We have developed an automated system to create sentences which can be answered with a simple true/false response. The automatically generated sentences have minimal redundancy and comprehension is required to correctly score them as being true or false. The reading speed function produced using the generated sentences is qualitatively similar to that measured using the MNREAD test, and these sentences give similar peak reading speed results to the MNREAD test.
This system can be used in a stand-alone fashion without the observer reading sentences aloud, making it possible to measure reading speed under conditions where speaking is not possible, such as within a MRI scanner. A staircase procedure could easily be added to measure reading speed or to maintain stimulus presentation near threshold for functional imaging of the brain whilst reading. The true/false response given by the participant also removes the potential source of error of an investigator determining whether words are read correctly or not. The very large number of possible sentences would be of particular benefit for longitudinal studies or those where repeated measures of reading speed are required.
There are a few caveats to our reading speed test. First, participants must be instructed to accept statements at face value and not to over-analyse the sentences before responding true or false. For example, the sentence "all dogs have legs" would be scored as true, although perhaps a very small number of dogs may have no legs. Similarly, participants had to be informed that "most" was not exclusive of "all": "most children were born" is scored as true even though on a semantic level it may not be strictly accurate. Despite careful instruction, we assume that these confusions contributed to the high lapse rate indicated in table . Second, although the (rather simplistic) Flesch-Kincaid reading age of our sentences suggests they could be read by a young child, some of the sentences have quite difficult meanings: consider "no snakes are mammals" (true) or "some bakers are mortal" (true). It is important to note that our software is written such that editing the word lists used is straightforward: they could easily be altered to be appropriate for assessing children, for example.
The MNREAD test has been designed to only include words from the 2,000 most frequently occurring words in American English. The sentence generator corpus was not written with a specific word frequency in mind. Post-hoc analysis of forty-eight randomly generated sentences was performed to identify the frequencies fo words appearing. 62% of the words were in the 2,000 most frequent words in British English, with only 18% being outside the top 6,000 words [26
]. Low frequency words included atheist, butchers, panthers, sparrows and cheetahs. In each condition, each subject read 24 sentences at each exposure duration and 48 sentences at each text size; and for every observer the sentences were different and selected at random. It is extremely unlikely that word frequency effects would have accounted for the word-size effects which we show in figure , or the psychometric functions in figure . To confirm this, a post-hoc analysis of 48 sentences with randomly assigned exposure durations was performed. This confirms that there is no relationship between exposure duration and word frequency (r2
< 0.00001). However, in subsequent experiments using fewer sentences, word frequency effects could be controlled by editing the corpus.
Finally, when pilot testing was performed in the laboratory of GEL, it was found that some cultural references were missed by those who had never lived in Europe (for example, "voting conservative" had no meaning; and a Peugeot was not identified as a car). Again, editing of the database can eliminate this problem.
A further limitation of our sentences is that they are only 4 words long and so would fit on one line: page reading (involving retrace to the start of subsequent lines and page navigation) can not be assessed easily using such short sentences. This is in contrast to MNREAD sentences which are typically presented over three lines.
Our data on a small number of observers is not enough to establish this test as being comparable to the MNREAD cards or any other test of reading speed, and in this methodological paper we do not aim to suggest that this is the case. A Bland-Altman analysis of at least 50 participants is required to accurately determine the limits of repeatability and variability of any clinical technique [27
]. Rather, we aim to show in this manuscript that an automated sentence generator can be used to produce intelligible sentences which can be scored using a dichotomous true/false outcome and which produce reading speed functions qualitatively similar to those created with a more traditional test such as MNREAD.