neurodudes

This article in the latest issue of the Journal of Neuroscience is interesting in the sense that they are do human brain stimulation of the hypothalamus, for the treatment of cluster headaches – but they then do positron emission tomography (PET) to examine the downstream neural circuits responsible for the abolition of the perception of headache.

Hypothalamic Deep Brain Stimulation in Positron Emission Tomography

This moves the field of brain stimulation from simple stimulate-and-see-what-happens, towards more of a study of human neural circuitry and how stimulation drives activity in connected locations. It’s possible this will lead, in the future, to better and more focal stimulation protocols, as people figure out what the “circuit-level” phenomena are that correct particular aspects of neural dysfunction. Perhaps someday we will have a map of the “hot spots” where stimulation of a small chunk of matter can modulate a wide degree of neural circuitry for the better.

(Last year, Helen Mayberg and colleagues’ deep-brain-stimulation-and-depression paper got at this issue as well, in which they stimulate the cingulate and (perhaps surprisingly) sent depressed patients into remission, and furthermore changed the activity of frontal structures from the abnormal state, back to a more normal pattern of activity.)

These studies are perhaps setting a good precedent for brain-stimulating neuroclinicians to follow.

— Ed

What gives humans the unique ability to construct novel sentences from the building blocks of language? A recent article in Behavioral and Brain Sciences proposes a “neural blackboard architecture” is capable of just this.

From the article (doi: 10.1017/S0140525X06009022):

“This paper aims to show that neural “blackboard” architectures can provide an adequate theoretical basis for a neural instantiation of combinatorial cognitive structures. [...] We also discuss the similarities between the neural blackboard architecture of sentence structure and neural blackboard architectures of combinatorial structures in visual cognition and visual working memory [...]”

As with all main articles in Behavioral and Brain Sciences, this one is followed by extensive comment and criticism from colleagues, and finally a reply by the authors. This provides a very deep look at the article and the issues surrounding it.

An older, but freely available, version of the article is available here.

A recent study in the Journal of Cognitive Neuroscience has isolated activation in the brain during a 3-stage model of deductive reasoning.

The study shows that during the ‘premise processing’ stage, there is more activity in occipito-temporal areas. During the ‘integration phase’, anterior prefrontal cortex is more active. During the final ‘valdiation phase’, the find more activity in posterior parietal and prefrontal areas.

AI started working on reasoning early on. Will studies like this lead us to the next advance in building models of reasoning?

Looking at static pictures of people running versus pictures of people standing still “evokes a delayed response in an area that overlaps with motionsensitive cortex (hMT+)”. Past studies have indicated a similar response for images depicting a falling cup versus a cup resting on a table.

The paper discusses the role of top-down influence from the temporal lobe as a possible cause for the response. How could this kind of brain activity be influencing our ability to recognize objects in scenes? Is this evidence of the activation of a distributed cortical representation of a moving object?

Should the field of AI be trying to figure out how to replicate a similar top-down influence in next-generation object recognition algorithms?

Abstract from the Journal of Cognitive Neuroscience is available here.

Stanford Neuroscientist Bill Newsome wants to implant an electrode in his own brain to study consciousness in ways that would be difficult with volunteer human subjects.

When considered alongside the story of Kevin Warwick who had a 100-electrode array implanted in his arm in 2002 in order to study electrical signals from his hand, one must wonder: is this a starting trend?

From the article:

TR: Do you really want to do this?

BN: Well, I’ve thought about it very carefully. I’ve talked to neurosurgeons, both in the United States and outside the country where the regulatory environment is less strict, about how practical and risky it is. If the risk of serious postsurgical complications was one in one hundred, I wouldn’t do it. If it was one in one thousand, I would seriously consider doing it. To my chagrin, most surgeons estimate the risk to be somewhere in between my benchmarks.

–Stephen

I haven’t read these myself, but if anyone’s interested, Aniruddh Patel does neuroscience research on the relation between music and speech. This 2003 Nature article also has a review of some cognitive science models of musical perception as they relate to testable predictions; and this article looks for correlations between linguistic and musical idiosyncracies in different cultures (specifically, if a culture’s language is “stress-timed” vs. “syllable-timed”, does the rhythm of their music reflect that?).

If you’re into music cognition, I compiled a brief list of links (starting from Patel’s publication list and a Google search, I don’t know this field) at NeuroWiki:MusicCognitionResources.

A few places on the internet are talking about a study conducted on Amazon tribespeople which demonstrates that basic concepts about geometry are independant of culture and level of education.

Here’s the story from Science News and here’s the story from Slashdot.

