UNIT- 5

Cognition
Unit 1: Elements of thought
Concepts
Concepts: Represent a class of objects or events, allowing for abstract thinking
without focusing on distracting details.
Concept Formation: The process of categorizing information into meaningful groups,
involving both positive and negative instances.
Experience-Based Learning: People learn concepts through experiences, such as
understanding the category of "dog" or "cat."
Rule-Based Learning: Adults often form concepts through rules (e.g., a triangle has
three straight sides), which is more efficient than examples.
Examples' Role: While rule learning is efficient, examples still play an essential role
in accurately categorizing complex concepts (e.g., different music genres).

Types
1. Conjunctive Concept: A concept that is defined by a set of attributes, every
member of which must be present for the concept to apply.
For example, the concept brother requires the joint presence of the attributes (a) male
and (b) sibling, neither of which may be omitted.
2. Disjunctive Concept: A concept that is based on possession of any one of a
set of attributes. These concepts are harder to learn due to their flexibility.
For example, A vehicle is considered an emergency vehicle if it has a siren, flashing
lights, or is marked as an ambulance/police car.
A car with flashing lights but no siren is still an emergency vehicle under this concept.
3. Relational Concept: Objects classified based on their relationship to
something else or between features (e.g., "sister" is defined by the relationship
to another person with the same parents).

Prototype
 In concept formation, the best or average exemplar of a category.
For example, the prototypical bird is some kind of mental average of all the different
kinds of birds of which a person has knowledge or with which a person has had
experience.
 We determine the difference between similar concepts (e.g., tall cup vs. vase)
by mentally comparing them to an "ideal" model.
 Identifying concepts can be challenging when a relevant prototype is not
available.
Concepts have two types of meaning:
Denotative Meaning: The direct, explicit, and literal meaning of a word or
phrase, as found in a dictionary, without any emotional or cultural associations.
Connotative Meaning: The secondary, implied, or emotional associations that
a word or phrase evokes in a specific context or culture, beyond its literal
definition.
Example - Word: Home
1. Denotative Meaning: A building or place where one resides or lives.
2. Connotative Meaning: Warmth, security, comfort, or a sense of belonging.

Concept formation
The process of forming a general idea or concept by observing specific examples.
This involves identifying the key features or characteristics that define a group or
category (e.g., what makes something a bird) or are necessary to recognize
members of that group (e.g., what defines a triangle, the idea of "above," or the
action "move").
The process of concept formation involves four elements: experience, abstraction,
generalization, and analysis.
 Experience: Direct participation in an action to gather information.
 Abstraction: Identifying common elements in various situations after experiencing
them.
For example, a child may initially apply the word "dog" to any four-legged animal, as
they recognize the common element of "four-leggedness."
 Generalization: Expanding the concept to include objects with a quality in
common, even if they have not been directly experienced, refining through trial
and error.
 Analysis: A systematic procedure, similar to task analysis, used to examine and
understand concepts, often applied in academic content or job design.
Mediation Theory: Focuses on the process of forming connections between things
that were previously not connected.
Hypothesis-Testing Theory: Views concept formation as an active process where
individuals generate hypotheses about stimuli, test them, and either accept or discard
them, formulating new ones in the process.

Propositions
 According to Anderson (1990, 2009), the meaning of a sentence can be
represented by a propositional network, which is a pattern of interconnected
propositions.
 A proposition is the smallest unit of knowledge that can be judged as true or
false. For example, "white cat" is not a proposition because its truth value
cannot be determined.
 Examples of propositions:
1. Susan gave a cat to Maria.
 2. The cat was white.
3. Maria is the president of the club.
 These propositions can be combined into a sentence like "Susan gave a white
cat to Maria, who is the president of the club."
 A propositional network represents the relationships between propositions
using nodes (representing the propositions) and links (arrows representing
relationships).
 Propositions are abstract and do not represent specific wording, but instead the
underlying meaning.

Mental imagery.
Mental imagery is the capacity to form vivid images in the mind. It plays a vital
role in various cognitive functions, shaping our perception, memory, problem-
solving abilities, emotional regulation, and motor skills.

Key Functions:
Memory and Learning:
 Enhances encoding and recall by forming strong mental associations.
 Helps organize and consolidate information, aiding learning and retention
(Sternberg,
Problem-Solving:
Facilitates visualization of solutions, promotes creative thinking, and assists in
evaluating options by simulating outcomes (Eysenck, 2012; Sternberg, 2016).
Emotional Regulation:
 Influences emotional responses by recalling positive or calming images.
 Aids in empathy and stress management through guided imagery techniques
(Goldstein, 2014).
Motor Control and Performance:
Enhances motor skills and mental rehearsal, improving performance, particularly
under pressure, which is frequently used by athletes and performers.

Theories of concept formation and mental imagery

Analog coding
Mental Representation: Analog coding creates mental images that closely resemble
the sensory or physical aspects of the original object or scene.
Sensory Similarity: The mental representation is similar to how we actually see or
experience things in the real world.
Continuous Representation: Analog coding allows for a sensory-like, continuous
depiction, forming a “picture” or “map” in the mind.
How it Works:
 Visualization of Scenes: Enables detailed, pictorial visualization of scenes in the
mind.
 Creation of Mental Images: Allows creation of mental images that capture an
object’s size, color, layout, and movement.
 Support for Spatial Tasks: Assists in tasks such as visualizing spatial layouts,
mentally rotating objects, and recalling images.
Example:
1. Detailed Imagery: Instead of verbal description, analog coding enables
visualization of specific details (e.g., dog’s color, movement of trees).
2. Sensory Elements: Includes sensory aspects like sounds (leaves rustling) and
visual effects (sun filtering through branches).
3. Lifelike Experience: The imagery resembles a video or snapshot, making the
scene feel realistic.
Research (Kosslyn, Ball, & Reiser’s Image Scanning Experiment):
Experiment Setup: Participants memorized a fictional map with landmarks (e.g.,
a hut, tree, rock).
Mental Travel Task: Participants mentally traveled between landmarks on the
map.
Key Finding: Time to mentally travel increased with greater mental “distance”
between landmarks.
Support for Analog Coding: Demonstrates that mental representations mimic
physical space, with mental distances behaving like real-world distance
Advantages of Analog Coding:
1. Rich Representation: Enables detailed and vivid mental imagery, aiding tasks
like mental rotation and spatial puzzle-solving.
2. Holistic Understanding: Provides a “big picture” perspective, useful for
grasping complex scenes or environments.
Disadvantages of Analog Coding:
1. Cognitive Load: Requires significant working memory and cognitive resources,
making complex tasks harder to manage.
2. Ambiguity: Visual images can be imprecise and interpreted differently, leading
to subjective representations and possible misunderstandings

