Final Review.docx

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Cognitive Sciences
Jim K Lee

Introduction and History: - Disciplines in cognitive science - Empiricism, nativism, behaviorism, functionalism o Empircism: o Nativism: o Behaviorism: o Functionalism: - Marr’s three levels o Implementation  How is perceptual and cognitive processing, the remembering of information, and so on, actually done with neural hardware in the brain?  Often this is the focus of cognitive neuroscience o Algorithmic  What processing steps are made to make a decision, or produce behavior, or so on?  Often this is the focus of cognitive psychology o Computational  Why does the cognitive capability behave like it does? What is its goal or purpose?  Often this is the focus of artificial intelligence or machine learning Concepts and categories: Concepts: mental entities Categories: collections of stimuli in the real world - Definitional, prototype and exemplar theories o Definitional: (set-theoretic) approach assumes stimuli are grouped using a set of necessary and sufficient properties. Does not work for real-world domains.  List of conditions that need to be met for a stimulus to belong to a category.  Example: triangles are closed shapes with three straight edges. o Prototype: Assumes people categorize stimuli by similarity to a prototype, which is an “ideal” instance of the category.  Example: A bird is more likely to be a robin (more typical) than an emu (less typical outliers). o Exemplar:  Every instance (exemplar) of a category is remembered  New stimuli are categorized by the average similarity they have to all category exemplars - Schemas, scripts o Schemas: Example: kitchen schema, items that belong in a structure, stereo typical, such as stove, fridge, sink etc. (Not like a toilet, bed, lounge…)  Having ‘slots’ filled with ‘variables’ o Scripts: Schemata for events, rather than structures. Capturing the stereotypical pattern, but allowing for constrained. Example: Going to a restaurant and ordering a meal. Some flexibility and exceptions. - Ad-hoc and goal-derived categories o Ad-hoc categories: Emphasize concepts that are not part of long term knowledge structures but can be created ‘on the fly’ in response to specific goals and circumstances.  Example: “things to take from a burning house”  Can become more permanent and well-defined through frequent use. o Goal-derived categories: Categories that are well established are sometimes called “goal-derived”  Example: Apple as ‘snack food’ - The basic level o Basic level is preferred level such as Chair verses furniture or Windsor. Perception, action, cognition - Top-down apperception and bottom-up perception o Top-down apperception: top-down, cognitively driven sources of information  Memory, knowledge, concepts, .. o Bottom-up perception: bottom-up, sensory driven courses of information from external stimuli  Visual stimulation, auditory stimulation, .. - Context effects of similarity o This shows that when comparing two items whether stating the differences or similarities, it makes them more similar in recall. Ruling out any uninteresting possibility compared stimuli because of a shared feature “things I compared” - Change detection and change blindness o People fail to detect large changes to visual arrays and scenes if they are briefly occluded o Change detection: The task  Example: Transparent video with women walking through with an umbrella. Non-transparent of a gorilla walking through. o Change blindness: the inability to perceive change  Example: building picture with more of the same picture but missing something. People fail to notice. - Perceptual illusions (visual, McGurk effect) o McGurk Effect: McGurk and MacDonald (1976) studied how visual cues affect auditory perception  Presented an auditory [ba] sound being paired with the lip movements for a [ga] sound  People perceive da [da] or [tha] sound - Categorical perception - Template matching, feature detection theory, and their applications o Template matching: having infinite number of templates to recognize a standard. o Feature Detection Theory: Such as find T in a groupe of Z vs find T in a group of Y. - Perception as inference - Embodied cognition o Embodied cognition emphasizes intelligent agents being situated in, and acting within an environment.  Embodiment makes it possible to think about non- perceptual stimuli, by replying on spatial metaphors. Decision making - Deductive and inductive decision making o Deductive: Possible to deduce the correct answer  Example: Watson Task, or boat with animals o Inductive: Human decision-making from reasoning from a specific observation to a more general conclusion  Example: Which McValue meal to choose, what courses to enroll in, etc.  Two approaches - “Rational” and heuristic decision making o ‘Rational’: describe human decision making in terms of maximizing benefit or utility o ‘Heuritic’: describe human decision making in terms of “rules of thumb” or “approximate solutions” - Heuristics and biases approach o Representativeness, availability, anchoring and other heuristics (law of small numbers, ignoring base rates, …)  Representative: people have the tendency to judge probabilities or likelihoods accround to how much one thing resembles another.  Example: Feminist bank teller  Availability: People assess the frequency of a class or the probability of an event by the ease with which instances of occurrences can be brought to mind.  Example: Much easier to image a death by shark, much easier to image a word beginning with k, etc.  Anchoring: Decisions are disproportionately influences by the first available pieces of information  Example: solve math problem, because the largers numbers were first, people believed that it was a larger income vs placing smaller numbers in beginning.  Example: Estimation, when given a certain percentage people would make a guess percentage related to the given.  Law of Small numbers: o Prospect Theory  Value function for gains is concave  Value function for losses is convex and relatively more steep  This implies that being risk averse for gains but risk seeking for losses  Losses “loom large” and are weighted more heavily than gains! - Fast and frugal heuristics approach: Limited search and simple decision- making because the world is competitive and resources are valuable, so you need to be FAST! The worl is changeable , so you need the robustness that comes from simplicity. o Recognition heuristic, theory and applications  Recognition heuristic: When one of the two objects are recognized, the recognized object has the higher value  Unless, the recognized object is negative then it will have the lower value.  Example: Comparing cites!  “LESS IS MORE” Effect  if recognition heuristic is more accurate than knowledge, partial familiarity will lead to the greatest accuracy. - Correlated environments: where the fist pieve of information predicts the rest o Example: Real-estate agents believe the first pieces of information (approaching house) determine wheater a viewer will decide they want to buy the house - Environments of diminishing returns: where the first pieves of information are more important. o Example: The starting give in backetball are much more important than the bench in determining the outcome. - Klein’s recognition-primed decision-making approach o Do not need to compare options, can j
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