To survive and thrive in the high-tech world requires the ability of humans to understand our own minds at both the metacognitive and control levels. When we are not in mindful engagements, we are at times vulnerable to biases and errors if we allow the mindless thinking to take over. Research have shown that human tend to make poor decisions (Stanovich, et al., 2000) and we act on irrational behaviors to avoid anxieties (Ariely, 2008), therefore, designers should consider metacognitive analysis to design of products and services so that they may guide users through the decision-making process and provide support for the users’ areas of weaknesses. There is a strong correlation between meta-awareness and meta-monitoring (Schraw, et al., 1994) and as such, this review examines their parallel influence in users’ decision-making process on the website with complex interactions and high information density of Morningstar, Inc.
Metacognition
Psychologist John Flavell described metacognition as knowledge about cognitive entities, cognitive motives, cognitive emotions, and the monitoring of cognitive processes (Flavell, 1979). Metacognition is typically distinguished as consisting of two functions: cognition knowledge which relates to what we know, and cognition regulation associated with how we manage this knowledge (Schraw, et al., 1994). Metacognition running along with cognitive processes, and in the executive functions, allows humans to monitor, assess, and adjust their cognitive and decision-making strategies (Jiménez, et al., 2009). Designers need to be aware that metacognition competes with normal cognitive process for the limited resources of working memory, and this additional loading enhances the possibility users can make decisional errors when interacting with products and applications (Kahneman, et al., 2009).
Metacognitive Knowledge
Metacognitive knowledge is the cognitive knowledge people maintain about themselves, and about the strategies or tasks that they may employ while solving problems, learning, or interacting with world systems (Flavell, 1979). Flavell further proposed that metacognitive knowledge is guided by three variables which affect our beliefs in the output of cognitive processes: person variables which identify what individuals understand about their own abilities to learn and comprehend, strategy variables providing the correct approach to solve problems, and task variables defining the amount of cognitive effort required in a given situation (Ozturk, 2017, Flavell, 1979). Educational psychologists Janis Jacobs and Scott Paris theorized three types of metacognitive knowledge: declarative, procedural, and conditional (Jacobs, et al., 1987). Declarative knowledge is what humans understand about their ability to learn, along with the factors that affect that ability. Procedural knowledge defines the approaches, or strategies that will be employed for various learning processes. Conditional knowledge sets boundaries on when and how to deploy certain approaches to learning (Jia, et al., 2019 & Jiménez, et al., 2009). Skilled learners have increased levels of declarative, procedural, and conditional cognitive knowledge, which drives increased performance (Schraw, et al., 1994).
Metacognitive Regulation
Metacognitive regulation involves the thinking processes humans take to manage learning and other cognitive processes (Son, et al., 2000 & Stanton, et al., 2015). Key components for self-regulated learners are: (1) identify and set appropriate goals, (2) ascertain knowledge of personal strengths and weaknesses, (3) provide an execution strategy, (4) evaluate ongoing performance, and (5) modify plans to counteract errors (Schraw, et al. 1994 & Jacobs, et al., 1987). Metacognitive skills—sometimes described as thinking about thinking—create cognitive load, work in the executive, and compete for the limited working memory resources (Roebers, 2017). Skill refinement will enhance mental models and develop expertise (Sternberg, 1998). Jacobs and Paris describe reading psychology and reading style skills as affecting people's comprehension abilities. (Jacobs, et al., 1987). Pre-attentive processing and existing mental models can drive processing priorities. Search skills have been equated to the foraging, and scent detection, of early hunters and gatherers (Pirolli, et al., 1999). Limited literacy and cognitive disabilities need to be considered in the design of critical applications. Individual learning styles determine the strategies used to attain goals (Ozturk, 2017). This paper will consider the role of decision-making skills in relation to descriptive theory and prospect theory (Kahneman, 1979).
