Chapter 7 – The Landscape of Memory Representing our knowledge Knowledge representation – form for what we know about ideas, things, events, etc. Declarative knowledge – facts Birthday, Columbus Day Procedural Knowledge – steps you can implement Tying your shoes, making a PB&J Communicating wDon't forget about the age old question of Who is Wilhem Wundt?
We also discuss several other topics like anat101
We also discuss several other topics like What pertains to the measue of how many magnetic field lines pass through a surface?
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Don't forget about the age old question of recitatif setting
We also discuss several other topics like What is the smallest unit of speech?
hat we know Describe broccoli versus describe loyalty Symbolic representation – relationship between a word and what it represents is arbitrary The word broccoli doesn’t tell us anything Imagery – mental representation of things not sensed or seen currently You can imagine smells, tastes, sights, feelings DualCode Theory Dualcode theory – use pictorial and verbal codes for representing information Analog codes – resemble objects they represent Symbolic code – stands for something else arbitrarily Storing Knowledge Propositional theory – we don’t store mental representations in images or words, but in propositions “The mouse bit the cat” would be stored as ”bite, mouse, cat” Limits of Mental Images and Propositional Theory Mental images are imprecise and subject to interpretation The car I visualize and the car you visualize depends on our experience with cars Some say we don’t need propositional code to manipulate information because we can manipulate mental images directly Manipulating Images Functional equivalence hypothesis – even though visual imagery isn’t identical to visual perception, it is functionally equivalent to it We use images rather than propositions for concrete objects we can picture in our minds Schizophrenic patients often report auditory hallucinations because they are unaware of their internally generated speech Mental Rotation Mental rotation – rotating an object’s image in your mind Harder the more the image is rotated Harder for older people More intelligent people are better at this Men better than women because of increased gray matter in women’s brains Mental Image Scaling The more detail we see, the less we can see the whole object It takes longer to describe a small object than a large object Mental Models Knowledge structures that individuals construct to understand and explain experiences Each model represents a possibility The greater number of alternative models needed, the harder it is Mental models only represent what’s true Do we have multiple codes? Multiple hemispheres Right hemisphere represents visuospatial knowledge in a manner similar to perception Left hemisphere more proficient in representing and manipulating verbal/symbol based knowledge Multiple images Visual imagery – images represent colors/shapes Spatial imagery – images represent depth dimensions, distances, and orientations Making Cognitive Maps Cognitive Maps – Internal representation of physical environment, centering on spatial relations Spatial cognition – acquisition, organization, and use of knowledge about objects in 2D or 3D We use landmarks, routeroad knowledge, and survey knowledge to make mental maps Using Cognitive Maps Heuristics – rules of thumb that influence our estimations of distance Is Reno, Nevada west of San Diego, California or east of it? Relative position heuristicChapter 8 – Organization of Knowledge In The Mind Declarative & Procedural knowledge The difference between knowing “what” and knowing “how” As cognitive psychologists, we want to know the “what” of our knowledge structure but we also want to understand the “how” process of using that knowledge Declarative Knowledge Concept – an idea about something that provides a means of understanding the world Concept of an apple, the number 3, pizza Category – grouping items together because they share similar features Category of fruits Schemas – mental framework of organization that holds multiple related concepts Birds Categories Natural categories – groups that occur naturally such as plants, birds, monkeys Artifact categories – groups that are designed by humans for a function Cars, office supplies, dishes People seem to agree about what goes in each category A lizard is always a reptile, an oven is always used to cook things Concepts People don’t seem to agree on stable concepts. They change frequently depending on where/who you are Concepts have a basic level which is the preferred level of description people use Basic level here is computer (Picture of three computers) But if I know a little more about computers, I know that some are Mac some are Windows If I know a lot about Macs, I would say that the one is a post 2012 iMac and the other is a post 2012 Mac Book Pro 13in. The more you know, the more specific your language gets about something Think of something you know a lot about, and