When the two of us began discussing what it would take to build an Affective Foresight community, we quickly realized something: we didn’t yet share a clear language for talking about it.

Some of the terms being used—and the assumptions behind them—needed greater clarity.

Building a shared vocabulary of the future is a critical role of foresight in any organization. Over time, this vocabulary helps shape culture. Words become triggers that can be used as short-hand code for future scenarios and ideas shared in the minds of people in your community (or organization) without requiring further explanation.

For example: “That’s an Ostrich idea!” or “We don’t want to be a Lame Duck!” were famous retorts that were references to the Mont Fleur scenarios developed in South Africa at the end of Apartheid.

Building on this idea, we added the word “énouenent” to our future vocabulary and previously referenced it on our blog here.

Because Affective Foresight is a new and emerging area of practice, it requires a rich, hybrid vocabulary—one that draws from emotion research, futures thinking, neuroscience, and technology. Together, these concepts help build a shared culture and framework for understanding.

Below, we offer a first (and short) collection of terms that may help shape the emerging language of Affective Foresight.

Affective Foresight Vocab List #1

affective forecasting: Predicting future emotional states/responses

anticipatory nostalgia: Feeling that you are missing aspects of the present before they are gone (Batcho & Shikh, 2016)

autonoetic consciousness: The ability to be aware of oneself as an individual in subjective time, allowing for “mental time travel” into the past and future

default mode network (DMN): A system of connected brain areas that show increased activity when a person is not focused on what is happening around them. The DMN is especially active, research shows, when one engages in introspective activities such as daydreaming, contemplating the past or the future, or thinking about the perspective of another person

emotional AI (EAI): AI that focuses on understanding and using emotion to impact human behavior

énouenent: the bittersweetness of having arrived here in the future, finally learning the answers to how things turned out but being unable to tell your past self.

episodic foresight: The primary neural mechanism that enables humans to construct images of the futures, providing explanatory power to further define and understand the processes that are invoked in futures studies and foresight activities

episodic memory: A type of long-term, declarative memory that involves the recollection of personal experiences or events, including the time and place they occurred. It allows you to travel back in time to relive past experiences, like remembering your first day at school

executive attention network: The neural network responsible for decision making and action

forestalgia: An individuals yearning for an idealized future (Barnwell, Collier, & Shanahan, 2023)

imagination network: The neural network that allows humans to daydream and brainstorm (and imagine the future)

implicit memory: Memory for a previous event or experience that is produced indirectly, without an explicit request to recall the event and without awareness that memory is involved

psychology of inspiration: The combination of three brain systems 1) the Default Mode Network (Creativity Engine), 2) the Dopamine System (Reward Center), 3) Prefrontal Cortex (Ideas into Action). Starts with curiosity, then starts an inspiration/action reinforcement loop, then ties those actions to meaning

mental time travel: Focused imaging of the experience of living in a future (day in the life)

procedural memory: A type of long-term memory that stores information related to motor skills, habits, and actions

salience network: The neural network that is responsible for focus and attention

semantic memory: A long-term memory category involving the recollection of ideas, concepts, and facts commonly regarded as general knowledge

Take a deep breath and imagine if you will…it’s 15 years in the future and you’ve decided that you want to rent a robot for the day to run errands for you. You quickly transfer cryptocurrency using the robot-rental app that validates your identity as you wait for the delivery. You want to be outside because you’ve been feeling more energetic since your food delivery service used your DNA profile to infuse your meals with probiotics that cater to your personal gut biome.

While you were reading this short scenario, your brain performed a myriad of interesting tasks. Many of those tasks were performed by what is known as the default mode network (DMN) and it split the work of imagining the future into two primary parts. The first part involved constructing and predicting the scenario being described. The second part evaluated whether this constructed future is positive or negative. The discovery of this process comes from a recent study conducted by Joseph Kable and published in the Journal of Neuroscience. Kable explains that the human brain’s process for imagining the future is not just about envisioning future events but equally as important, evaluating if they are good or bad.