Decision Making
Decision making is a type of task where humans need to select an appropriate response to a given scenario. It usually involves reviewing multiple options, and the variables associated with each, to draw an acceptable conclusion. Decision making involves risk which can lead to stress, and generate fatigue. Early work by mathematicians and economists lead to the development of utility theory which treated humans as logical and rational beings who could be expected to make reasonable choices (Fishburn, 1970 & Von Neumann, et al., 1944). Utility theory was based on the principle of what humans were supposed to do, but did not match normal human behavior. Psychologists then developed theories of descriptive decision making that considered factors such as heuristics, biases, emotions, stress, and fatigue—which seem more closely mapped to how humans actually arrive at decisions (Tversky & Kahneman, 1974; Basu, et al., 2022).
Rational Agents
Normative decision making assumes that human beings are rational agents who make informed, logical, and optimal decisions. Mathematician Peter Fishburn modeled human decision making against mathematical decision theory, which proposed that the decisions humans make in everyday life would be unbiassed and selected for the maximum likelehood of success (Fishburn, 1970). This theory is based on the precept of utility where humans will weigh the variables and associated options across multiple potential solutions and pick the optimal one without consideration of the time or level of cognitive effort required (Von Neumann, et al., 1944). Normative decision making does not account for the possibility of peoples biases, the affects of fatigue or stress, or the tendancy to provide quick responses. The theory seemed to be appropiate for decisions made in engineering, scientific, and economics design and research, but it does not match well with decisions people make in their daily lives.
Descriptive Models
The descriptive decision-making model accounts for the non-optimal choices people make in everyday decisions. Psychologists Keith Stanovich and Richard West noted the difference between normative and descriptive decision making could be accounted for by the systemic cognitive irrationalities in humans (Stanovich, et al., 2000). Humans prefer a more simplified decision-making process than the resource-intensive normative approach, and will replace the goal of finding the optimum solution with 'satisficing' where a 'good enough' choice is selected (Simon, 1957). Using minimal data stored in long-term memory, people take the mindless heuristic and bias-based approaches to make decisions. The simpler rule-based approach with heuristics can enable quicker decisions, but it can lead to non-optimal biased solutions, and allows for decision bias which is particularly troublesome for novices (Kahneman, et al., 2009). Three prominant biases are typicality (a representative heuristic will be selected without considering other possibilities), availability (a heuristic is picked that is readily available or has been recently accessed), and anchoring (an obtained information, or value, sets a bias point for future decisions) biases (Tversky & Kahneman, 1974; Lieder, et al., 2008). Humans prefer to simplify choices by weighting options and elimination by aspects. Heuristic possibilities will only be considered if they posess key variables or values, and others will be ignored (Kahneman, 2011). The framing effect occurs when people are given decision choices in either a positive (gain) or negative (loss) context, as humans are biased towards selecting positive outcome options. Human emotions, along with perceived cognitive load, affect decision outcomes (Stanovich, et al., 2000). Decision making is more likely to be driven towards short-term gratification, than longer-term priorities (Luhmann, 2009 & Kahneman, 2011).
Prospect Theory and Bounded Rationality
Psychologists Amos Tversky and Daniel Kahneman introduced prospect theory primarily in light of how people make financial decisions, and proposed that the three dominant factors affecting choice were certainty, isolation effect, and loss aversion. With certainty, an option that provides a guaranteed result is more likely to be chosen over other riskier alternatives that may provide better gains. The isolation principle states that choices are made in isolation without considering other possible related choices. People are risk aversive and more likely to select a choice that minimize losses than one that provides gains. (Kahneman & Tversky, 1979).
Daniel Kahneman described bounded rationality as the distinction between the decisions humans make based on their beliefs and biases, and the more optimal choices they should make proposed by rational agent models (Kahneman, et al., 2009). Biases, heuristics, emotions, fatigue, and limited working memory prevent humans from achieving unbounded rationality, the near perfect rationality described by economists. Bounded rationality ties into Simon's notion of satisficing and the principles of prospect theory (Simon, 1957 & Kahneman & Tversky, 1979).