come up with the basic level for it. How do we put things in categories? Theory #1 – defining features theory All the features associated with a concept are necessary to put it in that category ∙ For something to be a mammal, it must have fur, be warm blooded, etc. ∙ But what happens when we have something that doesn’t fit all the criteria? Theory #2 – prototype theory Things are grouped together by how similar they are to the average model of that category ∙ Prototype – abstract average of all objects in the category ♦ Has characteristics features that describe the category but aren’t necessary for it So birds usually fly, but an ostrich is still a bird even though it can’t Theory #3 – Synthesis of feature and prototype theories Each category has a prototype and a core ∙ Prototype – We have several typical examples of the category that can be used for comparison purposes when trying to categorize a new item ∙ Core – defining features something must have to be an example of a category Theory #4 – Theory based view of meaning People understand and categorize concepts in terms of implicit theories they have about concepts ∙ Think about athletes. What features come to mind? How many of you would put a cheerleader into that category? A bowler? Semantic Networks The concepts in our minds are connected like a spider web Collins and Quillian’s Network Model – hierarchal pattern Web of elements of meaning or concepts (called nodes) that are linked ∙ Collins and Quillian’s asked participants questions about animals like “is a shark a fish” or “is an ostrich tall” ∙ The questions asking about information further in the hierarchy took longer to answer Maybe not organized by hierarchies, but by semantic features (Smith, Shoben, & Rips, 1974) Instead of coming up with features for each animal, maybe we come up with a few key features and measure each animal in their level of that feature Schematic Representations How we organize the concepts in our mind Schemas are more taskoriented than semantic networks Your schema for something differs based on your experience with the thing Several characteristics: ∙ Include other schemas ∙ Encompass typical, general facts that vary by instance ∙ Vary in degree of abstraction Schemas Schemas include information about relationships: Concepts – links between things Attributes within concepts Attributes in related concepts Concepts and particular contexts Specific concepts and general background knowledge Schemas are like file cabinets where we store all information associated with an encounter or object Script – type of schema Contains information about the order in which things occur Less flexible than schemas Script of a trip to the movies∙ Props – seats, movie screen, money, tickets ∙ Roles – customer, movie attendant, managers, popcorn guy ∙ Opening conditions – customer wants to see movie and has money for it ∙ Scenes – entering, buying tickets, ordering popcorn, sit down, watch movie Jargon – specialized language used within a script for a particular group Example of script Immediate family, everyone gets to open one Christmas present three days early (because we’re all impatient it became a tradition), and then we open all of our presents on Christmas Eve Procedural Knowledge We acquire it through practice and implementation Once you’ve mastered a procedure, the knowledge is implicit It’s hard to explain how you know how to do something Serial processing – information is handled in a sequence of operations Production – generation and output of a procedure If you want to accomplish a task, you use a production system that comprises the set of rules for executing the task or using a skill Nondeclarative Knowledge Procedural knowledge – motor skills, perceptual skills, cognitive skills Simple associations – classical and operant conditioning Simple nonassociative knowledge – habituation and sensitization Priming Unscrambling words based on food, and coats ∙ Both ended with ACOT, some said coat and some said taco Integrative Models Adaptive control of thought (ACT) – procedural knowledge is represented in the form of production systems. Declarative knowledge is represented in the form of propositional networks ACTR – integrates a network for declarative knowledge Spreading activation – when nodes communicate with each other within a network This is how you lose train of thought sometimes. You activate a node and it then activates the nodes around it Connectionist Model Parallel processing – multiple operations go on at once Parallel distributed processing (PDP) model – handling multiple cognitive operations at once all over the brain In the PDP model, the connections represent neuronal units of knowledge, not specific concepts. So at any given time there are 3 states of a neuron: Inactive – neurons aren’t stimulated Excitatory – neurons release neurotransmitters that stimulate other neurons Inhibitory – neurons release neurotransmitters that inhibit other neurons Criticisms: Doesn’t explain how people remember single events Doesn’t explain unlearning