Propositional coding
Abstract Representation: Information is stored in an abstract, language-like format.
Use of Propositions: Information is represented as propositions—statements or
descriptions about an object or scene.
Non-Visual Format: Does not resemble the physical appearance of the object;
instead, it uses logical statements or descriptions.
How it Works:
 Descriptive Units: Information is divided into smaller, descriptive units called
propositions.
 Structured Relationships: Propositions represent relationships between concepts
in a logical structure.
 Mental Description: Instead of visualizing, the scene is mentally “described”
through propositions that capture key details
Example:
1. Use of Statements: The scene is represented through descriptive statements
(e.g., “The dog is running,” “The dog is brown”).
2. Fact-Based Processing: Focuses on processing facts or descriptions rather
than visualizing.
3. Non-Visual Representation: Information is mentally handled as abstract
descriptions rather than pictorial images.
Research (Reed’s Pattern Extraction Experiment):
 Experiment Design: Participants determined if a specific pattern was part of a
previously seen image.
 Results: Participants performed only slightly better than chance, suggesting
limited recall of detailed images.
 Support for Propositional Coding: Findings suggest that participants stored
abstract descriptions (e.g., “triangle on top of a square”) rather than detailed
images, aligning with propositional coding theory.
Advantages of Propositional Coding:
1. Clarity and Precision: Offers clear, unambiguous representations, making it
easier to manage complex ideas and relationships.
2. Logical Relationships: Well-suited for representing abstract or non-visual
information, such as cause-effect chains or complex logical sequences.
Disadvantages of Propositional Coding:
1. Oversimplification: Converting experiences into abstract propositions can
eliminate sensory details, limiting the depth of understanding for richly detailed
information.
2. Rigid Structure: The structured nature of propositional coding may reduce its
adaptability, making it less useful for creative tasks or situations requiring
flexible thinking.

Theories Related to Analog and Propositional Coding:

1. Dual-Code Theory (Paivio, 1971):
 Two Processing Systems: Paivio proposed that we have distinct systems for
verbal (language) and non-verbal (images) information.
 Verbal Code: Used for language-based information (e.g., words, sentences).
 Non-Verbal (Imagery) Code: Used for mental images (e.g., picturing an apple).
 Interaction: These systems work together to enhance memory by using both
verbal and visual pathways.

2. Propositional Theory (Pylyshyn, 1973):

 Propositional theory is an alternative to the dual-code theory, proposing that
mental representations are not stored as images or words but as abstract
propositions.
 Mental images are considered epiphenomena—secondary effects resulting from
more basic cognitive processes.
 A proposition represents the underlying meaning of relationships among
concepts, rather than visual or verbal forms.
 Researchers like Anderson and Bower have developed more complex models
incorporating multiple forms of mental representation.
 Some proponents, such as Pylyshyn (2006), continue to strongly support the
original conceptualization of propositional theory.

Unit 2: Models of knowledge organization (in semantic

memory):
In cognitive psychology, understanding how information is represented and organized
in the brain is essential for grasping processes like learning, decision-making, and
problem-solving. Knowledge is generally divided into two main types:
1. Declarative Knowledge (Explicit Memory): Information that can be
consciously recalled, including:
o Episodic Memory: Personal experiences and specific events (e.g.,
remembering your birthday).
o Semantic Memory: Factual information and general knowledge (e.g.,
knowing Paris is the capital of France).
2. Procedural Knowledge (Implicit Memory): Knowledge of how to perform
tasks without conscious thought, such as motor skills (e.g., riding a bicycle) and
habitual actions (e.g., typing)

Declarative Knowledge Organization:

 Concepts are fundamental units that represent ideas, such as an "apple" concept
that might connect to roundness, redness, or fruit.
 Categories group similar items, helping the brain organize and retrieve
information. For instance, apples fall under the fruit category.
 Schemas provide complex frameworks for organizing knowledge. For example, a
kitchen schema includes concepts like "stove," "fridge," and "utensils."
Different Category Types include:
 Natural: Occurring naturally, like trees.
 Artifact: Man-made, like kitchen appliances.
 Ad Hoc: Created for specific purposes, like “things you need to write a paper.”
 Nominal: Defined by set rules, like “bachelor.”
Theories on Concept Formation:
 Classical View: Based on necessary and sufficient features. For example, a
bachelor must be an adult, male, and unmarried.
 Prototype View: Concepts have idealized examples (prototypes) that capture the
essence of a category, like a robin as a typical bird.
 Exemplar View: Categorization is based on specific stored instances or examples.
 Schemata View: Combines prototypes and exemplars, with schemas acting as
frameworks.
 Knowledge-Based View: Categorization is rooted in context and purpose rather
than features alone.

Procedural Knowledge Organization:

 Often hierarchical, with general procedures broken into specific routines (e.g.,
riding a bike requires balancing, pedalling, and steering).
 Represented by "if-then" production rules, such as “If the light is green, then go.”
 With practice, procedural tasks become automatic, such as driving or typing.

