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Memory & Cognition (Lecture Notes).docx

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Ted Petit

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PSYB57 – Lecture Notes Week 7 – Associative Theories of Long Term Memory Representations and their Formats  Knowledge is information about the world that is stored in memory, ranging from everyday to the formal  Without knowledge, you‟d be unable to get beyond the surface of the objects and sensations that surround you in the world  Unable to categorize things  Categorization – ability to establish that a perceived entity belongs to a particular group of things that share key characteristics  Example: “Cakes” form a category of entities that people perceive as related in their structure and use  Without knowledge, you can‟t categorize so the cake on the table meant nothing to you  Categorization then allows you to draw inferences so that you can derive information not explicitly present in a single member of a category but available because of knowledge of the characteristics of the group(s) to which it belongs  Example: If you know the particular object is a cake, associations arise such as is this a celebration?  A key aspect of knowledge is that it relies on representations What is representation?  Representation – a physical state that stands for an object, event, or concept  Carry information about what they stand for  Example: A map of a subway system; the map is a representation that stands for various lines, stops, and connections, and carries information about them  Hypothetical internal cognitive symbol that represents external reality or some sort of mental process that makes use of such a system What are the criteria for being a representation?  Intentionality criterion  Representation must be constructed intentionally to stand for something else; it‟s the ability to store information by unconscious automatic mechanisms in the brain  We have the unconscious goal of storing information about experience, independent of your conscious goals  So is the intentionally criterion met? Yes because the brain at an unconscious level has the design feature of storing info about experiences of the world to stand for those experiences  Intention to capture information is built into brain system whether you consciously direct each memory  Example: camera is set by a photographer to take a picture every second whether photographer is present or not  Power of minds to be about, to represent or to stand for things, properties, and states of affairs in the world  Information-carrying criterion  A representation must carry information about what it stands for  How is it that you‟re able to draw on your memory of an object and describe it? It‟s because the memory of the object carries information about it (i.e. details of its shape, color, function)  Evidence that your memory carries info is that you‟re able to categorize from it  Example: if you see another cake that doesn‟t look completely identical to the first cake you ever saw, you can still categorize it as being part of the group “cakes”  Because memory carries info, it can help produce useful inferences about other objects/events encountered Four Possible Formats for Representations  One aspect of a representation is its format  Format refers to the type of its code  The elements that make up a representation and how these elements are arranged  Relies on characteristics of the processes that operate on them to extract information  Representations may be: a) Modality specific – make use of perceptual or motor systems b) Amodal – residing outside the perceptual and motor modalities  Another aspect of representation is its content – the information it conveys Modality-Specific Representations: Images  Images such as those that a camera captures are one possible representational format, which depicts information  The brain constructs a similar type of representation  Image has 3 elements which taken together determine its content: 1. Spatiotemporal window  A photograph taken of the scene (ex. Table with birthday cake and presents) in front of the camera doesn‟t capture everything in that scene but only that part of it within a spatiotemporal window (ex. Just the birthday cake) 2. Storage units  An image contains an arrange of storage units of the image in the spatiotemporal window (ex. Pixels of the photo) that‟s laid out in a grid  Each storage is sensitive to the light impinging on it; each individual unit also has a spatiotemporal window that captures only the information within a bounded spatial and temporal region nested within the larger window of the entire image 3. Stored information  In the case of the photograph, the information in the storage units is the intensity of light as visible wavelengths in each storage unit  The collective information of all the storage units specifies the content of the image  Do images constructed like the one of the birthday cake on the table exist in the brain?  Much scientific evidence supports the presence of images in the human brain  Brain anatomy research and neural evidence found that the pattern of activation in the monkey‟s brain surface roughly depicts the shape of the stimulus they were looking at  Suggests that the cortex of early visual processing areas is laid out somewhat like the pixels of a digital image and responds similarly  When neurons are arranged in the manner fire, the pattern of activation forms a topographical map  The presence of many of such topographically organized anatomical structure in brain suggests presence of images  There‟s also been much behavioural evidence:  Researchers asked participants to construct mental images while performing a cognitive task  Found that perceptual variables (color, shape, size, and orientation of the mental image) affected task performance  Suggest that participants had constructed mental images having perceptual qualities  Kosslyn (1975)  Studied the perceptual challenge of recognizing something when it‟s far away and small as oppose to it being close up and large in the visual field (this was used to demonstrate