Key Concepts in Cognitive Science and Information Processing

Decision tree

a branching representation of all possible paths through a problem space starting from an initial point

Formal property

a physical property of a representation that is not semantic

Intentional realism

the thesis that propositional attitudes can cause behaviors

Predicate calculus

formal system for exploring the logical relations between formulas built up from symbols representing individuals, properties, and logical operations. Includes quantifiers (ALL or SOME) that allow representations of generality.

Propositional attitude

a psychological state that can be analyzed into a proposition and an attitude toward that proposition

Semantic property

a property of a representation that holds by virtue of its content, that is, how it represents the world

Action potential

electrical impulses fired by neurons down their axons to other neurons

Boolean function

a function that takes sets of truth values as input and produces a single truth value as output

Computational neuroscience

the use of abstract mathematical models to study how the collective activities of populations of neurons could solve complex information-processing tasks

Neurotransmitters

neurochemicals that are transmitted across synapses in order to relay, amplify, and modulate signals between a neuron and another cell

Perceptron

a single-unit artificial neural network

Perceptron-convergence rule

a learning algorithm for perceptrons. It changes a perceptron's threshold and weights as a function of the difference between the unit's actual and intended output.

Synapse

the site where the end of an axon branch comes close to a dendrite or the cell body of another neuron. This is where signals are transmitted from one neuron to another.

Attractor

a region in the state space of dynamical systems on which many different trajectories converge

Dynamical systems hypothesis

radical proposal to replace information-processing models in cognitive science with models based on the mathematical tools of dynamical systems theory.

Dynamical systems theory

a branch of applied mathematics using difference or differential equations to describe the evolution of physical systems over time.

Object permanence

the knowledge that an object exists even when it is not being perceived.

State space

a geometrical representation of all the possible states that the system can be in. It has as many dimensions as the system has independently varying parameters.

Binocular rivalry

phenomenon that occurs when different images are presented to each eye. Subjects experience an alternation of the images.

Conditional probability

the probability that some proposition A is true, on the assumption that some other proposition is true. Often written as p(A/B).

Likelihood

important concept for Bayes's rule, measuring the probability of some evidence E, conditional on a hypothesis H. Often written as p(E/H).

Neuroeconomics

interdisciplinary area where concepts and tools from economics are used to illuminate brain functioning

Posterior probability

the result of applying Bayes's rule. It is the probability of a hypothesis H, conditional on some evidence E. Often written as p(H/E)

Prior probability:

in applying Bayes's rule, it is the probability assigned to the proposition before taking the evidence into account