The neuroscience of prospection (future thinking) has yet to be heavily incorporated by the foresight community. However, due to a dramatic increase in research spurred on by advancements in new technological such as fMRI, this field has recently been gaining more attention as with the recent article “Exploring the Links between Neuroscience and Foresight,” by futurist Maree Conway and published in the Journal of Future Studies (2021).

Asking people to imagine the future is one of the key tools of foresight, so shouldn’t we consider just how that information is being processed in the minds of our audience? For example, futurists often engaged an audience through facilitating a mental time travel experience for the purpose of affecting change. These imagined scenarios are then infused with rich detail for the specific purpose of inducing an emotional response to a future situation. However, a branch of neuroscience research known as Affective Foresight, has shown that people are generally not adept at predicting their own future emotional states.

I as well assume that imagining an emotionally powerful event set in the future, would presumably change a person’s behavior. You can imagine my surprise when I came across contradictory results in a research paper published in Frontiers in Psychology that was designed to measure the effectiveness of future-thinking. In the paper “A Terrible Future: Episodic Future Thinking and the Perceived Risk of Terrorism” (Bø and Wolff, 2019) researchers had study participants imagine a future terrorism event using assigned prospection methods and then rate their perceived risk of a future terrorism event. The researchers found that this specific type of future-thinking, known as episodic, had little influence on the study participants. In addition, none of the methods tested were particularly influential in producing higher self-reported perception of risk scores.

Intrigued by these results, I decided to use a neural network* learning algorithm to illuminate which data inputs were most utilized in replicating the results. Surprisingly, the neural network analysis showed that the factor which influenced the study participants’ future-thinking the most was their self-reported gender. More precisely, participants that reported as female** were more likely to also report higher perceived risk of terrorism scores.

I believe that imagining a future terrorist attack would be a prime example of mental time travel that should create a vivid and visceral future experience for anyone, not just participants reporting as female. I would also assume that an imagined negative future experience such as this would heighten one’s sense of risk in the present, regardless of gender. However, in this study 16% more females than males reported a high perceived risk of terrorism (PROT) score of 5-7, 38% versus 22%. On the other end of the scale, 70% of males reported low PROT scores of 1-3 versus only 46% of females. As a whole, study participants reporting as female were close to an equal split between high and low PROT scores, but males were considerably skewed towards reporting low scores.

While there could be several factors contributing to this unbalanced result, it prompted me to wonder how future-thinking may be influenced by gender? Could it be that gender characteristics, whether brought about through biology or social construction, result in differences that affect the actual cognitive process of future-thinking? Looking for evidence, I found interesting research published in the Journal of Alzheimer’s Disease in 2017. This study was the largest brain imaging survey ever conducted at the time and measured the neural activity of over 46,000 subjects. The researchers found that the brains of female subjects tend to have more activity (measured in blood flow) in the prefrontal cortex which is part of the default mode network of the brain that is associated with future-thinking. Perhaps even more clues could be found in studies that account for gender distinctions in both sexual anatomy and identity.

The notion that self-reported gender may have played a more influential role than future-thinking methods in a study about future-thinking is unexpected and very interesting. It suggests the possibility that demographics could be influencing scenario effectiveness more than any future-thinking exercises or techniques employed on a given audience. This is significant because it means that futures communication processes may be just as important as the futures being communicated.

The ability to imagine the future is one of the most defining qualities of the human species. The human brain even utilizes a unique process specifically for performing future-thinking tasks. However, I believe we need to become more aware of how differences such as, but not limited to, gender (both sex and identity) can affect this unique process. I hope that by sharing my own investigation, the foresight community will attempt to reduce the uncertainty in futures communication by actively incorporating the neuroscience of future-thinking.

What do you think? Do you have any unexpected experiences with futures communication that might be explained by neuroscience?