that people have mental images)  Participants were asked to visualize a target object (ex. A goose) next to one of two reference objects (ex. A fly or an elephant) o Each pair of objects was to fill the frame of participant‟s mental image (ex. Image of goose would be larger when paired with fly) o Participants heard the name of a property and had to decide as quickly as possible whether target animal has that property by referring to image  Found that participants were faster to verify properties when imagined the target object next to fly  Suggests that participants used images to answer the questions  Camera is a useful metaphor but brain images differ significantly from those taken by camera  Brain images are not as continuous and complete as photographs  People‟s perceptual images don‟t have uniform level of detail – some areas not as well represented as others (due to selective attention) Modality-Specific Representations: Feature Records  Representations must be considered in more sophisticated ways than those taken by cameras  A meaningful entity – object or event that plays an important role in an organism‟s survival and pursuit of goals  Pixels is a relatively meaningless entity because we don‟t just want to know whether light impinges on a particular point in space but rather what those patterns of pixels represent in the world  Meaningful representations are derived from images  Example: if you were a frog what would be meaningful to you? Bugs, but what does a frog need in order to get bugs? A motor system that can capture a bug flying by but to do so, frog must be able to detect the bug  Nature has applied meaningfulness and interpretation to the problem of representation  Early work showed that neurons in frogs visual system respond differentially to small objects moving within the frog‟s visual field  Found that some neurons fire in response to small young objects whereas others fire in response to object movement and together, these two sets of neurons allow the frog to detect the presence of a small, round, flying object  Different populations of neurons appeared to detect different types of information in VS  The info that these neurons detect is info that‟s meaningful to the frogs o “small”, “round”, and “moving” are features of flying insects  Feature – meaningful sensory aspect of a perceived stimulus  Neurons respond only when information meaningful to the individual is present  Don‟t constitute an image of the VS but instead interpret regions of images as indicating the presence of a particular feature  When these feature-detecting neurons become active, they categorize a region of n image as containing a meaningful feature of an object or event  Feature detecting is accomplished by populations of neurons which allows for graded response  Neurons are sensitive to more than a single feature and the info to which they respond may change both with experience and with goals at a given time  Form visual input, populations of neurons extract features along pathways of occipital, temporal, and parietal lobes and at later stages of processing, conjunctive neurons in various brain regions combine these features to form integrated featural representations of perceived entitites  Do feature-detecting neurons meet criteria for a representation?  Yes, in terms of intentionality, these neurons have been honed by evolution to stand for things in the world  Yes, in terms of information, the neurons themselves, by firing, carry info about the world  Further along the processing stream, populations of conjunctive neurons integrate featural information extracted earlier into object representations  Conjunctive neurons – build representations out of „associations‟ of elementary, image-like feature maps  Able to integrate information about size, shape, and movement to establish a featural representation of an object  Feature detectors do not correspond to spatial points of contrast but instead draws on different meaningful features of the object Amodal Symbol  Amodal symbols (abstract and arbitrary) describe the properties of an relations among meaningful entities in the scene  Resides in a knowledge system that constructs and manipulates descriptions of perceptual and motor states  Describes the contents of a visual state but lie outside the visual system and are part of a more general system that‟s used in language  Amodal symbols build three types of amodal representations: 1. Frames – structure that specifies a set of relations that links objects in the environment  Example: the gifts are to the left of the cake and this left-of configuration is above the table 2. Semantic networks – represents essentially the same relations and objects in diagram form 3. Property lists – names the characteristics of the entities belonging to a category  Example: properties of cake include frosting, candles, etc.  Amodal symbols complement images in that they categorize the regions of an image meaningful  Continues the interpretive process  Symbol could categorize an entity inaccurately; example. Categorizing the cake as a hat when seeing it in the dark  Summary of amodal representation: Physical stimulus > Info about physical stimulus travels up sensory channels > Neurons in feature maps fire to produce a sensory representation > Perceptual states are transduced into non-perceptual representational format (frame, semantic network, feature list) Statistical Patterns in Neural Nets  Another possible means of representation is the neural net – construct in which the cake in the birthday scene is presented by a statistical pattern such as 1100101000101  Offers greater scope for two reasons: 1. Elements can be viewed as neurons or as populations of neurons that are on or off (fire or not fire) o Each 1 in pattern represents a neuron that fires o Each 0 represents one that does not fire 2. Multiple statistical patterns can represent the same category o Flexibility offered by varying statistical patterns reflects the reality in the world (example: not all cakes are exactly the same) Multiple Representational Formats in Perception and Simulation  Representations play many roles in the