“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.” (Albert Einstein)
Although our brains are structured to make predictions, while being wired for linear thinking, we are not pre-programmed to think exponentially. How can we reverse and rewire the process to embrace exponential changes?
Many before me have already tried to explain. Thus, in this article, I will also try to answer this question by exploring major differences between linear and non-linear thinking and why humans are tuned to think linearly from a cognitive and probably neurophysiological perspective. Lastly, I will highlight a few suggestions about how we can adapt to the Era of exponential changes and benefit from it while learning to rewire our way of thinking.
The 21st-century and the 4th industrial revolution: an extraordinary era
The 21st century and the 4th Industrial Revolution (4IR) represent an extraordinary Era made of innovative discoveries and fast changes that manifest at a speed that our brains cannot yet comprehend and visualize. This shift does not follow a linear equation; instead, it follows an exponential curve which is not often found in nature. For this reason, when looking at growth and change, our minds have been conditioned to think linearly because this is the most familiar and simple way to approach innovation. However, although humans cannot understand the switch from deception to disruption, technological advancement is a fact. Indeed, the futurist Gerd Leonhard said that technology is exponential, and humans should not try to compete with machines. Instead, humans should focus on what they have inherited: being creative and compassionate and what machines cannot yet copy.
Gordon Moore already predicted this exponential trend in 1965. He forecasted that the number of components on an integrated circuit would double every year while reaching several 65,000 by 1975. This was a great prediction about technological development today known as Moore’s Law, forecasting that transistors on a chip would double every two years. Indeed, the 4IR saw the rapid evolution of technologies from DNA sequencing, nanotechnologies, artificial intelligence (AI) systems, self-driven vehicles, drones, robotics, augmented/virtual reality (AR/VR), 3D printing, energy storage, blockchain, Internet of Things (IoT), mobile payments, and much more. We could say that each technology we came across, from simple mobile phones to laptops, reflects Moore’s prediction. Even though some argue that we are almost at the end of Moore’s law, others say that the historical outcome of Moors’s law is accelerating. Indeed, the principle behind Moore’s law is still true, and this is even represented by the exponential (not linear) advancement of technologies accompanied by a greater increase of AI systems, machine learning, and deep learning implementations, and so shifting from modeling to interfacing.
However, keeping up to date with this technological progression generates a “technological vertigo” due to our linear mindset. So far, this natural attitude of humans to think linearly goes in parallel with an incremental change characterizing most business trends. However, that contrasts the digital and technological disruption brought by the 4IR.
So, what’s the difference between linear and non-linear thinking?
Linear vs. non-linear thinking
The way we think influences how we perceive and interpret the world. Consequently, determining our actions and decisions in our environment. Furthermore, the process of thinking leads to our learning, knowledge, comprehension, and it plays a relevant role in evolving problem-solving strategies. Hence, two different approaches have been identified: linear and non-linear thinking.
With linear thinking, we approach an idea/process that begins from a point, and through linked, sequential steps, it ends at another point. It is an analytic, methodic, rational, and logical style. This way of thinking is also defined as sequential thinking because it approaches things as having a sequence. Thus, linear thinkers have a tendency for consistency, rules, and formulas that can be applied in different situations to make predictions based on a logical relationship between facts (e.g., B comes after A, if x =y, y=z, then x=z). Not surprisingly, linear thinkers have a good attitude toward maths, science, and technical subjects while working and concentrating on cause-and-effect processes.
On the other hand, non-linear thinking is not sequential, and it refers to the ability to create connections between concepts/ideas, even when they are not related. This type of thinking is more abstract and less logical. Indeed, non-linear thinkers are more creative, intuitive, artistic, emotional and rely on their imagination to understand and solve problems (e.g., open-ended questions, brainstorming). They show a clear aptitude for art, humanistic, or social science subjects.
Nevertheless, whether linear thinkers lack in understanding and connecting facts that are not sequential (and that go after that ending point), non-linear thinkers due to their attitude in abstracting facts they might not perceive things that have a definite cause without understanding their effects. A simple example in business, linear thinkers would suggest a daily use product (e.g., soap). In contrast, non-linear thinkers would come up with a more creative, outstanding idea (e.g., a mobile App) that even customers wouldn’t have yet wished for.
The importance of understanding non-linear patterns in business
However, thinking linearly seems in contrast with a world where nonlinear principles may apply. Decades of research in cognitive psychology proved that the human mind has difficulties understanding non-linear relationships between facts and elements. However, awareness and knowledge about the nonlinearity process are extremely important in nowadays businesses. Thus, overcoming linear biases would be beneficial when coping with real-world scenarios. A simple example regards the generation of profit in a business. The relationship between costs, volume, and price is not following a linear approach, and managers’ intuition about these processes is not always correct (e.g., executives tend to focus on costs and volume changes instead of price). Another example is related to customers' attitudes (e.g., customers declare they care about sustainability. However, they are not willing to pay more for eco-friendly products). Customers' actions and thoughts are often nonlinear. Being aware of non-linearity is also relevant when analyzing performance metrics (e.g., assessment of the management effectiveness).
