System 1 vs. System 2 Thinking

Abstract

This paper explores the dual-processing hypothesis of the mind, Systems 1 and 2, by examining debates between cognitive and evolutionary psychologists. I structure the discussion in a back-and-forth manner to emphasize the differences. I show that, while the majority of cognitive psychologists now embrace the dual-processing theory of the mind, Systems 1 and 2, there are still some who disagree. Most evolutionary psychologists, in contrast, dispute the existence of System 2, a domain-general mind, although some disagree. However, a consensus is growing in favor of System 2, although evolutionary psychologists’ concerns must be addressed. The uniqueness of this review is that it contrasts the perspectives of cognitive psychologists with evolutionary psychologists, which is uncommon in the cognitive psychology literature, which tends to overlook evolutionary viewpoints.

1. Introduction

It is widely accepted that the human mind is specialized for specific domains. But is there a domain-general mind? Cognitive psychologists concur; however, evolutionary psychologists find this notion too pricy to accept. The specialization of different cognitive processes to handle specific types of information or tasks makes the mind domain specific. This means that the mind is not a single, general-purpose processor capable of handling all types of information equally well, but rather a collection of specialized modules, each optimized for a specific type of information or task.

Most cognitive psychologists now accept the dual-processing theory of the mind, which states that the mind operates in two separate but interconnected systems: the automatic system and the controlled system. The automatic system is a collection of subsystems known as System 1. It is fast, intuitive, and works unconsciously. It is responsible for processing information that is readily available in our memory and has been repeated several times. This system is in charge of our first reactions to situations and emotions. In contrast, the controlled system, or System 2, is slower and more deliberate. It is in charge of conscious thought, reasoning, problem solving, and decision making. This system is more adaptable and flexible, allowing us to override our initial reactions and critically evaluate information. System 1 is often referred to as the “gut feeling” mode of thought because it relies on mental shortcuts known as heuristics to make decisions quickly and efficiently. When the information presented is new, complex, or requires conscious thought, System 2 is used. It is frequently referred to as the “thinking” mode of thought because it relies on effortful processing to reach a conclusion.

Both systems collaborate to help us process information and make decisions, but which system is used depends on the situation and type of information being processed. Understanding the differences between these two systems can help us better understand why we sometimes make intuitive decisions and why we sometimes need to exert conscious effort to solve more complex problems. This theory has important implications, not only in psychology, neuroscience, and economics, but also in marketing and education, where it is used to explain why certain messages are more convincing than others and why certain learning approaches are more effective than others. Seminal works on System 1 and System 2 thinking are [1,2,3,4,5,6,7,8].

Meta-analyses generally support the dual-processing theory of the mind, finding evidence for the existence of two distinct cognitive systems and the idea that they can influence behavior and decision making in different ways [6,9,10,11,12,13]. In contrast, certain researchers have contended that the theory lacks empirical backing and that the differentiation between System 1 and System 2 thinking is not distinctly outlined or universally applicable. They claim that the theory is based on anecdotal evidence. This criticism should not be dismissed in the face of supportive meta-analyses because meta-analyses alone cannot resolve an issue. A meta-analysis is a statistical method used to merge findings from several studies, aiming to derive a conclusion regarding a particular research question. Meta-analysis has the advantages of increased statistical power, improved precision, and finding integration. However, heterogeneity, study quality, selection bias, data availability, and model selection are all issues. Meta-analysis has grown in popularity in recent years. However, it is overused. For instance, textbooks improperly utilize their findings to resolve a controversy [14]. Some basic references on the pros and cons of meta-analysis are [15,16,17,18,19,20,21,22].

An additional critique is that the differentiation between System 1 and System 2 thinking is excessively simplistic and fails to encompass the intricacies of human cognition. Several critics argue that there is no clear distinction between these two systems and that many mental processes involve the integration of both. The dual-processing theory oversimplifies complex human cognition processes and does not fully capture how the mind has evolved to process information and make decisions. Detractors contend that the dual-processing theory of the mind frequently relies on laboratory experiments that inadequately capture the intricacies of decision making and reasoning in real-world scenarios. They also claim that the theory is too vague and does not provide clear guidelines for determining whether a situation requires System 1 or System 2 thinking. This can lead to ambiguity in the interpretation of results.

Besides that, some evolutionary psychologists are skeptical of the theory because they do not see clear evolutionary mechanisms explaining why humans evolved to process information in two ways. As a result, more nuanced and detailed accounts of the processes involved are required, as is a better understanding of the interactions between the two systems. This should be taken seriously as a research agenda. Furthermore, some evolutionary psychologists argue that the dual-processing theory is based on Western, individualistic perspectives and fails to take cultural and individual differences in thinking styles and preferences into account. This should also be considered in the future research agenda.

The mind is domain specific for a variety of reasons. First, the brain has evolved to deal with a diverse set of environmental challenges and opportunities, and different types of information necessitate different types of processing. Processing visual information, for example, necessitates different neural circuits and mechanisms than processing auditory or linguistic information. Second, domain specificity improves efficiency and processing speed. The brain can optimize neural circuits and processing mechanisms for each type of information or task by specializing different cognitive processes to specific domains. Third, domain specificity can aid in the prevention of interference between cognitive processes. If different types of information or tasks were processed by the same domain-general cognitive mechanisms, the risk of interference between them would be increased, potentially impairing overall cognitive performance. This supports modularity. However, there are evolutionary reasons for the modules to communicate with one another.

Evolutionary psychologists are willing to accept the dual-processing theory of the mind if it is based on evolution by natural and sexual selection. The difference between System 1 and System 2 thinking must be the result of evolutionary pressures shaping how humans think and process information. From this perspective, fast and intuitive System 1 thinking is an adaptation that has evolved to process survival-critical information quickly and efficiently. For instance, the ability to quickly identify possible threats and respond to emergencies is a crucial survival trait that evolution would have favored.

Most evolutionary psychologists believe that there is little evidence to suggest that System 2 thinking evolved as a distinct cognitive mechanism. What cognitive psychologists call System 1 and System 2 thinking are more likely products of the same evolved cognitive processes. However, an increasing number of evolutionary psychologists now believe that slower and more deliberate System 2 thinking evolved to allow for more careful and controlled decision making in situations where quick and intuitive decisions are insufficient. Perhaps System 2 thinking would have been useful for solving complex problems and making long-term decisions about survival and reproductive success. In such cases, the modules must communicate with one another. Specialized mechanisms must be interchangeable or transferable across domains. As a result, a more nuanced view of the mind that recognizes both domain-specific and domain-general processes is possible. This is evidenced in this review.

