Cognitive Psychology

The Basics

Sandie Taylor, Lance Workman

• Publisher: Routledge
• Year: 2021
• ISBN:
  9781000429732
• Edition: 1
• Topic: Psychology
• Subtopic: Cognitive Psychology & Cognition
• Index: Psychology

Cognitive Psychology: The Basics provides a compact introduction to the core topics in the field, discussing the science behind the everyday cognitive phenomena experienced by us all. The book considers laboratory and applied theory and research alongside technological developments to demonstrate how our understanding of the brain's role in cognition is improving all the time.

Alongside coverage of traditional topics in the field, including attention and perception; learning and memory; thinking, problem-solving and decision-making; and language, the book also discusses developments in interrelated areas, such as neuroscience and computational cognitive science. New perspectives, including the contribution of evolutionary psychology to our understanding of cognition are also considered before a thoughtful discussion of future research directions. Using real-world examples throughout, the authors explain in an accessible and student-friendly manner the role our human cognition plays in all aspects of our lives.

It is an essential introductory text suitable for all students of Cognitive Psychology and related disciplines. It will also be an ideal read for any reader interested in the role of the brain in human behavior.

1

What is cognitive psychology?

DOI: 10.4324/9781003014355-1

Cognitive psychology is concerned with how we process information. It has been eloquently defined by the American Psychological Association (APA) in 2020 as “the branch of psychology that explores the operation of mental processes related to perceiving, attending, thinking, language, and memory, mainly through inferences from behavior”. The exploration of mental processes is not new. For a very long time, philosophers and ‘scientists’ have been fascinated by the connection between our mind, mental processes and the brain. Studying the brain using ‘scientific’ methodology began during the Renaissance period; a time in European history arguably between 1400 and 1700. It was during this period that scholars became interested in studying nature, which included the mechanics of the human body. Great thinkers such as René Descartes pondered over the philosophical approach of ‘dualism’ to understand the difference between mind and body. He believed the two were separate from each other such that our body (including the brain) represented a physical entity while the mind represented a spiritual one. The implication of this is that the mind can exist independently of the body. Conversely, monism described the mind and body as coexisting. With the development of scientific technology used to map the brain, the notion of the mind existing separately from the physical brain became very unlikely. Nevertheless, Descartes helped expand our way of thinking about the mind and body interface. Even today with our extensive explorations inside the working brain, finding direct connections between structure and function is not always straightforward (see Chapter 2). It could be argued that ideas from dualism and monism spurred questions about whether it is possible to replicate a model of how the mind works. For example, a sub-group of researchers in Information Technology (IT) have sought to develop a robotic brain based on a series of inputted commands and algorithms. This is designed in such a way as to emulate the connections of neurons (nerve cells) in the human brain. This type of neural networking comes out of research in computational modelling, which aspires to imitate cognitive functioning in humans. Despite this, development of a ‘brain’ that is able to learn independently of further human instruction is still beyond our reach.

The reason for this short history lesson is to provide us with a context of how cognitive psychology has developed. Not only this, but how the different disciplinary routes of science and philosophy have found their way as sub-disciplines into modern cognitive psychology. In this chapter, the objective is to outline these different contributory disciplines, and their contributions towards an understanding of how our brain processes information. The four main areas of contribution include: cognitive psychology; cognitive neuroscience; cognitive neuropsychology and computational cognitive science.

Cognitive psychology

The development of cognitive psychology was, in part, a reaction to the focus on overt behaviour advocated by ‘Behaviourism’. The behaviourist movement was concerned with only what you can see and not that which is hidden from scientific scrutiny. In other words, aspects of brain function, such as how we think and remember, were off limits. For behaviourists, such brain activity was encapsulated in a ‘black box’ that could not be researched objectively. Nevertheless, even as behaviourism maintained its hold over academic psychology, there were key scholars who contributed towards developing a method for studying mental processes. George Miller is one such scholar who, in 1956, introduced an experimental method for studying memory. Alongside Miller, there were others who devised ingenious experiments to understand mental processing. Also, with the popularisation of information processing during the 1970s, an all-encompassing approach for understanding mental (or cognitive) processing was introduced. The information processing approach helped cognitive psychology to develop further. It enabled cognitive psychologists to formulate models to represent, for example, how attention, perception and memory might operate. There were many types of information processing models, some of which highlighted the similarities in function between the human brain and a computer. As we will see in Chapter 3, information processing models have helped cognitive psychologists understand the transition from sensory pick-up of stimuli in our environment to forging meaning in the brain. Models of attention have brought with them new terminologies such as bottom-up versus top-down processing, and serial versus parallel processing (see Chapter 3). Different types of memory storage have been introduced such as short-term versus long-term memory (see Chapter 4).