Understanding non-linear patterns are necessary. Embracing them would lead to embracing a better outcome. Hence, non-linear patterns can be:
Increasing gradually, then rising more steeply: linear thinking managers tend to underestimate that customers who are more likely to stay bring more benefit to a company. Indeed, small increases have more benefits and high retention rates (instead of focusing on customers who are likely to defect and then propose marketing programs to increase retention rates).
Decreasing gradually, then dropping quickly: a typical case of thinking linearly is given by mortgage payers when selling a property after a few years, and they have to pay brokerage costs. Thus, they end up being surprised because they have small net gains.
Climbing quickly, then tapering off: linear thinking managers tend to underestimate the importance of price on generating profits. So instead, they focus on volume.
Falling sharply, then gradually: linear thinking managers tend to underestimate the effect that short payback periods would have on the annual rate of return. Indeed they tend to think that the return of investment is higher in a portfolio containing two projects that are expected to pay back in two years, instead of having one portfolio with a project with one-year payback and another project with four-year payback.
Thus, to overcome biases brought by a linear thinking approach, a few suggestions were given, such as enhancing awareness about a linear approach and those limits it may create through educational programs, focusing more on outcomes and not indicators, detecting and understanding the type of non-linearity you are approaching, and map non-linear relationships by using algorithms based on AI, or by plotting data to increase data visualization in order to make a decision even in a “what if” scenario.
Now that we clearly understand how linear and non-linear thinkers think, how can we explain linear thinking patterns and how human brains are “wired” by evolution to apply this approach? Let’s have a look at a few theories from cognitive psychology regarding human perception.
The seven principles of the Gestalt
Founded by Max Wertheimer, Wolfgang Köhler, and Kurt Koffka, Gestalt psychology, gestaltism, or configurationism [1,2,3] is a theory of human perception that originated as a school of psychology in the early 20th century in Germany and Austria. This theory was aimed to contrast the principles of Elementalist and structuralist psychology supported by Wundt and Titchener [4,5]. The German word Gestalt means “pattern” or “configuration,” highlighting that the human being perceives global patterns and configurations instead of single, individual components. Seven are the principles that govern the Gestalt theory to describe how humans perceive visual stimuli and their interaction with different objects located in the environment:
● Proximity: objects close to each other are seen as related, whereas objects far apart are not.
● Closure: when we encounter a complex configuration, we look for a single and recognizable pattern while bringing meaning to that structure. This process happens because our eyes try to fill in “missing data” to make sense of what we are seeing.
● Similarity: elements that share common characteristics are perceived as more related than those that do not.
● Continuity: elements that are on a line or curve are perceived as more related than those that are not on that line or curve.
● Perception: individuals perceive elements as either figure (focal point) or ground (background). Hence, when objects are overlapping, humans tend to create a sense of the spatial relationship between them, even without over-visual cues. So, simple visual objects are used to generate a sense of the relationship between these objects, also relying on past experiences.
● Organization: it regards five principles that are uniform connectedness, common regions, common fate (synchrony), parallelism, and focal points. Uniformed connectedness refers to the fact that visually connected elements are perceived as more related. Common regions refer to the fact that visual elements within the same region are perceived as a group. Common fate refers to elements that move in the same direction are perceived as more related than elements that are stationary or moving in other directions. Parallelism is related to the fact that parallel elements are perceived as more related than non-parallel ones. Lastly, focal points are points of interest on which our focus is directed and capture the attention (e.g., create emphasis on a design: size, color change, position, dramatic whitespace).
● Symmetry: in simple words, symmetrical elements are perceived as a unified group.
The seven principles of the Gestalt explain how our brain tries to perceive and interpret the world outside with the most simple processes. Indeed, proximity, closure, similarity, perception, organization, and symmetry appear as “obvious” ways to perceptive patterns. Thus, the tendency to think linearly might be influenced by these processes, in which one step follows the other, and predictions are made possible based on a sequential occurrence of facts.
However, this is not all. Our memory plays a significant role in encoding, storing, and recalling information. In this way, we learn patterns, we recognize patterns, and we can predict patterns. These processes are indeed called: pattern separation, completion, and recognition.
