The module comes in two parts, the first part involves learning the techniques required to present stimuli for experiments using computers. This involves appreciating the technical constraints on display technology and the methodological considerations required to present stimuli accurately, precisely and with the ability to communicate replicability of method for other investigators. The second part of the module involves analysis of data created from technical methods and covers a selection of classic and contemporary advanced methods.
The aim of this Module is to take a problem-based learning approach to the appreciation of the techniques required to conduct research in contemporary Psychological Science. The goal is to equip students with the ability to make informed judgments about appropriate methods and to select or implement the correct technique based on the requirements of the research question.
By the end of this module the student should be able to:
1. Evaluate the strengths and weaknesses in different methodological approaches to data collection and analysis in Psychological Science.
2. Select or implement an effective and appropriate technical or analytical solution suitable for addressing an empirical question in Psychological Science.
3. Use appropriate presentation format(s) to enable other investigators to replicate or appreciate the techniques used to solve an experimental problem or challenge.
1 Technology in Psychology
Appreciating the opportunities of using computers to assess human performance to precisely control displays or to enable large volumes of data to be collected.
2 Control of computer displays
Strength and weaknesses of CRT Monitors, Flat panel displays, tablet computers etc. Using computers to run experiments on visual processing.
3 Sources of Error and Artifacts in Experiments
Showing how a knowledge and appreciation of technological limits of equipment can eliminate or reduce experimental artifacts.
4 Automation of data collection methods
Creating or modifying software to control the sequence, timing and data collection of experiments on analog or digital visual displays.
5 Visual Cognition and Vision Science
Measuring the limits of human visual performance. Using adjustment, staircase or constant stimuli methods. Understanding thresholds and bias. Explaining different methodological approaches to the assessment of eye-guidance in scene perception and natural vision.
6 Data challenges In Psychology
Many areas of psychology create exceptionally large data sets, either through imaging techniques such as brain imagery, or distributed computing techniques such as mobile devices and social media. Each of these present opportunities for Psychological Science, but also challenges.
7 From Items Analysis to Linear Mixed Models
The inclusion of F1 and F1 Clark and Clark, Raijmaakers in the need for items analyses. Leading to the contemporary use of Linear Mixed Models. Using SPSS and R to calculate these.
8 Bayesian Statistics and the limits of NHST
Reviewing contemporary thinking on errors in inferential thinking using Null Hypothesis Significance Testing. Assessing the pros and cons of alternative methods such as Bayesian Statistics.
9 Historical and Conceptual Issues
Using case studies to illustrate how technology has informed the creation of theoretical models of human processing. Understanding how technological evolution has driven developments in novel paradigms in vision science and visual cognition.
Statement on Teaching, Learning and Assessment
The module comprises a series of practicals. The activities provide a focus for problem-based learning and provide formative feedback prior to the submission of the coursework. Laboratory classes will provide hands-on experience of some of the contemporary technology available for vision science and visual cognition. These include demonstrations and mini-experiments based on the indicative content. Structured feedback week will include student presentations of work in progress. Students will be expected (and encouraged) to actively engage with the material presented in this module. They will also be expected to independently source their own relevant reading material to use as evidence to support the arguments they present in the assessments. Unsupervised lab time is provided to allow access to equipment and facilities for coursework preparation. The assessment is in two units, the first requires the presentation of a draft method section for the students chosen topic. The second unit is a results section, presenting the analysis of the data created by the class based on selected sample problems. The assessment facilitates an appreciation of the place of technological knowledge in the decision-making processes involved in research, and the communication skills required to enable replication in research. The learning process is particularly focused on preparing students for their honours project and postgraduate research. This learning process is situated in the centre of the Abertay Attributes Triptych of Intellectual, Personal and Professional. In order to be an active citizen, it is crucial that students are able to acquire new methodological and analytical skills and present these in a manner that allows others to follow in their footsteps.
Teaching and Learning Work Loads
|Supervised Practical Activity||29|
|Unsupervised Practical Activity||13|
Credit Value – The total value of SCQF credits for the module. 20 credits are the equivalent of 10 ECTS credits. A full-time student should normally register for 60 SCQF credits per semester.
We make every effort to ensure that the information on our website is accurate but it is possible that some changes may occur prior to the academic year of entry. The modules listed in this catalogue are offered subject to availability during academic year 2017/18 , and may be subject to change for future years.