Marc Berman

speaker

637 appearances 1 recordings 1 series first heard Jul 2026 last heard 6 Jul

Marc Berman’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas · 360 | Marc Berman on the Science of Touching Grass · 6 Jul 2026 podcast

I became really, really interested in this idea of how do we quantify mental effort or cognitive effort?

And with some colleagues of mine, this is when I was a postdoc at the University of Toronto.

I was working with his grad student there, Nathan Churchill, and he was doing some interesting things with nonlinear dynamics.

And we thought it'd be kind of interesting to apply some of these measures to some of these different data sets that we had in the laboratory.

And I had mentioned to you this idea of fractalness in space, but you can also think about fractalness in time.

So you can have like a time series, maybe you're measuring one voxel in the brain and you're measuring its activity level over time.

And you can also sort of try to measure how fractal or scale free that signal is in time.

So basically that's sort of asking the question, okay, if you look at the signal at one second, 10 seconds, 60 seconds, two hours, does that signal have sort of the same characteristics?

And one of the way when we talk about same characteristics here is sort of converting that signal into the frequency domain and looking at its power spectra.

And it's thought that signals that show power that's proportional to 1 over frequency, this 1 over f kind of shape, that that sort of maybe suggests sort of a signal that has this sort of repeated structure.

And so what we did is, and sort of the perfect thing to get a little bit more in the weeds here,

It's, you know, power is proportional to one over F to the H, or H is this thing called the Hurst exponent.

And when H equals one, people say, okay, that signal is perfectly fractal in time.

So what we did is we basically measured how fractal people's brains were when they were doing different cognitive tasks.

So when people were doing a really easy task or even no task at all, like just sitting at rest or doing a very easy task versus a very hard task, we found that the signal when people were doing a very hard task was much less fractal than when they were doing the easy task.

So it seemed like the brain deviated from this one over F or F to the one power to have a Hurst exponent of one.

It deviated from that when you were doing something difficult.

Sorry, when you were doing something difficult?

Difficult.

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