Sereno, Brewer, and O'Donnell: "Context Effects in Word Recognition" (2003)

This paper is a mystery to me. It presents EEG evidence that ambiguous words are more difficult to process in a "biasing context," but it lumps together the figures for the sentences that primed the dominant of the word, and those for the sentences that primed the subordinate meaning.

I have no idea why anybody would ever want to do that, and it puzzles me even more since the two conditions seem to have been kept apart in the actual execution of the experiment.

Independent Variables

The materials used for the experiment are not reprinted in the paper except for the following twelve example sentences:

Word type	Context	Set	Example sentence
Ambiguous	Neutral	1	James peered over at the bank.
High-frequent	Neutral	1	She looked over the book.
Low-frequent	Neutral	1	To our surprise we saw a hawk.
Ambiguous	Neutral	2	The counted the number of feet.
High-frequent	Neutral	2	Sally knew about the drug.
Low-frequent	Neutral	2	They navigated through the cove.
Ambiguous	Biased	1	They measured in terms of feet.
High-frequent	Biased	1	The pharmacist distributed the drug.
Low-frequent	Biased	1	Pirates headed out to the cove.
Ambiguous	Biased	2	The mud was deep along the bank.
High-frequent	Biased	2	She read the new book.
Low-frequent	Biased	2	Flying to its nest was a hawk.

It would seem that the natural statistical tool for such a design would be a three-dimensional analysis -of-variance with 12 = 3 x 2 x 2 data cells; but as mentioned above, the authors seem to just throw the "Set" variable out the window for no particular reason (even though it seems like the most important one). Instead, they perform a two-dimensional analysis with 6 = 3 x 2 cells.

Dependent Variables

Even though the stuff that the authors are interested in is the amount of electrical activity at the scalp of the head, they actually consider two different dependent variables, both of them rather complicated. They come to the same conclusions in both cases.

The reason that they don't simply end up with a single number right away is that they used 129 electrodes and measured the electrical activity 256 times during each trial. This means that they start out with a huge amount of raw data, and they need some kind of dimensionality reduction to make sense of it.

To bring it down to edible size, they thus performed a principal component analysis; this is a method for bending and stretching the axes of the data space in such a way that all linear correlations between the dimensions disappear (as far as I understand). The problem of doing this turns out to be equivalent to finding the eigenvectors of a certain matrix; and once you have done it, the dominant eigenvector will then tell you how to reduce the data set into a single dimension in the most informative way.

They report that this principal component analysis was "spatial," and that they did it using a so-called quartimax rotation. I'm not quite sure what either of that entails exactly.

At any rate, the two dependent variables they consider are, if I'm not mistaken,

1. the amount of variance that the first (most important) component accounted for, i.e., the amount of variance in the dimension of this eigenvector relative to the total variance of the data set (measured in percent);
2. the mean value of the data when projected into this dimension (measured in microvolts).

How the former of these can be a negative number beats me (cf. their Fig. 1). But maybe I should just push some more big, red buttons and not worry so much.

Mud On the Bank

Here's how the authors sum up their results in the discussion section:

First, we found significant frequency effects in both neutral and biasing sentence contexts […] Second, although there was no context effect for HF [= high-frequent] words, LF [= low-frequent] words were (marginally) facilitated in a biasing context […] Finally, and critically, we examined context effects on ambiguous words. In a neutral context, ambiguous words behaved like HF words; in a biasing context, they behaved like LF words. A neutral context neither facilitated nor inhibited emergence of the dominant (HF) meaning, but a subordinate-biasing context selectively activated the subordinate (LF) meaning (the fate of the dominant meaning is less certain). We believe the pattern of results unambiguously establishes the existence of context effects very early on in the ERP record. (p. 331)

The fact that the ambiguous words behaved like the high-frequent unambiguous words should not be too surprising: the frequency of the high-frequent words were calibrated so as to match the dominant meaning of the ambiguous words as exactly as possible. So the key observation is that the ambiguous words "behaved like LF words" in the biased context.

This means that their ambiguous words required about as much mental effort to process on average as an unambiguous word with the same frequency as the least common meaning of the ambiguous word. But it seems to me that this piece of information is almost completely useless as long as these numbers are based on an average over both the sentences that were biased towards the dominant meaning and the sentences that biased towards the subordinate.