Computational Modeling

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Contents 1 Introduction (What is computational modeling?) 2 Why would you want to use a computational model? 3 Different phenomena that investigators have modeled 4 Different types of models 5 Selected references 5.1 By model type/use 5.2 By content topic 5.3 modeling software

Introduction (What is computational modeling?)

• It is not the same as a process model or a structural equation model
• It is governed by mathematical relationships, and requires computation because it would take too long to do it all by hand (usually because there are complex relationships that require many computations).
• Typically one sets up a simulation with the desired parameters and lets the computer run. One then looks at the output (whatever that may be, usually the experimenter knows ahead of time what they want to look at) to interpret the behavior of the model.

Why would you want to use a computational model?

• In short, to do something that is hard or impossible to do with a lab experiment.
  • If too many people are involved for it to be practical
  • If you want to simulate things that current technology doesn't allow us to measure
  • To validate with a more detailed model a hypothesized process derived from actual lab data
  • To propose a theory and test it for things that are not currently observable or practical.

Different phenomena that investigators have modeled

• Group decision making, jury decision making
• Social influence, development of norms
• Attitudes, attitude change
• Causal attribution
• Impression formation
• Stereotyping, group impressions
• Interpersonal processes (attachment, couple dynamics)
• Person Memory
• Self-concept
• Personality, personality dynamics

Different types of models

• cellular automata
• connectionist networks
• neural networks
• agent-based models
• symbolic models
• difference-equation models

Selected references

By model type/use

• cellular automata
  • Latane (1996)
  • Latane, Nowak, & Liu (1994)
  • Nowak, Szamrej, & Latane (1990)
• analytic/difference-equation models
  • Stasser & Taylor (1991)
  • Stasser & Vaughan (1996)
• connectionist networks
  • Kunda & Thagard (1996)
  • Read & Marcus-Newhall (1993)
  • Read & Miller (1993)
  • Read & Montoya (1999)
  • Shultz & Lepper (1996)
  • Spellman, Ullman, & Holyoak (1993)
  • Thagard (1989) (ECHO model)
  • Thagard (2000)
  • Thagard (2003)

By content topic

• attitudes/attitude change
  • Van Overwalle & Siebler (2005)
• causal attribution
  • Read & Montoya (1999)
  • Van Overwalle & Van Rooy (2001)
• cognitive dissonance
  • Shultz & Lepper (1996)
• group discussion
  • Stasser (1988)
  • Stasser & Taylor (1991)
  • Stasser & Vaughan (1996)
• personality
  • Read & Miller (2002)
• social influence/group opinion dynamics
  • Latane (1996)
  • Latane (2000)
• stereotyping
  • Smith & DeCoster (1998)

modeling software

• O'Reilly & Munakata (2000), "Computational Explorations in Cognitive Neuroscience", MIT Press.
• PDP++ homepage
• Matlab neural network toolbox
• Stuttgart Neural Network Simulator