All posts by Kathryn Ambroze

Brainstorming: The Evolution of Thought Theories

Various types of mental frameworks have been developed in attempts to conceptualize the mind and the way we, as humans, think. Classifying the mind, perception and rationality is intended to aid in the explanation of consumer decision-making. This attempt to clarify conscious versus subconscious behavior, motivation and anticipation has inspired numerous researchers to form creative ideas about how the mind works. While applications of decision-making research are infiltrating new fields constantly, the foundational philosophies have supported it for decades. Deliberate and intuitive thinking are approached in various ways. Below is a highlight reel of important theorists who impacted the trajectory of classified thinking. Many theorists are not included in this condensed summary; however, it is important to note at least some of the vast research dedicated to the mysterious theories of the mind. 

The Head and the Heart:

It seems fitting to start with one of the most famous founding fathers of psychoanalysis, Freud. Sigmund Freud introduced a tripartite model of the psyche: id, ego and superego (Freud, 1923). The id is defined as the unconscious, instinctual and primitive aspect of the mind. The superego involves moral consciousness instilled from a parent or authoritative figure during development. The ego mediates between the id and superego as a conscious force through reason. Freud was one of the few psychoanalysts who introduced the idea that behavior is a result of both implicit and explicit factors. Freud’s attempt at explaining the mental life set a foundation for many theorists to follow. Researchers such as Nisbett and Wilson (1944), Stanovich (2004), Wason and Evans (1975), and Wilson and Dunn (2004) all agree with the idea that unconscious processes control behavior without our awareness. Other psychologists, such as Bargh and Ferguson (2000), further believe that the subconscious can even act deliberately. Discussions about thinking can easily turn to a question of ethics, which it often does. Conversations about free will and predetermined destiny are very much a part of the dialogue. It is the relationship and influence of implicit and explicit responses, or dual-processing thinking, that sparks such commentaries and questions that remain consistently at the forefront of our curiosity. 

Each dual-processing theory involves both rule-based processing and associative processing. Rule-based processing is conscious, explicit, motivated behavior, while associative processing is a learned behavior that occurs automatically. Theories springboard from rule-based and associative processing into unique interpretations about how each component, either independently or simultaneously, influence behavior (Smith & DeCoster, 2000).

Kahneman and Frederick (2002) discuss how implicit and explicit modes of thinking operate in conjunction with one another within their literature review. The discussion analyzes how terms have varied over time with ultimately the same meaning behind the new phrases (Kahneman & Frederick, 2002). There are so many synonyms associated with dual systems of thinking (see Table 1a and 1b) that information becomes unclear. Although it can be challenging at times, it is important to not be bogged down by the synonyms for rule-based processing and associative processing. Ultimately, the vast literature is trying to find the right terms to be able to convey an abstract concept into an educational message. The public became much more familiar with the dual system thinking through Daniel Kahneman’s novel, Thinking Fast and Slow which helped unify the terminology. System 1 is defined as the unconscious or instinctive reaction, while System 2 is considered a slower, more rational or deliberative way to comprehend ideas or stimuli (Kahneman, 2011). An honest attempt to keep thinking about thinking simple.

Tables 1a and 1b: Taken from Evans (2008), lists various dual-processing synonyms commonly used within the literature (1a). 1b includes attributes of dual systems of thinking, categorized into clusters.

The irony in problem-solving how we, as humans, problem-solve pushes research to further the conversation and provoke challenging discussions. Petty and Cacioppo (1986) developed a dual-processing theory model to explain how a resulting attitude varies based on the way information is processed. The effectiveness of a message and its various apparatuses to persuade one towards a certain behavior is not only the focus of Petty and Cacioppo’s work, but the overall objective of marketing. Many researchers, including Petty and Cacioppo, worked to understand how different approaches warrant various responses. Fazio (1986) also contributes to this dialog by proposing that attitudes drive behavior via associations given to objects, except when there is deliberate motivation to bring an awareness to one’s attitude.

Attitudes and biases that effect how consumers act are often connected to dual-processing theories. Other runoffs of dual processing include the study of implicit or explicit bias in stereotypes by Devine (1989). By studying the relationship between stereotypes and prejudices, Devine (1989) concludes that implicit responses may be independent of personal explicit impressions. A lot of social science work involves observing how we as humans react through our conscious bias.  

While these contributions are helpful, any dual-processing theory is subject to critics and rebuttals which help to inspire innovative ideas. Evans (2008) summarizes not only the different labels and attributes of numerous dual-process theories in literature, but also reminds readers that there is no strong evidence to support two thinking styles—it is just a way to empirically classify abstract ideas. Evans also argues that assuming every single component of thinking fits under one of two umbrellas may be faulty (Evans & Stanovich, 2013). Keeping a fresh perspective can be challenging since it is easy to get confused by the complexities of this type of research; however, Freud did not solely inspire the mass contributions to the formation of process thinking. As we continue to skim the surface of thought theories, there are a few other notable philosophers to address.

Responses: Automatic or Conditioned

Shifting the perspective to the origins of stimulus-response relationships brings us to Ivan Pavlov. Pavlov is known for his classical conditioning experiments where dogs were given food by experimenters in white coats. The dogs eventually developed an association between white coats and food, thus, were excited to see an experimenter in anticipation of the food (Seligman, Railton, Baumeister, & Sripada, 2013). Classical condition experiments have been replicated and applied to various environments and subjects, as you can see in Figure 1. The free learning model inspired one of the two components utilized in Pezzulo and Rigoli’s 2011 decision-science study. Pezzulo and Rigoli (2011) analyzed conditioned participants responses to a stimulus with the addition of having the participants consider the implications of the action. Three simulations included various choices that led to different motivating implications. Think about if a friend offers you food when you are famished compared to when you just ate a meal. Depending on your level of hunger, your response may vary. Moreover, Pezzulo and Rigoli’s work also intertwines the analysis of positive and negative projections (aka anticipation or dread). The study concluded that considering the overall objective rather than one’s current state leads to a higher reward since the future internal states are given values, thus influencing a decision. So, the anticipation could be the difference between that glorious first bite of a meal while your stomach is growling or feeling as if you are about to roll out the door from overeating. How you predict the food will make you feel will play a part in deciding if you will accept or decline your friend’s offer.