processes that constitute cognition and it‟s implausible that only a single format would serve all these roles  More likely that multiple formats (images, feature detectors, amodal symbols, and statistical patterns) are required  Perception system – on perception of a scene, your brain construct a patchy visual image of it  As image develops, feature detection extract meaningful features and statistical pattern becomes active  Each element in the statistical pattern develops associations back to the image and feature units that activated it  Together these processing phases establish a multilevel representation of the scene  Simulation – a statistical pattern that can reactivate image and feature information even after the original scene is no longer present (basically the process of perception backwards)  Example: hearing someone say the word “cake” activates statistical pattern that was previously used to integrate info about the cake in the past and in turn, partially reactivate the features extracted for the cake along with the image From Representation to Category Knowledge  How do large assemblies of representations develop to provide knowledge about a category?  Category knowledge develops first from establishing representations of a category‟s individual members and second from integrating those representations  Say you have 5 different cakes and each produces a statistical pattern  The cakes are so similar that they produce similar statistical patterns but they differ enough that the patterns are not identical  The 5 individual patterns however share the common eight units which allows for the integration of the 5 memories of each cake  Since all 5 memories shared these 8 units, all the memories become associated to a common hub – results in the representation of a category (“cake”)  At one level, all category members become linked by the common statistical units they share  At another level, these shared units constitute a statistical representation of the category (not just of one member)  Shared units offer a means of retrieving category members from memory (since all category members become associated with a common hub, the hub serves as a mechanism for remembering category members at later times)  In a top-down manner, hub reactivates the image and the feature processing associated with the category member, thereby stimulating it  This process may mix memories of multiple category members together during retrieval to produce a blending  This results in the stimulated category member to be more like an average category member than like a specific one  This process of simulating average category members provides one mechanism for generating prototypes  Summary of how individual memories of a category become integrated to establish category knowledge:  5 individual cakes perceived on different occasions are each represented with a unique statistical pattern; the conjunctive units common to all are highlighted (ex. The 8 common units shared amongst all 5 cakes)  The shared conjunctive units establish a representation of the cake category which further integrate memories of the image and feature processing that occurred across cakes  The shared statistical pattern becomes active in the absence of a particular cake and produces a simulation of image and feature processing that is roughly the average of previously experienced cakes Convergence Zone & Connectionist Network  Depending on the category (cakes vs. guitars), a different profile of information across the six modalities of vision, audition, action, touch, taste, and smell is salient  Integration is key but how does the brain combine category name with all the relevant information across modalities?  Convergence zone theory  Convergence zone – population of conjunctive neurons that associates feature information within a modality  These patterns integrate info from image and feature analyses within a given modality (ex. Vision)  For cakes, image & feature info would similarly be integrated within the taste modality as well as smell modality  Higher order convergence zones integrate category knowledge across modalities  Throughout the brain, convergence zones integrate category knowledge in various ways in which the category knowledge captures the multimodal character of category members (all relevant features across modalities for a category become integrated) Structures in Category Knowledge Exemplars and Rules  Simplest structures that category knowledge contains are memories of individual category members known as exemplars  Schema – structured representation that captures the information that typically applies to a situation or event  Schemata are described as “structured” because they are not lists of independent properties but instead establish a coherent sets of relations that link properties Week 8 – Concepts & Language Categories Provide Information  A category refers to a set of entities that are grouped together  Concept – mental representation that‟s used for a variety of cognitive functions such as memory, reasoning, and using and understanding language  Categorization – process by which things are placed into groups called categories  Category – a partitioning or class to which some assertion or set of assertions might apply  Categories as existing in the world and concepts as corresponding to a mental representation of them Conceptual Representation  How are concepts represented?  