When discussing the disagreements between cognitive and evolutionary psychologists regarding the roles of Systems 1 and 2, I use a back-and-forth methodology to underline the differences. The goal is to show that, while the majority of cognitive psychologists now support the dual-processing theory of the mind, some still disagree. However, there is rising support for System 2, despite the concerns of evolutionary psychologists. The significance of this review is that it highlights this contrast, a unique perspective in cognitive psychology literature that typically overlooks evolutionary viewpoints. The literature on this topic is vast; thus, I will focus on works directly linked to the controversy. Nonetheless, in the final section, I comment on recent developments that may indirectly add to the debate.

The review is organized as follows after this introduction. Section 2 presents the standard viewpoint of evolutionary psychologists who challenge the existence of System 2. Section 3 examines cognitive psychologists’ reactions to the denial of a domain-general mind. Section 4 crystallizes the cognitive psychologists’ point of view. Section 5 shows that other cognitive psychologists are likewise doubtful of System 2. Section 6 shows how some evolutionary psychologists are beginning to accept a domain-general mind. Finally, Section 7 provides some concluding remarks.

2. Attack of Evolutionary Psychologists

Mainstream cognitive psychology is founded on a number of fundamental assumptions that evolutionary psychology challenges. Cognitive psychologists believe that cognitive architecture is all-purpose and devoid of content [23]. For example, information processing devices responsible for food choice would be the same as for mate and habitat choice. The capacity for reasoning, learning, imitation, goal-oriented actions, recognizing similarities, conceptualizing, and memory retention exemplify general-purpose mechanisms.

Conversely, most evolutionary psychologists hold the opposing viewpoint: the mind comprises numerous specialized mechanisms, each finely tuned to address distinct problems [24]. This modularity explains why people have different cognitive strengths and weaknesses, and why some people are better than others at certain types of processing. Modular processes are fast, mandatory, domain specific, informationally encapsulated, cognitively impenetrable, facilitated by specific neural architecture, susceptible to idiosyncratic pathological breakdown, and of fixed developmental sequence [25,26]. Modularity enables faster processing and greater accuracy in specific domains and provides a parsimonious explanation of cognitive processes by implying that different types of processing can be accounted for by different specialized modules rather than a single domain-general mechanism. Seminal works on modularity are [25,26,27,28,29,30,31,32,33].

Given that cognitive psychology views the mind as a versatile information-processing tool, limited consideration is directed towards the selection of stimuli for cognitive experiments. Frequently, cognitive psychologists choose stimuli primarily based on their ease of presentation and experimental control. Triangles, squares, and circles are used in studies rather than natural categories such as relatives, partners, enemies, or edible objects. Because artificial stimuli lack “content”, they are preferred. For instance, in memory studies, nonsensical syllables are occasionally employed to avoid interference from real words with meaningful syllables. Nevertheless, the rationale behind using such devoid-of-meaning artificial stimuli becomes valid only if the mind functions as a general-purpose information processor. If cognitive mechanisms are specialized to process information in specific tasks, it makes less sense [24].

According to evolutionary psychologists, another core presumption of cognitive psychology is “functional agnosticism”, asserting that the study of information processing mechanisms can occur without a full grasp of the specific adaptive issues they evolved to address. Ultimate (functional) explanations supplement purely proximate (causal) explanations by framing them in terms of survival value or function. Why does that bird sing so much more in the spring? The proximate explanation is that longer days cause hormonal changes, whereas the ultimate explanation is that it attracts breeding partners. The goal of evolutionary psychology is to find ultimate explanations for the study of human cognition [24]. In a similar vein, evolutionary psychologists posit that comprehending the functioning of human cognitive processes—such as categorization, reasoning, judgment-making, and memory retention—necessitates a prior understanding of these processes’ underlying functions, much like we need to grasp the primary role of the human liver (filtering out toxins) to understand it. These activities are carried out by cognitive mechanisms.

The following are the fundamental assumptions of evolutionary psychology [28]: (1) The human mind consists of evolved information-processing mechanisms, intricately woven into the nervous system. (2) These mechanisms and their developmental programs are adaptations shaped by natural and sexual selection across ancestral environments over evolutionary epochs. (3) Numerous mechanisms exhibit functional specialization, driving behaviors that effectively address particular adaptive challenges, such as mate selection, language acquisition, and cooperation. (4) For effective functional specialization, many mechanisms necessitate a comprehensive content structure.

Most cognitive psychologists, in contrast, accept the hypothesis of domain-general cognitive mechanisms, which stem from the behaviorists’ domain-general learning process [28,34]. One flaw in this hypothesis is that it ignores the existence of information sets that cognitive mechanisms have been specifically designed by evolution to process. A computer is also a domain-general information processor, but without “combinatorial explosion” issues. If the mind were a domain-general program with no processing rules, it would be confronted with a dizzying array of options. Assume you need to make 100 decisions in the first minute and another 100 in the second. There will be 10,000 possible combinations (100 × 100) after only two minutes. One million in three minutes (100 × 100 × 100), and so on [28]. The computer does not experience this combinatorial explosion because we program specific tasks for it, greatly reducing its set of decisions to make. However, the domain-general mind hypothesis of cognitive psychology does not specify these types of programs. Evolutionary psychology, however, sees them as issues of survival and reproduction.

Cognitive psychology will always be rooted in computational theories [35]. A computational theory defines a problem as well as the mechanism for solving it. This means that it specifies the information processing mechanism’s function [23]. The foundational principles of computational theory include: (1) Problem solving involves information processing mechanisms. (2) These mechanisms solve problems by considering their inherent structure. (3) To elucidate a mechanism’s structure, comprehension of the problem it was designed to solve and its purpose is essential. However, computational theory on its own does not outline how a mechanism resolves an adaptive problem, as each problem possesses multiple potential solutions.

Computational theories do not negate the requirement for scientific experiments to test hypotheses about problem-solving strategies in organisms. Instead, they narrow down the possibilities by outlining what is necessary for a successful solution. This process allows computational theories to eliminate many non-viable solutions for an adaptive problem. An important constraint for evolutionary psychologists is that information pertinent to solving human adaptive problems must have been recurrently present in ancestral environments. Consequently, humans (and possibly other species as well [36]) possess numerous specialized psychological mechanisms, each dedicated to addressing specific adaptive challenges [24].