Figure 1: To provide another example of classical conditioning, (1) demonstrates a dog drooling in the presence of food and (2) not drooling at the sound of a bell. However, by ringing the bell when food arrives (3), the dog creates an association where hearing the bell=in the presence of food= drooling. Therefore, if the dog hears the bell and assumes food is coming, the dog will start to salivate—even if there is no food (4). 

Rescorla-Wagner’s model also branches off the work of Pavlovian conditioning in a different direction by analyzing motivation and reward based on anticipation of an unconditioned stimulus. According to Rescorla and Wagner, if something exceeds your expectations, the causal connection is strengthened. Contrastingly, if you are anticipating something and are let down, the connection is weakened, and in turn, you will try to avoid it in the future (Siegel & Allan, 1996). By anticipating an exhilarating feeling, chances of being disappointed increase (van Dijk, Zeelenberg, & van Der Pligt, 2003). Having this type of human response could be why so many critics were not impressed with the last Game of Thrones season. The hype from the previous episodes caused viewers to have extremely high standards that were not met, thus, causing people to react negatively. The model Siegel and Allan (1996) propose expands to various topics of human judgement, learning, perception and regulation. Reasoning is also discussed in this model to determine why a decision is made. It concludes that humans use transitive inference abilities when making decisions. So, when you are presented a situation repeatedly, you will be better equipped to determine how to respond. Critics are eagerly looking for loopholes (such as an unintended Starbucks cup cameo in a world of dragons), since the final season did not match the attention to detail as previous seasons. According to Siegel and Allan (1996), the Rescorla-Wagner model is a large part of the groundwork of basic learning processes research. We can thank the world in Winterfell for reminding us the cliché-but-true quote, “no expectations, no disappointments.”

Whether watching Game of Thrones or learning more about biometrics, advances in technology pushes content in research and entertainment. Kable and Glimcher (2007) examined aspects of decision-making science with the use of an fMRI to pinpoint certain brain regions that may be associated with reward. Their findings indicate that the ventral striatum, medial prefrontal cortex and posterior cingulate cortex all experience an increase in activity when a participant receives or expects a reward (Kable & Glimcher, 2007). While these findings are under certain assumptions due to the nature of fMRI, the research implies that the brain reacts to rewards when goal-oriented choices are decided. When deciding between choices, whether it be a second helping of mashed potatoes or marrying someone, it involves reflection on information and determining what response will have the most advantageous outcome. The expectation or delivery of a reward is a part of how we continuously strive for the best option. 

“Act as if what you do makes a difference. It does.”

– William James

Motivation to pursue anything, especially in finalizing a decision, is a major aspect in what drives our behavior. Strack and Deutsch (2004) reviewed literature on motivation with an overall takeaway that suggests social behavior includes impulsive actions and reflective behavior. From their findings, these responses have underlying adaptive mechanisms to either avoid or approach a specific behavior. These reoccurring social trends can be considered either motivating or demoting, thus, encouraging or deflecting a certain action. Purchasing another box of thin-mints when you walk out of a grocery store may be an impulse action, but it may also be influenced by the charming scouts who need to sell a few more boxes before reaching their goal. The environment, context and descriptors of the situation have just enough impact to convince you to tack on an additional goodie, since it is for a good cause after all. 

While the research that focuses on motivation, avoidance and attraction are all intertwined in explicit and implicit thinking, there is one more component to address in the way we critically assess the mind. Thinking as a design for doing is introduced by the American philosopher and psychologist William James. James believed that there is a difference between expectations based on probability and fantasies that are part of a thought stream when having a futuristic thought. Subscribing to the impression that doing will only influence the future has been coined as “prospection” by Gilbert and Wilson (2007) and Buckner and Carroll (2007). 

Figure 2: (From left to right) William James, Ivan Pavlov and Sigmund Freud.  From “Who Are Some of the More Famous Psychologists?” by K. Cherry, 2017, www.verywellmind.com/pictures-of-famous-psychologists-4020319. Public domain image.  

Future thinking, just as most of these topics, are applied in decision making, action planning and emotional regulation. Superficial thought, inner speech, vague images, vivid and consuming scenarios can all be considered forms of future thinking (Neroni, Gamboz & Brandimonte, 2014). Forms of future thinking can also vary from immediate to distant. Research has demonstrated that temporal aspects of future thinking, as well as viewing it as an expectation or a fantasy, influences the type of thoughts that occur. For example, thoughts focusing on the near-future tend to involve concrete details, anticipating how something is going to be accomplished and revolve around errands. Thoughts in the distant future seem more goal-oriented and rooted in relationships (D’Argembeau, Renaud & Van der Linden, 2011). Furthermore, having a positive expectation of these ideals increases motivation since there is an association of past successes tied to the thought, while a positive fantasy decreases that sense of motivation (Oettingen & Mayer, 2002). Depending on if you perceive your thought as a fantasy or an expectation, this makes sense. If you just expect yourself to cut all carbs and sugars and envision yourself with a six-pack, it can be demotivating by feeling overwhelmed by an idealistic goal and drastic demands. Now, imagine that you plan to make it to the gym this week because you are aware of the negative health risks of not being physically fit. There is both reflection on past experiences about learning that going to the gym pays off and recognition that you want to improve your current situation. It is a short-term objective with real concerns to keep motivation high.