Brain Areas Associated with Knowledge:

 Hippocampus: Key for consolidating declarative knowledge.
 Prefrontal Cortex: Involved in working memory and decision-making.
 Basal Ganglia: Supports procedural learning and motor tasks.
 Parietal Lobe: Deals with spatial knowledge and attention.
 Cerebellum: Important for fine motor skills and procedural tasks.

Prototype
 Concepts are mental representations of categories that help us make sense of the
world.
For example, concept of fruit apples, bananas, or mangoes.
 Conceptualization is the mental process through which we group or classify
information into categories. This process simplifies our cognitive load by organizing
complex data into familiar and manageable units. It helps individuals create
structured understanding, making it easier to store and retrieve information
 Two prominent approaches are the prototype and feature comparison models
 Each approach offers a different way of understanding how our brains organize
concepts, objects, and categories

Prototype Theory:
 Proposed by Eleanor Rosch in 1973.
 The theory suggests that categorization is based on similarity to a central or most
typical example, known as the prototype
 Prototype is an item that is the most representative or ideal example of a category.
Eg. Sparrow- prototype of bird
 When we see a new item, we compare it to the prototype.
 If it closely matches the prototype, we categorize it as part of the group.
 It also emphasizes that members of a category differ in their prototypicality, or
degree to which they are prototypical.
A robin and a sparrow are very prototypical birds, whereas ostriches and penguins are
non-prototypes
 According to this theory there are various levels of categorization in semantic
memory. An object can be categorized at several different levels
 Experiments Supporting Prototype Theory
Rosch's Experiments (1975): Rosch conducted a series of experiments to show that
people categorize objects based on similarity to a prototype.
In one famous experiment, she asked participants to rate how well different examples
fit into a category. “Robins” and “sparrows” were rated as better examples of birds
than “penguins” or “ostriches”, supporting the idea of prototypicality
Strengths
 Helps categorize things easily by focusing on a typical example (prototype) instead
of strict rules.
 Explains how people classify everyday items, even when examples aren’t clear-cut.
 Reduces the need to remember all details of every item by focusing on the most
typical member.
Limitations
 Prototypes can vary depending on culture or situation, making categories less
consistent.
 It’s hard to clearly define where a category begins or ends.
 Doesn’t work well for abstract or unique items that don’t fit a typical example

Feature Comparison Model

 Proposed by Smith, Shoben, and Rips in 1974
 According to this theory, concepts are stored in memory according to a list of
necessary features or characteristics.
 People use a decision process to make judgments about these concepts
 Features used in this model are either defining features or characteristic features.

Defining features are those that must be present for an item to fit (feathers for
birds).

Characteristic features are typical but not essential (flying for most birds).
A parrot has defining features (feathers, wings) and characteristic ones (mimicking
speech).
Experiments Supporting Feature Comparison
Smith et al. (1974) used the sentence verification task to test this model.
In this task, participants were asked to verify statements like "A robin is a bird" or "A
penguin is a bird.“
People responded faster to typical examples (like 'A robin is a bird’) and slower to
atypical examples (like 'A penguin is a bird'), supporting the model's explanation of
the typicality effect
Strengths
 Uses specific features to decide if something belongs in a category, making it easy
to follow.
 Best for categories with strict rules, like shapes or objects with clear-cut features.
 Provides reliable categorization when features are straightforward.
Limitations
 Can’t handle categories that have variety or don’t fit exact rules.
 Requires a lot of thought to compare every feature for complex items.
 People usually rely on similarity, not strict features, to group things in real life

Prototype theory Feature Comparison theory

Focuses on the most typical example Emphasizes a list of features
Items are categorized by comparing Items are categorized based on the
them to prototype. presence or absence of specific features.
Allows variability More rigid
Reduces cognitive load Can increase cognitive load as it requires
checking multiple features.
Heavily influenced by personal Less influenced by experience
experiences
Useful in everyday categorization and Useful in formal logic and specific
language processing categorization tasks.
Hierarchical model
The hierarchical model of semantic memory suggests that information in our
long-term memory is organized in a structured, hierarchical way. This model was
developed by Collins and Quillian in the 1960s and is based on the idea that
concepts are stored in a network of nodes, with more general information at higher
levels and more specific information at lower levels.
Key Features:
 Nodes: Each concept or piece of information is stored as a "node" in a semantic
network. For example, the concept of "animal" or "bird" would be nodes.
 Hierarchical Structure: Concepts are arranged hierarchically, with more abstract
or general concepts at the top and more
specific concepts below them.
For instance:
"Animal" is a broader category.
Under "Animal," you would have more
specific concepts like "Bird" and "Mammal."
Under "Bird," you might have specific
examples like "Sparrow" or "Eagle."
 Cognitive Economy: The idea of cognitive economy suggests that properties or
attributes of a concept are stored at the highest level where they apply. For
instance, the property "has wings" would be stored at the "Bird" level rather than
repeating it for every specific bird like "Sparrow" or "Eagle."
 Inheritance: Lower-level nodes inherit properties from higher-level nodes. So, if
you know that "Bird" has "wings," you don’t need to explicitly store this fact for
every type of bird; they inherit this characteristic from the "Bird" category.
 Spreading Activation: When a concept is activated (e.g., you think of "bird"),
activation spreads through the network to related concepts, such as "wings,"
"feathers," and "fly."
Example:
 Top level: "Animal"
 Middle level: "Bird," "Mammal"
 Lower level: "Sparrow," "Eagle," "Dog," "Cat"
Strengths:
 Efficient use of memory by organizing concepts in a structured way.
 Explains how related concepts are linked and how activation spreads across a
network.
Weaknesses:
 Problems with exceptions: Some concepts don't neatly fit into this structure.
For example, a "penguin" is a bird, but it doesn’t fly, challenging the cognitive
economy of storing such exceptions at the top level.
 Flexibility: The model doesn’t account for more complex relationships or
overlapping concepts. It also struggles to explain how concepts can be linked in
non-hierarchical or associative ways (like "cat" and "dog" both being pets).
This model has been influential in understanding how semantic memory might be
structured, but later models have expanded and modified these ideas.

