Concept Knowledge

Exam 3

concept

building blocks from which all knowledge is created

generalization

applying general knowledge to new cases

inference

draws broad conclusions from experiences

prototype

An idealized, average, or most typical representative of a category.

• We categorize objects and concepts based on a "prototype", the most typical or idealized example of a category

• A category that has all of the characteristic features

• Compare new exemplars to this ideal prototype to determine category membership

  • A category’s prototype will differ across individuals

• Category membership is judged based on how much something resembles the prototype (typicality)

exemplar

Specific, remembered instances or examples of a category encountered in past experiences.

• specific, real-life examples of category members stored in your memory. 

• they take up more mental "storage" (making them less economical), they allow for much more flexibility.

theories

• represented by networks of interrelated conceptual knowledge.

  • Act like mental frameworks that help us understand the "why" and "how" behind a concept, rather than just what it looks like

beliefs

• woven into a broader network that stores all long-term memory information

• they directly influence and shape our daily categorizations and reasoning

definitional view

Every complex concept can be broken down into a set of individually necessary conditions

• A cat having 4 legs, soft, and meowing

family resemblance

• No defining features

• Characteristics

• The most characteristic features an object has, the more likely it is to belong to that category

  • A cat probably has four legs, probably meows, and is probably soft and furry

  • A creature without these features is unlikely to be a cat.

sentence verification task

participants are briefly presented with simple statements and must judge whether they are "true" or "false"

• Judgements more distant from the prototype take longer to make

production task

• “Name as many fruits as you can”

• Predicts that individuals will list prototypical items first

rating task

• Items closer to the prototype are rated as more typical of the category

• typicality effects

typicality effect

individuals process, recognize, and categorize typical examples of a category much faster and more accurately than atypical examples

• A Labrador is viewed as "more of a dog" (closer to the prototype) than a Corgi or a Pug

graded membership

a concept in cognitive science and psychology stating that category boundaries are not strictly binary

• items belong to mental categories on a spectrum based on how closely they resemble a central prototype

• Objects close to prototype are better members of the category than objects further away from the prototype

conceptual knowledge

• A mix of exemplar and prototype.

• Early learning often involves exemplars

• Experience often involves averaging exemplars to get prototypes

When using a prototype or exemplar, you rely on a judgment of resemblance. That judgment of resemblance depends on other knowledge

exemplar based reasoning

Categorization which relies on knowledge about specific category members rather than the prototype

superordinate

the most general level, offering the least specific visual and functional detail

• fruit, furniture

basic

the sweet spot level, the most functional level for everyday interaction and thought

• provides max detail with the min cognitive effort

• chair, apple

Characteristics of the Basic Level

• Representation by a single word

• Default for naming objects

• Easy-to-explain commonalities

• Basic categories learned first

subordinate

the most specific level for rapid identification or general communication

• kitchen chair, gala apple

keil

a skunk cannot be turned into a raccoon

a toaster can be turned into a coffeepot

essentialism

The belief that categories (especially ”natural” kinds like animals or plants) have an underlying, unobservable essence that makes them what they are

typicality and membership

resemblance is not the sole criterion for how we categorize things

typicality does not equal membership

• If you take a lemon, paint it with red and white stripes, inject it with sugar so it’s sweet, and run it over with a truck, it looks and tastes nothing like a lemon. Yet, it is still a lemon because of its biological origin

natural kind

you cannot turn a skunk into a raccoon by painting a stripe on it or altering its surgery. Its "essence" is biological, so its category membership cannot be changed by surface features.

artifacts

You can turn a toaster into a coffeepot by altering its parts. Because man-made objects are defined by their function rather than a biological essence, changing their features actually changes their category membership.

deep beliefs

overrule surface features

• when an object has all the perfect typical features of a category but is still rejected as a member because it lacks the correct history or essence.

• The Counterfeit Bill Example: A perfect counterfeit hundred-dollar bill looks, feels, and functions exactly like real money. It has 100% typicality. However, it is rejected as real currency because it didn't come from the official government mint

complexity of similarity

you don't just count up random matching features. Instead, you focus only on the attributes that you believe are essential for that specific concept. Your broader beliefs and theories about the world dictate which features matter and which ones can be ignored.

category based inferences

cognitive shortcuts where we assume an individual shares the traits, behaviors, or properties of a broader group it belongs to

• Guided by typicality

• Guided by theories and broader beliefs

• More likely to infer from a typical case to an entire category than from atypical cases

  • Ex: People infer that a fact about robins is true of ducks, but not the reverse

features

One way we characterize concepts, where we define or group items strictly based on their physical properties or observable traits (e.g., a ball is round and smooth).

goal-driven

A conceptual category where completely different-looking objects are grouped together because they serve a specific purpose or help achieve a goal (e.g., exercise equipment like a treadmill and a yoga mat).

relational

A category defined by the web of relationships, interactions, or systems between elements, rather than the physical traits of a single object (e.g., hunting).

event

Conceptual groupings based on specific occurrences, happenings, or structured episodes in time (e.g., a "visit to the dentist" or a "birthday party").

dissociation

The finding that different brain regions handle distinct types of concepts. Living things (Natural Kinds) rely heavily on processing perceptual properties, while non-living things (Artifacts) rely on functional or motor properties.

embodied cognition

The theory that concepts are not just abstract symbols, but actually include direct motor and sensory representations in the brain.

• For example, simply thinking of the word "kick" actively triggers your brain's motor areas.

hub

the Anterior Temporal Lobe (ATL). It serves as the centralized master area that integrates and connects all of your diverse conceptual knowledge

spokes

the specialized sensory and motor brain areas that hold specific, localized features of knowledge (e.g., visual traits or functional uses).

ATL damage

causes a loss of general, high-level category knowledge, drastically impairing your ability to name and recognize unique items like specific people.

nonredundancy

a property should only be stored at the highest possible hierarchical level (to save cognitive space).

• Problem: It often fails empirically because people verify statements like "Peacocks have feathers" incredibly fast, even though "has feathers" should logically only be stored higher up at the "Bird" level.

induction

drawing broad conclusions about a whole category from prior experiences.

• it is guided heavily by typicality

• you are far more likely to infer a new fact is true of an entire category if it starts with a typical member (e.g., inferring a fact about a robin applies to ducks, but not the reverse).

propositional

A network where knowledge is represented by distinct, rule-based combinations of nodes (propositions) that express precise, language-like relationships (e.g., "A robin is a bird"). It processes information serially and locally.

connectionist

A brain-inspired network where ideas are not stored in single nodes. Instead, each concept is represented by a specific pattern of activation spread across the entire network, processing data concurrently (Parallel Distributed Processing).

distributed

The representation style used in Connectionist Networks, where information is spread out.

• Concepts are defined by which combinations of units are active at the same time, much like a digital light display forming letters.

proposition

The smallest unit of knowledge that can either be true or false. Individual words (like "dog") are not propositions, but a complete, testable thought (like "The dog chased the cat") is.

nodes

these represent individual concepts (e.g., "Boy", "Kicked", "Ball").

agent

The specific semantic role for the node that represents who or what is doing the action in a propositional network (e.g., "Boy" in "The boy kicked the ball").

relation

The semantic role describing the action or state being expressed between nodes in the network (e.g., "Kicked").

object

The semantic role designating who or what is receiving the action from the agent within the network structure (e.g., "Ball").