Stachenfeld, Botvinick, and Gershman (2017) investigate the way information is stored in our brain to predict the future. Essentially the phrase “practice makes perfect” is how we work to reach most goals. There is a repetitive cycle of trying over until the most efficient way is conditioned into our behavior. Prediction also influences motivation, especially when the associations relate to awards (Schultz, Dayan & Montague, 1997). For example, if there are three steps necessary to play a game (identifying the game via packaging, setting up the game, and then playing the game), this research supports the notion that exposure to the packaging and setting it up to play the game will motivate you prior to playing. Simply put, the package may become motivating.

Animals allocate resources or evaluate environments to further insure the likelihood of a prediction. For example, by burrowing acorns in the dirt, gray squirrels can revisit them in the future as a food source.  Noting landmarks in the surrounding area gives the gray squirrel an advantage in retrieving the acorns when food is scarce. Dayan suggests that truncation—the strategy to proactively and continuously search for information—help animals adapt to situations well (Seligman, Railton, Baumeister & Sripada, 2013). This supports the notion that decisions are based on trying to improve the current moment rather than planning the end results in the beginning (Seligman, Railton, Baumeister & Sripada 2013). Sloman and Lagnado (2015) conducted research on how your focus determines the information you know, which in turn drives your reaction. Your mind anticipates likely outcomes and rationalizes through what to expect. By creating several continuous predictions, you can complete a mental simulation through associations (Sloman & Lagnado, 2015). Each of these efforts are utilized to better our ability to respond to a situation. 

Future Memories and Other Ironies

Future-oriented cognitions require forms of memory to represent what may happen in the future. Personal experiences or general knowledge all influence prospective thinking. Szpunar, Spreng, and Schacter (2014) developed a framework in which episodic (or experiential) and semantic (or factual) forms of memory can be applied to the four modes of future thinking: simulation, prediction, intention and planning. Each mode uses parts of episodic or semantic memory differently. For example, episodic intention focuses on setting a goal to do something in the future, while a semantic intention is the mental act of setting a general or abstract objective (Szpunar, Spreng, & Schacter 2014). It would be the difference between intending to buy a new pair of shoes versus learning a new language. Both components work together as a continuous dimension rather than a variable in which a memory can be categorized.

Figure 3: The time course of the three different system processes. It is important to note that each system is based on associations, therefore, are linked to memories even if the process focuses on present or future thinking.

The work of prospective thinking must involve discussions of memory since that is how associations are developed. Neroni, Gamboz and Brandimonte (2014) discuss if episodic future thinking can help carry out an intended activity in the future. Developing not only a future objective but also creating a plan about how to implement it was shown to improve remembering when compared to either spatial mapping or no preparation. Following the trend of Kahneman’s System 1 (implicit) and System 2 (explicit) thinking, System 3 has emerged to categorize future thinking. With the use of prospection, System 3 involves considering a hypothetical situation to determine not only how someone would react, but also to determine what would be considered the ideal. With the use of imagination and exploring various potential outcomes, future thinking gives us the agency to create and innovate ideas or plans. The endless possibilities of the future allow us to create whatever we want, since nothing is certain.

Even in the midst of writing this blog, new research has been released investigating how humans gather information to decrease uncertainty in social situations. FredmanHall and Shenhav (2019) explain how three interrelated mechanisms—automatic inferencing, controlled inferencing, and social learning— work together to alleviate the adverse feeling of being uncertain. Automatic inferencing involves predicting behavior based on both social norms of an environment and appearances. It is during automatic inferencing where inaccurate and systematical bias can impair how we anticipate an interaction. Then controlled inferencing is initiated to update the initial impression with information gathered from the interaction, as well as using prospective thinking to develop some sense of sympathy by envisioning what it would be like to be the other person. The final mechanism accounted for is social learning, where the information from both the past and current encounter are considered to inevitably determine how to approach a situation and diminish levels of uncertainty with the newly created perspective (FredmanHall & Shenhav, 2019). This research provides insight into how uncertainty motivates social behaviors claiming we have these mechanisms in place to “minimize the aversive affect associated with being uncertain about our own future states.”

Past, present and future thinking are not as much a linear timeline as they are intertwined processes. There are patterns that begin to emerge from selected thought theories mentioned. Taking these trends into consideration, we can accumulate a better approach for ideas that may better conceptualize how to discuss, categorize and implement mechanisms of thinking into research. Through the lens of implicit, explicit and prospective thinking, we can unpack components of memory, motivation and anticipation to really learn about how humans choose to approach the world around us.

Citations:  

Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the brain. Trends in cognitive sciences11(2), 49-57.

Cherry, Kendra. “Who Are Some of the More Famous Psychologists?” Verywell Mind, 2017, www.verywellmind.com/pictures-of-famous-psychologists-4020319.  

D’Argembeau, A., Renaud, O., & Van der Linden, M. (2011). Frequency, characteristics and functions of future‐oriented thoughts in daily life. Applied Cognitive Psychology25(1), 96-103.

Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of personality and social psychology56(1), 5.

Evans, J. S. B. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annu. Rev. Psychol.59, 255-278.

Evans, J. S. B., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on psychological science8(3), 223-241.

Fazio, R. H. (1986). How do attitudes guide behavior. Handbook of motivation and cognition: Foundations of social behavior1, 204-243.

Feldmanhall, O., & Shenhav, A. (2019). Resolving uncertainty in a social world. Nature Human Behaviour. doi:10.1038/s41562-019-0590-x

Freud, S. (1923). The ego and the id. SE, 19: 1-66.

Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the future. Science317(5843), 1351-1354.

Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature neuroscience10(12), 1625.

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. Heuristics and biases: The psychology of intuitive judgment49, 81.

Oettingen, G., & Mayer, D. (2002). The motivating function of thinking about the future: Expectations versus fantasies. Journal of personality and social psychology83(5), 1198.

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and persuasion (pp. 1-24). Springer, New York, NY.

Pezzulo, G., & Rigoli, F. (2011). The value of foresight: how prospection affects decision-making. Frontiers in Neuroscience5, 79.

Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science275(5306), 1593-1599.

Siegel, S., & Allan, L. G. (1996). The widespread influence of the Rescorla-Wagner model. Psychonomic Bulletin & Review3(3), 314-321.

Sloman, S. A., & Lagnado, D. (2015). Causality in thought. Annual review of psychology66, 223-247.

Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems. Personality and social psychology review4(2), 108-131.

Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017). The hippocampus as a predictive map. Nature neuroscience20(11), 1643.

Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and social psychology review8(3), 220-247.

Szpunar, K. K., Spreng, R. N., & Schacter, D. L. (2014). A taxonomy of prospection: Introducing an organizational framework for future-oriented cognition. Proceedings of the National Academy of Sciences111(52), 18414-18421.

Van Dijk, W. W., Zeelenberg, M., & Van der Pligt, J. (2003). Blessed are those who expect nothing: Lowering expectations as a way of avoiding disappointment. Journal of Economic Psychology24(4), 505-516.

Back to the Future: System 3

Our whole lives are spent anticipating what is yet to come. The time course of decision-making varies depending on the outcomes of both action and inaction. Based on the posed inquiry, you may have limits to how long you can consider your options to reaction. Deciding if you take the next exit on the highway, change the color of a brand logo, or dodge the football heading towards your face all require various amounts of influence from different styles of thinking. Past, present and future systems of thinking are strongly integrated. The way we prepare for the future is a large part of how we conduct ourselves in the present moment, and yet, both are influenced by past experiences. Through anticipation and imagination, the intentions and thoughts towards the future can prove themselves to be useful for leading action. 

Systems of thinking are utilized in literature to categorize abstract information such as thought processes. The research of Epstein (1994) states, “…there are two independent systems for processing information, experiential and rational, and that experiential relative to rational processing is increased when emotional consequences are increased.” Epstein’s approach at dual cognitive processing is expanded upon by Daniel Kahneman’s Thinking Fast and Slow. The book states that System 1 is unconscious, instinctive, and fast reactions. Thought of as a reflex, System 1 triggers an automated thinking process. Since it is considered nearly an instant decision, System 1 can have systematic errors for everyday decisions. There is little to no attention required for System 1, thus, making it possible to quickly decide the multiple decisions necessary to get through the day (Kahneman, 2011). System 1 thoughts can vary from running from the sounds of danger to preferring a brighter color bottle of shampoo. Small impressions enable us to create shortcuts, therefore, quickening our choices without deliberate thought. System 1’s thought process creates a continuous flow of interpretations. We can engage with the world around us while processing the vast amount of information experienced at any given moment because of System 1 thinking.

Figure 1: A general overview of System 1 and System 2.

 Unlike System 1, System 2 is described as the slower, more rational and deliberative notion of attention. System 2 requires our focus because it is a part of how we consciously do work, build relationships, and develop ideas. The self-aware process is critical and logical by constructing thoughts in a series of steps (Kahneman, 2011). If our attention on the stimulus is wavering, it is reflected in the performance. Fully engaging with information, such as providing feedback, is part of System 2 thinking. Challenging questions during an interview, bracing for the starter gun in a race or trying to solve a murder mystery on Netflix are a part of the System 2 thinking process.     

While these two concepts cover a large portion of thought processes, there has been an argument for an additional type of thinking involved in decision-making. System 3 considers the use of imagination by using past experiences to anticipate the future. Being able to create a sensory experience in your mind by conjuring up different scenarios is a unique progression that assists choosing what action to ultimately choose. Both System 1 and System 2 styles of thinking are utilized in the process by acting on associations and focusing on past experiences. With those contributions from the other systems, System 3 creates predictions in the mind and then prioritizes what is the best likely outcome of a potential decision. System 3 thinking is combined with the other modes of thinking by allowing consumers to deliberate on theoretical outcomes, and thus, experience an emotional preview that could be elicited by future-facing possibilities. Consider how you would feel if Google started to charge you each time you searched a new topic. Would you be confused, frustrated, appalled or a combination of these emotions? Do you think you would start using Bing in protest? These theoretical situations provide an idea of how you would feel and react to a potential future change. It is within the practical strategy of System 3 that provides an opportunity to understand what motivates consumers to make choices.

Figure 2: A visual representation of the three systems primary time courses. The systems overlap since the root of processing is associations. Therefore, retrospective thoughts can be applied to future thinking.

A major component of System 3 is the notion that prospective thinking requires generating, exploring, and evaluating past experiences to create multiple alternatives for the present. Developing mental simulations about how to progress in alternative ways create more advantageous decisions (Baumeister, Maranges, & Sjåstad, 2018). Prospection includes four aspects of simulation: navigational, social, intellectual, and memorial (Seligman, Railton, Baumeister, & Sripada, 2013). The ability to map out and envision different routes to get from one place to another is the navigational simulation. Social simulations, or episodic future thinking, include imagining hypothetical events that may happen (Szpunar, 2010). Additionally, proposing an idea in front of a board or to a close friend could be an example of a social simulation. Anticipating reactions, questions or consequences to specific conversations involves considering both the reaction of your audience and your response and is a valuable aspect of social simulation that allows you to prepare mentally for an interaction. The third type of prospective thinking involves critically thinking about how to explain or expand on an idea. Intellectual simulation would be the act of planning to conduct additional research about a topic or simply preparing how you will approach the topic with others. Finally, the fourth sector of prospective thinking involves recalling a situation and developing alternative approaches. Being grateful for wearing a helmet before a bike accident or wishing you turned sooner to avoid the pothole are both examples of this type of thinking. Remembering past decisions and considering hypothetical events that could have happened is known as memorial simulation (Seligman et al, 2013) or episodic counterfactual thinking (De Brigard & Parikh, 2019).

