Is problem solving a psychological process? Illustrate your answer with the steps and methods involved in problem solving. Differentiate between human and computerized problem solving.

Problem Solving as a Psychological Process

Problem-solving is undeniably a psychological process, rooted in cognitive functions like perception, memory, attention, and reasoning. It isn’t merely a mechanical process but is heavily influenced by individual differences, emotional states, and prior experiences. The process can be broken down into several key stages:

• Problem Identification: Recognizing that a discrepancy exists between the current state and the desired state.
• Problem Definition: Clearly articulating the nature of the problem, its constraints, and goals.
• Generation of Potential Solutions: Brainstorming and developing a range of possible solutions. This stage often involves divergent thinking.
• Evaluation of Solutions: Assessing the feasibility, effectiveness, and potential consequences of each solution.
• Selection of a Solution: Choosing the most promising solution based on the evaluation.
• Implementation of the Solution: Putting the chosen solution into action.
• Evaluation of the Outcome: Assessing whether the solution successfully resolved the problem and making adjustments if necessary.

Methods Involved in Problem Solving

Psychologists have identified various methods individuals employ when tackling problems:

• Algorithms: Step-by-step procedures that guarantee a solution if applied correctly. They are reliable but can be time-consuming and inflexible. Example: Using a specific formula to solve a mathematical equation.
• Heuristics: Mental shortcuts or “rules of thumb” that simplify problem-solving. They are faster than algorithms but don’t guarantee a solution. Example: The availability heuristic, where we estimate the likelihood of an event based on how easily examples come to mind.
• Insight: A sudden realization of a solution, often after a period of incubation. It’s characterized by a “Eureka!” moment. Example: Kohler’s experiments with chimpanzees, where they suddenly realized how to use tools to reach bananas.
• Means-End Analysis: Reducing the difference between the current state and the goal state by identifying subgoals.
• Analogical Reasoning: Using similarities between the current problem and previously solved problems to find a solution.

Human vs. Computerized Problem Solving: A Comparative Analysis

While both humans and computers can solve problems, their approaches and capabilities differ significantly. The following table highlights key distinctions:

Feature	Human Problem Solving	Computerized Problem Solving
Approach	Intuitive, heuristic-based, often relies on pattern recognition and common sense.	Algorithmic, systematic, relies on pre-programmed rules and data.
Flexibility	Highly adaptable to novel situations and ambiguous information.	Limited adaptability; struggles with situations outside its programmed parameters.
Learning	Capable of learning from experience and adapting strategies.	Requires explicit programming for learning (machine learning).
Creativity	Can generate novel and creative solutions.	Limited creativity; primarily focuses on optimizing existing solutions.
Emotional Influence	Affected by emotions, biases, and motivation.	Unaffected by emotions; operates purely on logic.
Speed & Accuracy	Variable; can be slow and prone to errors.	Generally faster and more accurate for well-defined problems.

Modern AI, particularly machine learning, is bridging some of these gaps. Deep learning algorithms can now recognize patterns and make predictions with increasing accuracy, even in complex environments. However, they still lack the general intelligence and common sense reasoning that humans possess. For instance, a computer can beat a human at chess (a well-defined problem), but it cannot understand the social context of a conversation (an ill-defined problem).