COG250 - L_1

Definitions §

Definition	Discipline	Level of Reality
Mind	Neuroscience	Brain
Mind	Computer Science (AI)	Machine Learning
Mind	Psychology	Behaviour & Experiment
Mind	Linguistics	Language
Mind	Anthropology	Culture
Mind	Philosophy	Using Ontology & Epistemology creating conceptual vocabulary for theoretical grammar

Lecture §

• CASA: Cognitive Science and Artificial Intelligence Student’s Association
  • Meeting: Sept 15, 12:00 to 15:00
  • Instagram: casa.uoft
  • Website: https://cogsci.ca/
• What is Cognitive Science
  • The interdisciplinary science study of mind
  • Mind: key distinction of being a person
• What are 3 Problems of mind (Cognitive Science is the field to solve these problems)
  1. ★ Problem of equivocation: use the same word for different things (making argument treading off with different meaning, making the argument non-valid):
     • Definitions
     • Ex. Information
  2. Plausible Ignorance: ignoring the fact that all levels of reality that they are interacted
  3. Fragmentation: creating Nihilism for the general public in terms of what they belong (create coherent account of minds)
• What are 3 Vision of Cognitive Science
  1. Generic Nominalism (genus & categorization) Rejected
     • Does not solve problems of equivocation
     • JUST creating set of vocabularies and categories in the study
  2. Interdisciplinary eclecticism (折中主义) Rejected
     • Unstable (degenerates into generic nominalism)
     • Only brief bridges are built
  3. ★ Synoptic Integration (综合集成)
     • Looking to building strong bridges between disciplines
     • Aims to challenge and change all related fields (solve problems between discipline, not within)
     • Big picture framework: additive processing, 4E cogscience, 4P model
• How do we do Synoptic Integration and how to do it well?
  • Metaphor (bridging between domains)
    • Connecting: identity and insight
    • Balance: keep relevant identity, compare salient insights (adpt-transfer)

    • "Sam is a pig"

      • Pig: (domain of animal) a set of salient characteristics
      • Sam: (domain of human)
      • Bridging between: using Pig’s lens to perceive Sam

  • Multi-aptness: can apply it for many different domains, and transfer deep (elegance)
    • Affords transfer to many different domains
  • Convergence: many argument support it (trustworthiness) Milligram
  • ★ Plausibility: both elegant and trustworthiness (where one should take seriously) ^fb6070
    • (trivial) Convergence --- [Balance] --- Elegance (far-fetched)
    • Liar: depends you care about true
    • Bullshit: depends you care about salience
    • Deepity: equivocating (dual-meaning) between trivial thing with far-fetched thing

      • "Love is just a word"

        • Trivial ==> Far-Fetched: using dual-meaning to apply trivial statement to far-fetched applications
        • Actually doesn’t have any evidence (far-fetched statement)

    • Motte and Bailey: far-fetched to trivial

      • "Morality is socially constructed"

        • Trial <--> Far-Fetched: switch between using a trial (defensible & uncontroversial) statement and arguing a far-fetched (controversial) statement
        • Motte (trial): moral is socially constructed; Bailey (far-fetched): no such things as right or wrong

• What is the purpose of synoptic integration, what is the 3 naturalistic imperative for cognitive science
  • Human need plausible definition to account for complexity of scientific explanations
  • 3 Naturalistic Imperative
    1. Analysis
       • Thales: “all is the moist”
       • Plausible (since his lives were in Greece, surrounded by water)
       • Analysis (analyzing complex phenomenon into basic phenomenon)
       • Lodestone has psyche
    2. Formalization
       • Descartes: “invented Cartesian Plane” (mathematic in the foundation for science)
       • Formalization (explain mental in non-mental terms, that is mathematically measurable)
       • Solve: Homuncular Fallacy (从前有个山，山里有个庙，庙里有个和尚说“从前有个山…”)
    3. Mechanization
       • Alan Turing (taking the design stand: how would you put it into a machine)
       • Mechanization (engineering end run: a machine to simulate the mind)
       • Solve: Hard to study complex mind: always try to please them and deceive them

Active Studying §

Summarize today’s lecture §

• [::Most important/focused topic] 4 (3)s (3 problems, 3 visions, 3 solution, 3 naturalistic imperative)
• [::Most difficult part, why, how to resolve] Deepity vs. Motte & Bailey (used ChatGPT, helpful)

Review Dates §

• 2023-09-11: 🟨
• 2023-09-18: 🟩
• 2023-09-22: 🟩
• 🟩 🟨 🟥