Steven Pinker [37] encapsulates the viewpoint of evolutionary psychologists as follows (p. 75): “The idea that a solitary, general substance could excel in perceiving depth, managing hands, alluring a mate, nurturing offspring, eluding predators, and outwitting prey, devoid of some level of specialization, lacks credibility. Suggesting that the brain overcomes these challenges solely through ‘plasticity’ is akin to asserting that it accomplishes them through magic”. Yet, while the majority of evolutionary psychologists agree with this viewpoint, there are those who believe that in addition to specialized mechanisms, humans have also developed domain-general mechanisms [38,39,40,41,42,43].

Examples of these general systems include general intelligence, concept creation, analogical reasoning, working memory, and classical conditioning. Working memory is the portion of short-term memory that pertains to conscious perceptual and verbal processing occurring in the present moment. Classical (Pavlovian) conditioning refers to association-based learning. General intelligence is the fluid capacity to integrate multiple cognitive abilities in order to tackle a novel problem. Evolutionary psychologists who recognize domain-general mechanisms contend that while recurring features of adaptive challenges lead to specialized adaptations, humans encountered numerous novel problems without frequent recurrence for dedicated adaptations to develop.

One instance illustrating the interplay between domain-specific and domain-general processes is found in Baddeley and Hitch’s working memory model. This model underscores the significance of temporarily storing and manipulating information for cognitive tasks. While the model primarily centers on the central executive and the phonological loop, it also introduces the notion of domain-specific “slave systems”, such as the visuospatial sketchpad and the phonological store. This situation presents a notable tension between the concept of distinct sensory modules and the assumption of universal computational operations in working memory, often rooted in learning mechanisms such as Hebbian plasticity (the process through which information is encoded and retained in neurons in the brain) and long-term potentiation (the strengthening of synapses resulting in a lasting enhancement of signal transmission between neurons).

Within the realm of working memory, Baddeley and Hitch’s model does imply a degree of domain specificity through its separate slave systems catering to different types of information. Nevertheless, the central executive component, responsible for directing attention and manipulating information, is commonly viewed as more domain-general in nature. Consequently, it becomes evident that domain-specific modules may indeed collaborate with domain-general processes to facilitate the execution of complex cognitive tasks.

Furthermore, we tackle outdated adaptive problems in innovative ways. For instance, we procure food from vending machines, tools from hardware stores, and partners through the Internet. Humans were able to thrive in environments that were continually changing and vastly dissimilar to the Pleistocene [41]. General intelligence appears to have developed to address non-recurring difficulties (associated with quickly changing surroundings) in pursuit of evolutionary goals or to discover novel answers to existing problems. Quickly changing ecosystems include unpredictable temperature changes, variations between ice ages and hot, dry summers, and rapid changes caused by the activity of volcanoes and earthquakes [41]. Throughout the course of human evolution, numerous information patterns were highly varied, which may have favored the creation of more generic, open-minded psychological systems. There is a need for domain-general mechanisms to deal with novelty, unpredictability, and variability [40,44].

Despite these arguments, most evolutionary psychologists are skeptical that domain-general mechanisms evolved. General intelligence could potentially represent a domain-specific adaptation targeting a particular category of challenges—specifically, those that are evolutionarily unprecedented [45]. Demonstrating proficiency in evolutionarily novel activities such as using the Internet or driving a car does not necessarily indicate domain-general adaptations. We can train a bear to ride a bicycle or a dolphin to groove to rock music, but this does not automatically infer that the adaptations facilitating these new behaviors are widespread [46].

Hence, the presumption of general processing mechanisms presents at least two significant challenges: (1) The earlier-mentioned combinatorial explosion issue, wherein unrestricted general mechanisms could generate an infinite array of potential behaviors. This would leave individuals unable to discern effective adaptive solutions from the multitude of unsuccessful ones. (2) Defining a successful adaptive solution varies across domains. Qualities essential for sound food selection, for instance, differ from those essential for favorable mate choice.

In summary, within the realm of evolutionary psychology, it remains premature to definitively conclude whether humans possess domain-general mechanisms alongside the established domain-specific mechanisms. But one thing is certain: the domain-specific mind assumption has been successfully used to discover important mechanisms, and it remains to be seen whether the domain-general mind assumption will yield comparable empirical discoveries [24]. But another thing is certain too: the human mind cannot have separate and isolated mechanisms.

Evolution favors specialized mechanisms that collaborate well and in a variety of combinations. The adaptations interact with one another [24]. Certain mechanisms’ data provides information to others. Internal data from sight, smell, and hunger help determine whether a food is edible. There is no information encapsulation in adapted psychological mechanisms [47], and thus no modularity. If information were encapsulated, psychological mechanisms could only access independent information and could not access information from other psychological mechanisms. There must also be supermechanisms that specialize in ordering and regulating other mechanisms. A man walking through a forest, for example, may come across a hungry lion, a bush full of ripe fruit, and a receptive female. Initially, he might opt to steer clear of the lion, even if it means passing up on the fruits and sexual opportunity. He may choose to pick some fruits before fleeing from the lion if he is starving. His psychological mechanisms interact in a variety of ways and are activated and deactivated in unpredictable patterns [24]. In computer science, supermechanisms that specialize in ordering and regulating other mechanisms are known as daemons.

3. Cognitive Psychologists Fight Back

Keith Stanovich [48] summarizes cognitive psychologists’ responses to the fundamental criticisms of evolutionary psychologists. As previously stated, the majority of evolutionary psychologists deny the existence of domain-general processing mechanisms. As a result, they accept the modularity hypothesis. However, the majority of cognitive psychologists are opposed to modularity [49,50,51]. System 2 is the term cognitive psychologists use to describe domain-general processing mechanisms, as seen. System 1, also known as TASS (The Autonomous Set of Systems), is the term for domain-specific processing. In terms of genes, System 1 maximizes inclusive fitness, which is an indicator of an individual’s overall contribution to the next generation, accounting for both its own offspring and the offspring of its relatives. Based on the individual’s ultimate goals, System 2 calculates utility maximizing actions. Analytical processing is required for an individual to maximize their utility in situations other than those found in evolutionary adaptation environments. This necessitates System 2 overriding System 1 [48].

According to cognitive psychologists [48], the evolutionary psychologists’ adaptationist approach focuses on functional dimensions and minimizes individual differences among humans caused by genetic variability, which would be functionally superficial [28]. As a result, they regard general intelligence and other inherited personality traits as functionally secondary. The issue is that general intelligence and other personality traits are essential for maximizing personal utility, which includes the pursuit of status.