Say you are debating buying a new shirt on Amazon. As you scroll through different styles and colors, you click quickly on one of the top recommended choices [System 1]. Then, maybe you peek at the price and start to rationalize the purchase [System 2] by creating potential outfits or envisioning what the shirt will look like on you [System 3]. While developing these mental stimulations, you elicit a certain type of emotional response about how you would feel in it. During this very common scenario, each system thought-processes has some type of influence about not only if you decide to add the shirt to your cart but also if you are ultimately going to buy it. 

Since System 3 has a major anticipatory component, it useful when attempting to launch a new concept, product, or communications approach. It also may be helpful when first starting to build a brand to see what best resonates with a target audience. The analysis behind System 3 has a lot of potential to include an implication map to visualize how consumers are influenced or graphs to demonstrate different association strengths the consumer has with various attributes. Both qualitative and quantitative analysis can be applied to System 3 research. Play-based or projective testimonies share empirical responses of the consumer, while numerical data, such as time-pressured tasks or rating scales, provide statistical findings. By combining the information from both components of data, the researchers can suggest what is mentally rewarding.

How are market research studies with System 3 conducted?

While System 3 research is still being established, there are some initial attempts at intertwining this concept into a testing setting. One method that has emerged from the development of System 3 includes having a moderator lead a participant with the use of projective techniques. Simultaneously, there are individualized tactile tasks for participants to have the freedom to create, while sharing stories and metaphors to explain responses to deliberately chosen questions.

In the following example, the research topic is about Disney World. The study begins with a series of thought-prompts, asking participants to consider the current state of the industry. The questions are intended to stimulate participants’ imaginations by prodding sensory experiences about envisioning what they hear or see when they think about Disney World. The questions shift to forward thinking by asking what they expect may happen. The transition from present situations to upcoming predictions is to help them carry over current associations to their opinions about the potential future product. Being asked to consider how the company may plan forces them to consider various stakeholders’ perspectives on Disney World.

Figure 3: An example of an implication map from System 3 survey responses. Lighter blue are what participants tend to like, while darker blue is not as popular. Larger nodes are predicted to be influential, while the smaller nodes are less likely to be important. Note this is a hypothetical example not based off actual responses.

Following the series of questions is a basic, timed, check all that apply (CATA) question about what they feel will have the biggest influence on Disney World in the future. While CATA is not able to determine intensity, using this measure can be helpful if the participants find it difficult to verbalize perceptions. An open-ended prompt then asks the participants to name an additional factor not previously listed that may be influential. This encourages the consumers to use future-facing thinking to create personal contributions that could make an impact on the company. Finally, to get a better understanding of participants’ priorities among the CATA options, additional conjoint analysis questions are included to evaluate how each option would rank between each other. The questions emphasize potential scenarios based on the responses from CATA. It could say something such as, “If Disney Springs becomes extremely valuable, which of the following trends will be boosted as a result?” The responses are recorded and utilized to create a visual diagram expressing predictions (based on size) as well as fondness (based on color). The diagram is referred to as an implication map and is intended to provide a visual representation of the responses obtained during testing.   

Figure 4: Two types of tests used to gain an understanding of participants’ characteristics and emotional function: (left) the inkblots used during the Rorschach test and (right) building blocks methodology.

Another strategy to uncover insights about consumer anticipation or aspirations includes playing with building blocks (yup, think K’nex playtime). This System 3 methodology subscribes to the same concept of the Rorschach test. Both tests use different mediums to interpret characteristics and emotional function of the participant: Rorschach through inkblots, while System 3 is via building blocks. Essentially, a moderator poses to a focus group sequential questions that become more abstract as the conversation continues. Participants respond to these prompts both tangibly, by building block models, and verbally, through metaphor explanations or storytelling. For example, let’s say that the moderator is trying to learn about the best type of work infrastructure. Building a green box can symbolize nature because this participant values time outdoors. Furthermore, the participant shares experiences where nature has inspired great ideas or motivated the participant to think outside-the-box. By having a conversation during an activity, this method transforms thoughts into structures by remembering the past to imagine future outcomes. Intangible questions about feelings are intentionally asked to initiate metaphorical thinking.  Symbols are intended to be expressed through tangible objects, hence, a green box representing nature. The conversations that occur among the group inevitably elicit themes. Within a consumer space, it allows companies to gain a unique perspective on what participants view as futuristic proactiveness. 

There are some limitations with this version of System 3 analysis. Since participants are using small building blocks to create structures, there must be a certain level of finger dexterity. Best responses tend to be from participants under the age of 60 years old. While limiting a population sample is never ideal, this is not the only component to be cautious of with this methodology. This System 3 approach is extremely interactive. It relies heavily on the use of a moderator to provide prompts, create a group dynamic and maintain a relaxed atmosphere. Also, due to the high reliance on verbal communication, this approach works best with participants that are more extraverted. It may seem silly to point out, but introverts are consumers too! Limits based on personality-types of participants leave room for inaccurate depictions of what the general consumers are interested in.     

