses: 1) for the in-person study we didn’t obtain nearly enough men to make a good test of the hypothesis, 2) for the online study we couldn’t control the exact parameters for the color red.  Still, we found no strong evidence that incidental red influences perceived attractiveness.

Beyond the (disappointing) replication results, there are some really interesting developments to this story:

• Our replication work drew the attention of science journalist Dalmeet Singh who wrote a cool article summarizing the field and our contribution for Slate.  Dalmeet has made covering negative results a part of his beat–how great is that!
• There have been some questions about these studies almost from the start.  Greg Francis highlighted the fact that the original study of women rating men by Elliot & Niesta (2008) is just too good to be true–every study was statistically significant despite very low power, something that ought not to regularly happen (Francis, 2013).
• Although there have been some studies showing red effects (though often in subgroups or only with some DVs), there is a growing number of studies reporting little-to-no effect of red manipulations on attraction: (Hesslinger, Goldbach, & Carbon, 2015)(Peperkoorn, Roberts, & Pollet, 2016)(Seibt, 2015)(Lynn, Giebelhausen, Garcia, Li, & Patumanon, 2013)(Kirsch, 2015) plus a whole raft of student-led precise replications that were part of the CREP project (Grahe et al., 2012): https://osf.io/ictud/
• To help make sense of the data, Elle and I embarked on conducting a meta-analysis.  It has turned out to be a very big project.  We hope we’re nearly ready for submission.
• Andrew Elliot, the original investigator, was extremely helpful in assisting with this replication.  Then, as the meta-analysis progressed, he became even more involved and has now joined the project as a co-author.  The project’s still not complete yet, but I’ve really enjoyed working with him, and I’m proud that this will (hopefully) become an example of how collegial and productive replication work can be towards better and more cumulative science.