Pattern separation, completion, and recognition
“There are common ways we see patterns. Patterns are the laws of nature and life that present themselves in all disciplines of life — from the smallest microorganism to macrocosm… While patterns aren’t always apparent, they are continuous and autonomous.” (Amy Oestreicher)
A brain structure called the hippocampus has often been related to the activity of two mechanisms responsible for processing information and learning through encoding, storing, and recalling information. In particular, the ability to differentiate or associate information is important due to the number of stimuli that the brain constantly receives from the environment. Thus, these two mechanisms are pattern separation and pattern completion, respectively [6]. The former, localized in the dentate gyrus (DG) that is a part of the hippocampal formation, refers to the ability of the brain to encode different stimuli and to store that information separately (e.g., it allows to encode the information about where you did leave the keys yesterday and today into memory). Wheres, the latter, localized in CA1 and CA2 structures of the hippocampus, refers to the ability to recall a previous encoded and learned pattern from the observation of a partial cue (e.g., recognizing individuals when a partial view of them is given), and this ability can also facilitate generalization of that information when presented in a nosy environment (e.g., recognizing the same person but in a novel context) [6].
Initially, this approach was formalized into a mathematical model by Marr (1970) [7], later evaluated computationally [8,9,10] and experimentally by implementing behavioral [11,12], neuropsychological [13,14], and functional magnetic imaging techniques (fMRI) [15,16]. The hippocampus plays a relevant role in storing similar patterns without creating interference and recalling stored patterns from a noisy/degraded cue. In this way, through a pattern separation process, information is encoded and stored separately and successively recalled when a partial cue is presented through a pattern completion process. A few studies demonstrated that when these processes are dysfunctional, disease conditions such as autism, schizophrenia, or neurocognitive aging [17,18,19]. Moreover, an extensive literature review was carried out [6] to better investigate these mechanisms from a computational and psychological perspective. Findings showed that pattern separation happens during encoding and this process would interest any stimulus type such as sensory/perceptual, spatial, temporal, affect, and response information [6]. Differently, pattern completion manifests during retrieval, meaning that impaired learning processes cannot be attributed to this mechanism, and likewise the previous process, even pattern completion can involve any attribute.
Pattern recognition and machine learning
Another mechanism is called pattern recognition. This concept, mostly used in the context of machine learning (ML) algorithms, finds its applications in computer vision and graphics, statistical data analysis, image processing, and segmentation analysis, fingerprint identification, seismic analysis, signal processing, radar signal analysis, speech recognition, information retrieval, and bioinformatics. Pattern recognition can be viewed as a type of problem, whereas machine learning is a type of solution [20]. Pattern recognition refers to the automated recognition of patterns and regularities in data by using ML. By recognizing patterns, data are classified based on previous learning or statistical information extracted from patterns and representations. Among endless possibilities and cases, recurrent sequences of data, observed across time, are implemented to forecast trends, configurations of features to identify objects, combinations of words and phrases (e.g., neural language processing), or clusters of behavior. Thus, pattern recognition is the process of classifying and clustering patterns. A few characteristic features are [20]:
● The process relies on data;
● Familiar patterns, as well as, unfamiliar objects are classified and recognized quickly and with high accuracy;
● Patterns can be recognized even from different perspectives;
● Patterns can also be identified when they are hidden;
● The process of recognition relies on learning information from data, and it becomes an automatic process.
Training and learning are the main characteristics of pattern recognition, and this process also shows a few advantages in its implementation [20]:
● It can interpret DNA sequences;
● It can be applied in astronomy, medicine, robotics, and remote sensing by satellites;
● It can solve classification problems;
● It can recognize specific objects even from different angles and perspectives;
● It can be implemented in visually impaired blind people for cloth pattern recognition.
From a border perspective, pattern recognition relates to the human ability to connect patterns that share similarities or even differences. This ability is the foundation of our knowledge because humans rely on patterns to understand the worldand give sense to their perceptions. Hence, several daily situations involve pattern recognition, whether it is learning our language at birth, the development of specific behaviors in response to repeated situations, the identification of what causes a disease looking at similarities generating the disease itself, or simply the Netflix algorithms that choose your best movies based on your previous selections.
Nevertheless, although it has been discussed that the brain is wired for linear thinking and possibly explained by perception processes as we observed through the Gestalt principles, pattern separation, completion, and recognition, the nature of the human nervous system (NS) activity are very complex. It can be understood in terms of non-linear neural connections and patterns.
Neural networks to predict non-linear behaviors
Although human behavior is mostly inclined to think linearly, the nature of the human NS is one of the most complicated systems. Even the activity from a single neuron to the higher system-level, complex non-linear behaviors were demonstrated [21]. Several analyses and methods, from correlation, coherence to Granger causality, have been implemented to investigate and explain neural connectivities and input-output interconnections. However, these linear approach methods are limited in demonstrating just a few neural activities, thus incapable of explaining more complex and higher-level behaviors [21].
