![]() ![]() While neither approach was fully supported, a possible means of reconciling the two approaches and directions for future research are proposed. The results are discussed in relation to the multiple capacities account of working memory (e.g., Martin and Romani, 1994 Martin and He, 2004), and the cue-based retrieval parsing approach (e.g., Lewis et al., 2006 Van Dyke et al., 2014). However, a measure of phonological capacity (digit span) and a general measure of resistance to response interference (Stroop effect) did not predict individuals' interference resolution abilities in either online or offline sentence comprehension. For comprehension question reaction times, a measure of semantic STM capacity interacted with semantic but not syntactic interference. For offline sentence comprehension, as measured by responses to comprehension questions, both general WM capacity and vocabulary knowledge interacted with semantic interference for comprehension accuracy, suggesting that both general WM capacity and the quality of semantic representations played a role in determining how well interference was resolved offline. For online sentence comprehension, as measured by self-paced reading, the magnitude of individuals' syntactic interference effects was predicted by general WM capacity and the relation remained significant when partialling out vocabulary, indicating that the effects were not due to verbal knowledge. ![]() We examined interference arising from a partial match between distracting constituents and syntactic and semantic cues, and related these interference effects to performance on working memory, short-term memory (STM), vocabulary, and executive function tasks. Interference effects occur when readers incorrectly retrieve sentence constituents which are similar to those required during integrative processes. This study investigated the nature of the underlying working memory system supporting sentence processing through examining individual differences in sensitivity to retrieval interference effects during sentence comprehension. 2Haskins Laboratories, New Haven, CT, USA.1Department of Psychology, Rice University, Houston, TX, USA.The command above also indicates there's a header row in the file with header=TRUE.Yingying Tan 1†, Randi C. Mydata <- read.table("filename.txt", sep="\t", header=TRUE) So if your separator is a tab, for instance, this would work: If your data use another character to separate the fields, not a comma, R also has the more general read.table function. In this case, R will read the first line as data, not column headers (and assigns default column header names you can change later). Mydata <- read.csv("filename.txt", header=FALSE) If that's not the case, you can add header=FALSE to the command: The read.csv function assumes that your file has a header row, so row 1 is the name of each column. A data frame is organized with rows and columns, similar to a spreadsheet or database table. More on this in the section on R syntax quirks.)Īnd if you're wondering what kind of object is created with this command, mydata is an extremely handy data type called a data frame - basically a table of data. (Aside: What's that <- where you expect to see an equals sign? It's the R assignment operator. To import a local CSV file named filename.txt and store the data into one R variable named mydata, the syntax would be: R has a function dedicated to reading comma-separated files. Also, R does have a print() function for printing with more options, but R beginners rarely seem to use it. There are better ways of examining a data set, which I'll get into later in this series. You'll get a printout of the entire data set if you type the name of the data set into the console, like so: (I'm not sure from what year the data are from, but given that there are entries for the Valiant and Duster 360, I'm guessing they're not very recent still, it's a bit more compelling than whether beavers have fevers.) One of the less esoteric data sets is mtcars, data about various automobile models that come from Motor Trends. And some online tutorials use these sample sets. Not all of them are useful (body temperature series of two beavers?), but these do give you a chance to try analysis and plotting commands. Into the R console and you'll get a listing of pre-loaded data sets. If you just want to play with some test data to see how they load and what basic functions you can run, the default installation of R comes with several data sets. Here are several ways to get data into R for further work. But for any kind of serious work, you're a lot more likely to already have data in a file somewhere, either locally or on the Web. Yes, you can type your data directly into R's interactive console. Once you've installed and configured R to your liking, it's time to start using it to work with data.
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