Lecturer: Dr. Benjamin Amponsah, Dept. of Psychology, UG, Legon Contact Information:

Transcription

1 Lecturer: Dr. Benjamin Amponsah, Dept. of Psychology, UG, Legon Contact Information: College of Education School of Continuing and Distance Education 2014/ /2017

2 Session Overview Attempts to explore alternatives to the informationprocessing approach in the early 1980s led to the development of the framework known as the Parallel-Distributed Processing (sometimes called Connectionist model). This model may be seen as a model of memory which is fundamentally different from the other models of memory. Slide 2

3 Session Objectives At the end of the session, the student will be able to Describe another contemporary model of memory that builds on earlier models with somewhat different conceptualizations Explain the assumptions underlying the parallel distributed processing Evaluate the theory in terms of its strengths and weaknesses Slide 3

4 Session Outline The key topics to be covered in the session are as follows: Topic One: Distributed Representation Topic Two: Characteristics of Parallel-Distributed Processing Topic Three: Criticisms of PDP Slide 4

5 Reading List Ashcraft, M. H. (2006). Cognition (4 th edn.), London: Pearson Education Int. Galotti, K. M. (2004). Cognitive Psychology: In and out of the laboratory (3 rd Edn.). Belmont, CA: Wadsworth. Hunt, R. R. & Ellis, H. C. (1999). Fundamentals of Cognitive Psychology (6 th edn.), New York: McGraw-Hill. Willingham, D, B. (2001). Cognition: The thinking animal. NJ: Prentice-Hall. Slide 5

6 Topic One DISTRIBUTED REPRESENTION Slide 6

7 What is Parallel-Distributed Processing? Efforts to explore alternatives to the information-processing approach in the early 1980s led to the development of the framework known as the Parallel-Distributed Processing (sometimes called Connectionist model). Its name is derived from models which depict cognition as a network of connections among simple but numerous processing units. These units are sometimes compared to neurons (the cells that transmit electrical impulses and underlie all sensation and muscle movement), they are sometimes called neural networks. Slide 7

8 What is Parallel-Distributed Processing? The Model is relatively new regarding the processes of memory. It states that information is not inputted into the memory system in a step by step manner like most models but instead, facts or images are distributed to all parts in the memory system at once. Older models (e.g., the stage model) hypothesized that information would consolidate first into sensory memory, then move to short-term memory, and then finally go to long-term memory. Slide 8

9 What is Parallel-Distributed Processing? Each unit is connected to other units in a large network. Each unit has a level of activation at any particular moment in time. We may conceive the idea that the human brain is massively parallel, with richly interconnected set of processes. The neurons may act as simple processes with each making a very small contribution to the overall memory capacity (Fulcher, 2003). Slide 9

10 A Representation of Parallel Distributed Processing Slide 10

11 Figure 6.1 (A) A network description of robin. (B) A connectionist network showing an alternative depiction of the same information as in part (A). From Galotti (2004). Slide 11

12 Node Figure 6 C. Parallel Distributed Processing Slide 12

13 Topic Two CHARACTERISTICS OF PDP Slide 13

14 Characteristics of PDP Let us look at some important characteristics of the PDP approach as suggested by Matlin(2000). PDP theorists argue that all cognitive processes can presumably be explained by the activation of these networks starting from the node. See Fig 6 C. Connections between these neuron-like units are weighted and the connection weights determine how much activation one unit can pass on to another unit. Slide 14

15 Characteristics of PDP When a unit reaches a critical level of activation, it may affect another unit either by exciting it or by inhibiting it (if the connection weight is negative). Notice that this design resembles the excitation and inhibition of neurons in the human brain. Every new event changes the strength of connections among relevant units by adjusting the connection weight. As a result, you are likely to respond differently the next time you experience a similar event. The neural networks are specifically designed to learn from experience (Hahn & Chater, 1997). Slide 15

16 Characteristics of PDP There are situations where we have partial memory for some information, not perfect memory. The brain s ability to provide partial memory is called graceful degradation. For example, the tip-of-the-tongue phenomenon, which happens when you know exactly which target you are seeking; but you cannot retrieve the actual target - the target refuses to leap into memory. Graceful degradation also explains why the brain continues to work somewhat accurately, even when an accident or stroke has destroyed portions of the cortex (Baddeley, 1997). Slide 16

17 Topic Three CRITICISMS OF PARALLEL DISTRIBUTED PROCESSING MODELS Slide 17

18 Criticisms PDP models have a lot of explanatory power, however there are some weaknesses. One problem is that many PDP models suffer from what is called catastrophic interference. That means that if the model learns one set of associations (Set A) and then learns a different set (Set B), Set B will over-write Set A. Set A will be completely lost from the model. The problem is observed only if you train the sets sequentially. But this is not a characteristic of human memory. Slide 18

19 Criticisms Another problem is the ability of PDP models to learn rules. Very simply, it is argued that there are many subtle aspects of children s language that the model did not account for and that these aspects required some rule-based representation. It appears that memory might be organized in some manner consistent with spreading activation, but the spreading activation hypothesis has not been immune to criticism. Slide 19

20 Criticisms Until we have a preferable alternative theory, most cognitive psychologists will prefer spreading activation. As to whether the representation is local or distributed, that point is also waiting research evidence. Slide 20

21 Sample Questions What is the Parallel Distributed Processing? How different is the PDP from the Stage mode? Slide 21

22 References Baddeley, A.D. (1999). Essentials of human memory. East Sussex, England: Psychology Press. Fulcher, E. (2003). Cognitive Psychology. Glasgow: Bell & Bain Ltd. Hahn, U., & Chater, N. (1997). Concepts and similarity. In K. Lamberts & Shanks (Eds.), Knowledge, concepts and categories (pp.43-92). Cambridge, MA: MIT Press. Galotti, K. M. (2004). Cognitive Psychology: In and out of the laboratory (3 rd Edn.). Belmont, CA: Wadsworth. Maltin, M. W. (2002). Cognition (5 th ed.). Thompson, Wadsworth. Slide 22