Hence, in a review, He and Yang (2021) [21] explored recent discoveries in investigating nonlinear neural systems based on a time and frequency domain analysis and nonlinear techniques to measure neural connectivity. Nonlinear modeling analysis is needed to understand neural functioning while detecting neuronal processing and the transfer of signals across systems.
Therefore, finding solutions to nonlinear problems needs computing to be reinvented to simulate the architecture and functioning of the human brain. Thus, neuromorphic computing has been predicted by Jack Kendall of Rain Neuromorphic, Suhas Kumar, of Hewlett Packard Labs, has predicted neuromorphic computing as a novel way of computation happening in the coming years [22]. The future of computing is not about adding components on chips, but Kumar said that computers need to be reinvented to match the complexity of how neural systems process information due to neuroplasticity and sparsity. The authors mentioned that nowadays, computers could be comparable to an insect's brain when processing multiple information. Therefore, innovating and enabling current computing approaches toward nonlinearity, causality, and sparsity with disruptive architectures (e.g., deep neural networks) would guarantee the possibility to solve complex problems such as accurate predictions and gene sequencing.
Considering the complex, non-linear nature of the human NS, and hardly replicable even by advanced computational analysis, which factor may influence human thinking toward linearity? In my opinion, to shape, tune, and strengthen neural patterns toward a linear framework should be a process that starts at the early stages of development and can influence thinking and behavior. Thus, culture can be a potential factor influencing human thinking and the way humans approach and perceive events and the environment.
A cultural bias?
Several cross-cultural studies on human thinking demonstrated how cultural differences might influence the way of thinking [23]. A distinction has been made between Eastern and Western cultures, dominated by different types of thinking. Besides socio-ecological differences, the study of Yama and Zakaria (2019) [23] investigated and explained the fundamentals behind these differences. Originating from Ancient Greece culture, Western cultures are characterized by analytic cognition, focusing on the concept of an independent self. These factors lead to the generation of a linear thinking approach. Differently, based on the philosophical tradition of China’s Taoism, Confucianism, and Buddhism, Eastern’s cultures evolved a holistic cognition, centered on an interdependent self and leading to a dialectic thinking, and in which the study of Wong (2011) aimed to understand this type of thinking approach from a cultural and historical perspective [24].
Moreover, about the fact that dialectical thinking has been related to the construction of beliefs associated with a tolerance of contradictions, change expectation, and holism, the study of Li, Li, and Ito (2021) [25] investigated the effect of dialectical thinking on anticipating climate change, comparing cultures that promote dialectical thinking (e.g., a sample of Chinese participants) with cultures that support linear thinking (e.g., a sample of North American participants). Thus, two studies were conducted to explain whether a different type of thinking shaped by culture would influence the perception of individuals in predicting climate change. Results from the first study did find a significant difference between Chinese (dialectic thinkers) and North American participants (linear thinkers) in predicting the trend of climate change, where the former were less likely to anticipate an increasing trend but more likely to anticipate a stable trend for future climate change. Although only marginally significant, similar results were also found in the second study, which was carried out to avoid possible biases related to the cultural way of influencing thinking. Nevertheless, this study highlighted two main findings. First, independently of the culture, both groups were able to anticipate that climate change will accelerate in the coming years and that educational programs in both cultures successfully raised awareness about this environmental issue as a global concern. In addition, behaviors and psychological constructs [26, 27] are generated and shaped by the culture, influencing the way individuals respond to external events [28, 29]. Indeed, participants from a culture that promotes dialectic thinking showed a tendency toward a stable trend. In contrast, participants from a culture that promotes linear thinking were more likely to predict an increasing trend, concluding that the consideration of cross-cultural differences influencing thinking should be evaluated in educational programs, especially when discussing global topics (e.g., climate change issues) [25].
Besides scientific studies, clinical psychotherapist Claire Nara supports the idea that dialectical thinking can be usefulwhen facing the different layers of our reality against the view of a binary sense of the world. In this perspective, dialectical thinking is paradoxical, which tries to see things from different angles starting from understanding their opposites. This can be a useful approach that allows the development of multiple, alternative strategies to overcome situations while recognizing that there are other perspectives to look at things, leading to a more cognitively flexible and adaptable mindset able to release negative feelings. Furthermore, gaining control over our way of thinking is beneficial because our actions are closely related to our thought patterns. Thus, modifying our internal structures would lead to the actuation of different behaviors, consequently impacting the environment around us.
Having seen how culture may influence our thinking pattern while shaping our behavior and understanding of the environment around us, the effect of different types of thinking in a workplace? For example, which type of thinking is more effective in completing tasks, making decisions, or in leading teams?
