Key Concepts in Cognitive Science

Cognitive science uses many specialized terms from psychology, philosophy, neuroscience, linguistics, and artificial intelligence. This glossary explains key concepts clearly.

Basic Concepts

Cognition

Cognition refers to the mind's information processing: perceiving, remembering, thinking, learning, language, and decision-making. It's what cognitive science studies.

Examples of cognitive processes:

Recognizing a face in a crowd
Solving a math problem
Learning a new word
Deciding what to order from a menu

Representation

A representation is an internal model of the external world. The brain doesn't process the world directly but its representations.

Think of it this way: When you see an apple, light hits your retina. Your brain constructs a representation—an internal model of the apple that includes its shape, color, and location.

Representations can be:

Symbolic – Language and concepts
Analogical – Mental images, maps
Distributed – Neural network activation patterns

Information Processing

The view that the mind processes information like a computer: receiving inputs, processing them, and producing outputs. This was the cognitive revolution's core idea.

Processing stages:

Sensory input (senses)
Encoding (converting information to internal format)
Processing (operations on representations)
Storage (memory)
Retrieval (recall from memory)
Output (behavior, speech)

Modularity

The idea that the mind consists of specialized modules that process specific types of information. Jerry Fodor's 1983 theory claims that language, face, and motion perception occur in separate modules.

Characteristics:

Modules operate quickly and automatically
They are informationally encapsulated (don't know other modules' contents)
Only input and output formats are compatible with the rest of the mind

Computational Mind

The theory that the mind is a computational system—it runs algorithms on representations. This makes it possible to model the mind with computer programs.

Levels:

Computational level: What task does the system perform?
Algorithmic level: How is the task performed?
Implementation level: In what physical structure is the algorithm realized?

Perception

Bottom-up vs. Top-down Processing

Bottom-up (data-driven): Processing proceeds from sensory data upward toward more abstract representations. "What the senses tell you."

Top-down (concept-driven): Prior knowledge, expectations, and context guide processing. "What you expect to see."

Example: You see something in the dark. Bottom-up: Outlines, shadows. Top-down: "I'm in the forest, that could be a bear"—expectation shapes perception.

Change Blindness

The phenomenon where we don't notice even large changes in our visual field when our attention is elsewhere. Shows that perception isn't a perfect copy of the world.

Gestalt Principles

Perceptual organization principles described by German psychologists in the early 1900s:

Proximity: Objects near each other group together
Similarity: Similar objects group together
Continuity: We see continuous lines and forms
Closure: We complete missing parts into shapes

Attention

Selective Attention

The ability to focus on specific information while ignoring other things. The cocktail party effect: you can follow one conversation in a noisy room.

Divided Attention

The ability to process multiple tasks simultaneously. Limited: performance decreases as tasks increase.

Attentional Bottleneck

The limitation of attention: we can't process all information consciously. At some point, processing requires attentional resources, which are limited.

Memory

Working Memory

A short-term system that keeps information active and processes it. Capacity is limited (about 4–7 units). Alan Baddeley's model includes:

Phonological loop – Verbal information
Visuospatial sketchpad – Visual and spatial information
Central executive – Coordinates and controls
Episodic buffer – Integrates information

Long-term Memory

More permanent information storage, practically unlimited capacity:

Declarative memory – Consciously remembered
- Episodic: Personal experiences
- Semantic: General facts and concepts
Procedural memory – Skills and habits (often unconscious)

Encoding and Retrieval

Encoding: Converting information into a storable format Retrieval: Recovering information from memory Encoding specificity: Retrieval works best when the retrieval context matches the encoding context

Consolidation

The process where memory stabilizes over time. The hippocampus is initially critical, but memories gradually transfer to the cortex. Sleep is important for consolidation.

Language

Linguistic Competence vs. Performance

Competence: Linguistic knowledge that enables language use Performance: Actual language use, affected by memory, attention, and other constraints

Syntax, Semantics, Pragmatics

Syntax: Language structure, word order, grammatical rules Semantics: Meaning, the content of words and sentences Pragmatics: Language use in context, speech acts

Linguistic Universal

A feature that occurs in all world languages. Chomsky argued that universals reflect innate grammar.

Thinking and Decision-Making

Heuristics

Quick, rule-of-thumb reasoning strategies. Often effective but can lead to systematic errors.

Availability heuristic: We estimate probability based on how easily examples come to mind
Representativeness heuristic: We judge how much something resembles a typical example
Anchoring: Estimates are biased by initial values

Cognitive Bias

Systematic deviation from rational reasoning. Tversky and Kahneman documented dozens of biases affecting decision-making.

Dual Process Theory

Theory that thinking has two systems:

System 1: Fast, automatic, intuitive
System 2: Slow, effortful, analytical

Neuroscience

Neuron

A brain cell that sends and receives signals. The brain contains about 86 billion neurons.

Synaptic Plasticity

The ability of synapses (connections between neurons) to change with use. "Neurons that fire together, wire together."

Brain Regions

Prefrontal cortex: Decision-making, planning, impulse control
Hippocampus: Memory formation
Amygdala: Emotion processing
Visual cortex: Visual information processing
Broca's area: Speech production
Wernicke's area: Speech comprehension

AI and Cognitive Science

Neural Network

An AI model inspired by brain structure. Consists of layered connected "neurons."

Deep Learning

Training multi-layered neural networks on large amounts of data. The foundation of current AI.

Symbolic vs. Subsymbolic

Symbolic AI: Uses explicit rules and symbols Subsymbolic AI: Uses distributed representations (neural networks)

Philosophical Concepts

Qualia

The subjective quality of experience. What something feels like from the inside. E.g., the redness of red, the painfulness of pain.

Hard Problem (of Consciousness)

Why does subjective experience exist at all? Why does information processing feel like something?

Functionalism

The philosophical view that mental states are defined by their functional role—that is, how they relate to inputs, outputs, and other mental states.

What Is Cognitive Science? – Basics
Cognitive Psychology – Psychological concepts
Neuroscience and Cognition – Neuroscience concepts
Consciousness and Cognitive Science – Philosophical concepts
AI and Cognitive Science – AI concepts