Cognitive psychologists observe that evolutionary psychologists often downplay the impacts of discrepancies between the environment of evolutionary adaptations and the present-day environment. There would not have been much of a difference between then and now. Nonetheless, there are unnatural pressures for decontextualization in the modern world. Conflicts between System 1 and System 2 result from this, as demonstrated by Kahneman and Tversky’s agenda of heuristics and biases [52]. The settings in which System 1 performs best—frequently rehearsed, frequency-coded, time-constrained, recognition-based decisions—do not always exist in the modern world. Representativeness, availability, sunk cost, confirmation biases, overconfidence, and other consequences that hinder an individual’s capacity to maximize utility result from the conflict between System 1 and System 2.

Cognitive illusions, for example, arise when confronted with probability representations other than the frequentist one. For example, “40 people out of 1000 have symptoms”, rather than “4% have symptoms”, is more understandable. However, the frequentist representation does not eliminate the cognitive bias any more than the Muller-Lyar visual illusion is eliminated after we “learn” to see it. Lines with arrow-like tails are used in the Muller-Lyar illusion, with one line having inward-facing tails and the other having outward-facing tails. The line with inwardly pointing tails appears shorter, while the line with outwardly pointing tails appears longer.

Cognitive biases are systematic errors in thinking that can lead to incorrect judgments and choices. In the dual-processing theory of the mind, cognitive biases are often associated with System 1 thinking. System 1 makes decisions quickly and efficiently using heuristics, which are simple rules of thumb, but this can lead to systematic biases. In contrast, System 2 thinking is thought to be less prone to biases because it is more likely to engage in critical evaluation of information, consider multiple perspectives, and weigh the evidence carefully. In practice, however, it is not always the case that System 2 thinking is free from biases, and many biases can persist even when we are engaging in more deliberate and reflective thinking. For example, memes are another source of persistence of bias in System 2 thinking. Memes are units of cultural transmission that are analogous to genes. This highlights the complex interplay between System 1 and System 2 thinking and suggests that biases are the result of a more complex interplay of multiple cognitive and emotional processes, rather than being solely the result of one system or the other.

Contrary to the heuristics and biases approach, some cognitive psychologists agree with evolutionary psychologists who do not recognize System 2. Earlier frequentist representations were shown to remove cognitive biases in non-frequentist probability format experiments [53]. System 1 understands frequentist representation but not non-frequentist representations such as Bayesian probability. However, many frequentist versions of base-rate problems are computationally simpler and thus cannot be directly compared to the non-frequentist version [54,55]. The System 1 heuristics, developed for the Pleistocene era, are not suited for attaining rationality in the contemporary world. Many modern decisions did not exist in the environment of evolutionary adaptations, and we did not have time to train and collect data on their frequency. As a result, we must use various inference rules to make logical and probabilistic inferences. Furthermore, we must filter a large amount of information coming from our autonomous modules that could stymie a decision.

Evolution by natural and sexual selection, according to evolutionary psychologists, built the human mind’s decision-making machinery, and this set of cognitive devices defines and constitutes the universal human principles that guide decision making. Therefore, evolutionary psychology ought to furnish a compilation of universally shared human preferences, along with methodologies for obtaining and rearranging supplementary preferences [56]. Cognitive psychologists do not deny this, but they caution against ignoring the role of culture in determining human preferences. However, culture has evolutionary roots as well. Slow biological evolution has given way to rapid cultural evolution. Even so, this is also the result of natural and sexual selection, and the agents through which this selection operates are memes. Memes live in people’s heads and replicate like viruses, infecting other people’s heads. Cultural evolution eliminates less adapted memes while spreading more adapted memes, greatly accelerating cultural change [57].

4. The Reasserted Point of View of Cognitive Psychologists

Evidence from experimental psychology and neuroscience, according to cognitive psychologists, points to the existence of two minds in the brain [4,58]. This has nothing to do with the dualism mind–brain, as the mind can still be considered simply as brain activity [59]. Two systems compete to influence our perceptions and actions. System 1 is an ancient evolutionary mechanism shared with other animals. Its subsystems comprise innate input modules and domain-specific knowledge acquired through a domain-general learning mechanism. On the other hand, System 2 is a recent evolutionary development that is distinctly human. It facilitates abstract reasoning and hypothesis-driven thinking. System 2 correlates with general intelligence measures. However, it has a limited working memory capacity. Our actions often rely on System 1 decision-making processes rooted in previous experiences, wherein we repeat successful past approaches. These intuitive choices demand minimal contemplation. Alternatively, decisions can be formed by constructing mental models or simulating future scenarios, a task for which System 2 is employed. The literature assigns several labels to Systems 1 and 2 [6,7,25,48,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75]. However, we cannot expect these labels to be consistent from one author to the next [58].

A classic experiment to study attention is the Stroop task, which shows the conflict between automatic System 1 and controlled processing System 2 thinking. It is the most straightforward evidence for the existence of the two systems. Participants are given a list of words that are printed in various colors. The task is to identify the color of the ink used to print the word while ignoring the word itself. The task is made more difficult by presenting incongruent stimuli, such as words printed in different colors of ink, such as “blue” printed in red ink. Participants encounter a conflict between a task they intend to complete using System 2 and an automatic response from System 1 that interferes with it. That is, System 2 is in charge of self-control [1]. It is difficult to break the habit of responding to the color in the Stroop task after years of learning to read words. When participants succeed, however, they increase activity in the areas of the cortex responsible for color vision while decreasing activity in the areas responsible for word identification [59].

According to cognitive psychologists, there is also evidence of dual-process reasoning in tasks exhibiting the belief bias effect [76]. In these tasks, participants attempt to reason logically according to the instructions, but prior beliefs interfere with the answers provided. There are four types of syllogisms: (1) a legitimate argument and a credible conclusion (absence of conflict); (2) a legitimate argument and an unbelievable conclusion (presence of conflict); (3) an illegitimate argument and a credible conclusion (presence of conflict); and (4) an illegitimate argument and an unbelievable conclusion (no conflict). When accepting or rejecting the conclusions of these syllogisms, the belief bias effect appears. Participants are more likely to accept a conclusion if it stands on its own merits. The belief bias effect makes it difficult to deduce the conclusion logically from the premises. Cognitive psychologists believe that this indicates that System 2 is having difficulty removing the belief suggested by System 1.

