| Feature | Theoretical Models | Computer Models |
|---|---|---|
| Format | Visual diagrams and flowcharts | Computational software and AI |
| Focus | Conceptual stages of processing | Functional simulation of the brain |
| Example | Multi-Store Model of Memory | Artificial Intelligence algorithms |
Define 'Inference' Clearly: In exams, always explain that inference is necessary because mental processes are 'private' and cannot be directly observed.
Evaluate with 'Machine Reductionism': A high-level critique is that the computer analogy ignores human emotion and motivation, which significantly affect cognition but are absent in computers.
Understand Soft Determinism: Recognize that while the cognitive approach assumes our 'mental hardware' limits us, it also allows for free will (the ability to choose how to process information).
Check for Validity: Be prepared to discuss how lab-based cognitive experiments might lack ecological validity because they use artificial tasks that don't reflect real-world thinking.
Confusing Inference with Proof: Students often mistake an inference for a proven fact; remember that an inference is a logical 'best guess' based on evidence, not a direct measurement.
Over-extending the Computer Analogy: Avoid suggesting that the mind is a computer; it is only like a computer in how it processes information. Humans are prone to forgetting and emotional bias, whereas computers are not.