Cognition: Thinking, Problem-Solving & Decision-Making

TAKEAWAYS:

• Cognition is the mental process of acquiring, storing, and using knowledge (perception, attention, memory, language, problem-solving).
• Problem-solving uses algorithms (step-by-step) or heuristics (mental shortcuts), but biases (e.g., confirmation bias) can distort decisions.
• Language is a cognitive tool: Chomsky’s theory explains innate grammar, while Whorf’s hypothesis links language to thought.
• Decision-making is influenced by emotions (amygdala) and logic (prefrontal cortex), seen in Ncell’s tariff choices or Daraz’s discount psychology.
• Cognitive biases (e.g., anchoring, availability) explain why users trust eSewa’s reviews or ignore NEPSE’s volatility.
• AI mimics cognition: Chatbots use rule-based systems (like early GPS navigation) or machine learning (like YouTube’s recommendation engine).

What Is Cognition?

Cognition is the mental process of acquiring, storing, transforming, and using information. It includes:

• Perception (interpreting sensory input)
• Attention (focusing on relevant stimuli)
• Memory (encoding, storing, retrieving)
• Language (symbolic communication)
• Problem-solving (finding solutions)
• Decision-making (choosing actions)

Why it matters: Cognition shapes how we interact with technology (e.g., designing intuitive apps) and make life choices (e.g., investing in NEPSE).

1. Attention: Selecting What to Process

Attention is the cognitive filter that prioritizes information. Types:

• Selective attention: Focusing on one task (e.g., ignoring traffic while driving in Kathmandu).
• Divided attention: Multitasking (e.g., texting while walking—risky!).
• Sustained attention: Long-term focus (e.g., studying for TU exams).

How it works: The cocktail party effect (Cherry, 1953) shows we filter irrelevant noise (e.g., hearing your name in a crowded room). The spotlight model (Posner) explains how attention shifts like a searchlight.

Real-world example:

• Pathao drivers use selective attention to navigate traffic while tracking orders.
• WhatsApp notifications use visual cues (vibrations, colors) to grab attention.

2. Problem-Solving: From Algorithms to Heuristics

Problem-solving is cognitive effort to reach a goal. Two main strategies:

Common heuristics (and biases):

• Availability heuristic: Judging probability by ease of recall (e.g., fearing plane crashes after news coverage).
• Representativeness heuristic: Stereotyping (e.g., assuming a quiet person is shy).
• Anchoring: Relying too heavily on the first piece of information (e.g., Daraz’s "original price" vs. discount).

Worked example: A Daraz seller lists a phone for ₹50,000 but marks it as "₹60,000 (20% off)."

• Anchoring bias: Buyers focus on ₹60,000, not the actual value.
• Cognitive shortcut: They perceive ₹50,000 as a "great deal" without comparing other sites.

3. Decision-Making: Logic vs. Emotion

Decisions involve rational (prefrontal cortex) and emotional (amygdala) systems. Models:

1. Dual-process theory (Kahneman):
   • System 1: Fast, automatic (e.g., recognizing a friend).
   • System 2: Slow, effortful (e.g., calculating loan interest).
2. Prospect theory (Kahneman & Tversky): People prefer avoiding losses over gaining equivalent rewards (e.g., NEPSE investors panicking during drops).

Real-world ties:

• Ncell tariff choices: Users weigh data limits (logic) vs. peer pressure (emotion).
• eSewa payments: Fear of fraud (amygdala) vs. convenience (prefrontal cortex).

mindmap
  root((Decision-Making))
    Factors
      Logic["Prefrontal Cortex\n(Weighs pros/cons)"]
      Emotion["Amygdala\n(Fear, excitement)"]
    Biases
      Loss Aversion["Prefer avoiding losses\n(e.g., NEPSE sell-offs)"]
      Sunk Cost Fallacy["Continuing failed investments\n(e.g., Daraz seller holding stock)"]
    Models
      Dual-Process["System 1 vs. System 2"]
      Prospect Theory["Losses loom larger than gains"]

4. Language: The Cognitive Tool

Language is a symbolic system that shapes thought. Key theories:

• Chomsky’s Universal Grammar: Humans are born with a "language acquisition device" (LAD) for grammar rules.
• Whorf’s Linguistic Relativity: Language influences thought (e.g., multiple words for snow in Inuit languages affect perception).

Components of language:

1. Phonemes: Smallest sound units (e.g., "b" vs. "p" in "bat" vs. "pat").
2. Morphemes: Meaningful units (e.g., "un-" in "unhappy").
3. Syntax: Rules for word order (e.g., Nepali SOV vs. English SVO).
4. Semantics: Meaning of words/sentences.

Real-world example:

• Google Translate uses algorithms to map phonemes/syntax but struggles with idioms (e.g., "kick the bucket").
• Nepali vs. English: Nepali’s context-dependent grammar (e.g., verb endings for subject) reflects cultural communication styles.

5. Cognitive Biases in Tech and Business

Biases distort judgment. Examples in Nepal’s digital economy:

Worked example: NEPSE investors see a stock drop from ₹500 to ₹400.

• Loss aversion: They panic-sell, worsening the drop.
• Hindsight bias: Later, they claim they "knew it would crash."

In the Real World

1. eSewa’s Trust System:

   • Uses social proof (reviews) and anchoring (showing "₹X saved") to reduce cognitive load in payments.
   • Why it works: Nepalis trust peer recommendations (availability heuristic) over cold data.

2. Pathao’s Dynamic Pricing:

   • Adjusts fares based on supply/demand heuristics (like Uber).
   • Cognitive link: Riders accept higher prices during peak hours due to loss aversion (fear of missing the ride).

3. Ncell’s Tariff Confusion:

   • Overlapping plans exploit choice overload (too many options paralyze decision-making).
   • Solution: Simplified menus use chunking (grouping similar plans) to reduce cognitive strain.

4. YouTube’s Algorithm (Global):

   • Uses availability heuristic: Shows trending videos first, reinforcing what’s easily recalled.
   • Nepali example: Political news dominates feeds during election seasons.

5. Khalti’s Security Warnings:

   • Uses framing: "Your money is safe" (positive) vs. "Hackers target weak passwords" (negative).
   • Psychology: Negative frames trigger stronger emotional responses (amygdala activation).

Exam Tip

1. Define and differentiate:
   • Distinguish algorithms (e.g., GPS routes) vs. heuristics (e.g., "if it’s raining, take the bus").
   • Compare Chomsky’s LAD vs. Whorf’s linguistic relativity.

2. Apply to real scenarios:

   • Problem-solving: Explain how a Daraz seller uses heuristics to set prices.
   • Biases: Analyze why NEPSE investors make irrational decisions (loss aversion, hindsight bias).
   • Language: Relate Chomsky’s theory to how Google Translate fails with Nepali grammar.

3. Diagrams are key:

   • Draw dual-process theory (System 1 vs. System 2) for decision-making.
   • Sketch attention models (spotlight, filter) for perception questions.

4. Common pitfalls:

   • Don’t confuse memory (Unit 4) with cognition (this unit). Focus on processing, not storage.
   • Avoid vague answers like "people think differently." Specify how (e.g., "due to anchoring bias").

5. Case study practice:

   • Question: "How does eSewa use cognitive principles to encourage payments?"
   • Answer:
     • Anchoring: Shows "₹X saved" to highlight discounts.
     • Social proof: Displays "10,000+ users trusted" to reduce risk perception.
     • Simplification: Limits steps in the payment flow to reduce cognitive load.

Visual Summary: