Unsupervised Thinking
a podcast about neuroscience, artificial intelligence and science more broadly

Wednesday, April 26, 2017

Episode 20: Studies on the State of Science

Sometimes scientists decide to turn their tools of inquiry inward to understand their own fields and behaviors. For our 20th episode, we're diving into this meta-science by reading some papers about papers written by scientists studying scientists. In particular, we start with a commentary discussing the increasing size of scientific teams, and what that means for credit assignment. Do we need to move to a more Hollywood approach by highlighting specific achievements in different roles? Also, when will we address the fact that most young researchers on these teams will not have a career in academic science? We then get into a modeling study that aims to show how incentivizing the publication of novel results can ultimately lead to a widespread decrease in scientific quality. This raises questions of whether individuals or the system is to blame for high rates of shoddy publications. We then touch on a small experiment that the conference NIPS (Neural Information Processing Systems) performed on their peer review system, showing that (spoiler alert!, or probably not if you've been subjected to peer review...) the process can appear somewhat random. Finally, we go over a report that tracked trends in neuroscience research over the past ten years. We find that a meta-study of a field can seem very different from the view inside of it. Finally, we mention how studies of science done by scientists differ from those done by the humanities, and how both may be of use.    

We read:
Together We Stand
The Natural Selection of Bad Science
The NIPS Experiment
The Changing Landscape of Neuroscience Research, 2006–2015: A Bibliometric Study

To listen to (or download) this episode, (right) click here or use the player below

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Thursday, March 30, 2017

Episode 19: Gender Science

Way back in Episode 16 we paired up with Always Already to talk about a book on gender and the interaction of science and society. Unsurprisingly, that conversation spanned far beyond the scientific study of gender and so we never really got into the biological weeds. Our intent with the current episode was to go back to gender, with a focus on the explaining the current state of the science. What we quickly learn, however, is that it's very difficult to talk about gender without talking about society. So we first work through this by airing our anxieties on the topic, and our personal motivations for finding this science interesting.

Eventually though, we break into the biology of embryonic sexual differentiation and certain "natural experiments" that alter the course of this differentiation. People with abnormal differentiation offer a chance to see what happens when things like chromosomal sex (XX vs XY) and external genitalia are decoupled, which offers some insight into normal gender development. Next we cover some biological hypotheses on sex that didn't pan out (but are still being promoted...). Finally we turn to the better controlled world of animal experimentation and cover what factors impact gendered behavior in macaque monkeys.

As it turns a lot of findings on gender don't replicate, but here's one that does: whenever the Unsupervised Thinking crew has a conversation on gender it takes more than an hour.

We read:
The biology of human psychosexual differentiation  (a recommended read!)
Hormonal influences on sexually differentiated behavior in nonhuman primates

And Conor mentioned:
Why Has Critique Run out of Steam? From Matters of Fact to Matters of Concern
by Bruno Latour

To listen to (or download) this episode, (right) click here.

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Tuesday, February 21, 2017

Episode 18: Does Neuroscience Need More Behavior?

For most people, the desire to study neuroscience comes from a desire to understand how, in some form, the brain leads to behavior. Generally, neuroscientists focus on the brain side of that relationship, but what obligation do they have to study behavior? Is it even possible to do proper neuroscience without a clear documenting of the behavior we seek to understand? We use a recent opinion article as a jumping off point to discuss these issues. In the paper, the authors argue that behavior is being neglected amongst neuroscientists and it must return to its status as "epistemologically prior." In particular, there are arguments for studying more natural behavior and quantifying behavior more precisely.

In this episode we explain our general sympathies with this argument, but question the extent to which change is required. Should all neuroscientists stop what they're doing and study behavior? Are modern technologies drawing scientists away from the "bigger questions"? No, probably not. But this article does bring up questions about how we, as individuals and as a field, choose what to study. Different implicit beliefs about what levels of explanation are satisfying lead to different research priorities. Progress in neuroscience would be best suited by neuroscientists who better understand these implicit beliefs in themselves and others.

We read:
Neuroscience Needs Behavior: Correcting a Reductionist Bias

And mentioned:
Episode 8: Neuroscience vs. Psychology
Episode 7: Optogenetics 

To listen to (or download) this episode, (right) click here.