Cognitive psychologists also find evidence from neuroscience for the dual-reasoning process. Though neither system has a single location in the brain [1], in brain images taken by functional magnetic resonance imaging (fMRI), there is evidence of neural differentiation of reasoning with abstract material and with material that uses semantic-rich problems [77]. Content-based reasoning engages the left hemisphere’s temporal lobe, while abstract formal problem solving triggers activity in the parietal lobe. Commonly shared neural regions include the bilateral basal ganglia nucleus, the right cerebellum, the fusiform gyrus, and the left prefrontal cortex. For syllogistic reasoning, two distinct regions are involved. In tasks inducing the belief bias effect, Goel and Dolan [78] utilized fMRI. In syllogism tasks where participants make logically correct decisions, the right inferior prefrontal cortex activates. Conversely, belief-biased incorrect responses activate the medial ventral prefrontal cortex—a component of the mammalian brain. Supporting this, the medial ventral prefrontal cortex is linked to intuitive and heuristic responses akin to those from System 1 [78]. Furthermore, in a study using positron emission tomography (PET), access to deductive logic was found to involve the right medial ventral prefrontal cortex, which is an area known to be associated with emotions [79].

Cognitive psychologists see the correspondence bias, which can be seen in the Wason selection task, as additional evidence that mental processes are dual. Wason [80] wanted to know if people can be Popperian: if they are well equipped to test hypotheses in everyday life by looking for evidence that potentially falsifies them. The Wason selection task evaluates the potential violation of the conditional hypothesis “if P then Q” across four distinct situations, each illustrated using a set of four cards. When P is true but Q is false, an assumption of the form “if P then Q” is violated.

In the abstract version of the Wason selection task, participants are presented with a conditional statement and presented with four cards. Each card displays a letter on one side and a number on the other. The objective is to determine which card or cards need to be flipped over to ensure that a statement such as “If there is an A on one side of the card, there is a 3 on the other side” remains consistent without being proven false. Remember that the statement “if P then Q” is only violated when P is true but Q is false. As a result, the logically correct answers are P and not Q. This means that when we turn the P card over and find a not Q behind it, or when we turn the not Q card over and find a P on the other side, the rule is broken. The stated rule would not be broken by turning over cards A and 7. The most common choices, however, are A or A and 3, in which case the correspondence bias occurs. Typically, less than 25% of participants correctly answer A and 7. There is a proclivity to select cards that are lexically corresponding (A and 3), regardless of their logical status.

The correspondence bias is a System 1 heuristic. Humans are not born with the ability to detect violations of descriptive or causal rules. The brain evolved to aid in survival and reproduction, not to uncover the truth. When social contracts are involved, however, performance on the Wason selection task improves [81]. Applying the identical format as the earlier abstract example, wherein the statement is “If a person is drinking beer, that person must be over the age of 18”, the correct P and not Q cards—namely, “drinking beer” and “16 years old”—are reversed on more than 70% of occasions. According to cognitive psychologists, there is evidence in neuroscience that the correspondence bias reflects the existence of two minds. Those who successfully overcome the bias activate distinct brain regions [82].

Lastly, cognitive psychologists highlight archaeological findings indicating that humans developed System 2, meant for domain-general reasoning, subsequent to the existence of autonomous subsystems (System 1). Notably, approximately 50,000 years ago, there was a sudden rise in representational art and religious imagery, along with swift transformations in instrument and artifact design [83].

However, the dual-mind idea contradicts evolutionary psychologists’ focus on the mind’s massive modularity, even when engaged in domain-general reasoning, and the fact that domain-specific mechanisms make more evolutionary sense than domain-general reasoning abilities. The potential late evolution of System 2 implies differentiating between evolutionary rationality (the logic of System 1) and individual rationality (the logic of System 2) [84]. With less direct genetic influence, System 2 enables humans to follow their own goals rather than those of their genes [48].

The cognitive reflection test [85] is a direct assessment of individual cognitive ability, gauging the interplay between their Systems 1 and 2. Higher scores on this test indicate greater proficiency in utilizing System 2 to counteract System 1 inclinations. Comprising three questions tailored to evoke compelling yet incorrect automatic responses, the cognitive reflection test unfolds as follows: (1) A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost? (2) If it takes 5 machines 5 min to make 5 widgets, how long would it take 100 machines to make 100 widgets? (3) In a lake, there is a patch of lily pads. Each day, the patch’s size doubles. If it takes 48 days to cover the entire lake, how long would it take to cover half of the lake? The accurate answers are 5, 5, and 47, correspondingly, whereas the intuitive (yet incorrect) answers are 10, 100, and 24.

In a separate approach, [86] present an alternative questionnaire. While the test maintains a positive correlation with general intelligence assessments, it remains distinct from them. As it evaluates the capacity to counteract impulsive initial responses (known as cognitive reflection), the cognitive reflection test holds the potential for superior predictive accuracy in decision making, particularly within scenarios concerning risk and intertemporal choice [85]. Better decision making, after all, is dependent on the test’s broad rationality rather than algorithmic intelligence. Women perform worse on the cognitive reflection test than men. Furthermore, atheists outperform religious people [87]. A weaker System 2 is not always a bad thing in terms of evolution, however. Perhaps women and religious individuals are “more evolutionarily adapted”, resulting in a more robust System 1.

In the context of the dual-processing theory of the mind, “nudges” can be viewed as a method of influencing System 1 thinking. Nudges are low-cost, gentle interventions that aim to influence behavior by making it easier for individuals to make good decisions. They are commonly used in behavioral economics and are based on the idea that individuals are frequently influenced by the context in which they make decisions rather than their underlying preferences. Nudges are frequently designed to take advantage of the automatic, intuitive, and quick-acting nature of System 1 thinking. Nudges can influence people’s decisions by making certain options more visible, accessible, or prominent, without requiring them to engage in more deliberate and reflective System 2 thinking. A nudge could be as simple as presenting options in a specific order or using specific cues or labels to highlight certain options. These nudges work because they influence the unconscious processes associated with System 1 thinking rather than requiring people to conduct a more deliberate and reflective analysis of their options. Nudges, while effective at influencing behavior, can also be contentious.

Nudges have the following advantages: (1) they are low-cost and simple to implement; (2) they can be more effective than traditional policy instruments; and (3) they are generally non-intrusive. The disadvantages include: (1) they can be seen as paternalistic or manipulative; (2) they can be designed and implemented in ways that reflect the biases and interests of those who design and implement them; (3) they can be less effective than traditional policy instruments in some situations; (4) they can have unintended consequences and can be dangerous in the age of big data [88].

