Cognitive Science Overview

Definition: The study of the mind and intelligence. Not phenomenology, psychoanalysis, behaviorism, or biology
Cognitive Approach: Views the mind as a computer processing information and utilizing algorithms.
- Algorithms: Step-by-step processes for generating outputs from inputs, requiring goals and constraints.
- Perception: Shaped by prior experiences and assumptions about the environment, with two main views:
  - Constructive: Involves imagining an object based on past experiences.
  - Mirroring: Assembling data to make assumptions about the environment.
- Global Workspace Theory of Consciousness: Perceptual information is selected for broadcast to other brain systems, entering awareness.
- Memory and Availability Heuristic: Memory availability is a type of mental shortcut, where we estimate probabilities based on how readily we recall examples.
- Internal Maps: Studies like the mouse study show that we have internal representations of the world.

Computational Systems: Different abstraction levels are needed to ask diverse questions about a system.
- Levels of Abstraction: Goals, inputs/outputs, representation, transformation, physical implementation.
- Marr's Three Levels:
  - Computational Theory: Determines the system's goals and properties.
  - Representation and Algorithm: Details the information format and manipulation rules.
  - Hardware Implementation: Describes the physical components involved.
Ultimatum Game Example: Illustrates the application of Marr's levels to decision-making.
- Representation: Choices are represented as continuous-valued intensities.
- Algorithm: Intensities compete with each other.
- Implementation: Neurons execute the process.

Early Computing Devices: Charles Babbage created a mechanical adding machine and Ada Lovelace devised a computer program for it.
Turing Machine: A theoretical model with a tape, current position, state, and a lookup table for instructions.
Church-Turing Thesis: Any computation humans can perform using pencil and paper can be done by a Turing machine.
- Turing Machine Implementation: Represents the algorithmic/representational level of Marr's framework.
- Universal Turing Machine: Capable of performing any possible computation given the right inputs.
NAND Operation: A universal operation, meaning other logical operations can be built from it.
Classical Computational Theory of Mind: The mind is a computational system employing algorithms and representations similar to a Turing machines.
- Computational Framework: Memory representations are discrete symbols, algorithms consist of steps, and it allows for non-traditional elements like parallel computation or probabilistic transitions.
- Connection to Physical Systems: Mental processes can be implemented using physical objects, like neurons.
Descartes' Dualism: Matter and mind are distinct substances.
General-Purpose Programmable Computer: Humans can be programmed to compute anything computable, but specific mental processes might not be programmable.

Limitations of Classical Computers: Underperform at tasks like categorization, generation, and problem solving compared to humans.
Artificial Neural Networks: Models of human thought inspired by the brain.
- Evolution of Neural Networks: Became more sophisticated in the 1980s, with breakthroughs in image recognition in 2015.
- Components: Nodes (neurons), weights, activation functions, objective functions, and learning algorithms (e.g., backpropagation).
Neural Network Process: Input is fed through the network, the output is measured, and backpropagation adjusts weights for improved accuracy.
Comparison to Human Mind: Some similarities exist, but human brains require less training and have different learning mechanisms.
- Evolutionary Training: Could have provided baseline learning for humans.
- Intuitive Physics: Artificial neural networks struggle with tasks that are easy for humans.
Brain as a Neural Network: The Carl Cox study on monkey brains has influenced this view.

Synesthesia: Perceiving one sense in terms of another.
- Types: Sound-color, color-sound, auditory-tactile, day-color, grapheme-color
- Possible Explanation: Proximity of sensory brain regions.
Format: The family of features of a representation.
- Access: Who understands the representation.
- Types: Basic (colored regions), complex (concrete objects/abstract truths), population codes (allowing for more representations and robustness to errors).
Content: Identifying the content of representations.
- Methods: Task performance, response time, errors, and location of representation.
- Imagery Debate: Whether internal representations are sentence-like or picture-like.
  - Pylyshyn: Sentences.
  - Kosslyn: Pictures.
Aphantasia: Inability to visualize.
Hyperphantasia: Extremely vivid imagery.

Modular Features: Encapsulation, specialization, localization.
Evidence for Modularity: Aimes rooms (evidence for modulation) and classroom experiments (evidence against).

Nativism: Innate elements of cognition.
- Learning: Refines representations and algorithms.
Empiricism: General-purpose pattern detectors.
- Knowledge Acquisition: Through life experiences.
Core Cognition Hypothesis: Humans are born with specialized cognitive systems for numbers, objects, and agents.
Cognitive Development:
- Object Permanence: Understanding objects' continuous existence, even when hidden.
- Centration: Reasoning based on a single, salient aspect of a situation.
- Theory of Mind: Understanding that others have different minds.
- Overregularization: Grammatical errors in language development (e.g., "goed" instead of "went").

Evolutionary Influence: Genes for brain development determine cognitive processing.
- Shared Genes: Explain similarities across individuals.
Environmental Impact: Monkey study demonstrates that lack of visual experience impacts behavior, supporting the nurture perspective.
Heritability Index: Explains the genetic contribution to a trait's variability.
Twin Studies: Compare identical twins with fraternal twins to assess genetic contributions.
Genome-Wide Complex Trait Analysis: Models the relationship between genes and traits.
Life Experience: Twin studies suggest that family environment may be less significant than life experiences.
Gene Expression: Genes influence the brain and guide environmental selection.
Genotype and Phenotype: Evolution focuses on genotypes (genes).

Brain Size and Energy Consumption: High energy consumption despite relatively small size.
Galapagos Finches: Illustrates how environmental changes cause anatomical shifts.
Phenotype Plasticity: Genes expressed under specific conditions.
Pleiotropy: Single gene influencing multiple phenotypes.

Neuroscience Basics: Dendrites receive information, synapses transmit messages, and chemical signaling alters neuron voltage.

Neuroimaging Methods: Provide information about brain structure and function.
- Measures: Brain activity, structure, and connectivity.
- Applications: Understanding cognition, identifying brain disorders, and monitoring brain changes over time.