Psychology and Technology: Advanced Methods


The module involves learning the techniques required to design systematic approaches to scientific enquiry of the mind using the latest techniques and technologies. This includes identifying testable hypotheses and tractable questions. It also involves appreciating the technical constraints on display technology and the methodological considerations required to present stimuli accurately, precisely. Finally, it emphasises the importance of attaining the ability to communicate scientific methods for the benefit of others.


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.

Learning Outcomes

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.

Indicative Content

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, Virtual Reality 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-movements 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 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 an alternating series of lectures and practicals. The practical 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. The lectures will introduce the module, the coursework requirements and outline the methods, techniques, issues and technologies relevant to contemporary visual cognition. Example of case studies will be reviewed in the lectures. 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 comprises two units, the first requires the presentation of a structured abstract for each student’s chosen research topic. This outlines the background topic, the proposed method of data collection, the analysis technique(s) required, and the potential scope or implications of the findings. The second unit is a draft report with introduction and method section for the proposed research topic. A results section, describing the statistical technique(s) required to analyse the data is also required, as is a short discussion section placing the anticipated results in their theoretical context. 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

Total 200
Lecture 14
Tutorial/Seminar 0
Supervised Practical Activity 14
Unsupervised Practical Activity 13
Assessment 52
Independent 107

Guidance notes

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 2019/10 , and may be subject to change for future years.