Research and Application of Metacognition and Decision Making

What is Metacognition?

Metacognition refers to the second-order cognition of the mind; thoughts about thoughts, knowledge about knowledge, and self-reflection of abilities are all within the realm of metacognition. Flavell defines metacognition as the “knowledge that takes as its object or regulates any aspect of any cognitive endeavor” [1, P. 8].

Metacognition plays a critical role for humans because of its usefulness and adaptiveness [2]. In fact, according to Flavell, humans have characteristics that make metacognition a necessity [3]. For example, people are error prone and fallible, thus careful monitoring, regulation, and assessment is in constant need. Survival instincts require humans to plan ahead and make decisions based on critical evaluation of alternative choices. In order to make such decisions, metacognitive skills are required. Finally, human beings are conscious organisms that can think about and communicate psychological events. The act of thinking and explaining such events is in itself a metacognitive engagement.

In his seminal research in cognitive development theory of children, Flavell defined the term metacognitive knowledge and metacognitive experience [4]. Metacognitive knowledge is knowledge stored in long-term memory that refers to “people as cognitive creatures with their diverse cognitive tasks, goals, actions, and experiences” [4, P. 906]. It involves the knowledge of one’s own cognition. Metacognitive experiences are realization of certain momentary experiences. It can vary in length of duration, level of consciousness, and complexity. The sudden, anxious feeling that one may encounter after realizing one does not understand an exam’s material, is an example of a metacognitive experience. Such phenomena are likely to happen when there is high, concentrated thinking and feelings [4].

Metacognitive experiences can have important effects on cognitive goals, tasks, strategies and metacognitive knowledge. Experiences can add, delete, or revise metacognitive knowledge; similar to how new information is assimilated and accommodated into long-term memory [5]. Experiences such as puzzlement and frustration can also lead to a re-establishment of new goals and revision of previous plans. Furthermore, they can activate cognitive or metacognitive strategies. For instance, the act of self-testing oneself with questions and noting how well they were answered is a metacognitive strategy aimed at the metacognitive goal of assessing knowledge.

Efklides added a third phenomenon of metacognition called metacognitive skills [6]. Metacognitive skills are conscious orchestration of mental processes to plan, monitor progress, allocate attentive resources, and regulate cognition and strategy use. Researchers such as Flavell, Butler and Winne argue that individuals who can accurately self manage and regulate are the most successful at learning because they can utilize the right set of tools to achieve goals and modify strategies based on awareness of effectiveness [4], [7].

Decision Making

Decision making is a complex, dynamic process that requires metacognition. Zeleny and Cochrane defines it as “a complex search for information full of detours, enriched by feedback from casting about in all directions”, where one must constantly gather and discard information under fluctuating uncertainty [8, P. 86].

Utility theory states that under ideal circumstances, the economic man will choose the option that provides the greatest pleasure and utility [9]. This theory assumes three characteristics of the decision maker: the individual 1) has complete information, 2) has infinite sensitivity, and 3) is completely rational. The decision making then becomes a process of obtaining an adequate measurement of attractiveness for each alternative and choosing the one with the highest merit.

These characteristics are of course, unrealistic and false. Humans rarely have complete information, and so under uncertainty, decision making becomes a problem of heuristics. Humans are also irrational creatures and are easily influenced by biases such as the framing effect. Furthermore, humans lack the cognitive resources to optimize in a practical manner due to limitations of working memory, information, and computational facilities [10]. According to Schwartz et al, people who attempt to maximize utility tend to experience less happiness, optimism, and satisfaction [11]. Maximizers also tend to rely on external sources for information, increasing dependency and leading to procrastination of choice [12].

Instead of maximizing, there is a tendency for humans to satisfice, or select what is “good enough”. Simon defines satisficing as “decision making that sets an aspiration level, searches until an alternative is found that is satisfactory by the aspiration level criterion, and selects that alternative” [13, P. 168]. For example, a group may decide on a suboptimal plan, not because it offers the most utility, but because it was the first plan that was unanimously agreed upon.

The Process of Decision Making

Zeleny and Cochrane describe utility theory as an outcome-oriented model of decision making [8]. Under circumstances of uncertainty however, decision making is more complex and is better modeled by a process-oriented approach. Decision making happens in three stages: pre-decision, decision, and post decision.

Pre-decision Stage

A decision making process is initialized by a sense of conflict [8]. This conflict arises from the lack of suitable alternatives and the infeasibility of the ideal alternative. The decision maker’s goal is to resolve the pre-decision conflict either by finding the ideal (rare), or by choosing amongst a set of alternatives. Information that further separates and distinguishes choices are gathered in an objective fashion. This stage is also iterative with continuous reinterpretations and reassessments. Once alternatives are sufficiently divergent, a decision can be made.

Decision Stage

As the individual gets closer to finalizing a decision, alternatives that are further away from the ideal choices are discarded, and partial decisions are made. When alternatives are discarded, a re-evaluation of remaining choices ensues. Priority order may change, displacing the ideal choice with an alternative closer to the feasible set [8]. For instance, after an exhaustive search for an unrealistic deal for a car that is priced significantly lower than the MSRP, the individual may discard the ideal for a more feasible choice that is valued at standard pricing. Partial decisions are also important to make because it reduces the initial conflict. When the final decision is made, the initial pre-decision conflict is fully resolved.