For those extraverts who are open to sharing, there are still some questionable influences due to the reliance of a trained moderator. Subliminal influences can affect how a participant responds. Something as small as vocal intonations or body language can potentially alter a participant’s experience. Yet, a huge component of this System 3 approach is collaborative. Models created by different participants are intentionally meant to build upon each other through the group dynamic and conversation. Research where parent-child partners or male versus female groups have utilized this qualitative research approach has shown this method can cause greater influences on how an individual would respond to a prompt.

The behavioral research process of analyzing prospective thinking relies on a lot of metaphors and symbols to explain what a model means. These narratives and conversations eventually lead to themes of the session. The type of personalities within each group influences the outcome of each research session. Another aspect of the sample that is skewed involves the personality types that qualify for this type of research. Participants are screened to be more susceptible to being verbally expressive. By only analyzing a certain type of personality-type, the findings being measured can be disputed. It can be challenging to overcome these types of concerns, especially if a small sample size is acceptable in this approach. 

The two different approaches mentioned in this article are not the first—nor the last— to utilize System 3 thinking. These methodologies are meant to gain a deeper understanding of how a new product experience would appeal to consumers. These approaches focus on gaining insight into company developments such as trends, team strategies, and product uses. The notion of future thinking allows the consumer to imagine multiple possibilities and gives them agency to envision ideals for businesses to use for future developments.

When would System 3 be beneficial?  

While System 3 is a newer concept and has plenty of components to revise, the use of future thinking in general can be extremely helpful. In everyday life, we engage in “what ifs” to learn from the past to better our future (Smallman & Summerville, 2018). There are also times where we recall experiences of great success with relief to appreciate that a situation had a positive outcome, thus, reinforcing if a similar situation is to occur again, we will be prepared. Fear and anxiety are often caused by future unknowns (Brown, Macleod, Tata, & Goddard, 2002). By formulating calls to action through future thinking, we exercise a sense of control.    

System 3 is a recent development within decision-making research, which means that there are plenty of kinks that need to be worked out. The most glaringly noticeable concern regarding how System 3 works is the inconsistency in its approach. As touched on earlier, some applications insist on including a moderator that prompts participants to discuss the stimulus. Associations, by nature, have bias interconnected with it. By having a moderator, with his or her own bias, involved in the projection technique leads to some concern. No one can control the implicit bias interspersed within the intonation of the way a script is presented.  Although the moderator can consciously (*shout-out to System 2*) attempt to remain consistent among all the trials, there is no way to guarantee each probe will remain the same. This could impact the participant experience, and thus, influence the answers provided in the study. However, there are always exceptions. In situations that involve a participant who struggles to articulate his or her responses, having a moderator help understand what the participant is struggling to express can be useful. Other methodologies use online questionnaires in which the questioning implies future-thinking; yet, there is no standardized language for prospection. With the novelty of prospection research, it raises the question about the definite claims capable of being made from this type of research. This is not a suggestion to halt the studies, but rather to analyze the results like a pilot study— the information reported may be accurate, but generalizations should not be made.

The “System” labels are theoretical constructs designed to help create a language to discuss the functions and processes of the brain. While this concept is not by any means new, developing a type of diction that is universally accepted will help advancement of how the field is able to address behavioral and market research. System 3 is still very much being refined and fine-tuned to find its space within the field. Moving forward with both System 3 and other labels that emerge, it is important to be simultaneously critical and open-minded to these progressions. System 3 thinking does not solidify the future but actively works to shape alternative possibilities. An appreciation for maybes and what-ifs further the development of any critically thought-out idea. Harnessing the importance of what a consumer believes may or may not happen guides us to better steer in the direction of a successful desired outcome.   

Citation:

Baumeister, R. F., Maranges, H. M., & Sjåstad, H. (2018). Consciousness of the future as a matrix of maybe: Pragmatic prospection and the simulation of alternative possibilities. Psychology of Consciousness: Theory, Research, and Practice5(3), 223.

Brown, G. P., Macleod, A. K., Tata, P., & Goddard, L. (2002). Worry and the simulation of future outcomes. Anxiety, Stress & Coping15(1), 1-17.

De Brigard, F., & Parikh, N. (2019). Episodic Counterfactual Thinking. Current Directions in Psychological Science28(1), 59-66.

Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American psychologist49(8), 709.

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

Seligman, M. E., Railton, P., Baumeister, R. F., & Sripada, C. (2013). Navigating into the future or driven by the past. Perspectives on Psychological Science8(2), 119-141.

Smallman, R., & Summerville, A. (2018). Counterfactual thought in reasoning and performance. Social and Personality Psychology Compass12(4), e12376.

Szpunar, K. K. (2010). Episodic future thought: An emerging concept. Perspectives on Psychological Science5(2), 142-162.

The Underlying Red Flags of Usability Testing

Usability testing has emerged in the market research world as a promising method to measure how consumers interact with a product, service or system. The goal of usability testing is to assess concepts such as understandability, learnability, operability to attractiveness or compliance (Rusu et al., 2015) in the consumer experience. Generally, when conducting usability testing, consumers complete a task while being observed by a researcher who notes if issues are encountered. At the core of usability testing, the goal is to minimize or eliminate distraction and find ways to optimize the information for the consumer. Usability testing also aims to discover ways to re-evaluate how to maximize product effectiveness and efficiency, in addition to improving consumer satisfaction (Rusu, C., Rusu, V., Roncagliolo, S., & González, C, 2015). These recommendations on how to improve performance issues are then utilized to further product development.  

Staying competitive in any market is a challenge. Those striving to be “the best” must be able to outline what and how to beat the competition in whatever category is prioritized at the top. A prime example of this can be seen in the transportation industry. The growth of mobile apps has influenced companies like Uber, taxis and Lyft. To remain competitive, Lyft invested in usability testing for its mobile app. The results suggested that the app needed to be revamped. The company redesigned the interface to fit the consumers’ wants and needs through simplifying maps and driver information (Chen, 2016). By continually striving to improve and remain up-to-date, Lyft remains competitive in the field.

