An ET point of view of neuroscience

How would an extraterrestrial view the state of the art of our neuroscience research? The paper "Neuroscience and the correct level of explanation for understanding mind: An extraterrestrial roams through some neuroscience laboratories and concludes earthlings are not grasping how best to understand the mind–brain interface" (a really long title...) in Trends in Cognitive Sciences (2010 Jul; 14(7)) describes a possible point of view from ET (or more realistically, from a high-level point of view of the big picture). Specifically, the author argues that studying the low-level neural mechanisms, like the behavior of individual neurons, may not be the right way to understand mind, just like we do not need to understand quantum mechanics before understanding the Newtonian mechanics. This reminds me of the long-standing symbolism vs. connectionism debate in AI research. What is the right level of research towards an artificial general intelligence? Symbols? neurons? Or somewhere in-between?

Mind Reading

Decoding neural activities using machine learning methods is an emerging area since a few years ago. The neural data is usually obtained by presenting the word and/or image of a concept to an experiment participant and recording his brain images (e.g., fMRI, EEG). The types of concepts tested so far are very simple (e.g., concrete nouns, and more recently adjective-noun compositions), but I believe experiments on more complex and abstract concepts are to be expected in the near future (or are already on progress!). Given the neural imaging data, one natural task is to find out the mapping between concepts and images. An intermediate layer of semantic features can be added between concepts and images, which is intuitive and also makes things more tractable. So now the problems are what the right semantic features are, and how to find out the mappings between these layers.

For the first problem, in earlier work this is somewhat manually constructed. In "Predicting Human Brain Activity Associated with the Meanings of Nouns" (Science, May 2008), the semantic features are the co-occurrence frequency of the stimulus noun with 25 manually selected verbs in a large corpus. In a more recent paper, "A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes" (PLoS ONE, Jan 2010), these features are discovered from the fMRI data by means of factor analysis (and the result is very interesting: the three main features are related to manipulation, shelter and eating, all of which are the most important things for the survival of our primitive ancestors). With the semantic features specified, the second problem can be done by simply applying common machine learning predictors like Naive Bayes.

Modeling online sentence parsing by particle filter

This is an interesting paper from NIPS2008: modeling the effects of memory on human online sentence processing with particle filters. When we read or listen to a sentence, we receive the words incrementally (i.e., one after another), and construct a mental comprehension of the sentence. Dynamic programming can be used to parse a sentence incrementally, but since it can always find the right parsing, it can't explain why people may fail to comprehend "garden-path sentences" in their first attempt. For example, when reading the sentence "the horse raced past the barn fell", we may fail at the last word because we are likely to take "raced" as the main verb before reading the last word "fell". Previous work used pruning to model such an effect: only a set of high-probability partial parsings are kept after receiving each word, so the correct parsing may be dropped halfway. This paper adopts a different idea: in a resource-bounded way, estimating the posterior of partial parsings given the words that have been received. So particle filter becomes a natural choice, where each word is an observation and the partial parsings are the hidden states. Again, the right parsing may be dropped halfway because only a set of particles are maintained, which explains the garden-path effect. In addition, this method can explain the "digging-in effect", which says a longer garden-path sentence is harder to comprehend than a shorter one. For example, compare this sentence with the previous one: "the horse raced past the barn that is big and old fell". The explanation is, with more words in the sentence before the disambiguation point, the particles for the right parsing are more likely to be dropped due to resampling.