Post-decision Stage

Even when the final decision unfolds, a sense of post-decision dissonance may ensue [8]. Regret is likely to manifest as well, especially when a decision is made between two equally attractive, but not identical alternatives. This is because the ideal alternative has been displaced by a feasible, but less attractive option. In order to counteract this, a bias will arise toward the decision made. Humans will seek consonant information that conforms to their decision to increase confidence [8].

It is important to note that during any stage of the decision making process, cognitive load for the individual can be high. This is especially the case if the decision needs to be made in a short period of time. Edland and Svenson reported that under workload overload, input selectivity becomes high, accuracy decreases, and strategies become less diverse [14]. It is recommended that designers make information available, interpretable, and salient for the most objectively important tasks. For instance, visualizing options for different laptop models in a chart format will help lower cognitive load because all the information is externalized and does not need to be stored in working memory. Even relieving small amounts of cognitive load by automating tasks can be a sufficient remedial measure [15].

Irrational Behavior and Bias

Individuals do not always make rational, consistent decisions across tasks and situations. Research has shown that individual differences can affect decision making, including those of risk aversion and risk judgments, and decision making competence [16].

Decision making can be influenced by emotions. Shiv and Fedorikhin found that when consumers do not allocate resources to decision making, the consumer is more likely to decide based on affect rather than cognition [17]. For instance, given a binary choice of chocolate cake or fruit salad, consumers will have a higher chance of selecting chocolate cake when decision is based mainly on affect. This also suggests consumers whose cognitive processes are constrained are more likely to impulse buy.


Decision problems can be formulated in multiple ways, and often times the framing of the problem itself can sway the preferences in choice. This is known as the framing effect and occurs when two “logically equivalent (but not transparently equivalent) statements of a problem lead decision makers to choose different options” [18, P. 36]. For example, an economic program that results in 90% employment gains public support, yet if the same program states it results in 10% unemployment, opposition rises [19]. Generally, individuals tend to prefer risk-aversion when the problem is framed in a positive manner (gain), but shift to risk taking when the alternatives involve loss [20]. Duckman argues however, that framing effects can be diminished with the availability of credible advice [19]. Framing effects can also happen with outcomes; prospect theory states that a loss is more significant than an equivalent gain.

Expert Decision Makers

Proficient decision makers are able to consult and exploit past experiences to handle uncertainty and novelty. According to Cohen’s R/M model, they undergo a process called meta-recognition [21]. During the meta-recognition process, a gap is found during assessment and a skilled decision maker will patch the flaw or weakness that is found. These weaknesses can be a discovery of incompleteness, conflict, or unreliability [22]. Correction occurs by an external action, attention shifting, and/or assumption revision. These actions are meant to stimulate retrieval of new, potentially relevant information either from an external source, or from long-term memory. They will then reevaluate the results and continually test the current state of comprehension. Various strategies, such as a quick test, are adopted to fill in the gap of understanding and inconsistency in knowledge. This meta-recognitional skill is analogous to meta-comprehension skills in proficient readers [23].

Case Study

Flight Control is a mobile strategy game where the objective is to land as many aircrafts to their landing zones while avoiding mid-air collisions. The game has numerous characteristics that differentiate it from a more static strategic game such as chess. The positions and timings from which flights appear are unknown. As time progresses, more flights emerge with variations in speed and size. Such dynamic characteristics of this game make it impractical to take normative, object-oriented approaches. To deal with such uncertainties, a player must use metacognition to monitor performance and generate winning strategies.

Figure 1: Screenshots of Flight Control. White lines signify path of flight. Red circles indicate potential crashes.

During the early stages of the game, it is easy to compute the optimum path from a flight’s current position to its landing zone. The player’s initial strategy may be to simply draw a linear line between the flight and the target. However, as more flights enter the field, it becomes cognitively taxing to monitor all flights simultaneously. Not only does the player need to keep track of the position of the flight, but also the direction and the speed at which it is flying relative to other flights in the field of vision. Deciding on a path to draw becomes more complex than to draw a linear line. It is at this point that the player realizes that with the current strategy, a crash is inevitable. Such metacognitive experiences push the player to reassess and switch strategies.

Figure 2: A situation where a simple strategy will not suffice.

A novice player may simply “stick with” the path that was first drawn because it satisfices the goal at a satisfactory level. However, this is prone to crash threats appearing at unforeseeable time as the game progresses. Expert players on the other hand, are able to plan ahead by making decisions based on past experience and heuristics. For instance, they may have learned to recognize spots on the field in which collisions are more likely to happen and avoid such areas.

As more flights emerge, cognitive load is put on the user and game difficulty increases. This is obviously intentional to make the game challenging. However, if this were to hypothetically be an actual flight control program, cognitive load and dissonance are components that are to be avoided at all costs. Certain designs can be made to the program to help the flight controller, such as visually displaying locations where collisions are guaranteed to happen, given the current set of flights and paths drawn. In fact, the entire program could be automated, relieving the user from heavy mental computational work.


Metacognition plays a critical role for humans. As error-prone organisms with survival instincts, humans must make use of metacognition to monitor progress, assess and revisit strategies, and make decisions based on critical evaluation of alternative choices. Decision making is a complex process of selecting the option closest to the ideal. During the this progress, the individual may undergo heavy cognitive load, and it is important for the designer to externalize as much information to prevent workload overload.


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