Balancing linear and nonlinear thinking in effective leadership
It is common to say that linear thinkers’ brain is differently wired from non-linear thinkers. Although solid neuroscientific evidence is lacking, linear thinkers indeed show a different way to process information and solve problems. Indeed, linear thinkers are usually identified with engineers, information technology specialists, accountants, and other research-based professionals. A linear approach fits the way to perform these jobs and tasks. However, linear thinkers may find difficulties when assuming higher positions, such as leadership roles [30].
Previously, a distinction between linear and non-linear thinkers was given. In addition, linear thinking is mostly characterized by a data-driven, analytical approach, tuned to reaching results and dominated by a repetitive and methodological framework. In this way, neural connections are generated by following the same patterns and strengthened by repeating similar tasks. But what happens when a linear thinker is called to lead a team?
Dealing with uncertainty and interpersonal differences that characterize humans do not fit into a mathematical equation or law. Thus, linear thinkers may find leadership positions a challenging situation and probably be incapable of understanding team dynamics. The reason is related to a lack of creativity, visions, inability to inspire and motivate others, effective communication, and a lack of seeing a “big picture” that characterize effective leadership of this century. Linear thinkers usually show a “tunnel-like” vision, driven by a purpose and a destination. Indeed, they tend to apply the same old behavior to approach new situations. Unlike non-linear thinkers, a lack of emotional intelligence, spontaneity, relationship avoidance, and communication is observed differently. Hence, linear thinkers can be categorized as more efficient than effective in their workplaces. However, to develop into effective leaders, they need to acquire new skills, broaden their vision and perspective, to “rewire” their neural connections toward a non-linear thinking approach [30].
This process can be emotionally and psychologically challenging since linear thinkers live their life with a binary mindset. Thus, stimulating open-ended questions when approaching problems, brainstorming, flexibility, adaptability, curiosity, communication, and active listening would be relevant points to consider and forge while developing a non-linear thinking modality. Coaches aim to shift linear thinkers' attitude from saying “I already know it” to “I want to know more.” What characterizes non-linear thinkers is a mindset built around emotional intelligence, curiosity, continuous learning, self-awareness, self-esteem, and mindfulness.
To overcome obstacles that linear thinkers may face while leading teams evolve into a novel mindset, a few suggestions are given [29]:
Strength flexibility: linear thinking is mostly characterized by rigidity, due to a binary framework of viewing and solving problems. Thus, developing flexibility is useful when adapting to diverse situations and strengthening resilience and facing challenges through innovation.
Develop emotional intelligence: linear thinkers usually avoid close relationships, emotions, and connections with others. However, to evolve into an effective leader, emotional intelligence is needed to generate a positive impact and engage the team toward effective collaborations. Indeed, empathy is the key to a successful business when understanding customers ’ needs and assuming their perspectives.
Learn and master somatic (non-verbal) communication: linear thinkers have difficulties in exploiting a productive dialogue and connecting with others. In addition, effective communication is usually non-verbal. Thus, learning to understand behaviors and body communications is central for developing effective communication.
Look for opportunities that lead to self-growth and self-empowerment: effective leaders know that seeking opportunities is an important step toward growth and self-development. Hence, effective leaders show interest in attending workshops, events, classes, and are always up-to-date while readings and informing themselves. Thus, to rewire linear thinking pathways, the need to create new habits while “doing something different” is optimal to strengthen and move into a non-linear way of thinking. Moreover, linear thinkers can rely on their great skills of planning, scheduling, and executing when organizing and building new activities.
Be aware of limits, and learn to overcome them: different from non-linear thinkers, linear linkers tend to ignore their limits. Thus, learning to ask for help is necessary when evolving into an effective leader. In addition, being open and asking for feedback is a behavior that needs to be mastered toward professional growth and goals achievements.
However, extremes are never an optimal choice. Thus, to be an effective leader, a balance between linear and non-linear thinking should be considered. Both types of thinking have advantages and disadvantages, and where their combination creates a perfect match; learning to look outside the box but with a purpose-driven, goal-oriented behavior [30].
Saying that, how can we rewire our linear thinking toward a non-linear approach?
Rewire your brain to think exponentially
In 2016, Ray Kurzweil, director of engineering in Google, commented in a talk that “although our brains are hardwired to predict the future, we’re not pre-programmed to think exponentially” and he relates this idea to the way that humans approach the exponential growth of technological advancement. Indeed, he discussed that computers are getting more powerful, cheaper, and mostly at a fast rate. At the same time, he continued that this event generates an impact on our brains, modifying the way we communicate and interact through technologies. Thanks to our prehistoric nature, our brains can make predictions estimating “what’s next”, and this evolutionary process did not change across generations. However, even though the development of technologies follows a predictable trajectory, the brain does not understand this pattern. The challenges that we had to face centuries ago when our brains were developing, were just “local and linear.” In contrast, today we have to deal with drones, automated vehicles, or even gene editing. Thus, our brains and thinking modes struggle to rewire according to exponential changes. In this regard, Kurzweil suggests that to align with an exponential change, embracing technologies would augment our brains to evolve into a “superhuman intelligence.”