Having an unbiased consumer complete tasks or interact with an item allows the researcher observing the participant to gain a better understanding of enjoyment, satisfaction, confusion or criticism in real time throughout the entire test. Companies like Lyft utilize usability testing to advance in the market without exploiting funds or time. Additionally, feedback from the target audience directly minimizes the risk of a product failing, thus, improving sales and helping the company better position itself within the market (Hawley, 2010).

So, if usability testing works, why isn’t everyone using it? It is a good question with a couple of red-flags among the answers…  

The first red flag of usability testing lies within the typical sales pitch about why it is so great: it’s overpromising by guaranteeing more concrete solutions than it can provide. A big part of the usability testing appeal is the claim to uncover a lot of answers. Having such broad and lofty goals creates vague conclusions that are challenging to apply to real-world situations (from deliriously opening a bottle of medicine from bed during a sick day to online shopping at midnight for the perfect date outfit). The oversimplified gray areas in usability testing can create confusion. Tractinsky (2017) questions if usability contributes to satisfaction, or if satisfaction contributes to usability. Odd coexisting variables, such as satisfaction and usability, are feasible due to the fluidity of the current definitions. The lack of consistency makes it possible for current researchers to structure usability testing to best satisfy the goals of a project. What usability is, and the way it should be tested/measured, should remain consistent, regardless of the type of design activity being applied.

Usability testing umbrellas many different trial methods, so much so that there is no standard approach. Having generic guidelines can lead to various interpretations of overarching measures to create note-worthy conclusions. Whatever mode of usability testing is applied to a study should neither be determined by findings, nor should it be altered to fit the framework of a research question (Arnowitz, Dykstra-Erickson, Chi, Cheng, & Sampson, 2005). Let’s say that you wanted to test a new type of scissors. Comparative usability, where multiple scissors are compared to one another, verse explorative usability, an approach to testing content and functionality of a single pair of scissors, are vastly different approaches to determining how the new scissors model resonates with a consumer. This concern could be rectified with a consistent, standardized methodology (i.e. questionnaire) or definition. If you want to run a scissors study with usability testing, a clear step-by-step approach to get valid results should be meticulously determined.  Essentially, walk, don’t run with scissors.  

Usability testing delays research progression just as interactive product design is hindered by debatable measures (Veral & Marcias, 2019). A system must be developed to help build a solid foundation of preliminary research for future research to progress from the initial findings. Such ambiguity makes it near impossible to compare studies since there is such a wide difference in approaches. If the objective is to create a reputable usability test, then it should have a consistent framework with rigorous definitions and strong evidence to reach sound conclusions.

Part of the general problem involves recognizing that any type of usability testing is a construct, not a real-world phenomenon. Maybe your dog is barking, your roommate is on the phone or the neighbor’s car alarm is going off for the third time today. Whatever the situation may be, a controlled environment will never duplicate a real-life scenario. This is especially true because environments are constantly changing. There are constraints to all research in this regard, but that must be acknowledged prior to approaching how an experimental measure will be conducted. Similarly, usability seeks to gain access to the user experience; however, that is not entirely possible since the participant always has an awareness that he or she is a part of a study. The non-naturalistic methodology (aka being told to complete a task) will not reflect the consumer’s real experience.

There are various quantitative metrics used to validate design concepts in usability testing: time spent on a task, success and failure rates, number of clicks needed to complete a task, etc. These measures cannot guarantee that the result is because of the product, system or service. For example, there is no guarantee that the person is focusing on the task rather than a meal from two days ago, and therefore could affect time spent using the item. For a stimulus to become important for a user, there must be some type of motivation to take part in the task (Hertzum, 2016). Understanding these initial mechanisms will develop a stronger methodology to analyze usability results.

The lack of a stable procedure in usability testing can lead to components of the research being problematic. One controversial point within usability research is sample size. It is common practice in usability testing to have 5-7 participants in a sample size. This range of participants is heavily debated; however, a sample size of 5 is utilized in practice frequently because it is budget-friendly (Lindgaard, G., & Chattratichart, J, 2007). While the sample size for all research remains to be subjective, it should be noted that having only a few participants provides a limited amount of feedback.

There are other common research methods that are frequently used and should be questioned for their effectiveness and efficiency. For example, one usability approach involves what is called “thinking aloud” where the participant speaks during a task while an evaluator observes behavior and listens. This method can be divided into two segments: classic and relaxed thinking aloud. Both force the participant to evaluate his or her experience in the explicit, which may or may not be what is verbally expressed. The consumer can say anything that comes to mind, which may be unnatural and forced, especially for prolonged testing periods (Hertzum, 2016). It may also be distracting for the consumer to complete the task while simultaneously explaining each step verbally, creating a time lag for task completion. With such obvious confounds, why still use this as a method? It may be thatthere hasn’t been another measure developed to extract consumer opinions that supersedes the thinking aloud method.

Qualitative data in usability testing includes observing body language, hand movement, facial expressions and facial changes like squinting. A lot of these metrics are left up to interpretation of the observer, who comes with his or her own set of bias. Yes, you can have a recording of the research session; however, a video recording only provides one angle of the respondent and still does not give insight into what the consumer is considering. Think about the subjectivity in hand motions. A gesture’s intention can vary culturally and socially. Hand gestures are used to communicate, but it’s very challenging to know exactly what something like holding your head up with your hands could mean.   