Connectionist Models - Parallel Distributed Processing

 Parallel Distributed Processing (PDP) Model: Developed by McClelland,
Rumelhart, and Hinton, the PDP model is a connectionist model that mimics the
brain’s neural network.
 Parallel Processing: Unlike traditional computing models that process
information sequentially, PDP uses parallel processing, where multiple processes
happen simultaneously across a network of nodes.

Key concept
 Nodes and Connections: In
the PDP model, concepts or
knowledge are represented by
nodes, which are
interconnected. The activation
pattern across nodes
represents knowledge.
 Excitatory and Inhibitory Connections: Some connections increase the
likelihood of activating a node (excitatory), while others decrease it (inhibitory),
allowing for adaptive learning.
 Learning Through Strengthened Connections: Connections strengthen or
weaken based on frequency and type of usage. Frequently activated pathways
become stronger, making information more accessible.

Functioning of PDP
 Pattern of Activation: Knowledge is represented by patterns of connections
across nodes, not stored in a single node. Activation flows through these
patterns, adjusting them as new information is processed, making memory
adaptive.
 Handling Incomplete Information: The PDP model can process incomplete or
degraded information, allowing the network to reconstruct correct or near-
correct patterns, showcasing cognitive flexibility.

Application
 Perception and Recognition: PDP explains how we recognize patterns,
objects, and language, even with partial information.
 Learning and Adaptation: The model is applied in artificial intelligence to
simulate how humans learn, remember, and adapt, including applications in
language learning and addressing conditions like dyslexia.

Criticism
 Complexity in Neural Realism: While the PDP model resembles neural
processing, it doesn't account for all neural properties and may not fully explain
how we handle unique or single events.
 Quick Adaptation Challenges: The model struggles to explain how we quickly
unlearn or adapt established patterns, which is often necessary in real-life
situations requiring rapid learning adjustments.

Networks models –Quillian

The Network Model of Semantic Memory proposed by Quillian (1966) is a
conceptual framework designed to explain how concepts and their relationships are
organized and stored in human memory. This model, also known as the Semantic
Network Model, suggests that concepts are represented as nodes within a network,
and that these nodes are connected to other related concepts or nodes.

Key Features of Quillian's Network Model:

 Nodes: Each concept or piece of information is stored as a "node" in the network.
A node represents a category or concept such as "Bird," "Animal," or "Dog."
 Links/Connections: These nodes are connected by links, which represent
relationships between concepts. The links can be of different types, such as:
o ISA links: Represent hierarchical relationships. For example, "Dog ISA
Animal" means a Dog is a type of Animal.
o HAS links: Represent attribute relationships. For example, "Dog HAS Fur"
means a Dog has fur.
 o Part-Of links: Represent part-whole relationships. For example, "Wheel IS
PART-OF Car."
 Hierarchical Structure: Similar to the hierarchical model, Quillian's model
assumes that concepts are organized in a hierarchy. More general concepts are at
the higher levels, and more specific concepts are stored beneath them. This allows
for cognitive economy, as higher-level nodes store general attributes that apply
to many concepts at lower levels.
For example, the property "has feathers" would be stored at the "Bird" level, so
specific birds (like "Sparrow" or "Eagle") would inherit this feature automatically
without needing separate storage for each one.
 Cognitive Economy: This refers to the principle that memory storage is optimized
by storing attributes at the highest level in the hierarchy where they apply. This
reduces redundancy and saves cognitive resources.
 Spreading Activation: When a concept is activated, the activation spreads across
the network to related concepts. For example, activating the node for "Dog" will
also activate related concepts like "Animal," "Pet," or "Fur." The activation spreads
from node to node, and the closer the nodes are to each other, the faster and
stronger the activation.
 Search Time and Distance: The model suggests that when we need to retrieve
information, the system searches through the network. The search time or
retrieval time depends on the distance between the nodes. The greater the
distance, the longer the retrieval time.
For example, to retrieve the fact that "a dog has fur," the system may need to
traverse through the "Dog" node to the "Animal" node and then find the relevant
attribute.
Example of the Network:
 Top level: "Animal" (with links to various animal categories like Bird, Fish,
Mammal)
 Middle level: "Bird" (with links to specific bird types like "Sparrow" or "Eagle,"
and properties like "has feathers")
 Lower level: "Sparrow" (specific instance of a bird, inherits properties from
"Bird" like "has wings," "can fly")

Strengths of Quillian's Network Model:

 Efficient storage: It accounts for how concepts and their properties are stored
in a way that minimizes redundancy.
 Explains relatedness: It provides an intuitive explanation for how concepts
are related and activated, such as the spreading activation process.
 Cognitive economy: It optimizes memory use by storing general properties at
higher levels, preventing the need for repetition.

Weaknesses of the Model:

1. Exceptions and Irregularities: The model struggles to handle exceptions. For
example, a "Penguin" is a bird but doesn’t fit neatly into the typical "has wings"
category, which may not be efficiently represented in this hierarchical network.
 2. Context and Flexibility: The model doesn’t easily account for the fact that the
meaning or relationship of a concept can change depending on context. For
example, the word "bat" might activate different concepts based on whether it's
referring to the flying mammal or a sports equipment.
3. Distance in the Network: While the model emphasizes search times based on
network distance, it doesn't always align well with human experience. Some
associations seem to be faster than the direct distance between nodes might
suggest.