As previously discussed, linear thinking is characterized by a sequential approach of thought, in which predictions are possible based on a sequential order of events (e.g., a company producing widgets). Differently, thinking exponentially is related to the ability to compound events together; indeed, the cumulation of events is greater than their aggregate parts(e.g., a platform with network effects). However, these different types of thinking support business models that are in contrast with each other. So, why should entrepreneurs invest in an exponential framework? Which are the benefits of learning to think exponentially? Are there any disadvantages? [31]
Considering the early stage of investments, the production of goods/services grows incrementally with marginal costs at a slightly low rate with a linear approach. This linear approach is practical and easy to manage. Predictions are possible from one year to the next 3-5 years, the revenue stream is proportional to the production capacity. and rarely anomalies would occur. Differently, when implementing an exponential framework, besides the development of advanced technologies, estimating profit and production of goods/services might be difficult to believe (remember the 10x growth) unless investors would shift their mindset toward innovative disruptions. One example representing the exponential models is Software As A Service (SAAS) companies. Indeed, these companies are capable of building platforms, generating cash flows from operations back into further improving and developing features to challenge competitors. When looking at earnings, a type of business that follows an exponential trajectory seems that for a long period nothing happens, but suddenly it explodes with a rapid trend [31].
However, it may feel uncomfortable at its early stages due to a lack of visual progress and the fear that the business is not moving ahead. None like uncertainty, especially investors who are mostly impatient, and sometimes success might scare people because it requires hard work, constant learning, and new skills. But the 21st-century and 4IR need leaders that are willing to embrace uncertainty, leaders who hold a vision about “what if” we take the advantage of exponential trends to build businesses. For example, companies like Google, Netflix, or Airbnb were focused on hiring the “right” people early on their journey to generate a robust culture and ecosystem behind their business, allowing them to scale exponentially along the way [31].
The challenges brought by digital transformation and 4IR are no longer local and linear and those models built around linear growth aren’t any more applicable. However, before seeing earnings and growth, shifting toward an exponential model requires patients, especially at the beginning of the business. Finding the proper investors and the right team might be hard, because tasks and goals to be achieved are more challenging, hard, and demanding. Evaluating risks and managing activities is needed when implementing an exponential approach, however, once the fears are left behind, the path toward success can be predictable [31].
Hence, how can individuals and organizations prepare for disruptive changes? How can we benefit from this extraordinary exponential Era? A few strategies have been proposed:
● Plan with agility: changes are happening at a high rate, thus, to be agile, planning strategies should be adapted accordingly. Whether current job occupations will disappear, new opportunities will come. Hence, a career will transform into a series of evolving experiences in which learnings and skills are transferable. Planning becomes agile and flexible to embrace disruptive changes and that requires active engagement, effective communication, and rapid implementation.
● Explore: whether the dilemma has been for decades between exploiting existing skills or exploring new opportunities, to face the 21st-century and 4IR the shift toward more experimentation, lean startup approaches, entrepreneurship, and willingness to pivot and adapt to rapid changes are needed.
● Welcome and master changes: mastering effective management becomes a key principle that leaders should apply when leading organizations and team members toward achieving and positively adapting to exponential innovations and novel opportunities.
● Embrace technologies and collaborate with them: the interaction and active collaboration with machines and advanced technologies allow high productivity in an inter-connected worldwide environment. Furthermore, it enhances communication and cooperation with other individuals toward achieving extraordinary successes.
● Transform your learning into becoming a lifelong learner: a revised education is needed to prepare students when entering Industry 4.0. Hence, education 4.0 represents a novel framework that evolves into a lifelong learning process. Learning new skills while adapting to constant and rapid change is required when technologies and processes are evolving exponentially. Organizations and other workplaces should promote and offer training and educational programs in response to required skills and capabilities upgrades.
● Develop tenacity skills: although the shift toward exploration and more experimentation would lead to new opportunities, the risk for failures and obstacles will likely arise. Thus, developing an “exponential mindset” means more adaptability, flexibility, curiosity, and resilience in trying different ways to solve a problem. A positive attitude in response to disappointment is the key to success. Indeed, organizations should focus on training this skill in leaders and developing effective communication, transparency, and optimism. In addition, growing an environment supported by mentors, coaches, and individuals holding a similar mindset will facilitate achieving goals.