Certain components of usability testing have evolved substantially over the last few years. As interaction paradigms, technology, and software development rapidly increased, the potential avenues of usability testing have followed suit. Several studies have been conducted to refine components of usability testing such as the questionnaire and how to evaluate responses to make them more standardized. Advancements included simplifying ways to use number scales rather than descriptive words when describing a task or developing systematic ways to compare response charts automatically (Huisman & Hout, 2010; Merčun, 2014; Adikari, S., McDonald, C., & Campbell, J., 2016; Berkman, M. I., & Karahoca, D., 2016). This progress should not be undermined and suggests that professionals are aware of the strides necessary to appropriately practice usability testing. 

Usability testing, like anything in life, will have unexpected complications. Each interface upholds its own unique challenges, thus pushing the focus to develop more user-friendly goods or services (Krug, 2000). For example, web design testing can appear simple at first. However, most companies need the web design to now be compatible with not only a desktop, but also a mobile app, a tablet, and a smart phone. These different interfaces are dramatically different from a visualization perspective as well as for human-technology interaction. Something as simple as scrolling capabilities can have a huge influence on what may or may not be strong areas of interest on the stimuli. Learning to account for these unforeseen difficulties during research will help improve usability testing.

Changing trends of familiarity can also influence results within usability testing. Target audiences are another vital component of any study. Among the general population, 77% of Americans have smartphones, with younger adults being more likely to use the mobile device as their primary connection to the internet (Pew Research Center, 2018). Using a mouse or a touchscreen can make a huge impact on how someone interacts based on his or her performance with technology. Finding measures to scale hidden issues, such as the target audience’s technological capabilities, aids the battles that usability testing is facing.


Considering all these red-flags, it makes sense why usability testing isn’t the magic one-size-fits-all solution. We live in a world that requires an extremely fast turnaround in technology. Some companies bypass usability testing completely and just release to the public as much content as possible to see what sticks. Other companies, such as Lyft, want to conduct studies with the fastest turnaround possible. The last decade alone has seen extensive growth in usability because of the emergence of smartphone advancements, AI, website design, etc. It is a challenge to meet the needs of such a rapidly growing market.  

Currently, usability testing can be applied in any field (alluding to the potential that it may be spread too thin to be effective). Usability testing tries to gain a grasp of how consistently a user interacts with an interface, or memorability, and the ability to understand how to use the app, or learnability. To further understand concepts like memorability and learnability, research tools such as eye-tracking have been integrated into usability studies to help determine parameters such as visual attention (Frith, 2019). Any item being tested includes variables which influence a participant’s ability to perform on a specific task. Color, texture, font, font size, images and format are just some of the many components that contribute to the overall picture of how a stimulus is presented. There is a potential for usability testing to provide truly powerful tools for optimizing products. However, it needs to define and progress with the competitive space in which its being utilized.   

Consumers have quirks that influence the interactions between a consumer and software, products or a service. Usability professionals must challenge themselves to recognize the disconnect among testing methodologies and objectives. Defined terms will help to develop a standard methodology, and ultimately, promote a stronger research.

At HCD, we advocate for always using the right tool for the right question. Our motto is “Prove it.” For usability testing, our goal is to make sure that the methodology we use is appropriate for the experience, and that the results are meaningful and actionable. This list of red flags highlights the need for this sort of approach. As a blanket field of research, usability testing can be useful, but fraught with misuse. But if you make sure to use the right tool for the right questions, you may just be able to ensure product success. Please contact Cara Silvestri (cara.silvestri@hcdi.net) regarding any additional information about how we can help you better your product, service, or system.


Citations:

Adikari, S., McDonald, C., & Campbell, J. (2016). Quantitative Analysis of Desirability in User Experience. arXiv preprint arXiv:1606.03544.

Arnowitz, J., Dykstra-Erickson, E., Chi, T., Cheng, K., & Sampson, F. (2005). Usability as science. interactions12(2), 7-8.

Berkman, M. I., & Karahoca, D. (2016). Re-assessing the usability metric for user experience (UMUX) scale. Journal of Usability Studies11(3), 89-109.

Chen, J. (2016, June 21). Lyft Re-design Case Study – UX Collective. Retrieved from https://uxdesign.cc/lyft-re-design-case-study-3df099c0ce45

Frith, K. H. (2019). User Experience Design: The Critical First Step for App Development. Nursing education perspectives40(1), 65-66.

Krug, S. (2000). Don’t make me think!: a common sense approach to Web usability. Pearson Education India.

Lindgaard, G., & Chattratichart, J. (2007, April). Usability testing: what have we overlooked? In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 1415-1424). ACM.

Hawley, M. (2010). Rapid Desirability Testing: A Case Study. Accessed online15(04), 2010.

Hertzum, M. (2016). Usability testing: too early? too much talking? too many problems?. Journal of Usability Studies11(3), 83-88.

Huisman, G., & Van Hout, M. (2010). The development of a graphical emotion measurement instrument using caricatured expressions: the LEMtool. In Emotion in HCI–Designing for People. Proceedings of the 2008 International Workshop (pp. 5-8).

Merčun, T. (2014). Evaluation of information visualization techniques: analysing user experience with reaction cards. In Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization (pp. 103-109). ACM.

Pew Research Center. (2018). Demographics of mobile device ownership and adoption in the United States. Retrieved from www.pewinternet.org/factsheet/mobile/

Rusu, C., Rusu, V., Roncagliolo, S., & González, C. (2015). Usability and user experience: what should we care about?. International Journal of Information Technologies and Systems Approach (IJITSA)8(2), 1-12.

Tractinsky, N. (2017). The Usability Construct: A Dead End? Human–Computer Interaction, 33(2), 131–177.

Veral, R., & Macías, J. A. (2019). Supporting user-perceived usability benchmarking through a developed quantitative metric. International Journal of Human-Computer Studies122, 184-195.