Spreading Activation - Collins & Loftus

Roots in Quillian’s Model (1960s):
Quillian proposed that memory functions
through a hierarchical structure, where
information retrieval occurs by activating nodes
that spread through connected nodes. This
model was initially developed to simulate
human semantic memory in computers.
Collins and Loftus's Advancements
(1975): Building on Quillian’s model, Collins
and Loftus introduced more flexibility by
suggesting that memory is organized by
semantic relatedness. Concepts are stored
based on associative strength rather than fixed
hierarchical categories.

Key components
Nodes and Links: Concepts are represented as nodes (e.g., "dog," "bird," "car"), and
links show relationships between them. Related concepts are connected by stronger
links.
Connection Strength: The strength of links varies based on the frequency and
closeness of the association. Stronger links occur between more closely related
concepts (e.g., "bird" and "canary" vs. "bird" and "animal").

How Activation Spreads:

Non-Hierarchical Spread: Activation can move across various links, and the spread
is faster for stronger, frequently accessed connections. When one node (e.g., “bird”) is
triggered, it activates closely related nodes (like “crow” or “canary”) more rapidly
than less connected nodes (e.g., “penguin”).
Priming Effects: The model accounts for priming effects, where activation of a node
facilitates faster retrieval of related nodes. For instance, seeing or hearing “bread”
may make related nodes like “butter” or “toast” more accessible.

Application
Memory Recall: The theory explains how one memory can trigger related memories,
as seen when thinking of "beach" might evoke "vacation" and "sunshine."
Language Processing and Understanding: It explains how we comprehend
language by activating networks of related words, helping us process language in
context.
 Strengths
Explains the Typicality Effect: Common or typical examples of a category (e.g.,
"sparrow" vs. "penguin") are recognized faster because they have stronger associative
links to the category.
Category Size Effect: Smaller categories (e.g., "fruit") are easier to recall than
broader ones (e.g., "living things") because activation spreads faster within a more
specific, closely-knit network.

Limitations
Difficulty in Prediction: The theory's adaptability makes it hard to test scientifically,
as it lacks clear, testable predictions, making it challenging to design experiments for
confirmation or refutation.
Descriptive Nature: The model is more descriptive than predictive, explaining
memory retrieval in broad terms but lacking precision in predicting specific behaviors.

Schemas
A collection of basic knowledge about a concept or entity that serves as a guide to
perception, interpretation, imagination, or problem solving.
For example, the schema “dorm room” suggests that a bed and a desk are probably
part of the scene, that a microwave oven might or might not be, and that expensive
Persian rugs probably will not be. Also called cognitive schema.

Unit 3: Reasoning
Inductive reasoning
Inductive reasoning is a logical process that starts with specific observations or
examples and generalizes them to form broader conclusions.
Specific to General: Starts with specific observations or examples and generalizes
them to form broader conclusions.
Bottom-Up Approach: Moves from particular instances to general rules or theories.
Probabilistic Conclusion: Unlike deductive reasoning, inductive reasoning does not
guarantee certainty but provides a probable or well-supported conclusion.

Key Concepts:
Probabilistic Nature: Inductive reasoning leads to conclusions that are likely but
not guaranteed, relying on observed patterns or data for support.
Forms of Inductive Reasoning:
1. Generalization: Drawing broad conclusions from specific observations (e.g., "All
swans are white" from observed swans).

2. Causal Inference: Inferring cause-and-effect relationships based on correlations

or patterns.

3. Reasoning by Analogy: Drawing parallels between similar cases to predict

outcomes (e.g., past economic policies improving the economy might do so again).
Cognitive Aspects of Inductive Reasoning:
 Involves generating and testing hypotheses based on data, and is more prone to
errors than deductive reasoning due to reliance on observation.
 Dual-Process Theory: Two cognitive systems are involved:
1. Associative System: Based on pattern recognition and past experiences.
2. Rule-Based System: Involves structured, deliberate analysis.
Applications: Inductive reasoning is essential in fields like scientific research,
medical diagnosis, AI, business analytics, and everyday decision-making, especially
for forming hypotheses and predictions without definitive data.
Limitations and Biases:
 Confirmation Bias: Tendency to focus on evidence that supports existing beliefs,
ignoring contradictory data.
 Overgeneralization: Making broad conclusions from limited observations, which
may not always be accurate.

Deductive reasoning
Deductive reasoning is a logical process that begins with a general statement or
hypothesis and moves towards a specific conclusion.
General to Specific: Begins with a general statement or hypothesis and moves
toward a specific conclusion.
Top-Down Approach: Follows a logical, structured process.
Logical Validity: If the premises are true, the conclusion must be true.
Applications: Commonly used in fields that require accuracy and certainty, such as
mathematics, law, and formal logic.

Key Concepts:
Logical Structure: Deductive reasoning relies on clear premises, where the
conclusion is contained within them, ensuring that if the premises are true, the
conclusion must follow.
Validity and Soundness:
 Validity: Refers to the logical structure of an argument, where if the premises are
true, the conclusion must logically follow, regardless of the truth of the premises.
 Soundness: Requires both validity and true premises to guarantee a true
conclusion.
Forms of Deductive Reasoning:
 Syllogistic Reasoning: Uses syllogisms, with two premises leading to a
conclusion. Examples include:
1. Universal Affirmative: "All A are B."
2. Universal Negative: "No A are B."
3. Particular Affirmative: "Some A are B."
4. Particular Negative: "Some A are not B."
5. Example: "All mammals have lungs. Dogs are mammals. Therefore, dogs
have lungs."
 Propositional Reasoning: Connects propositions through logical connectors like
“if-then” or “and.” Key rules include:
1. Modus Ponens: If P → Q and P is true, then Q must be true.
2. Modus Tollens: If P → Q and ¬Q (not Q) is true, then ¬P must also be true.
Cognitive Processes: Deductive reasoning involves cognitive effort, especially
with complex statements. Cognitive load can affect performance and introduce errors,
especially when dealing with complex syntax or negations.
Applications: Vital in fields requiring precise conclusions, such as mathematics,
formal logic, and legal reasoning, ensuring consistency with established principles.