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Wednesday, January 25, 2017

Episode 17: Ethics of AI

There's a lot to be said about the technical progress being made with artificial intelligence, but what about the impact these rapid advances have on the society in which they unfold? In this episode, we tackle a broad range of such issues, from the possibility of removing human bias from algorithms to how likely we are to fall in love with an AI (Conor might). We speculate on how difficult the transition from humans to self-driving cars will be and our wild uncertainty about the future of jobs/the value of human labor. Throughout you will see a poorly-veiled concern about the current political state of the world and how wealth and power will be distributed in the future. What we learn though, is that in addition to the economic and technological impacts, the use of AI  is having at least one major side effect: it's forcing us to explicitly define our goals and values, such that we can impart them to our digital offspring. Now if we could just agree on what those goals and values are...

We read:
The Ethics of Artificial Intelligence by the Machine Intelligence Research Institute
SciAm review of Weapons of Math Destruction
Who's Responsible When a Self-Driving Car Crashes?
Who Will Own the Robots?
The Guardian's review of Her

To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Tuesday, December 20, 2016

Episode 16: Gender, Biology, & Society

On this "very special" episode of Unsupervised Thinking, we partner with our fellow podcasters over at Always Already, a critical theory podcast, to burst out of our respective academic bubbles and tackle issues of science and society. The fodder for our conversation is Brain Storm, a book by Rebecca Jordan-Young, that lays out the evidence that prenatal hormone exposures influence gender differences in behavior later in life. In the book, she claims that the sum total of the studies she covers only offers weak support for the hypothesis, and that scientists need to appropriately incorporate other factors into their models such as socialization and environment.

While we use this book as a common starting point, our conversation quickly moves beyond the particulars of these gender science studies. We start by questioning who is the intended audience of this book and what it's trying to say to different groups. This moves us into a discussion on critiques of science made by non-scientists and the role that those should/could play in shaping research agendas. We also spend some time dissecting the two-way street between science and society: particularly, how are common notions of gender shaped by scientific studies and how do society's stereotypes seep into the methods of science? An underlying disagreement about the nature of truth peppers the discussion, but we hold off on a full blown debate on that. Ultimately it is clear that the extent and cause of gender differences in behavior is far from settled science, and that is something on which we all can agree.

We read:
Brain Storm (Preface, Ch 1, 8-10)
To get a sense of the book, check out reviews by the LA Times and Slate.

To listen to (or download) this episode, (right) click here.

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Wednesday, November 30, 2016

Episode 15: "Just-So" Stories of Bayesian Modeling in Psychology

In the late 1700s, English minister Thomas Bayes discovered a simple mathematical rule for calculating probabilities based on different information sources. Since then Bayesian models for describing uncertain events have taken off in a wide variety of field, not the least of which is psychology. This Bayesian framework has been used to understand far-reaching psychological processes, such as how humans combine noisy sensory information with their prior beliefs about the world in order to come to decisions on how to act.

But not everyone is riding the Bayesian train. In this episode, we discuss a published back and forth between scientists arguing over the use and merits of Bayesian modeling in neuroscience and psychology. First, though, we set the stage by describing Bayesian math, how it is used in psychology, and the significance of certain terms such as "optimal" (it may not mean what you think it does) and "utility". We then get into the arguments for and against Bayesian modeling, including its falsifiability and the extent to which Bayesian findings are overstated or outright confused. Ultimately, it seems the expansive power of Bayesian modeling to describe almost anything may in fact be its downfall. Do Bayesian models give us insight on animal brains and behaviors, or just a bunch of "just-so" stories?

We read:
Bayesian Just-So Stories in Psychology and Neuroscience

How the Bayesians Got Their Beliefs (and What Those Beliefs Actually Are):
Comment on Bowers and Davis (2012)

Is That What Bayesians Believe? Reply to Griffiths, Chater, Norris, and Pouget (2012)

And referenced a previous episode, the Unreasonable Effectiveness of Mathematics.

To listen to (or download) this episode, (right) click here.

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com) 

Wednesday, November 2, 2016

Episode 14: Computational Psychiatry

Computational psychiatry is a field in its infancy, but it offers potential to change the way mental disorders are diagnosed, treated, and understood. In this episode, we cover the different components of computational psychiatry, compare and contrast it to computational neuroscience (and old-school psychiatry), and discuss which of its promises are actually likely to be fulfilled. In particular, we get into:
-online games that inform diagnoses
-models of learning mechanisms that can explain disorder
-machine learning techniques that advise treatment plans
-neural circuit mechanisms that can('t) explain disease

We read:
TiCS Computational Psychiatry
Computational psychiatry as a bridge from neuroscience to clinical treatments
Characterizing a psychiatric symptom dimension related to deficits in goal-directed control

To listen to (or download) this episode, (right) click here

As always, our jazzy theme music "Quirky Dog" is courtesy of Kevin MacLeod (incompetech.com)