So how are cognitive information processing models derived? A succinct answer to this is by collating participant scores from cognitive-related tasks. This appears to be simple, but these cognitive-related tasks have to be controlled such that they test the same aspect of cognition under the same conditions. This means that to test problem-solving, for example, the same conditions of the study must be applied to all participants. Often two groups are compared where only one variable is changed. In this context, the term variable is used to denote something that can be changed or manipulated. An example of this might be that one group is given more time to complete the task or that they have to do so in the presence of a distraction. In this way we can compare the performance of the two groups for the effect of time or distraction on problem-solving. Different cognitive tasks are designed to test specific aspects of performance; such as, how many items participants recall within a set period of time, after memorising them for one minute. For each group of participants, it is possible to derive an average score of performance for the specific task in question. This average score can be considered as representative of how a particular population sample performs on the task studied (under each condition). The average performance of each group is known as a normative score. This is important as it can be added to existing knowledge about how people perform on such a task but under slightly different conditions. In our problem-solving example, it could be that resolution of the task fails when the time allowed is halved or when distracted. This information can then be added to existing theory or a model of problem-solving (see Box 1.1).

Box 1.1 How are cognitive information processing models derived?

An interesting way of developing scientific knowledge was outlined by philosopher Karl Popper (1959). He provided a protocol for devising experiments in his Deductive Model of Science. Not only was this embraced by cognitive psychologists, but by the scientific community as a whole. It describes a number of stages which should be followed as a means of building knowledge based upon existing knowledge. Sometimes the existing knowledge becomes extended, but it can also be refuted. The key element is to test and retest using the same variables and method; the experimental method. He described how it is important to make predictions or hypotheses about the phenomenon under investigation. For example, ‘Faces presented twice during learning are more likely to be recognised in a test than those faces shown once’. This hypothesis is making a statement of prediction and prescribes the best way of investigating this. The experimental method should have two conditions, one where participants see a series of faces, some of which are repeated and some of which are not. In the other condition, participants are shown the same faces but all of them are shown once only. The test should include the target faces with a set of new unseen faces. This means that all participants have the same test. If the hypothesis holds true, then participants shown the faces twice should recognise more of the faces than those participants who saw one presentation only. Results supporting the hypothesis are added to existing theories about memory for faces; in this case showing faces more than once improves facial memory. Hence, the hypothesis is supported, and findings are added to the general theory of memory for faces. We could devise a simple model based on this hypothesis (see Figure 1.1).

Figure 1.1 Fictitious model based on collated performance results

This example shows how it is possible to develop models based on participant scores from a cognitive task (face recognition). It makes sense, however, to include scores that are similar. If scores are ‘all over the place’, then the hypothesis will not be confirmed. Scores have to be evaluated carefully because it is possible that there are one or two scores that are odd, in that they fail to fit the majority pattern. These scores are considered as outliers and can be above or below the average score. Hence, most scores that are similar are considered the average (norm or mean) while other scores are distributed away from mean. The distance from the mean is calculated as standard deviations and these scores can still be included. Outliers that are ‘many’ standard deviations from the mean, fail to represent a normative population and can be excluded. Unless we are interested in the extreme scores (which we can be), the exclusion of ‘outliers’ is important if we want to obtain a consensus on individuals’ performance for specific cognitive tasks. This type of scoring fits with the bell-shaped Yerkes-Dodson curve (see Figure 1.2). Such results help cognitive psychologists understand how the brain processes information and enables the tweaking of existing models.

Figure 1.2 Yerkes-Dodson curve

Source: Adapted from Yerkes and Dodson (1908)

Cognitive tasks can be used as an objective method of exploring mental processes. Moreover, by adopting such a method, cognitive psychologists have a means of opening the ‘black box’ that behaviourists so vehemently argue cannot be accessed. There are many examples of where cognitive tasks have helped progress our understanding of cognition. One interesting area is mental imagery. Mental imagery is considered to be a series of pictures in the mind’s eye without input from the outside world. One study examined the hypothesis that mental imagery uses visual representations (see Box 1.2).

Box 1.2 Mental imagery

The study of mental imagery is attributed to Stephen Kosslyn, who devised an experiment in 1973 to examine how we can ‘see’ images in our mind, without actually receiving input from our visual system. He argued that we scan images introspectively in our mind, and that this is a cognitive process. Kosslyn found that when participants were instructed to imagine moving from one location to another, it took them longer to achieve this when the distance between the two points was further away. He also demonstrated that when asked to scan an object using mental imagery, such as a boat, the more distant the components of the boat were, the longer it took to ‘reach’ them. For example, when asked to scan for the anchor from the motor, participants moved in real time across the deck and bow to find it. In 1978, Kosslyn, Ball and Reiser wanted to know whether these mental images preserve the spatial distances between the parts of objects. They used a map of a fictitious island which had a series of objects on it, such as a lake, hut, rock, sand, grass and a well. The spatial distances between these objects were pre-defined and set in a way that some objects were closer together than others (see Figure 1.3).

Figure 1.3 Fictitious island map

Source: Adapted from Kosslyn, Ball and Reiser (1978)

Participants learnt the objects on the map, their locations and relative positions. The map was removed, and participants were told that they would hear the name of one of the objects on the map, at which point they had to mentally image the whole map and focus on the object. Participants effectively scanned their mental image of the map and pressed a button when they arrived at the location of the object. From this object’s location they had to travel mentally to the next object and so on. The length of time it took to travel from one object to the next was consistent with the spatial distances on the map. Hence, the further the distance between two objects, the longer it took to scan the mental image. They concluded that the cont...