In addition, nudges fail to acknowledge the influence of memes. While memes [57] are frequently linked with quick and spontaneous responses (System 1), they can also demand more deliberate and analytical thought (System 2), such as those that rely on wordplay or obscure cultural or political references. For instance, when an individual gets a tattoo or adopts an ideology, they are making a conscious memetic choice that reduces their utility and inclusive fitness. Because System 2 memes can also result in poor judgment and decision making, they are uncontrollable by nudges designed to promote System 2 dominance over System 1 thinking.

5. The View of Dissident Cognitive Psychologists

At this point, it is clear that most cognitive psychologists agree that there are two distinct mental processes known as System 1 and System 2, and that some evolutionary psychologists are starting to accept the two-mind theory as well [24]. However, a small group of cognitive psychologists, dubbed “ecological theorists” by their colleagues, agree with the still dominant view among evolutionary psychologists that denies the existence of domain-general mental processes (System 2).

According to these ecological theorists, intuitive and deliberate judgments are based on common principles [89,90,91]. They contend that the evidence supporting the dual theory is consistent with a single system theory [89]. Moreover, they contend that the theories of the two systems lack clear conceptual definitions, rest on questionable methodologies, and depend on insufficient and often inadequate empirical support [90]. They claim that dual-reasoning process theories demonstrate the retrocession of precise theories to substitutes [92]. Additionally, they provide reasoning and empirical substantiation in favor of a rule-based theoretical framework. This framework elucidates both intuitive and thoughtful judgments while challenging the notion of dual systems characterized by qualitatively distinct processes [91].

In the perspective of ecological theorists [91], rules serve as inferential tools for tasks such as categorization, estimation, pairwise comparisons, and judgment, extending beyond provided information. A rule takes the form of an if–then relation, akin to syllogistic reasoning: if (clues), then (judgment). Consequently, rule-based judgments follow a deductive approach. Notably, the same rules can underlie both intuitive and deliberate judgments. The accuracy of both types hinges on how well the rules align with the environment—an ecological rationality of rules. Thus, intuitive and deliberative judgments are both rooted in rules. These rules may adopt either an optimizing or heuristic nature. Nonetheless, a challenge arises in selecting appropriate rules for such judgments.

How do individuals choose a rule from their adaptive repertoire for a specific problem? This choice is bound by the task and memory content, narrowing down the viable rules. The ultimate selection of a rule, however, rests on processing capacity and perceived ecological rationality. When multiple rules possess similar ecological rationales, a rule conflict can emerge. The proper implementation of a specific rule might face interference from competing rules. These rules are rooted in fundamental cognitive abilities, such as recognition memory. Variations in these abilities among individuals impact how swiftly and accurately a rule is executed. Rules, which encompass both intuitive heuristics founded on stereotypes and deliberative logic-based rules, can exhibit varying degrees of ease or difficulty in application. This depends on their level of routine integration and their immediate accessibility. Individuals endowed with higher processing capabilities adeptly employ both easy and challenging rules, guided by their perceived ecological rationality. In contrast, individuals with limited processing capacity solely utilize straightforward rules to guide their judgments. The accuracy of both intuitive and deliberate judgments hinges on the ecological rationality of the employed rule. It is important to note that increased complexity in rules does not necessarily equate to heightened accuracy compared to simpler rules. Similarly, statistical rules do not inherently outperform heuristic rules in terms of accuracy.

Nothing is more intuitive and automatic than visual illusions. For ecological theorists, even the most basic perceptual judgments are rule-based. A figure with dots on the left that appear concave and those on the right that appear convex inverts after turning the figure upside down is an example of one of these illusions. This arises from the brain’s creation of a three-dimensional mental model, utilizing shaded parts of dots to speculate about the dots’ extension in the third dimension. In forming this speculation, the brain relies on two assumptions: (1) that light originates from above (in relation to retinal coordinates); and (2) that a single light source exists. Consequently, the visual illusion is founded on an inferential rule that hinges on these two environmental attributes [93]. In times when the sole light sources were the sun or moon, the brain adhered to a straightforward guideline: dots with shadows on top were perceived as receding into the surface, while those with shadows on the bottom were interpreted as protruding from the surface. This optical illusion aptly showcases how automatic, rapid, and effort-free intuitive processes conform to heuristic rules. The rule’s justification is ecological; it is suited to the environment. The heuristic rule that uses the above properties 1 and 2 leads to good inferences in the three-dimensional world; however, the rule leads to a visual illusion in the two-dimensional figure.

Ecological theorists emphasize ten adaptive toolbox heuristics that underpin both intuitive and deliberate judgments: (1) recognition [94], which establishes that if one of two alternatives is recognized, you should deduce that it is the most important; (2) fluency [95,96] asserts that if you identify two choices, but one stands out as being recognized more swiftly, you can infer that it holds greater significance; (3) choose the best [97], which states that you should first look for clues in expiration order, then stop looking for a track that is recognized, and finally choose the alternative that this track suggests; (4) tallying [98], which states that when estimating a criterion, you should ignore weights and simply count the number of positive clues; (5) satisficing [99,100], which refers to searching for alternatives and selecting the first that meets or exceeds your aspiration level; (6) equality [101], which states that resources must be allocated equally to each of n alternatives; (7) default [102,103], which states that if a default occurs, nothing should be done; (8) tit-for-tat [104], which refers to cooperating first and then imitating the other’s behavior; (9) imitate the majority [105], which refers to imitating the majority of your group’s behavior; and (10) imitate the successful [105], which establishes that you should mimic the most successful individual’s behavior.

Cognitive psychologists [9] concur that all System 1 and System 2 behavior can be described by rules and even modeled by computer programs. Nonetheless, they argue that labeling System 2 as “rules-based” is inaccurate, as it insinuates that System 1 cognition lacks rule involvement. Rules can encompass both tangible and conceptual aspects, and any automatic cognitive system amenable to computational modeling is also capable of adhering to rules. However, the fact that intuition and deliberation are both rules says nothing about whether these rules are derived from different cognitive mechanisms. They do not support a single cognitive mechanism, as [91] claims.

Contrary to what the theory of the dual mind implies, ecological theorists believe: (1) the dimensions that distinguish System 1 from System 2—judgment speed, ease, or resource dependence—are continuous, not discrete; (2) these dimensions are misaligned; and (3) Systems 1 and 2 do not work independently of one another [90]. However, cognitive psychologists argue that, in response to the criticism that mental processes are continuous and not discrete, the distinction between types and modes of processes must be considered. Modes, unlike types, can change on a continuous basis. Continuous variation in cognitive capacity, for example, determines the likelihood that an answer will be initiated by System 1 processing. This, however, does not invalidate the discrete distinction between Systems 1 and 2.

