In cognitive psychology, an algorithm is a systematic, exhaustive procedure that is guaranteed to produce a correct solution to a problem, given sufficient time and resources. Unlike heuristics (mental shortcuts that are fast but fallible), algorithms are thorough and reliable. Checking every possible combination to find a lock's code, trying every possible move sequence in a chess position, or systematically testing every suspect in a lineup are all algorithmic approaches. The trade-off is fundamental: algorithms ensure accuracy but can be prohibitively slow for complex problems.
Key Structures
- Prefrontal cortex — The anterior portion of the frontal lobe, critical for executive functions including planning, decision-making, working memory, and cognitive control.
- Frontal lobe — The largest lobe of the cerebral cortex, responsible for executive functions including planning, decision-making, working memory, and the voluntary control of behavior.
- Problem Solving — The cognitive processes involved in finding solutions to novel, non-routine challenges — from well-defined puzzles to ill-defined real-world problems.
- Insight — The sudden, conscious realization of the solution to a problem — the 'aha!' or 'eureka' moment — often preceded by an impasse and accompanied by a feeling of certainty and surprise.
- Heuristics — Mental shortcuts or rules of thumb that simplify complex judgments and decisions, enabling fast and often adequate solutions at the cost of systematic errors and biases.
Algorithms vs. Heuristics
The distinction between algorithms and heuristics is central to understanding human problem solving. Newell and Simon (1972) demonstrated that humans rarely use pure algorithmic search because the problem spaces for real-world problems are typically too large. A chess game has an estimated 10^120 possible game sequences — exhaustive search is impossible even for the fastest computers. Instead, humans rely on heuristics that efficiently navigate the problem space, accepting the risk of occasional errors in exchange for dramatic improvements in speed. The choice between algorithmic and heuristic approaches depends on the problem's complexity, the cost of errors, and the available time.
Computational Complexity
Different algorithms vary enormously in their computational demands. Simple algorithms (linear search through an ordered list) scale linearly with problem size. Others scale exponentially, becoming impractical for large problems. The concept of computational intractability — problems for which no efficient algorithm exists — has been applied to understand human cognitive limitations. When problems are computationally intractable, humans must rely on heuristic approximations, which may explain why human judgment systematically deviates from optimal solutions in many decision-making contexts.
While humans rarely use formal algorithms consciously, many learned procedures are effectively algorithmic. Long division, the procedure for solving quadratic equations, and recipes are all algorithms — step-by-step procedures that guarantee correct outcomes when followed correctly. Even in everyday cognition, certain well-practiced tasks (like making change or spelling regular words) may be accomplished through internalized algorithmic procedures that have become automatic through practice.
Relationship to Artificial Intelligence
The algorithm concept bridges cognitive psychology and computer science. Early AI research attempted to solve cognitive tasks using pure algorithmic approaches but found that the combinatorial explosion made this impractical for all but the simplest problems. This insight — that intelligence requires heuristic shortcuts rather than brute-force algorithms — was a key contribution of cognitive psychology to AI and remains central to understanding both human and machine intelligence.
Disorders
- Impaired algorithm application in frontal lobe damage
- executive dysfunction disrupts systematic problem solving