● Enhance human qualities: in a world of exponential changes driven by technologies, the revision of ethical frameworks and rules raised concerns and debates. Thus, humans should not compete with machines. Instead, they should value the importance of those skills that naturally belong to humans and are exclusive to our species, such as creativity, intuition, imagination, emotion, compassion, and ethics.
Learning to think exponentially can benefit the growth of a business while making more accurate predictions about future trends and keeping in line with technological advancements. In particular, the shift toward an exponential mindset is relevant for leaders who need to embrace the digital transformation to drive an organization to succeed. Peter Diamandis, the well-known entrepreneur, futurist, and co-founder of Singularity University with Ray Kurzweil, focuses on educating executives to develop an exponential mindset. Through Big Think+, Diamandis advises how to embrace the exponential shift to gain advantages [32]:
Take advantage of using disruptive technologies instead of being disrupted by them;
Go ahead with your thought when planning your future, without being influenced by past constraints;
Implement exponential tools (e.g., crowdsourcing);
To promote exponential growth, stimulate innovation and the generation of ideas while avoiding solutions that appear too straightforward and obvious.
Rewire your brain through neuroscience
In this article, the difference between linear and non-linear thinking was discussed, and a few suggestions were given about how to embrace an exponential mindset. The term “rewire” has been widely used in this context, as well as, “neural pathways”, “neural connections”, and “neuroplasticity.” However, we should be careful in using these terms while inferring that the brain can modify its activity, moving from one thinking modality to the other. Hence, I tried to search for proof about this phenomenon by looking at the scientific literature, although I could not find what I was looking for. Based on my scientific career and neuroscientific studies, it is possible to effectively train and “rewire” the brain to think exponentially.
When understanding which neural oscillations (e.g., EEG analysis), or area of the brain (e.g., fMRI analysis) is more active based on a task of interest (e.g., that requires exponential, non-linear thinking), techniques such as neurofeedback (NF) or transcranial direct current stimulation (tDCS) could be implemented to train that neural activity or directly stimulate that area of the brain, toward the development of an exponential mindset with a valid neural basis, who knows, maybe in the future brain hacking D.I.Y products will be developed to train and stimulate the brains of entrepreneurs to think exponentially fast while making accurate predictions of future trends, thus growing 10x successes.
In conclusion, I am aware I did give a very broad and imprecise explanation of what I mean when saying “rewire your brain through neuroscience.” This topic would need more than one article to be discussed, but if someone in the research field is interested to know more, I am always up for new and innovative studies to challenge, experiment and explore. Outstanding topics are always around the corner to be further explored! Leave me a message on LinkedIn or the OpenExO platform to connect!
“Our brains renew themselves throughout life to an extent previously thought not possible.” (Michael S. Gazzaniga)
“Our minds have the incredible capacity to both alter the strength of connections among neurons, essentially rewiring them, and create entirely new pathways. (It makes a computer, which cannot create new hardware when its system crashes, seem fixed and helpless).” (Susannah Cahalan)
References:
Koffka, K. (1935). Principles of Gestalt Psychology. London: Lund Humphries.
Wertheimer, M. (1923). “Untersuchungen zur Lehre von der Gestalt II,” in Psycologische Forschung, Vol. 4, 301–350. Available online at: http://psychclassics.yorku.ca/Wertheimer/forms/forms.htm
Wertheimer, M. (1924). “Ueber Gestalttheorie,” Lecture Before the Kant Gesellschaft, Reprinted in Translation in A Source Book of Gestalt Psychology, ed W. D., Ellis (New York, NY: Harcourt Brace), 1–11.
Titchener, E. B. (1898). The postulates of a structural psychology. The Philosophical Review, 7(5), 449-465.
Titchener, E. B. (1908). Lectures on the elementary psychology of feeling and attention. Macmillan.
Hunsaker, M. R. and Kesner, R. P. (2013). The operation of pattern separation and pattern completion process associated with different attributes or domains of memory. Neuroscience & Biobehavioral Reviews, 37, 1, 36-58. DOI:https://doi.org/10.1016/j.neubiorev.2012.09.014.
Marr, D. (1971). Simple memory: a theory for archicortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 262 (1971), pp. 23-81.
McNaughton, B. L., and Morris, R. G. (1987). Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends in Neuroscience, 10 (1987), pp. 408-415.
Rolls, E.T. and Treves, A. (1994). Neural networks in the brain involved in memory and recall. Progress in Brain Research, 102 (1994), pp. 335-341.
Rolls, E.T. (1996). A theory of hippocampal function in memory. Hippocampus, 6, pp. 601-620.
Rolls, E.T. and Kesner, R.P. (2006). A computational theory of hippocampal function, and empirical tests of the theory. Progress in Neurobiology, 79, pp. 1-48.
