Psychology of Learning

Module 09: Decision-Making 1

Summary

Choice & the Matching Law

Choice behavior examines how organisms allocate responses among alternatives.

• Judgment vs. choice: Judgment estimates likelihood; choice selects among options.
• Impulsivity paradigm: Pigeons prefer smaller‑immediate rewards over larger‑delayed ones, showing delay reduces reinforcement value.
• Marshmallow test: Children’s ability to delay gratification predicts later life outcomes.
• Matching Law (Herrnstein, 1961): Responses are distributed proportionally to reinforcement rates.
• Generalized Matching Law (Baum, 1974): Adds parameters for bias (consistent preference) & sensitivity (responsiveness to reinforcement differences). This law applies across species, describing how choices match reinforcement distribution, with deviations explained by undermatching or overmatching.

Theories of Choice Behavior

Why does matching occur?

• Behavioral economics: Combines psychology & economics to explain resource allocation. Utility represents subjective value, not just objective quantity.
• Optimization theory: Organisms maximize satisfaction, balancing diminishing marginal value. Variety increases utility (e.g., movie marathon, dung fly mating behavior). Matching reflects optimal reinforcement intake.
• Momentary maximization theory: Organisms choose whichever option is best right now. Matching can emerge from moment‑to‑moment comparisons, though optimization better explains long‑term exclusive preferences.
• Decision contexts:
  • Certainty: outcomes known exactly.
  • Risk: outcomes have known probabilities.
  • Uncertainty: probabilities unknown. These distinctions highlight how choice varies depending on available information.

Models of Decision-Making

Models distinguish between rational prescriptions & actual behavior.

• Normative models: Prescribe optimal strategies.
  • Expected utility theory: Choose option with highest expected utility (probability × value).
  • Subjective utility theory: Under uncertainty, use estimated probabilities.
  • Decision trees: Visualize options, probabilities, & outcomes.
  • Utilitarianism: Maximize collective utility, though pure forms conflict with moral intuitions.
  • Probability theory: Formal reasoning under uncertainty, balancing Type I & Type II errors.
• Descriptive models: Describe how people actually decide.
  • Satisficing (Simon): Choose “good enough” options rather than optimizing. Satisficers report greater happiness than maximizers.
  • Prospect theory (Kahneman & Tversky): Losses loom larger than gains, producing loss aversion, endowment effect, status quo bias, & risk preferences that violate expected utility.
  • Regret theory: Anticipated regret shapes choices, leading people to avoid options with high regret potential.
  • Compensatory strategies: Trade-offs across dimensions; cognitively demanding compared to simpler non-compensatory rules.

Conclusion

Module 09 shows that choice behavior is shaped by reinforcement patterns, optimization principles, & psychological biases. Normative models prescribe rational strategies, but descriptive models reveal systematic deviations—loss aversion, satisficing, regret—that better explain real-world decisions. Together, these insights bridge conditioning research with economics & psychology, laying the foundation for understanding heuristics & biases in later modules.