If this is the case, it suggests that there might be something special about geometry that made it evolutionarily advantageous to hard-wire into the brain. Or, from another perspective, some evolutionary adaptation makes geometry easy for our brains to understand. After all, a triangle is just the combination of three bars, which V1 is very good at responding to. As vision research continues to study the brain’s representation of increasingly complex objects, it may shed light on how this works from a systems neuroscience perspective.

–Stephen

By the way, this nytimes article got me thinking about mirror neurons. Mirror neurons are neurons that respond both when you do a certain thing, but also when you see others doing the same thing.

The article implies that mirror neurons could be important for imitation. I wonder if they could be a system to let your brain literally try to imitate the patterns in another person’s brain? If (and given the possibility of different representations in different brains, I think this is a big “if”) imitative learning turns out to be literally one person’s brain patterns matching a teacher’s brain patterns, then an imitative learning mechanism would be a mechanism for thought “synchronization”.

If you have a set of neurons in your brain that are mirror neurons for task A, and Katy has a set of mirror neurons for task A, and Katy does task A with you watching, then maybe your sets of mirror neurons will fire in roughly the same patterns at the same time. So the mirror neurons would be somewhat like a telephone line between your brain; nearby neurons could cause Katy’s neurons to change their pattern, and presuming that this causes her to change her outward behavior, your mirror neurons would absorb the changed pattern. So neurons next to your mirror neurons could receive a signal from neurons next to Katy’s mirror neurons.

I’d like to note that I don’t know much about mirror neurons, and I certainly don’t know if mirror neurons have the properties necessary to be actually capable of this. Do they influence behavior? Do sets of mirror neurons actually end up matching the patterns of firing of groups of another person’s mirror neurons, or do they just become more likely to fire on an individual level? It seems pretty unlikely that they’d do this.. but it’s neat to think about.

Anna Fisher and Vladimir Sloutsky have an interesting paper called “When Induction Meets Memory: Evidence for Gradual Transition From Similarity-Based to Category-Based Induction”. They are trying to get at the question of whether children around the age of six have category-based induction or similarity-based induction.

What is category-based induction and similarity-based induction?
If I tell you that object A has property P and object B does not, and then I show you objects X, Y, and Z and ask which ones probably have property P, how do you make the decision?

Do you rely on which categories the objects fall into? (“object A is a mammal and object B is a fish; X and Y are mammals, and Z is a fish, so I’ll guess that X and Y have property P, and Z does not”)

Or do you rely on similarity? (“object A is brown and object B is yellow; X and Z are brown and Y is yellow; so I’ll guess that X and Z have property P, and Y does not”)

Humans apparently tend to prefer category-based induction, at least when they are familiar with the categories. What about children around age 6? This study argues that they prefer similarity-based induction.

Please refer to the paper if you’d like a more detailed explanation, as well as a discussion of the history of the question and citiations to other experiments supporting similarity-based or category-based induction in children.

The details of the study
In the control condition, subjects were given pictures of animals and told to study them for a subsequent recognition test. In the experimental condition, subjects were given a picture of a cat, and told that it has “beta cells inside its body”. Then they were given pictures of other animals, some of which were also cats, and asked which animals had beta cells inside their bodies.

In both conditions, subjects were later shown some of the same pictures and some different ones, and asked which pictures they had seen before (a recognition test).

Adults (and 11-year olds) in the experimental condition were worse than adults (and 11-year olds) in the control condition at the recognition test. But kids of age 7 did about the same in both conditions.

The experimenters argue that this is because the adults in the experimental condition were doing category-based induction and the 7-year-olds were doing similarity-based induction.

More details

They support this assertion by redoing the experiments with some twists. Most notably they redo it with 7-year olds, but only after training them to do category-based induction (see page 590 of the paper for how they did this). These 7-years performed similar to adults on the recognition-memory test. This supports the assertion that the difference between the 7-year olds and the adults was the use of category-based induction.

They also do the opposite experiment, where they create a situation in which the adults don’t know what the categories are and so are forced to use similarity-based induction. In this case, the adult performance on the recognition test becomes similar to the children’s.

Discussion
This paper is methodologically similar to the pigeons study, although they are about different things. In both studies, we first identify a cognitive task that someone else can do better than human adults (someone else being children or pigeons). This in itself is interesting, because it implies that there are tradeoffs in “intelligence”, and that the greater performance of human adults on some tasks comes at the price of worse performance on others (see Chris Chatham’s weblog entry on this study for more on this).

It also allows us to do interesting experiments. In both studies, the researchers wanted to identify the cause of the bad performance of human adults. They did this by training the group who used to be better than human adults to be just as bad as them (or almost just as bad). This strongly suggests that whatever they taught the others was the cause of the bad performance in the human adults. It also shows that what they taught the others was something that they weren’t doing before.

