Research Writing for a General Audience, St. Mary's College of Maryland, Roger Stanton

For this assignment you will write a blog entry on a research-based topic.  This blog entry will be posted on the St. Mary's College of Maryland blog site created by Roger Stanton.  Not only will this assignment be posted on a blog, but the assignments is designed so that you are writing for a general audience--just as blogs are written.  This might be a little uncomfortable, but this will help me to get a feel for how well you write in the absence of all the structures of APA style.

For this assignment you can pick any topic within the fields of cognitive psychology or cognitive science.  That said, it should be a fairly specific topic because your blog post should be the equivalent of three pages, double-spaced, and your writing needs to be focused.

OK, let's think about the writing style.  It's for a general audience, so let's see two examples.  The first is an example of how we write for a scientific audience:

Within the categorization literature, there exists a debate between proponents of single-system models (Stanton, 2012; Stanton & Nosofsky, 2013).  Although a variety of multiple-system formats exist, most depict an explicit system and an explicit system (Stanton, 2008).  In a recent study, Stanton (2010) demonstrated that a single-system model was able to account for the results of reported dissociations of classification, and that a multiple-system model was not consistent will all of the results reported in their study.

Do you know what that means?  I do, but that's because it's based on my research.  OK, so that clearly is not written for a general audience.  Let's try this one:

Learning to parse perceptual objects or events into distinct categories (or classes) is an essential aspect of human cognition.  Our perceptual systems are constantly bombarded with an astonishing amount of information, yet rather than seeing the world as a blur of unrelated objects and events, we instinctively separate these percepts into equivalence classes.

Although there are many theories that attempt to explain how people learn to make these categorization judgements, most of these theories fall into the class of either single-system models or multiple-system models.  A single-system model is one that proposes only one process, or set of processes, that is responsible for learning all categories.  A multiple-system model is one that proposes two or more processes, and some categories are learned by one type of process (or system) and other categories are learned by another type of process (or system).  

A large amount of the evidence in support of the multiple-system's approach is based on a reported dissociations of classification.  However, recently Roger Stanton, a psychology professor at St. Mary's College of Maryland, has demonstrated that these reported dissociations do not hold up under scrutiny.  In his most recent research at St. Mary's College of Maryland, Roger Stanton and his assistants performed a series of experiments.  All of these experiments were performed with students at St. Mary's College of Maryland used as the research participants.  

One of the primary approaches that Roger Stanton used was to fit mathematical models to data obtained in the experiments.  Roger Stanton found that a single-system model was able to account for all of the results.  Additionally, a multiple-systems model was not able to account for new data that was generated in the research conducted at St. Mary's College of Maryland.

In a related line of research conducted at St. Mary's College of Maryland, Roger Stanton was able to show that a single-system model was able to account for a classic result in the categorization literature.  Taken together, the research conducted by Roger Stanton and his research assistants at St. Mary's College of Maryland suggests that a single-system account is sufficient to explain the reported dissociations of classification learning.  To view a list of articles published by Roger Stanton at St. Mary's College of Maryland view his Google Scholars page.