Kirwan, C.B., Hartshorn, A., Stark, S.M., Goodrich-Hunsaker, N.J., Hopkins, R.O., and Stark, C.E. (2012).Pattern separation deficits following damage to the hippocampus. Neuropsychologia, 50 (10), pp. 2408-2414.
Leutgeb, S., and Leutgeb, J.K. (2007). Pattern separation, pattern completion, and new neuronal codes within a continuous CA3 map. Learning and Memory, 14, pp. 745-757.
Leutgeb, J.K. , Leutgeb, S., Moser, M.B., and Moser, E.I. (2007). Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science, 315, pp. 961-966.
Bakker, A. , Kirwan, C.B., Miller, M., and Stark, C.E. (2008). Pattern separation in the human hippocampal CA3 and dentate gyrus. Science, 319, pp. 1640-1642.
Lacy, J.W., Yassa, M.A., Stark, S.M., Muftuler, L.T., and, Stark, C.E. (2011). Distinct pattern separation related transfer functions in human CA3/dentate and CA1 revealed using high-resolution fMRI and variable mnemonic similarity. Learning and Memory, 18, pp. 15-18.
Tamminga, C.A., STan, A.D., and Wagner, A.D. (2010). The hippocampal formation in schizophrenia. American Journal of Psychiatry, 167, pp. 1178-1193.
Femia, L.A. , and Hasselmo, M.E. (2002). Is autism partly a consolidation disorder? Behavioral and Cognitive Neuroscience Reviews, 1, pp. 251-263.
Yassa, M.A., Mattfeld, A.T., Stark, S.M., and Stark, C.E. (2011). Age-related memory deficits linked to circuit-specific disruptions in the hippocampus. Proceedings of the National Academy of Sciences U S A, 108, pp. 8873-8878.
Siva, C. (2018). Machine Learning and Pattern Recognition. DZone. Available at: https://dzone.com/articles/machine-learning-and-pattern-recognition [Accessed on December 07, 2021]
He, F. and Yang, Y. (2021). Nonlinear System Identification of Neural Systems from Neurophysiological Signals. Neuroscience, 458, 213-228. DOI: https://doi.org/10.1016/j.neuroscience.2020.12.001
Jack D., Kendall et al. (2020). The building blocks of a brain-inspired computer, Applied Physics Reviews.. DOI: 10.1063/1.5129306
Yama, H. and Zakaria, N. (2019) Explanations for cultural differences in thinking: Easterners’ dialectical thinking and Westerners’ linear thinking, Journal of Cognitive Psychology, 31:4, 487-506, DOI: 10.1080/20445911.2019.1626862
Wong, W. (2006) Understanding Dialectical Thinking from a Cultural-Historical Perspective, Philosophical Psychology,19:2, 239-260, DOI: 10.1080/09515080500462420
Li LMW, Mei D, Li W-Q and Ito K (2021) Dialectical Versus Linear Thinking Shapes People’s Anticipation of Climate Change. Front. Psychol. 11:623591. doi: 10.3389/fpsyg.2020.623591
Nisbett, R. E., and Masuda, T. (2003). Culture and point of view. Proc. Natl. Acad. Sci. U. S. A. 100, 11163–11175. doi: 10.1073/pnas.1934527100
Leung, K., and Bond, M. H. (2004). “Social Axioms: a model for social beliefs in multicultural perspective” in Advances in experimental social psychology. ed. M. P. Zanna (San Diego, CA, US: Elsevier, Academic Press), 119–197.
Pisano, I., and Lubell, M. (2017). Environmental behavior in cross-national perspective: a multilevel analysis of 30 countries. Environ. Behav.49, 31–58. doi: 10.1177/0013916515600494
Tam, K. -P., and Chan, H. -W. (2017). Environmental concern has a weaker association with pro-environmental behavior in some societies than others: a cross-cultural psychology perspective. J. Environ. Psychol. 53, 213–223. doi: 10.1016/j.jenvp.2017.09.001
Jody Michael Associates. Linear Thinkers: Overcoming Leadership Challenges. Available at: https://www.jodymichael.com/blog/linear-thinkers-overcoming-leadership-challenges/ [Accessed on December 03, 2021]
Gibb, S. (2018). Linear vs Exponential Thinking. Available at: https://samngibb.medium.com/linear-vs-exponential-thinking-558eab9cccbb [Accessed on December 03, 2021]
Big Think+ (2019). Your brain is wired for linear thinning. Learn exponential thinking instead. Big Think+. Available at: https://bigthink.com/plus/your-brain-is-wired-for-linear-thinking-learn-exponential-thinking-instead/ [Accessed on December 03, 2021]
Photo by JJ Ying and Taras Hrytsak on Unsplash
Graphic by Beatrice Barbazzeni on Canva
Comments