Memory notes 9-2 (Obj 3-5).

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Presentation transcript:

Memory notes 9-2 (Obj 3-5)

1.) Automatic Processing We process an enormous amount of information effortlessly, such as the following: Space: While reading a textbook, you automatically encode the place of a picture on a page. Time: We unintentionally note the events that take place in a day. Frequency: You effortlessly keep track of things that happen to you. OBJECTIVE 3| Describe the types of information we encode automatically.

2.) Effortful Processing a.) Committing novel information to memory requires effort just like learning a concept from a textbook. Such processing leads to durable and accessible memories. OBJECTIVE 4| Contrast effortful processing with automatic processing, and discuss the next-in-line effect, the spacing effect and the serial position effect.

3.) Rehearsal a.) Effortful learning usually requires rehearsal or conscious repetition. Ebbinghaus studied rehearsal by using nonsense syllables: TUV YOF GEK XOZ Hermann Ebbinghaus (1850-1909)

4.) Memory Effects Next-in-line-Effect: When you are so anxious about being next that you cannot remember what the person just before you in line says, but you can recall what other people around you say. Spacing Effect: We retain information better when we rehearse over time. Serial Position Effect: When your recall is better for first and last items on a list, but poor for middle items.

****Serial Position Effect TUV ZOF GEK WAV XOZ TIK FUT WIB SAR POZ REY GIJ Better recall Poor recall

****What We Encode Encoding by meaning Encoding by images Encoding by organization

****Encoding Meaning “Whale” Q: Did the word begin with a capital letter? Structural Encoding Shallow Q: Did the word rhyme with the word “weight”? Phonemic Encoding Intermediate OBJECTIVE 5| Compare the benefits of visual, acoustic, and semantic encoding in remembering verbal information, and describe a memory-enhancing strategy related to the self-referent effect. Q: Would the word fit in the sentence? He met a __________ in the street. Semantic Encoding Deep Craik and Lockhart (1972)

****Results