Five questions answered by Tom Griffiths

1 How can we find out how the brain works?

As a cognitive scientist I normally think about this question from the perspective of Marr’s levels of analysis. Understanding how the brain works is a question at what Marr called the “implementation” level, but I think a lot of insight can be gained by asking why the brain does what it does — a question at Marr’s “computational” level. Between those levels of analysis is the “algorithmic” level, which looks at the particular cognitive processes that are involved in solving a problem. Over the last few years a lot of progress has been made at both the implementation level and the computational level, but I think the algorithmic level gets neglected when we think about the brain. Understanding the algorithms that brains execute — and how brains learn to execute those algorithms — is going to be a critical part of finding out how the brain works.

2 What will your talk at CCN 2017 be about?

I will talk about a formal framework for linking these different levels of analysis. Computational-level explanations focus on the structure of abstract problems and their ideal solutions, explaining behavior in terms of optimal solutions to the problems that we face in everyday life. However, finding those optimal solutions can be extremely computationally expensive. So, my collaborators and I have started to look at the consequences of introducing resource constraints: if you know you have to solve a problem using a particular computational architecture, with associated computational costs, how should you solve the problem? This “bounded optimality” approach pushes the principle of optimization down to the algorithmic level, and gives us a new set of tools for exploring some classic questions about human cognition.

3 How can cognitive science, computational neuroscience, and artificial intelligence best work together?

I think these three approaches need to work together to give explanations of how the mind works all levels of analysis. Artificial intelligence can help us find good solutions to the kinds of problems that intelligence organisms face, providing the elements we need for constructing computational-level theories. Cognitive science puts these theories together with behavior to really understand the cognitive processes that people engage in. Computational neuroscience helps us understand how those cognitive processes could be executed by neurons. However, explanations at each of these levels can inform one another. For example, there is a productive feedback loop between AI and cognitive science, with AI leading to new theories of human cognition and cognitive science highlighting the ways in which AI still falls short. I think similar feedback loops can be constructed between all three disciplines.

4 What current developments are you most excited about?

The three things I am currently most excited about are understanding how people are so good at finding strategies for solving complex problems (something that is still beyond the limits of current AI), the potential for crowdsourcing technology to be transformative for the way that we conduct behavioral research (I think it is currently massively underutilized), and exploiting the advances in deep learning for representing complex stimuli such as images and text in order to study human cognition with naturalistic stimuli.

5 What do you hope to learn at CCN 2017?

I’m really interested in those feedback loops between different disciplines, so I hope to learn more about how machine learning and AI have been influencing contemporary neuroscience.