Concerning the criticism that the attribute clusters of Systems 1 and 2 are not aligned, cognitive psychologists argue that it is based on the false assumption that both cognitive systems have defining attributes. The fact that different cluster items are not always observed together would only be a problem if the two cognitive systems had distinguishing characteristics. However, when it comes to the criticism that dual-processing theories have multiple and ambiguous definitions, cognitive psychologists [9] agree that label proliferation has been ineffective. However, when it comes to the criticism that the evidence for dual mental processes is ambiguous and unconvincing, cognitive psychologists disagree and re-present the amount of evidence that exists on the various research fronts that we have previously shown. After responses from Keren, Kruglanski, and Osman to these remarks, Evans and Stanovich followed suit. Take a look at the discussion in [106,107,108,109,110].

6. The Perspective of Dissenting Evolutionary Psychologists

By now, it should be obvious that the majority of evolutionary psychologists disagree with the view held by the majority of cognitive psychologists that cognitive architecture is domain-general and content-free. Nonetheless, some evolutionary psychologists aim to explain complex cognitive functions and, by extension, the evolution of very high human intelligence [24].

While the human brain constitutes just 2 to 3% of the body’s weight, it accounts for a substantial 20 to 25% of calorie consumption [111]. The human brain is larger in relation to the rest of the body than that of other primates, which already have large brains. Over the last few million years, the human brain has tripled in size. Humans do not have the largest brain mass or the highest brain-to-body ratio among species, but we do have the most brain neurons [59]. Our large brains in terms of neurons contain sophisticated information-processing devices and intelligence that our primate cousins lack, such as abstract thinking, reasoning, learning, and scenario-building abilities. What caused these cognitive abilities to evolve? We have two hypotheses that may or may not be competing: (1) the hypothesis of ecological dominance and social competition [112,113] and (2) the hypothesis of deadly technological innovations [114].

According to the ecological dominance and social competition hypothesis, once ancient humans relatively mastered the “four horsemen of the apocalypse”—famine, perennial war, plague, and extreme weather—human competition became the new selective force. The new adaptive problems are forming coalitions, punishing traitors, detecting fraud, and negotiating social hierarchy positions. The risks of theft, cannibalism, sexual infidelity, infanticide, extortion, and other betrayals increased in ancestral social groups of 50 to 150 people [115]. These social adaptive problems necessitate the selection of larger brains in terms of neurons and higher social intelligence. Consciousness, language, self-awareness, and theory of mind (the capability to comprehend others’ beliefs and desires [116]) exemplify novel forms of intelligence.

In addition, scenarios are created to practice responses to various social situations [113]. Social competition drives adaptations in the formation of coalitions for hunting large game—a critical source of protein [117]. Forming cooperative hunting coalitions necessitates communication skills as well as the ability to detect and punish traitors. It also requires cooperation among members as well as rules for meat distribution. The increased scale of group hunting allows surplus meat to be stored in the bodies of friends and allies in exchange for a reciprocal return. Fighter coalitions quickly morph into war coalitions. Hunting weapons can be used to gain access to other groups’ resources [112,118,119]. This creates an arms race between adaptations for war and adaptations for defense, both of which require higher levels of intelligence.

The high level of intelligence of today’s humans can be attributed to intense group life, hunting, war, and bipedalism. Bipedalism liberated the hands for creation and application of tools. The ecological dominance and social competition hypothesis is supported by evidence. This hypothesis predicts that as population density increases, so will the selective pressure for greater intelligence, owing to increased social competition. Examining skulls spanning 10,000 to 1.9 million years ago unveils a correlation: regions with higher population density exhibit larger skull capacities [120]. However, ecological dominance and social competition does not have a strong correlation with general intelligence (IQ).

The other explanation for the evolution of human intelligence is the deadly technological innovations hypothesis. Individual differences in survival have not been eliminated by technological advances that have increased average human survival rates. Individual differences in survival are still associated with individual differences in intelligence. New technologies—fire, tools, weapons, and boats—brought with them new threats. These new technologies kill less intelligent people (deadly innovations), putting selective pressure on the evolution of general intelligence.

Hunting weapons kill more than the hunted animal among Botswana’s !Kung [121]. Avoiding accidents caused by new technologies requires cognitive ability, such as creating scenarios, anticipating bad luck, and taking precautions. Less intelligent people have more fatal accidents, and their children have a higher mortality rate as a result of their absence (double threat). More general intelligence is required as technology becomes more complex (spiral complexity). African emigration brought new technologies as well as new dangers, making it a ratchet emigration. Because intelligence is related to longevity, this supports the deadly technological innovations hypothesis.

Each additional IQ point reduces the risk of dying by 1% between the ages of 25 and 64 [122]. Thus, having an IQ of 115, which is 15 points higher than the average of 100, reduces this risk by 15%. Non-fatal injuries that reduce inclusive fitness are also linked to IQ. In aggregated data from the modern world, low IQ is associated with a higher risk of drowning; being involved in car, motorcycle, or bicycle accidents; being injured with a knife; being involved in explosions or being struck by falling objects; and being struck by lightning [114].

In short, both the ecological dominance and social competition hypothesis and the deadly technological innovations hypothesis suggest adaptive roles for the evolution of general intelligence—abstract thinking, scenario construction, and the capacity to learn from experience. In addition to domain-specific cognitive abilities, therefore, humans have evolved domain-general cognitive abilities [24].

The authors of the controversies mentioned in this review are listed in

Table 1. Cognitive vs. evolutionary psychologists on System 2.

System 2
Cognitive Psychologists	Evolutionary Psychologists
Yes	No
Evans, [4,58,67,68] Evans and Over, [66] Evans and Stanovich, [9,110] Evans et al., [55,76] Stanovich, [48,71] Stanovich and West, [2,84] Sloman, [3] Westen, [5] Strack and Deutsch, [6] Epstein, [7] Barsalou, [8] Samuels, [49] Samuels et al., [50] Over, [51] Macchi and Mosconi, [54] Kahneman, [1]	Cosmides and Tooby, [23,27,46,56,81] Tooby and Cosmides, [28] Fodor, [25,26,60] Spelke, [29] Carey, [30] Hirschfeld and Gelman, [31] Keil, [32] Leslie, [33] Barrett and Kurzban, [34] Pinker, [37] Kanazawa, [45]
No	Yes
Gigerenzer, [53,92] Gigerenzer and Goldstein, [97] Kruglanski and Gigerenzer, [91] Kruglanski, [107] Keren, [106] Keren and Schul, [90] Osman, [89,108] Thompson, [109]	Buss, [24] Mithen, [38,83] Livingstone, [39] Geary, [44] Geary and Huffman, [40] Chiappe and MacDonald, [41] Figueredo et al., [42] Premack, [43] Hagen, [47]

. Recent research that may contribute to this discussion can be found in the references [123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139].

