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Question 4
Evaluate the classic studies by Baddeley (1966b) and Watson and Rayner (1920) in terms of reductionism.
Step 1
Answer
Reductionism is a method for explaining complex phenomena, such as society, by breaking them down into smaller, more manageable units. This approach posits that understanding these smaller parts can illuminate the nature of the whole. Conversely, reductionism can overlook the interconnections and interactions between these elements.
Step 2
Answer
In his 1966 study, Baddeley investigated short-term memory (STM) and long-term memory (LTM) through a method examining acoustic and semantic coding. He asked participants to remember a list of words, concluding that different types of memory storage operate distinctly. This work represents a reductionist approach, as it analyzes specific memory components without considering broader cognitive processes.
Step 3
Answer
Watson and Rayner's research focused on conditioning, using Little Albert to demonstrate how fear responses can be learned through association. Their laboratory experiment isolated stimulus-response pairings, allowing them to establish foundational principles of behaviorism. This method reflects a reductionist outlook that simplifies complex emotional reactions to basic learned behaviors.
Step 4
Answer
While reductionism has its merits, simplifying memory and learning to isolated components can lead to an incomplete understanding. For example, Baddeley's study supports the Working Memory Model but may not account for the holistic and reconstructive nature of memory as proposed by other theories. Additionally, Watson and Rayner's focus on stimulus-response relationships underplays the role of social context and individual experiences in learning.
Step 5
Answer
Integrating a more holistic view could enrich the understanding of both studies. For instance, considering how emotional, social, and environmental factors influence learning and memory can lead to more comprehensive educational strategies. This suggests that while reductionism is valuable for isolating variables, a broader approach may yield deeper insights into the complexities of human behavior.
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