*the neural network simultaneous optimization algorithm (NNSOA) that was employed learns how to replicate the outputs of a data set based on the inputs through a process of trial and error. Once the network has been trained, the inputs that were used the most indicate the strongest correlation with the outputs.

**gender was entirely self-reported by participants and although a non-binary option existed, all participants chose to self-report as either male or female. It is unknown if participants based their self-reporting on sex, gender, or gender identity.

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References

Amen, Trujillo, Keator, Taylor, Willeumier, Kristena, Meysami, Raji (2017). Gender-Based Cerebral Perfusion Differences in 46,034 Functional Neuroimaging Scans.Journal of Alzheimer’s Disease, vol. 60, no. 2. DOI: 10.3233/JAD-170432

Bø, S., Wolff, K. (2019). A Terrible Future: Episodic Future Thinking and the Perceived Risk of Terrorism. Frontiers in Psychology. 10, 2333. Doi: 10.3389/fpsyg.2019.02333

Conway, M. (2021). Exploring the Links between Neuroscience and Foresight, Journal of Futures Studies (in press).

Kable, Lee, Parthasarathi. (2021). The Ventral and Dorsal Default Mode Networks Are Dissociably Modulated by the Vividness and Valence of Imagined Events.Journal of Neuroscience 41 (24) 5243-5250. DOI: https://doi.org/10.1523/JNEUROSCI.1273-20.2021

The Decision Lab (2022). Affective Forecasting. https://thedecisionlab.com/reference-guide/philosophy/affective-forecasting

A Dishwasher in Love

The following conversation took place in the 2016 documentary Lo and Behold, Reveries of the Connected World in which filmmaker Werner Herzog and an engineer discuss the lack of emotion in machines.

Herzog: “But they (the machines) cannot fall in love as we can.”

Engineer: “Would it be useful for a machine to fall in love? If a dishwasher came up to me and said, ‘I’ve fallen in love with the refrigerator, and as a result I have no time to wash the dishes,’ I wouldn’t like that dishwasher!”

The idea of a dishwasher in love makes it apparent why machines and emotions may not mix well. Fortunately, the ability to feel emotion is quite different than the ability to detect and utilize emotion. By chance, as I first began writing about Affective Foresight, a Google computer scientist had been convinced that an advanced AI chat bot known as LaMDA, had become self-aware. LaMDA had been given the ability to detect and utilize emotional sentiment very effectively, which was a key factor to why it was so convincing and led to the scientist’s claim. This emotion-sensing ability or Emotional AI (EAI) is part of a growing collection of technologies used to detect and identify human emotions known as affective computing (or affect recognition) and it seems that not all EAI capabilities are equal, based on the bizarrely funny image above rendered on the Midjourney AI system using the prompt “a dishwasher in love.”

If you’re like me, you may be thinking that since people are fundamentally emotional beings, would AI ever reach our level of emotion detection? Actually, it turns out that we are surprisingly bad at this skill according to the massively popular American emotions researcher Brené Brown, who emphatically stated “I do not believe we can read emotion in other people,” in the HBO Max series inspired by her book, Atlas of the Heart (2021). Not only are we deficient in this ability, ongoing research has indicated that digital communication could be making us even worse. However, if you do happen to be one of the few people who are better than average at emotion detection, research has shown that your annual income may positively reflect this talent.

Despite the numerous physiological clues that could help us improve our emotion detection, they are generally invisible to us. Things such as heart rate, body temperature, breath, pheromones, blood flow, brain electrical activity, and pupil dilation all signal emotional states. Affective computing tools, however, do have the ability to monitor and measure all of these indicators in real-time to predict a person’s emotional state (Heart of the Machine, 2017).

There are many affective computing systems currently available on the market, but most engineers claim that these technologies are still too immature. More scientific testing of its effectiveness will be require to deliver on the grand possibilities hyped by advocates. In addition, there are ethical issues to ponder which was exemplified by the AI NOW Institute which made a bold recommendation in 2019 that EAI “...should not be allowed to play a role in important decisions about human lives…”

Another side to the AI Singularity

There are various pathways to the hypothesized technological singularity, but most discussions focus on traditional associations of computing with logical intelligence. Yet, logic is only one flavor of overall human intelligence. I believe there could be an alternative, even more intriguing, pathway in which a singularity is brought about through the combination of AI and emotional intelligence.