Although not directly relevant to the debate between cognitive and evolutionary psychologists over System 1 vs. System 2, an intriguing line of research is worth mentioning. The more widely recognized view of the link between cognitive capacity, intuition, and reasoning ability is that individuals with higher cognitive capacity are better at correcting biased intuitions during reasoning tasks. However, there may be a positive link between cognitive capacity and accurate intuitive thinking. To some extent, cognitive capacity is connected with the ability to rectify incorrect intuitions, although it predominantly predicts accurate intuitive responses. This implies that smarter individuals may just have more accurate intuitions rather than being particularly skilled at correcting incorrect ones [140].

In other words, it is not just a case of System 2 overriding System 1 reasoning. As a result, while smarter deliberation influences reasoning correctness, it is not the entire explanation. Another theory is that people with higher cognitive capacity just have better intuition. They would not need to deliberate to rectify early intuitions in this “smart intuitor” perspective because their first hunch is already correct [141,142,143,144,145]. Cognitive capacity predicts the ability to have correct intuitions rather than the ability to rectify them through deliberation. So, rather than using the cognitive reflection test to capture “the smart deliberator view”, it is more crucial to consider the smart intuitor view using a two-response test [145]. Participants in the two-response paradigm respond to a problem in two steps: an initial intuitive reaction and a final response after reflection. Surprisingly, research reveals that those who deliver the accurate final response frequently match it with their first intuition. This implies that good reasoners do not always require reflection to rectify their intuitions. Individual differences studies show that those with higher cognitive capacity are more likely to have correct initial responses [129,146,147,148].

Another important piece of new research says that the dual-process theory should be changed so that it is based on the single principle of rational choice [149]. By doing this, it can explain better than the single-process theory where habits come from, why they slip up, and how they break. It is suggested that the way to decide if one theory is better than another is to look at how few principles it uses, and to avoid adding extra qualifications to explain anomalies.

7. Concluding Remarks

Examining the debates between cognitive and evolutionary psychologists, this article discussed the dual-processing hypothesis of the mind, Systems 1 and 2. Using a back-and-forth methodology to emphasize the differences, the goal was to show that, while the majority of cognitive psychologists now support the dual-processing theory of the mind, some still disagree. Nevertheless, it appears that System 2 is garnering support. This review is unique in that it contrasts the perspectives of cognitive psychologists and evolutionary psychologists, which is uncommon in the cognitive psychology literature, which tends to put evolutionary perspectives in the background.

Most cognitive psychologists agree that there are two mental processes, which Daniel Kahneman popularized as System 1 and System 2. These two systems compete for dominance over our inferences and actions. In evolutionary terms, System 1 predates the other and comprises a self-contained assembly of autonomous subsystems. System 2 enables abstract reasoning as well as the use of hypotheses. System 2 is thus a domain-general processing mechanism. Domain-specific processing mechanisms refer to System 1. The late evolution of System 2 suggests that a distinction be made between evolutionary rationality, which is System 1’s logic, and individual rationality, which is System 2’s logic. As a result of the emergence of System 2, humans can pursue their own goals rather than just the goals of genes.

Most evolutionary psychologists, however, deny the existence of a domain-general processing mechanism (System 2) and only accept the modularity of mind hypothesis. A minority of cognitive psychologists support this view and believe that intuitive and deliberate judgments are based on shared principles. While most evolutionary psychologists disagree with the notion that cognitive architecture is domain-general and devoid of content, some evolutionary psychologists are beginning to accept the theory of the two minds.

Even though recurrent features of adaptive challenges favor specialized adaptations, evolutionary psychologists assert that humans encountered numerous novel problems lacking sufficient regularity for specific adaptations to evolve. Therefore, prematurely assuming the existence of a domain-general processing mechanism alongside established domain-specific processing mechanisms is cautioned against by these psychologists. After all, the domain-specific mind assumption has been used successfully to discover important mechanisms, and it remains to be seen whether the domain-general mind assumption will yield comparable empirical results.

However, the human mind cannot have separate and isolated mechanisms because certain mechanisms’ data provide information to others. Internal data such as sight, smell, and hunger provide information that can be used to determine whether a food is edible. There is no information encapsulation in the adapted psychological mechanisms, and thus no modularity. This is due to the fact that information encapsulation would imply that psychological mechanisms would only have access to independent information and would not have access to information from other psychological mechanisms. There must also be supermechanisms, such as daemons, that specialize in ordering and regulating other mechanisms.

Analytical processing is required in situations other than those found in evolutionary adaptation environments, and this necessitates System 2 overriding System 1. A large number of cognitive biases emerge from the conflict between System 1 and System 2, as studied in Daniel Kahneman and Amos Tversky’s heuristics and biases agenda. These biases interfere with an individual’s ability to maximize utility. According to cognitive psychologists, evolutionary psychologists are incorrect in assuming that System 1 heuristics, which were adapted to the Pleistocene, are optimized for making sound decisions in the modern world. We have no other alternative than to apply a variety of inference rules to draw logical and probabilistic judgments using System 2 because many contemporary decisions did not exist in the environment of evolutionary adaptations, and we did not have time to train and gather data on their frequency. Furthermore, we must filter a large amount of information coming from our standalone modules (System 1) that may obstruct a sound decision.

Nudges are a method for influencing System 1 thinking. However, despite their effectiveness in influencing behavior, nudges can sometimes be controversial. In addition, because memes can contaminate System 2 thinking, poor judgment and decision making are not solely the product of System 1 dominating System 2 thinking—a circumstance that nudges cannot influence.

In summary, this review shows that there is a growing consensus in favor of System 2, but the challenges raised by evolutionary psychologists must be addressed. Even while acknowledging the possibility of both domain-specific and domain-general processes, more nuanced and detailed accounts of the System 1 and 2 processes are required, as is a better understanding of how the two systems interact. We also cited several fairly recent studies that are already taking this into account.