misprocessing

Misprocessing refers to the incorrect handling or transformation of information during processing by a system, whether biological (the human brain) or artificial (a computer program, database, or data pipeline). It describes results that diverge from the input, the intended rules, or expected outcomes. The term is not widely standardized and is used as a generic label for processing errors across disciplines.

In psychology and neuroscience, misprocessing denotes errors in perception, attention, encoding, or interpretation that lead to distorted beliefs or decisions. Examples include misperceiving stimuli, misremembering details, or misapplying rules due to cognitive bias. In everyday use, misprocessing can be invoked to explain why people reach incorrect conclusions despite accurate information.

In information technology and data processing, misprocessing occurs when a system produces incorrect outputs from valid inputs because of bugs, flawed logic, data corruption, or misaligned requirements. It can arise from programming errors, improper data types, edge-case handling failures, or integration problems between subsystems.

Consequences range from minor inconveniences to safety risks and financial loss. Mitigation strategies include validation and error handling, rigorous testing, quality assurance, provenance and auditing, and clear specifications. In cognitive contexts, training, metacognition, and decision aids can reduce misprocessing by supporting more accurate interpretation and memory.

Misprocessing is related to processing errors, cognitive biases, data integrity, and software bugs, and is used as a general term to describe failures in information handling across domains.