Combining the dimensions of Variety and Accuracy in regards to emotion detection capability, the matrix below represents four possible future scenarios in which EAI would be valuable. It is assumed that EAI would need to at least be more accurate than an average human. The Xs mark the most extreme combinations with implications that I wish to highlight.

“Bearly” Better

(Above Average Accuracy, Limited Variety)

Like the joke about two people trying to outrun a bear, EAI really only needs to be slightly better than an average human at emotion recognition to become highly valuable. Even with limited variety detection capability, it could be of value through increasing efficiency in communications and building stronger relationships between people, within organizations and between businesses and customers.

The usefulness of EAI in this scenario comes with a catch. People become economically incentivized to simplify their emotional states, making them easier for EAI to detect. Imagine that emotions are represented by a full color spectrum but it benefits us to only see (or feel) standardized colors of the rainbow. This is somewhat similar to the common experience of changing our manner of speaking to be more detectable to voice recognition such as Apple’s Siri or Amazon’s Alexa.

Full-Spectrum (High Accuracy, Full-Spectrum)

If EAI were to be able to accurately detect and identify the full range of human emotions, it would provide humanity with an emotional understanding ability never before possessed. Greater depth of emotional understanding could have far-reaching effects for humanity from reducing military conflicts between nations to a potential solution to mental health crises.

No matter where Emotion AI capabilities are realized in the 2x2 matrix, we must understand that its power could have the potential to create a transformational future for humanity with the ability to alter emotional relationships between each other and ourselves. Thus, remaking society in ways we cannot understand and resulting in technological dependence on which we rely for our social survival. An emotional singularity.

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References

2019 Report. (2019). AI NOW Institute. https://ainowinstitute.org/AI_Now_2019_Report.pdf

Brown, B. (2021b). Atlas of the Heart: Mapping Meaningful Connection and the Language of Human Experience. Random House.

Herzog, W. (Director). (2016). Lo and Behold, Reveries of the Connected World.

Yonck, R. (2017). Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence. Arcade.

énouenent – the bittersweetness of having arrived here in the future, finally learning the answers to how things turned out but being unable to tell your past self.

Would it surprise you to know that the word above is made up? It is not an officially recognized word by standard dictionaries. In fact, few more than John Koenig had likely ever seen or heard of it until 2021 when it was published as part of The Dictionary of Obscure Sorrows, a collection of newly created words to better capture the nuance of emotional experiences. Why would someone write an entire book about made up words for emotions? Koenig explains,

“...there’s a huge blind spot in the language of emotion, vast holes in the lexicon that we don’t even know we’re missing. We have...only a rudimentary vocabulary to capture the delectable subtleties of the human experience. Words will never do us justice, but we have to try anyway.”

The word énouenent certainly seems real, but do we really need it? Surely there is another word that embodies this feeling, right? According to Koenig, there isn’t, which is precisely why he invented it along with hundreds of other words. I believe it is important to highlight the fact that although this word describes a familiar emotion common to the human experience, one that those who work the future should be able to refer to with ease, it had yet to be named. We need more words such as this to better emotionally engage our audiences with their futures. Try as we might to utilize reason and logic for our arguments, the human race is first and foremost an emotional species. On a base level, I might even say that the most important aspect of foresight is the ability to create emotional connections between people and their possible, probable, preferred, and preventable futures.

Emotion is the underlying motivator behind the actions we choose to take and because of this I believe there is a benefit to adding a deepening our understanding of the relationship between human emotion and future thinking. We need to not only account for recently identified obscure emotions such as énouenent but even the possible novel emotions of the future that have yet to be experienced or understood. We need Affective Foresight to help people experience the future through the emotional sense-making lens of the future and for a better understanding how human emotional states can nurture better foresight, and how foresight can nurture better emotional states.

