Experts often have an edge in problem solving. In general, a person is considered an expert if they have at least ten years of experience solving problems in a particular field. Expertise in one field does not generalize to others. A chess master who's been playing for twenty-five years is an expert in chess, but not in cooking. Experts seem to differ from novices in problem solving in several ways. First, they have more knowledge to draw from than novices, and they organize their knowledge in a different way. Because of that knowledge, they perceive and frame the problem more efficiently. Experts also tend to break down the problem into chunks and subgoals. This allows them to navigate the problem space forward, from the initial state to the goal state, instead of backward as a novice would. However, expertise can be a disadvantage in some ways, as experts are also vulnerable to errors in memory.

First of all, experts with many years of experience solving a particular type of problem are bound to know more about that type of problem than a novice would. They have more examples of past problems to compare with the current one, which might help them find an appropriate solution that has been used in similar situations in the past. In addition to the sheer volume of problem-related knowledge, experts organize this information efficiently in memory. They draw many connections between past situations, finding similarities between them. The wealth of associations gives them easier access to their knowledge and lets them see patterns among the vast collection of individual examples.

Experts also tend to see patterns in the present problem. Chess experts, for example, use a strategy called chunking to help them remember the locations of pieces on a chessboard. The chunks are familiar set-ups of chessmen on the board that recur often in games. Instead of memorizing the placement of twenty different pieces, an expert would only need to remember three or four chunks, each containing five or six pieces. Chunking can help problem solving by making the problem more manageable. For example, say a chess problem requires nine moves to reach its solution. A novice would treat this as a problem with nine distinct steps, while an expert might see that the first five moves flow naturally from one another, and the next four are a well-known sequence. Thus, the expert can treat the problem as if it has only two steps. Reducing the number of operators needed to reach the goal reduces the load on working memory, so that its limited resources can be used more efficiently.

Experts also tend to break problems down into subgoals. This process is similar to chunking in that it involves condensing a large amount of information to make the problem more manageable. Instead of trying to get from the initial state to the goal state in one leap, which often leads to mistakes in navigating the problem space, experts can see the problem as a series of smaller problems to be solved. In order to get from point A to point D, experts see that they must first go through points B and C. So, they can concentrate on getting from A to B, then B to C, and finally C to D. By framing the problem as a series of subgoals, experts are more likely to see patterns and find easy ways to solve these smaller problems. Once an expert recognizes a familiar problem, he can work forward to solve it, rather than working backward from the desired solution to the initial state, as a novice would. However, if an expert does not recognize a problem, he will work backward through the problem; thus, we know that working forward depends on familiarity with the problem.

After solving similar problems for 10 years, many experts find that some of their problem solving skills have become automatic. An automatic skill takes up fewer resources from attention and working memory, allowing experts to solve the problem more quickly or to devote the saved resources to working on some other aspect of the problem. To take a familiar example, we are all experts in reading. If we were not, we would need to sound out each word and fit the phonic pieces together, then search our memories for the meaning to match that word, or for similar words to help us deduce its meaning. Because we have expertise in solving reading problems, we never need to think about what familiar words mean. Reading words in front of us takes very little effort; we can read while doing or thinking about other things, and, in fact, we cannot stop ourselves from reading words that we see. Thus, we say that reading has become automatic to us. However, if we encounter an unfamiliar word, one outside our expertise, we must revert to sounding it out and searching for similar words to figure out its meaning. Automaticity in expertise depends on practice and familiarity.

As we have seen above, one major limit on expertise is that it is not generalizable. Being an expert in one type of problem does not increase my skills on other problems. Expertise can improve memory for large numbers of items through chunking. However, experts' familiarity can harm the accuracy of their memories in other ways. Experts' memories for particular problems are less detailed than novices. They remember the general gist of a problem, the aspects that were important to determine a solution, but not the unimportant details. Novices, by contrast, do not differentiate between important and unimportant aspects, so they are more likely to remember many details that were not crucial to the problem. In addition, experts' memories for problems are vulnerable to intrusion errors. They tend to insert into a problem things that were not actually there, but that they had seen on previous, similar problems. To return to the chess example: Say that a king is usually guarded by a pawn. However, in this particular problem, the king stands alone. An expert asked to recall the problem will probably tell you that there was a pawn by the king; this mistake is an intrusion error. Experts' familiarity can make them more efficient at finding solutions, but it can hinder their memories for specific problems.