Intraproblem retention during learning-set acquisition in bluejays (Cyanocitta cristata)

Three experiments were conducted examining short-term retention within individual object-discrimination learning-set (ODLS) problems. In Experiment I, it was found that intraproblem retention decreased during ODLS acquisition. Experiment II demonstrated that this phenomenon was not due to simple recency of experience with other ODLS problems. Experiment III demonstrated that retention was not influenced by the number of trials per problem or number of problems per session. These results were interpreted as supporting a conditional discrimination model of ODLS acquisition in bluejays.

     

Knowing What the Patron Wants: Using Predictive Analytics to Transform Library Decision Making

Predictive analytics and machine learning are burgeoning areas of professional practice for large corporations especially businesses that offer products and services to customers. The power to better understand the movement of large amounts of data in a company and the capability to deploy that data to meet a customer's needs is invaluable from a services standpoint. Some in libraries have theorized that this type of data usage could possibly be used in a library service environment as well. In this article, we demonstrate how you can develop and use machine learning algorithms and predictive analytics to proactively understand library behavior. Although libraries are good at data collection, we often rely on statics or old data for assessment. Utilizing a machine learning system, called the Automated Library Information Exchange Network (ALIEN), we can better understand the movement of the items in the collection and better serve the needs of our customers the library patrons.