25 November 2013

How much neuroscience in 'Social'?

Psychologist Matthew Lieberman does like the fMRI!  In his new book 'Social' (2013) the UCLA professor and Director of the Social Cognitive Neuroscience Lab makes the case for the neural underpinnings of our social learning and behavior.  The question that came to my mind though was how much of the message was basically social psychology (which is valuable, don't get me wrong, but not dependent on fMRI findings).

The book features many diagrams of brains, pointing out various regions that are active during different cognitive tasks.  In general the correlations of active areas to cognitive tasks can be very useful to better understand the brain structures, if not to actually understand how the cognitive tasks are achieved. Most illuminating are the findings where either the same area is used during different types of tasks, or where different areas are used for what seem to be very similar tasks.  I think it's probably valuable to combine these types of findings with traditional psychology to see what may be illuminated.

Lieberman's key claim is that our 'default' brain mode is used for so-called 'mentalizing' - sorting through the social world, trying to understand other people's motives and intentions. This is shown by the activation of certain brain areas both while explicitly thinking about social problems and when not attempting to do other cognitive tasks.

We typically use a particular prefrontal brain region for general cognition (reading, memorizing, computing, etc.), and it was thought that these areas were the critical to all learning.  But various studies have found a 'social encoding advantage' in learning using the mentalizing system to form overall impressions of people and their intentions rather than simple memorization of people's behavior.  The finding was that 'the folks making sense of the information socially have done better on memory tests than the folks intentionally memorizing the material.' (284)  From the neuroscience angle:
Jason Mitchell, a social neuroscientist at Harvard University, ran an fMRI version of the social encoding advantage study. As in a dozen studies before his, he found that when people were asked to memorize the information, activity in the lateral prefrontal cortex and the medial temporal lobe predicted successful remembering of that information later on. According to the standard explanation of the social encoding advantage, the same pattern should have been present or event enhanced when people did the social encoding task, but that isn't what happened. The traditional learning network wasn't sensitive to effective social encoding. Instead the central node of the mentalizing network, the dorsomedial prefrontal cortex, was associated with successful learning during social encoding. (284-5)
Lieberman suggests a number of interesting applications of this finding to change and hopefully improve the way we teach kids, who are intensely interested in the social world and not so interested in memorizing facts - such as by teaching history more in terms of the social dramas (rather than actions and dates), and math by engaging students as both tutors and tutees.

The book has sections on three stages of social development, which he terms connection, mindreading (theory of mind), and harmonizing - and argues that significant brain resources are devoted to maintaining connection with other people.  Harmonizing is about taking on many of the goals and behaviors of our social group (particularly active during adolescence).  The idea here is that our sense of self as supported in the brain is very susceptible to the social messages we receive.

Overall I liked this book - not that it really lives up to the subtitle 'Why Our Brains Are Wired to Connect' - it's more about 'How' than 'Why'. At its best it reminds us that we are truly social creatures, and the neuroscience helps illustrate that point.

Will we understand science in the future?

Tyler Cowen suggests not in his book 'Average Is Over' (2013).  The book is a bit of prognostication about the near future, looking mainly at how the use of computers is and will change our world.  The basic idea is that the people who can add value to computer work in some way will reap most of the rewards.

For the purposes of this blog, I thought the part about computer-driven science was most interesting. Cowen lists three reasons why science may become harder to understand:
1. In some (not all) scientific areas, problems are becoming more complex and unsusceptible to simple, intuitive, big breakthroughs.
2. The individual scientific contribution is becoming more specialized, a trend that has been running for centuries and is unlikely to stop.
3. One day soon, intelligent machines will become formidable researchers in their own right. (206)
And here's one attempt at a summary:
The remaining human knowledge of science will be very practical, very prediction-oriented, and well geared for improving our lives.  Of course those are all positive developments. Still, as a general worldview, science will not always be very inspiring or illuminating. The general educated public will to some extent be shut out from a scientific understanding of the world, and we will run the risk that they might detach from a long-term loyalty to scientific reasoning. (219)
It will be interesting to see how much of this thinking will apply to neuroscience.