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.
Buckner, R. L., & Carroll, D. C. (2007). Self-projection and the brain. Trends in cognitive sciences, 11(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 Psychology, 25(1), 96-103.
Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of personality and social psychology, 56(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 science, 8(3), 223-241.
Fazio, R. H. (1986). How do attitudes guide behavior. Handbook of motivation and cognition: Foundations of social behavior, 1, 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. Science, 317(5843), 1351-1354.
Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature neuroscience, 10(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 judgment, 49, 81.
Oettingen, G., & Mayer, D. (2002). The motivating function of thinking about the future: Expectations versus fantasies. Journal of personality and social psychology, 83(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 Neuroscience, 5, 79.
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599.
Siegel, S., & Allan, L. G. (1996). The widespread influence of the Rescorla-Wagner model. Psychonomic Bulletin & Review, 3(3), 314-321.
Sloman, S. A., & Lagnado, D. (2015). Causality in thought. Annual review of psychology, 66, 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 review, 4(2), 108-131.
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017). The hippocampus as a predictive map. Nature neuroscience, 20(11), 1643.
Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and social psychology review, 8(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 Sciences, 111(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 Psychology, 24(4), 505-516.