Cognitive errors
Cognitive errors are common thinking mistakes that occur because our minds
rely on shortcuts or assumptions when processing information.

Types of Cognitive Errors:

1. Mental Set: This error occurs when individuals stick to familiar problem-solving
strategies, even when they no longer work. Example: In Luchins's water jar
problem, participants continued using a complex method instead of a simpler
solution.
2. Functional Fixedness: This error happens when individuals can't see
alternative uses for an object beyond its typical function. Example: In Duncker’s
candle problem, participants struggled to use a box of tacks creatively to mount
a candle on the wall.
3. Stereotypes and Stereotype Threat: Stereotypes are overgeneralized beliefs
about a group’s traits. Stereotype threat occurs when individuals perform worse
due to awareness of negative stereotypes about their group. Example: The
stereotype that "boys are better at math than girls" can negatively affect girls’
math performance.

Cognitive Biases:
1. Illusory Correlation: People perceive relationships between unrelated events,
often because certain events are memorable, leading to false associations (e.g.,
linking certain behaviors with specific personality traits).
2. Overconfidence: People overestimate the accuracy of their judgments or
knowledge, often due to a lack of understanding of the situation's complexity or
reliance on limited information.
3. Myside Bias: Individuals process information in ways that confirm their existing
beliefs, which can hinder objective evaluation of opposing views.
4. Hindsight Bias: After an event, people believe they "knew it all along" and
misremember their original judgment, falsely attributing foresight to
themselves.

Common Cognitive Fallacies:

 Gambler’s Fallacy: The belief that a random event is influenced by previous
events (e.g., thinking a coin is more likely to land heads after multiple tails in a
row).
 Sunk Cost Fallacy: People continue investing in a failing endeavor because they
have already invested resources, making it harder to stop even when it's no longer
rational.

Unit 4: Creativity
The mental processes leading to a new invention, solution, or synthesis in any area. A
creative solution may use preexisting elements (e.g., objects, ideas) but creates a new
relationship between them. Products of creative thinking include, for example, new
machines, social ideas, scientific theories, and artistic works.
Types of Thinking: Thought can be:
 Inductive (from specific facts to general principles)
 Deductive (from general principles to specific situations)
 Logical (based on explicit rules)
 Illogical (intuitive or associative)
Creative Thinking - features Creative thinking uses all these types of thought (in
various combinations) along with:
Fluency - The ability to generate ideas, words, mental associations, or
potential solutions to a problem with ease and rapidity. It is usually considered
to be an important dimension of creativity.
Flexibility - Flexibility is defined as the number of times you shift from one
class of possible uses to another.
Originality - Originality refers to how novel or unusual your suggestions are

Measuring Creativity: Creativity can be assessed by counting instances of fluency,

flexibility, and originality, which together indicate one’s capacity for divergent
thinking.

Stages of Creative Thought.

A good summary of the sequence of events in creative thinking proposes five stages
that usually occur. Of course, creative thought is not always so neat. Nevertheless, the
stages listed are a good summary of the most typical sequence of events.
a) Orientation. As a first step, the problem must be defined and important
dimensions identified.
b) Preparation. In the second stage, creative thinkers saturate themselves with
as much information pertaining to the problem as possible.
c) Incubation. the gradual generation of a solution to a problem at a
nonconscious or semiconscious level, often after an attempt at a conscious,
deliberate solution has failed.
d) Illumination. a moment of insight, such as the nature and processes of an
interpersonal relationship, the solution to a problem, or about the understanding
of an event.
 e) Verification. The final step is to test and critically evaluate the solution
obtained during the stage of illumination. If the solution proves faulty, the
thinker reverts to the stage of incubation.

The Creative Personality.

 IQ and Creativity: Creativity is not strongly linked to IQ. People with normal or
above-average intelligence vary in creativity, showing little correlation between the
two.
 Knowledge and Interests: Creative individuals have a wide range of knowledge
and interests, and they can fluently combine ideas from various sources.
 Openness to Experience: Creative people are open to irrational thoughts and are
comfortable expressing their emotions and fantasies.
 Focus on Symbolic Thought: They are drawn to ideas, concepts, and
possibilities, often valuing truth, beauty, and form over external recognition or
success.
 Independence and Complexity: Creative people value independence,
complexity, and originality, particularly in their work, though they are typically not
eccentric or bizarre outside of it.
 Common Traits of Creative People:
o Strong awareness of people, events, and problems
o High verbal fluency
o Flexibility with numbers, concepts, and social situations
o Originality, humor, and unique expressions
o Ability to abstract, organize, and synthesize
o High energy and persistence in tasks of interest
o Dislike for routine tasks
o Willingness to take risks
o Vivid imagination, often expressed in childhood through “fibbing” or
imaginary companions
This profile highlights that creative people are generally well-rounded, open-minded,
and engaged in the world around them, challenging common stereotypes about
creativity.

Convergent & divergent thinking

Convergent thinking
Critical thinking in which an individual uses linear, logical steps to analyze a number
of already formulated solutions to a problem to determine the correct one or the one
that is most likely to be successful.

