aggerdmon

I too am coming from psychology looking to make the transition into a career in tech, and would be very interested to hear more about your experience making the transition*. But I would like to offer my experience having just got out of school.

I agree that there is absolutely a need for a transition to more advanced statistical methods in the field. In cognitive psychology at least, you are starting to see growing interest in adopting Bayesian techniques and moving away from null hypothesis significance testing. But unless you come across it on your own, the difference between Bayesian and Frequentist statistics is unlikely to be referenced until the graduate level. I believe Bayesian methods may alleviate some issues. For instance, one of the studies we were starting up towards the end of my time at the lab had an interesting property in that using Bayesian methods the study expected to do what could be thought of as corroborating or supporting the null hypothesis. If Bayesian methods start seeing wider adoption, I have to wonder how careful people will be thinking about their choice of priors, but it's a step in the right direction.

With regards to the statistics that are being taught. Quite a few of the K300 (Statistics for Psychology) courses are now being taught using R, but my own K300 course emphasized learning to do it by hand and didn't allow calculators. An interesting point brought up on a podcast I was listening to [1], was that we still teach statistical methods in the order they were developed/discovered and not the order that makes the most sense. I could see how teaching ANOVA as a special case of linear models might be less hand wavy. Interesting podcast, the professor they're interviewing is advocating a technique called structural equation modeling which I would love to find time to read up on.

However the research the lab I was with does specializes in building mathematical models of category learning, and has a relatively strong quantitative focus, so I can't say how this extends to other subfields or other universities. I no longer have the paper, but I saw a survey of psychology departments awhile ago that I believe found the number of methods courses being required in graduate programs was declining and fewer universities having researchers that specialize specifically in methodology. The paper made an interesting point that when you're specialized in methodology, you may play an important role increasing the quality of everyone else's work. However if your work specifically targets researchers, you're unlikely to see the same kind of funding or high profile journal publications seen by people in more applied areas. We can't all be like Tversky, but hopefully we'll start seeing some of that return now especially after it kind of declined around whenever psychophysics decreased in prominence.

I guess part of where the apprehension of increasing the complexity of statistical methods may be coming from is (1) that people might be worried about decreasing the accessibility of their work or (2) if you don't have a strong understanding of the math, you run the risk of pushing complexity somewhere you aren't as equipped to deal with it. With regards to (1), I know that one of our frequent collaborators had developed a quantum dynamics model of decision making that was showing impressive results characterizing the data, but I do not envy the amount of effort I'm sure he has to put into explaining the math in his papers. (2) might be addressed through more interdisciplinary collaboration, but I think you need both the support for development of methodology and adoption.

If you don't mind me asking, what area were you working in? And how did you go about transitioning to becoming a software engineer? I started out programming doing simulations like Conway's Game of Life and the like in a course that taught programming for cognitive scientists, and along the way kinda fell in love with it. When I decided to do an honors thesis, I got the chance to do way more programming than is expected in an undergraduate psychology degree: Using an OWL ontology in the planning stage to help with the experiment's implementation, debugging the experiment, doing analyses, and writing a visualization program to simplify recovering the orientation of Multidimensional Scaling Solutions. Coming out of my undergraduate, I'm thinking a career writing software either as a developer or engineer looks preferable to going back to school right now, but python junior-dev positions are proving tricky to find in Indy.

[1] http://methodologyforpsychology.org/mfp017-mathematical-psyc...

[Ok, I just saw in another one of you're posts your MA involved perceptual control theory, so yah, right on. I have this written already and feel a little silly now, but I figure the links are good and worth posting.]

I am finishing up my undergraduate right now in psychology, and while I want to defend the work that is being done by cognitive psychologists on concepts at higher levels of abstraction (Many researchers working in more mathematical areas of psychology tend towards working on things like consciousness, perception, concepts and categorization. There are cool advances in it, but I won't go too much into it in this post. I guess I'm just trying to say that you end up working with constructs that have higher barriers of entry to communicating outside of groups of researchers who get excited about modeling and the perceptual measurement theory.) I digress with that though. The main reason I wanted to comment is that your comment on understanding things like pointing hit on one of my favorite areas of psychology that has tragically always been somewhat nitch, but provides a lot of interesting connections between different areas if you take the time to get into it. There is a thread in the field of psychology that does just this kind of research, termed sometimes ecological psychology. It is often referred to colloquially as Gibsonian psychology, termed after [JJ Gibson](https://en.wikipedia.org/wiki/James_J._Gibson) who is seen as the father of the field(its practitioner's are known colloquially as Gibsonians).

I was lucky enough to take the Geoffrey Bingham's (who specializes reach to grasp behavior iirc) Perception/Action course a few years back, and found it immensely rewarding.* The primary text for the course was JJ Gibson's classic book "The Ecological Approach to Visual Perception". The philosophical foundations of gibsons theory of affordances is to take a realist approach to perception. (Avoiding the troubles that mind-body dualism imposes; taking a more Heidegerrian approach). The classic mantra of the field is ask not what is in you're mind, but what you're mind is in. I wish I had time to say more now, but I need to run here. But I'll leave you with a few snippets of what I took away from the course, and some links I think you'll enjoy, and can furnish more if you would be interested.

The Archival Gibson Video Series (Whoever assembled these had perfect choice in music)

[Reversibility and event perception](https://www.youtube.com/watch?v=GwVLny6Ymsk)

[Optical Transitions:Visible To Invisible](https://www.youtube.com/watch?v=1qQLtIICXoE)

[Motion Paralax and Percieved Depth](https://www.youtube.com/watch?v=bVSaWXqQh0w)

Disclaimer I haven't watched this video, but the idea that perception of throw-ability may explain size weight illusions is a surprising one.

[Geoff Bingham - Haptics: throwing and the size-weight illusion](https://www.youtube.com/watch?v=Ud9JB4NYUvA)

* I really need to digitize those notes...

Btw: Awesome Arm.