Basic Concepts in Cognitive Science Flashcards
Phrenology
It tried to describe the mind based the on assumptions on the anatomical structure of the brain.
* The brain as the center of thought
* Modular organization of the
brain into anatomical regions
* Functional specialization of
individual brain regions
Brodman areas
In 1907, the German neurologist Korbinian Brodman proposed to divide the cerebral cortex into 52 different regions based on cytoarchitectonic properties
Magnetic Resonance Imaging (MRI)
imaging the anatomic structure of tissue in vivo. It
is an active sensing method based on the magnetic
properties of hydrogen atoms that dissipate energy
differently depending on the surrounding tissue
Diffusion Tensor Imaging (DTI)
DTI is an extension of MRI that measures the diffusion movement of water molecules and thereby can detect nerve fibers.
Electroencephalography (EEG) and Magnetoencephalography (MEG)
EEG and MEG enable the non-invasive monitoring of brain activity at high temporal resolution. While EEG measurements are based on measuring the electrical potentials resulting from neural activity via electrodes placed on the skull, MEG detects the corresponding magnetic field.
Positron Emission Tomography (PET) and Single-Photon Emission Computer Tomography
(SPECT)
PET and SPECT are nuclear imaging techniques that require medication with radiotracers.
Brain activity is monitored implicitly by observing the regional cerebral blood flow.
Functional MRI (fMRI)
fMRI is an extension of MRI that visualizes changes of the blood oxygen level caused by brain activity elicited through external stimuli or cognitive processing at high spatial resolution.
Motor and Sensory Regions of the Cerebral Cortex
Slide 78!
Cognitive Science unites Relevant Fields of Research
Engineering, Natural Sciences, Humanities
Different degrees of
biological realism of cognitive systems
Computational Models
Purely computational models implement cognitive functions solely based on a
functional view of the system without any reference to biology.
Bio-Inspired Models
Bio-inspired models implement cognitive functions by replicating known or
hypothesized mechanisms of cognitive processing from biological organisms.
Hybrid Models
Hybrid models combine computational and bio-inspired modeling.
Computational vs. Bio-Inspired Modeling
Slide 82
The Ultimate-Proximate Distinction
The ultimate explanation: Why does a cognitive system exhibit a certain behavior?
The proximate explanation: How is a certain behavior implemented in a cognitive system?
Ultimate explanations are concerned with why a behavior exists, and proximate explanations are concerned with how it works.
Levels of Abstraction in Cognitive Modeling
Computational Theory
High-level description of the system that states the goal of the computation and
the logic of the strategy by which it is carried out.
Representation and Algorithms
Realization of the computational theory in terms of input, output, representations and algorithms that perform the required transformations.
Hardware Implementation
Physical implementation of the representations and algorithms.
Name of Artificial Intelligence with focus on cognition
Artificial General Intelligence
Cognitivist Systems vs Emergent Systems
Cognitive functions are modelled as working computer programs
Emergent cognitive models, cognition is a continuous self-organizing process that is driven by the interaction between the agent and its environment.
Mind-Body Problem (Schools of Thought)
Substance Dualism (also Cartesian Dualism)
* René Descartes postulated that mind and body are two kinds of
different substances; their separation makes the soul immortal
and enables free will
Monism (also Physicalism)
* There is only one substance, which means that mental states are
physical states
* This would imply that individuals that share a mental property also
share a corresponding physical property
The Computational Theory of Mind
The computational theory of mind (CTM) holds that the mind is a digital computer: a discrete-state device that stores symbolic representations and manipulates them according to syntactic rules; that thoughts are mental representations – more specifically, symbolic representations in a language of
thought; and that mental processes are causal sequences driven by the syntactic, but not by the sematic, properties of symbols.
Physical Symbol Systems (theoretical foundations for the emergence of general intelligence from symbolic operations)
A PSS (Physical Symbol System) consists of symbols that can be created, modified, copied, and destroyed.
An expression (a group of symbols) designates an object if the system can interact with it.
The system can interpret an expression if it represents a process the system can perform.
Symbols and processes are recursive: symbols can represent other symbols, and processes can create other processes.
Problem solving in a PSS involves searching through possible symbol arrangements.
In summary, a Physical Symbol System manipulates symbols and expressions to interact with objects and perform processes. It uses recursive definitions to represent complex ideas and solves problems by searching through possible symbol configurations.
Physical Symbol System Hypothesis
A physical symbol system has the necessary and sufficient means for general
intelligent action.
→ Every cognitive system is a physical symbol system
Heuristic Search Hypothesis
The solutions to problems are represented as symbol structures. A physical
symbol system exercises its intelligence in problem-solving by search, that is, by
generating and progressively modifying symbol structures until it produces a
solution structure.
→ Cognition is reflected in an intelligent search strategy that finds a solution
without exhaustive brute-force search
Rationality
Rationality means that an agent acts in a sensible and purposeful way to
achieve its goals. A rational agent will always use as much of its knowledge as
possible to guide its behavior.
Rationality Views
An ideal agent, “given its belief-desire system, optimizes its choices.”
→ Not feasible in reality
Bounded Rationality
“Bounded rationality is rationality as exhibited by decision makers of limited
abilities.”
What does rationality depend on?
Rationality depends on the success criterion, the agent’s prior knowledge of the environment, the actions the agent can perform, and past percepts.
Emergent Systems
The main goal of cognitive processing an emergent system is to maintain and extend its autonomy.
Continuous self-organization of the system.
In contrast to the computational functionalism of cognitivism, knowledge and
representation are specific to the emerging system.
Connectionist Emergent Systems
Connectionist cognitive systems model cognition based on networks of simple
interconnected computational units. Cognitive processing in these networks is
distributed, parallel, and based on statistical properties instead of formal rules.
Knowledge is stored in the connection weights of the system. The most
prominent class of connectionist systems are neural networks.
Enactive Emergent Systems
An enactive emergent system “develops its own understanding of the world
around it through its interactions with the environment”
Enactive Emergent Systems mechanisms
Phylogeny:
This refers to the system’s innate abilities. The system’s physical and cognitive capabilities, which it is born with, determine how it interacts with the environment. It explains how species evolve and adapt to environmental changes through natural selection and genetic mutations.
Ontogeny
* The system is structurally coupled with the environment and makes sense of it
based its actions the perception of resulting actions of the environment
* Through sense-making, the system generates its own specific epistemology
that increases its cognitive capacity by capturing the regularities of interaction
(→exploration)
* The gradual increase of cognitive capacity results in development
It includes processes such as embryonic development, growth, maturation, and aging within a single organism.
evolutionary history vs individual development
Dynamical Emergent Systems
Dynamical emergent models of cognitive systems are based on time-dependent
differential equations that allow for a compact representation of complex
system behavior and the application of analysis methods from dynamical
systems theory
Main Properties of Dynamical Systems
Dissipation: the number of reachable states reduces over time
Non-Equilibrium System: stable function requires an external “energy” supply
Non-Linearity: complex behavior can emerge from a small set of state
parameters
Collective Variables: the system is represented by a small set of state variables