So, in the case of the pigeons, the researchers conclude that prior experience with matching tasks was the cause of the base rate neglect in the human subjects. In the induction study, the researchers conclude that children around age 6 don’t normally prefer category-based induction.

There’s an interesting series of studies from Edmund Fantino, Inna Kanevsky, Shawn Charlton, J Hartl, A Goodie, and D Case about heuristics in human decision-making. They find a task at which pigeons are actually better than humans, because they don’t have the same biases. Then they managed to train the pigeons to show the same biases as the humans. The studies are about base-rate neglect.

What is base rate neglect? (skip down if you already know)
To explain base-rate neglect, let’s say that in a certain sort of situation, one of two mutually exclusive events occurs, and generally event A happens with X% probability, event B with Y% probability (those probabilities are the “base rates”). Your job is to determine which event happened in some particular situation. There is also evidence left behind which helps you decide which event happened (for instance, event A may cause E to happen with a 5% chance, but event B has a 20% chance of causing E; so if you see evidence E, that pushes you in the direction of thinking that event B happened).

The logically optimal thing to do in these sorts of situations is to crunch the numbers using Bayes’ rule; there’s an equation which, when given all of the relevant probabilities and evidence, will tell you whether event A or B is more likely to have occurred.

But what humans often actually do is underestimate the effect of the base rates, and rely too much on the immediate evidence. To see why this is stupid, imagine that you’re coming home at the end of a cloudy day — you think it may have rained but you were inside at work all day and you’re not sure. When you get home you find that the window is open and your carpet is all wet next to the window. Your roommate accidentally leaves the window open about 50% of the time, so you get mad at him for leaving it open and letting the rain in. But he says, “Wait a minute man, yeah, okay, maybe I left the window open, I really don’t remember, and maybe it rained, I didn’t notice, but before you get all mad at me, consider this: we both agree there’s about a 50% chance of it raining on a day like this. So even if i HAD left the window open, there’s only a 50% chance that it would cause the carpet to get wet. But let’s say that wet, slimey aliens came through the window to look at our house during the day. If that had happened, there would be a 100% chance of causing the carpet to get wet. So, the evidence (the wet carpet) really supports the slimey aliens hypothesis better than your I-left-the-window-open hypothesis. So it probably wasn’t my fault”.

What’s wrong with your roommate’s argument? Base rate neglect. Sure, slimey aliens would be more certain to get the carpet wet than leaving the window open. But the prior probability of slimey aliens coming in on any particular day is much, much lower than the prior probability of your roomate leaving the window open by accident. The huge difference in the priors outweighs the fact that the aliens would be more certain to cause the evidence that you see.

Any adult human can see what’s wrong with the slimey aliens excuse, although they probably wouldn’t phrase it in terms of “base rate neglect”. But here’s a similar situation that humans tend to get wrong.

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

I won’t give away the answer here in case you want to work it out yourself. Please see this link for the correct answer, as well as a detailed explanation of how to get it. If you get it wrong, don’t feel bad; so do 85% of doctors (heh heh, I bet now you feel even worse, but for a different reason).

OK so what’s the experiment with the pigeons?
On each trial, first they showed the pigeons a picture of either a vertical or a horizontal line (they call this the “sample”). Then they gave the pigeons two choices; peck at a button with a picture of a vertical line, or peck at a button with a picture of a horizontal line. If they picked the vertical line, there was a 75% chance of getting a reward; if they picked the horizontal line, there was a 25% chance of getting a reward. The sample was a red herring; it didn’t give you any information about the right answer. The optimal strategy is always to pick the vertical line.

This is the task at which pigeons are better than humans. Pigeons do pretty close to the optimal thing. Humans choose the choice that matches the sample about 55% of the time (Goodie and Fantino (1995), but that’s not online; this article summarizes on pages 18-19).

The researchers hypothesize (on page 19 of the previous link) that this is because human have been trained over years and years to do tasks in which matching is important. In other words, for this task, humans are “overeducated”:

…from early childhood humans learn to match like colors and shapes at home, at play, and at school
(e.g., in playing with blocks and puzzles and in reading picture books with their parents).
Pigeons, on the other hand, have not experienced a rich history of matching.

They test this hypothesis by training the pigeons to do a matching task, and then they have them redo the original task (creating “overeducated” pigeons; Fantino has a funny cartoon picture in his talk but I couldn’t find it online). And indeed, they report that these pigeons do about the same thing that humans do, choosing the choice that matches the same about half the time.

So, they have succeeded in educating the pigeons until they become as stupid as us.