Metacognitive Analysis of Users’ Decision Making on Morningstar Website
A webpage with a non-intuitive, or overly complex, environment lends itself to poor decision making. Interaction design can be described as a controlled display of affordances (the good and bad choices users are allowed to make in a system) where designers build a webpage’s affordance landscape to reduce the complexity of choices, and the possibility of error (Gibson, 1966 & Kirsh, 2004). Coordinating affordances along with the metacognitive processes (goal setting, strategy selection, self-appraisal, self-monitoring, and validation) is a form of ethical nudging to emphasize that good interaction design facilitates positive decision making by providing a look ahead, or anticipation of what to expect next. Website effectiveness is dependent on the availability of relevant information, as well as its presentation and organization. Part of good interaction design is to take a functionally complex (high content) website and create a group of simpler systems, along with reasonable support or help. These supports, or metacognitive aids, reduce cognitive load and enhance metacognition (Kirsh, 2004). Proper framing of decision start points will also ease cognitive loading and allow for consistent, and more optimal decision choices. Building a complex website—such as that of the to-be-discussed Morningstar—as collections of navigable simpler systems can be a difficult task; however, a highly-organized and intuitive environment of affordances with appropriate nudges, and proper framing, would minimize the complexity of metacognition and decision making that reduces stress, anxiety, and cognitive load for the users (Kahneman, 2011).
Figure 1. Homepage of Morningstar, Inc. (https://morningstar.com)
Morningstar is an investment research firm that provides market analysis and investment ratings for funds, ETFs, stocks, and bonds. The company is considered a trustworthy tool where individual investors and institutions can get up-to-date market information, along with unbiased and thorough analyses on possible investment choices. Figure 1 shows Morningstar’s dense homepage with information, options, and textual descriptions that seem ideal for expert users. The top main navigation menu is well categorized and the information architecture within the side (hamburger) menu at top left corner is logically structured, however, the body of the homepage seems chaotic and is clustered with multiple group subjects that are competing for attention. The lingos and terminologies on the page are written for experienced investors which can exacerbate anxieties for novices. If the users do not have matching heuristics in their long-term memory, they may resort to satisficing (cognitive narrowing) where they select a prominent item on the homepage, or abandon the site (Simon, 1957, Tversky & Kahneman, 1974); the lack of prior heuristics and variables would eliminate the possibility of simplifying choices via weighting options and elimination by aspects (Kahneman, 2011). For novices, there is also the possibility of selective omission where they start to block out reasonable choices that are not understood, or might be competing against a decision already made. The possible decision-making errors for novices increased as the cognitive loading effects of stress, emotion, fatigue, and anxiety are all competing for the same limited resources in working-memory (Jiménez, et al., 2009).
Figure 2. Morningstar’s landing page for Medalist Funds (https://morningstar.com/best-investments/medalist-funds)
Figure 2 shows Morningstar’s clean layout for the Medalist Funds, scrolling from top to bottom reveals 855 funds. Sorting is available on individual columns but no filter option exists to allow a search on subsets (e.g., a user is not able to select all 4-stars or better ratings of Large Growth funds with 5-Year Return of 10% or higher). This lack of filtering prevents the application of proper heuristic strategies to refine choices, which leads to fatigue, stress, and anxiety for all users to gather information by scrolling down and up. With the limited ability to group choices, novice users may be guided by the prospect theory’s risk aversion and likely only select 5-star rated funds, without consideration that other funds might be more appropriate for certain investment goals (Kahneman & Tversky, 1979). The magnitude of information on this page limits foraging capabilities and the ability to gain a scent to provide direction (Pirolli, et al., 1999). For some users, the investment cost in time and effort to select appropriate investments may lead to irrational decision making and poor choices (Kahneman, et al., 2009).
Conclusion
Humans’ decision making correlate to our metacognitive processes that are oftentimes determined by contexts and biases. With properly executed designs, we can emphasize the importance of metacognitive analysis through goal setting, strategy selection, self-appraisal & monitoring, and validation. Designers can coordinate effective interaction structures to be cognitively efficient and influence users with optimal choices in their environments.
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