Divergent thinking
Creative thinking in which an individual solves a problem or reaches a decision using
strategies that deviate from commonly used or previously taught strategies. This term
is often used synonymously with lateral thinking.
Creativity indeed goes beyond just divergent thinking. While divergent thought is
essential for generating novel and varied ideas, true creativity requires additional
qualities:
1. Utility and Meaningfulness: Creative solutions need to be not only original
but also useful and meaningful, addressing the demands of the problem
effectively.
2. Critical Thinking and Reasoning: Creativity involves applying reasoning and
critical thinking to refine and evaluate novel ideas, distinguishing innovative
solutions from impractical or far-fetched ones.
3. Integration of Multiple Thinking Styles: Creativity combines divergent
thinking with convergent thinking, logical analysis, and practical assessment to
transform original ideas into valuable solutions.

Productive and reproductive thinking

These two types of thinking, originally distinguished by Gestalt psychologists
like Max Wertheimer, emphasize different approaches to problem-solving and
creativity.

Productive thinking
Productive thinking is the process of creating new solutions or insights that go beyond
routine knowledge, requiring the individual to restructure or reframe the problem to
find novel perspectives or creative solutions. It emphasizes innovation and originality.
Approach: Unlike linear problem-solving, productive thinking involves a holistic
reorganization of the problem space, often leading to an "aha!" moment when the
solution suddenly becomes clear.
Key characteristics
 Divergent Thinking: Productive thinking explores many possible solutions,
encouraging creativity by generating unconventional ideas and breaking away from
standard methods.
 Insight and Reorganization: It often involves insightful leaps that reframe a
problem, allowing the solution to emerge clearly by reorganizing the mental
representation of the problem.
 Going Beyond Familiar Knowledge: Productive thinking pushes beyond existing
knowledge, essential for innovation and tackling ambiguous, complex problems
where conventional solutions fall short.
Examples
 Science and Theory Development: Productive thinking helps scientists
create new theories or frameworks that offer fresh perspectives, such as
Einstein’s theory of relativity.
 Business and Product Design: It drives the creation of new products,
services, or business models that meet evolving consumer needs, like the shift
to eco-friendly products.
 Complex Problem-Solving: Addressing complex social issues, such as poverty
or climate change, often requires productive thinking, as conventional
approaches are insufficient for solving these challenges.
Cognitive process involved in productive thinking
 Creative Problem-Solving: Involves finding unique, previously unconsidered
solutions to problems.
 Cognitive Flexibility: The ability to view problems from multiple perspectives,
enabling alternative solutions.
 Holistic Analysis: Viewing the problem as an integrated whole rather than
separate parts, as emphasized in Gestalt psychology.
Advantages
 Innovation and Originality: Productive thinking fosters the discovery of
groundbreaking solutions and advancements that propel fields forward.
 Adaptability: It enables individuals to tackle complex, ill-structured problems
that don’t have clear goals or solutions.
Limitation
 Time-Consuming: Productive thinking can take longer because it involves
exploring multiple solutions and reorganizing how the problem is viewed.
 Mental Demand: It requires significant cognitive effort, as it involves moving
away from ingrained thinking patterns and dealing with uncertainties.

Reproductive thinking
Reproductive thinking involves applying established knowledge and solutions to
familiar problems. It relies on memory and past experiences rather than developing
new insights. This thinking is efficient in well-defined situations.
Common in Routine Tasks: It is used in tasks with clear goals and known solutions,
relying on previously learned patterns and established methods that are solidified
through repetition and practice.
Key characteristics
 Memory Retrieval: Solutions are often retrieved directly from memory, based
on past experiences or learned responses.
 Routine Application of Known Methods: Reproductive thinking involves
applying familiar techniques without generating new approaches, making it
effective for structured tasks.
 Reliance on Previous Knowledge: Solutions are based on known methods,
using established rules or algorithms to arrive at an answer, rather than
exploring new possibilities.
Examples
 Solving Math Problems with Formulas: Applying memorized formulas to
solve standard algebra problems is an example of reproductive thinking.
 Technical Tasks: Following a predefined set of steps to repair equipment
exemplifies reproductive thinking.
 Routine Administrative Tasks: Tasks like data entry, scheduling, or
bookkeeping rely on established protocols, requiring little new problem-solving.
Cognitive process involved
 Recall and Memory Retrieval: Reproductive thinking relies on retrieving
information or methods from long-term memory for efficient problem-solving.
  Linear, Step-by-Step Approach: Solutions are reached by following a
predefined sequence of steps that are known to be effective.
 Use of Algorithms and Heuristics: Problem-solving often involves applying
algorithms or heuristics that simplify the process.
Advantages
 Efficiency and Speed: Reproductive thinking is quick and efficient, making it
ideal for routine tasks that require established methods.
 Consistency and Reliability: It produces consistent and reliable results, as it
applies known methods with predictable outcomes.
Limitations
 Efficiency and Speed: Reproductive thinking is quick and efficient, making it
ideal for routine tasks that require established methods.
 Consistency and Reliability: It produces consistent and reliable results, as it
applies known methods with predictable outcomes.

Insight
Insight refers to the sudden and clear understanding of a solution to a problem,
often without obvious reasoning. The process of reaching the solution may not
always be clear, even after reflection.

Types of Insights (Sternberg & Davidson):

 Selective Encoding Insights: Identifying which information is relevant and
which is irrelevant to the problem.
 Selective Comparison Insights: Recognizing which information from long-
term memory is pertinent for solving the problem.
 Selective Combination Insights: Combining relevant information in a new
way to form a solution.

Unit 5: Psycholinguistics:
A branch of psychology that employs formal linguistic models to investigate language
use and the cognitive processes that accompany it.
Developmental psycholinguistics is the formal term for the branch that
investigates language acquisition in children. In particular, various models
of generative grammar have been used to explain and predict language acquisition in
children and the production and comprehension of speech by adults. To this extent,
psycholinguistics is a specific discipline, distinguishable from the more general area of
psychology of language, which encompasses many other fields and approaches.
1. Syntax: Refers to grammatical rules for organizing words into sentences,
focusing on sentence structure. Grammar encompasses both syntax and
morphology, examining word and sentence structure.
2. Semantics: Focuses on the meanings of words and sentences. Semantic
memory is our organized knowledge about the world.
3. Pragmatics: Studies the social rules underlying language use, considering the
listener's perspective. Pragmatics is essential for understanding how language
varies in social contexts.
Scope of Psycholinguistics: Includes topics like sound, meaning, grammar, and
social factors, reflecting language's complexity.
Research in Psycholinguistics: Covers a broad range, addressing issues in
comprehension, imperfect language, and neurolinguistics, with a historical perspective
on the field's development.