Features  Many objects are categorized/characterized by members that share many features  Members of a group that are characterized by high within-category overall similarity  Prototypes  Formed by averaging resemblance  Concepts are organized around family resemblances rather than features that are individually necessary and jointly sufficient for categorization  The prototype for a category consists of the most common attribute values associated with the members of the category  Categorizations can be predicted by determining how similar an object is to each of the prototypes  Theories  People‟s categorizations seem to depend on the theories they have about the world  Example: man jumping into a pool fully clothed o At this point, categorization of the man‟s behaviour does not depend on matching the man‟s features to the category “drunk‟s” features o Categorization by theory explains the behaviour  Theories involve organized systems of knowledge Rosch (1975) Typicality Ratings for Natural Categories  Categories have graded structure  The typicality effect refers to the phenomenon in which subjects are faster to respond to typical instances of a concept (ex: apple for the concept fruit) than they are to atypical instances (ex: pomegranate)  People perceive members or natural object categories (i.e. birds, trees, vehicles) and goal-derived categories (i.e. things to take on a picnic) to vary in their representativeness or typicality of the category  As category members become more typical, they gain increasing priority in a number of cognitive tasks  When subjects asked to list as many objects as possible, tend to list the most prototypical members of the category first  When subjects are asked to rate the typicality of animals like robin and eagle for the category bird, they reliably give different typicality ratings for different objects Rosch (1975) Priming Experiment  Priming occurs when presentation of one stimulus facilitates the response to another stimulus that usually follows closely in time  Rosch demonstrated that prototypical members of a category are affected by a priming stimulus more than are nonprototypical members  Participants first heard the prime (example: name of a color such as green) and two seconds later they saw a pair of color side by side and indicated by pressing a key whether the two colors were the same or different  The side-by-side colors were paired in three different ways: 1. Colors were the same and were good examples of the category (ex: primary reds, blues, greens) 2. Colors were the same but were poor examples of the category (ex: light blue, light green, etc.) 3. Colors were different with two colors from from different categories (ex: paring red with blue)  When the colors were the same, priming resulted in faster “same” judgements for the prototypical (good) colors (RT=610ms) than nonprototypical (poor) colors (RT=780ms)  In other words, when participants were primed with the word green and were shown two good example of the color green that are both the same, they were faster to make the “same” judgement” than when they were shown two poor example (light green) of the color green that are both the same Levels of Categorization  Three levels of categories: 1. Superordinate level – ex: furniture 2. Basic level – ex: table 3. Subordinate level – ex: kitchen table  Examples: Superordinate: Vehicle Basic: Truck Subordinate: Pickup, van Tanaka & Taylor (1991)  They asked bird experts and nonexperts to name pictures of objects (there were objects from many different categories such as tools, clothing, flowers, etc.)  Interested in how the participants responded to the four bird pictures  Results show that experts responded by saying the birds‟ names (robins, sparrow, jay, or cardinal) but the nonexperts responded by saying bird  Experts learned to pay attention to features of birds that nonexperts are unaware of  In other words, experts focused more on subordinate rather than basic whereas nonexperts focused on the basic rather than subordinate  Shows that in trying to understand how people categorize objects, necessary to consider not only the properties of the objects but the learning and experience of people perceiving these objects Medin (1989)  Arguments of how concepts are organized vary (either similarity-based approach or theory-based approach)  See table in lecture slides for comparison of the two Dynamic Representation  Not all possible information for a category is activated when the category is accessed  Information relevant to the current context is preferentially activated  Example: compare the information activated by “piano” for the following two sentences:  She injured her back when she tried to lift the piano  She injured her wrist when she tried to play the piano Levels of Language Representation  There are many components that contribute to the overall meaning of a sentence  Referr to the pieces at different levels of language representation and together they make up the grammar of language  Grammar – refer to the sum of knowledge that someone has about the structure of his/her language  Diagram of the different levels of language representation: Example: “The chef burned the noodles) Discourse level – coherent group of written or spoken sentences  Mentally represents the meaning of the entire sentence, beyond the meaning of the individual words  Proposition: relates the action, the one doing the action, and the thing being acted on (burn, chef, noodles)  Inference: Chef is incompetent Syntactic level – specifies the relationships between the types of words in a sentence (such as between nouns and verbs)  Way of representing sentence structure  In this example, the sentence is composed of both a subject noun phrase (“the chef”), a verb phrase (“burned”) and another noun phrase (”the noodles”) Word/Morphemes level – encode word meanings (example: chef refers to “someone skilled in cooking food”)  Morphemes – building blocks of words and are the smallest unit o meaning in language  Some words such as the and chef are composed of only a single morpheme whereas some are built up from several such as noodles which is composed of two morphemes (noodle + the plural morpheme s) Phonemes – smallest distinguishable units of speech sound that make up the morphemes in a given language Key Areas of the Brain for Language  Broca‟s area and Wernicke‟s area are important for language and are well documented in terms of language impairments of patients with damage to these areas  Other areas include the primary motor cortex which sends information to structures important in speech production and the auditory perception area where speech is perceived Language and Speech: Broca‟s and Wernicke‟s Areas  Patients who have had a stroke or other damage that affects parts of the left hemisphere of brain
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