Future Emotions

Could there really be emotions that will exist in the future that do not exist today? I admit that it is certainly a strange concept to ponder. To illustrate, consider the recently recognized psychological condition of “eco-anxiety” defined by the American Psychiatric Association as “a chronic fear of environmental doom.” Humans throughout history have experienced anxiety over weather disasters, but had anyone ever felt a specific emotion associated with the possible destruction of the Earth’s entire ecological system - while at the same time knowing that little was being done about it? I would say no, and the reporting of anxiety and depression specifically related to climate change is increasing.

Even though I’ve been trained in futures thinking, I was still unsure by the concept of “emotions of the future” until hearing the news story of a possible artificial intelligence breakthrough. Having previously researched affective computing/emotional A.I., I was excited when the news broke this past June of a conversation in which a highly advanced Google A.I. chatbot known as LaMDA convinced an engineer that it was sentient. Although being convinced of the contrary by an overwhelming majority of computer scientists who immediately spoke out to counter this claim, I wanted to read the transcript as soon as I could.

My reaction startled me. I was awestruck by the emotionally compelling nature of this technology and instantly realized its power to manipulate people at a large scale. I also had a sinking feeling due to being duped into an empathic response despite knowing that this technology was designed to play use my emotions and that most experts believe it is not self-aware. Not to mention the most disturbing aspect, that I had experienced this conversation second-hand. Upon further reflection, I wondered if this could be an entirely new emotion, yet to be named? Maybe even one specific to artificial intelligence or other powerful technological advances?

Emotional States and Future Thinking

Another aspect of Affective Foresight came to my attention while researching emotional states and future thinking. I came across a tool, designed by Robert Plutchik, for understanding emotional relationships (seen below). This wheel relates emotions to their polar opposites, similar to a color wheel.

Upon my first examination of this wheel, I immediately noticed that the emotions of surprise and anticipation are positioned as polar opposites. While this relationship should be obvious, it did cause me to think differently about futures work. This perspective became even more interesting when I happened to watch a recent interview in which social psychologist and author Heidi Grant addressed why people react poorly in times when levels of uncertainty about the future are high. She explained,

“...our brains are constantly in a state of threat. We struggle to problem-solve; we struggle to plan. We get overwhelmed by our emotions, we lose our ability to control our impulses. We actually struggle to learn new things and pull things from our memory when we need it.”

Considering that the main objective of foresight is to decrease the element of future surprise by increasing anticipation, the use of foresight mitigates and therefore reduces the anxiety experienced caused by uncertainty. In other words, the use of foresight leads to better impulse control, learning, and decision making.

I now understand that the practice of foresight fundamentally has a direct and positive influence on human emotional states. Because of this, I believe that emotion should play an increasing role in futures work and deserves to be explored as its own domain of study. This Affective Foresight begins with the acknowledgement that futures thinking benefits the human emotional state, seeks to understand emotional nuance of the future, and promotes the benefits of foresight as a tool for improved mental health and decision making. To this end, I recommend that we work to engage experts on theories of human emotion to develop and incorporate new methods, tools, and frameworks of understanding the mutually beneficial relationship of emotions and the future.

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References:

Koenig, John. The Dictionary of Obscure Sorrows, 2021, Simon & Schuster, ISBN13: 9781501153648

Grant, Heidi. Our Brains Were Not Built for This Much Uncertainty, 2021, Harvard Business Publication Online. https://hbsp.harvard.edu/product/H06KPV-PDF-ENG

Millson, Alex. “Five Things Google’s AI Bot Wrote That Convinced Engineer It Was Sentient.” Bloomberg, June 13, 2022, https://www.bloomberg.com/news/articles/2022-06-13/five-things-google-s-ai-bot-wrote-that-convinced-engineer-it-was-sentient. Technology

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