Final Review.docx

8 Pages
57 Views
Unlock Document

Department
Cognitive Sciences
Course
PSYCH 140C
Professor
Jim K Lee
Semester
Fall

Description
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
More Less

Related notes for PSYCH 140C

Log In


OR

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit