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The Science of Sensemaking

Learn about the science of making sense.

Total Read Time: 30 - 60 Minutes

Technology for Making Sense

On its own, make sense of complex things in life and the world can take a long time. But by building technology that leverages well established principles from information science, cognitive science, and learning theory, we can accelerate the process as well as amplify our ability to make sense.

We use models every day when we look at maps, weather forecasts or financial predictions. We create our own 'mental' models whenever we make future plans. The fundamental nature of our cognition is to build models or 'simulate' reality to understand it and predict the real world more accurately.

Creating models in a computer is one of the most powerful ways we make sense of complex systems. Effortlessly, models in the form of maps on our phones, spreadsheets about our finances, demographic maps, and weather forecasts help us to go beyond our direct senses to see things in ways that enhance our ability to make sense of the world around us, solve problems, and improve our decision making.

The Power of a Model of Cognition

At its core, Inqwire's technology is based on a one-of-a-kind, integrated, situated, process model of cognition.

One-of-a-kind, because creating a model for making sense of life requires a unique process of synthesizing expertise from across multiple domains, instead of just following a best practice or template from a single domain.

Integrated, because it leverages basic principles of information and cognitive science to both simplify problems by using constraints and accelerate the ability to find insights.

Situated, meaning it arranges concepts within a familiar language framework that reflects and leverages how people think naturally.

And, because this model is built out of processes, it allows for user-created adaptive solutions or custom 'recipes' that overcome the one-size-fits-all problem.

And, because it's a model it serves as scaffolding to bring in other concepts from modern cognitive science to inform algorithms further and serve as important sources of constraints.

The Power of Constraints

When it comes to making sense of life, each of us can articulate the big aspects of our life, such as important people, events, challenges, life chapters and so on. It isn't an infinite list. In Inqwire's model of cognition, there are 12 categories of life aspects. If we assume there are about 10 life aspects in each category, this means each of us has around 120 big picture aspects of life. Just as we use landmarks or constellations to orient ourselves and navigate a complex natural world, we use these big picture aspects to navigate our complex lives.

The process of making sense of any domain, including the domain of our lives, requires us to examine how all the parts fit together into a coherent and consistent whole – or to integrate them, like putting together a jigsaw puzzle. If the pieces don't fit together we get a sense that it just doesn't 'add up'. This means the cognitive process of making sense requires us to bring to mind different parts in many different ways to figure out how they all go together.

The problem is, whenever we have a problem that requires us to consider any possible combination of things, the number of combinations quickly becomes unbelievably large. For example, if we start with 120 important aspects of life, bringing together every possible combination of 7 aspects (the limit of what we can hold in our head), that would result in about 100 billion combinations. If we consider that it takes minutes to recall aspects of life 'cold', making sense of our entire life 'in our head' would take on the order of 500,000 years of doing nothing but thinking all day!

Clearly, this isn't the process we use to make sense of life. So what do we naturally do to make it faster?

What we do instead, is use natural language that implicitly contains categories and rules that act as constraints to reduce all the possible ways aspects of our lives can go together.

Inqwire's integrated process model of cognition is based on natural language and has these constraints built in. So, if we redo the calculation using Inqwire's model, we would go from about 100 billion combinations to about 100 thousand combinations and if we did nothing else to improve the process, we would go from spending on the order of a half a billion years of nonstop thinking to make sense of our lives, down to one year.

This may sound outrageous, but the power of constraints to simplify complex problems is well understood in information science within the areas of optimization and big graph data.

To better understand how constraints simplify problems, imagine that instead of ever seeing a map, you are given a list of billions of GPS coordinates for all the intersections in the world. Whenever you want to figure out what direction to go, you need to go through disorganized lists of this data you have to memorize in your head to search for a match at each intersection. Clearly, this would be impossible. But with a model for how that data is constrained to fit into a map (a coordinate system), all of a sudden the data becomes simplified and navigation is easy.

Regardless of whether we are trying to use a computer or a human brain to calculate combinations, it will always be faster the fewer combinations are under consideration. But if we happen to reduce the number of combinations in a way that is situated, like a map, then we get a huge additional advantage with a human brain that can leverage the power of situating concepts.

The Power of Situating Concepts

When we situate concepts, meaning we find a unique location for them relative to each other, we find we are able to hold immense amounts of information in our memory effortlessly, and this body of integrated concepts seamlessly becomes part of our thinking. Modern cognitive science tells us that this is because situating concepts, like placing locations on a map, takes advantage of our fundamental brain architecture.

While we may only just now be understanding how we do this, our use of this concept to help us make sense of things far predates cognitive science and computers. Arranged symbols in art and eventually written languages are all designed to situate complex concepts in such a way to hold them easily in our mind and help us make sense of things, through a process called 'the Method of Loci'. The invention of the book is arguably one of the most complex technologies we use to situate and make sense of complex ideas.

The other super power we get through situating concepts is that it takes advantage of our ability to use recognition instead of recall, which is a much faster process. As we saw above, it takes on the order of minutes (and can take much longer) to bring something to mind 'cold' - think of the 'tip of the tongue' phenomenon, where you know a word but you just can't bring it to mind. But if you saw the word, you would shortcut this work and instantly recognize it. This recognition happens on the order of milliseconds. So now if we redo our calculation from above on how long it would take to make sense of your entire life, and use recognition instead of recall, we now go from it taking a full year of nonstop recall (about 7000 hours), down to about one hour of recognition.

Information technologies invented to help us situate concepts and make sense of life have been discovered going back as far as the late 1200's. These technologies were made out of concentric paper rings to help people integrate aspects of their lives through bringing them together in meaningful combinations.

These inventions are considered by many to be the first real example of information science and were used widely for about 300 years for mental clarity and well-being until they were tragically, systematically destroyed by authoritarian institutions that were attempting to quell any source of free thinking, leaving future information scientists very little to go on. But the new science of cognition, particularly cognitive linguistics, has provided a healthy start to how we might recreate and even surpass the historic success of these early information technologies.

Adaptive Solutions

We can think of algorithms as 'recipes' that result in something useful. Anytime we write down a set of instructions, we could describe that as an 'algorithm'. They can often be very simple, but still invaluable. For example, we could write down an algorithm for how to brush our teeth, how to do the dishes, or even how to be a good listener.

When we have a model that describes a set of inter-related processes, or a 'process model', that means we can 'walk' through the model linking together steps to create many different paths. And every path through the model actually defines a unique process – or an algorithm. If the model grows with more data, then the number of these processes or algorithms grows with it quickly and can become virtually infinite.

At this point the system moves from a set of predefined, 'one size fits all' solutions or algorithms, into a cognitive modeling system that gives you the freedom to choose, shape and create your own processes. It's like you become a chef instead of just following recipes. This doesn't stop the system from suggesting recipes or ingredients if you want it to. But it can create them custom to you, based on your preferences, and based on your data or the 'ingredients' you have on hand. In this way, cognitive modeling systems are designed to enhance human cognition without interfering or attempting to replace it.


In this science story we saw how there is tremendous evidence that making sense of life is a fundamental function of our cognition, and that because of this, we each already possess the intelligence, the expertise, and the capacity to make sense of our own lives. We saw how our fundamental relationship to information, both in how it is represented, and how we engage it, comprises our largest barriers to making sense. And we revealed common-sense steps that everyone can follow to reduce these barriers along with exciting ways technology can help.


Contact Support

If you need help, want to give us feedback, want to suggest a topic for a topic, or need to report a bug, use this form to send us a message and we will get back to you as soon as possible.