Module details for Psychology and Technology: Advanced Methods

Description

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.

Aims

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 Identification of Research Questions

Using collaborative methods to establish problems in psychology amendable to systematic research on or making use of technology.

2 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.

3 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.

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.

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.

Teaching and Learning Work Loads