Linguistic relativity
Linguistic relativity, also known as the Sapir-Whorf hypothesis, is the idea that the
language people speak influences how they think about the world. It suggests that the
meanings of words and grammatical structures can differ between languages, and
that these differences can affect how people perceive and conceptualize the world.

Verbal deprivation hypotheses.

The Verbal Deprivation Hypothesis suggests that limited verbal interaction in
early childhood, especially in lower socioeconomic status (SES) environments,
may negatively impact language development.
Limited Verbal Stimulation: Children in verbally limited environments may miss
exposure to a rich vocabulary and complex sentence structures, which can hinder
linguistic skills.
Socioeconomic Factors: Lower SES families might face challenges like less time
for verbal engagement due to work demands or limited access to educational
resources, which can impact children's language development.
Long-term Effects: Reduced early verbal interaction can lead to ongoing deficits in
language skills, potentially affecting literacy, academic performance, and social
opportunities.
Criticism: Some argue that it’s not just the quantity but also the quality of verbal
interaction that matters. Factors like cultural communication styles, meaningful
engagement, and responsiveness also play crucial roles.

Stages of Language Development (based on Sternberg's

framework):
Pre-linguistic Stage (0-12 months): Babies respond to sound patterns, engage
in vocal play, and use gestures for communication.
Holophrastic Stage (12-18 months): Use of single words to convey ideas;
learning basic semantics.
Two-word Stage (18-24 months): Begin using two-word sentences, introducing
syntax.
Telegraphic Stage (24-30 months): More complex sentences, improved syntax,
and social interaction skills.
Complex Sentences Stage (3-5 years): Mastering sentence structure and
grammar; children engage in storytelling and hypothetical questions.
Refinement Stage (5 years and beyond): Advanced language use, including
idiomatic expressions and social adaptation.
Theories of language acquisition:
Skinner- behaviourism
Skinner's Behaviorist Explanation: Skinner (1957) explained language acquisition
through environmental influence and behaviorist reinforcement, where children learn
language by associating words with meanings, and correct usage is reinforced
positively.
Chomsky's Critique: Noam Chomsky argued that language acquisition cannot rely
solely on environmental input, as children need inherent tools to process an infinite
number of sentences.
Universal Grammar Theory: Chomsky proposed Universal Grammar, suggesting
innate grammatical categories (like nouns and verbs) that support language
development in children and processing in adults.

Chomsky (LAD) Lenneberg-genetic readiness.

Language Acquisition Device (LAD): Proposed by Noam Chomsky in the 1960s, the
LAD is an innate mental capacity that enables infants to acquire and produce
language.
Nativist Theory: The LAD is part of the nativist theory, which suggests that humans
have an inborn facility for language acquisition.
Argument from Poverty of the Stimulus: Chomsky argued that children's rapid
language acquisition implies significant innate grammar knowledge, as they receive
limited correction and instruction in their first language.

Critical thinking
A disciplined process of analyzing, evaluating, and synthesizing information to form
reasoned judgments.
Key Elements: Involves active, persistent, and careful consideration of beliefs or
knowledge, assessing evidence, and exploring possible conclusions.
John Dewey's Contribution: Dewey popularized the term and defined it as
"reflective thinking," highlighting its difference from passive thinking like rote
memorization or unsupported beliefs.
 Reflective Judgment: Involves evaluating beliefs and assumptions, making
informed judgments based on careful analysis rather than impulsive decisions. It
requires evidence to back conclusions.
 Understanding Over Memorization: Focuses on understanding principles rather
than just memorizing facts, enabling individuals to apply knowledge flexibly and
solve problems in various contexts.
 Raising Objections and Seeking Alternatives: Critical thinking involves
questioning assumptions, considering alternatives, and exploring counterexamples,
ensuring deeper understanding rather than accepting the first plausible answer.
 Self-Reflection and Evaluation: Good critical thinkers engage in self-reflection,
constantly evaluating their beliefs to avoid "mental laziness" and promoting a
search for new interpretations and solutions.
 Depth vs. Surface Thinking: Critical thinkers delve deeper, questioning and
analyzing answers from multiple perspectives to gain a nuanced understanding,
whereas surface thinkers stop once they find a reasonable answer.

Applications
1. Improved Problem-Solving: Critical thinking helps tackle complex problems
by understanding the core structure of issues and developing innovative
solutions when typical approaches fail.
2. Empowerment in Learning and Cognitive Development: Engaging in
critical thinking enhances cognitive abilities and improves learning outcomes by
fostering deep analysis and integration of information.
3. Promotes Independent Thinking: Critical thinking encourages individuals to
question information, form their own opinions, and navigate information
overload, which is essential for personal and intellectual growth.
4. Better Decision-Making: It leads to well-informed, rational decisions by
carefully analyzing facts and evidence, enabling individuals to weigh options
and predict outcomes effectively.

Challenges to Developing Critical Thinking

1. Mental Laziness: People often settle for superficial answers without exploring
alternatives, hindering critical thinking. Overcoming this requires active
engagement in tasks that require deeper analysis and reflection.
2. Over-Reliance on Rote Memorization: Traditional education’s focus on
memorization can limit students' ability to think critically. Emphasizing critical
thinking in education encourages a more inquisitive and reflective mindset.