We live our lives constantly involved in a series of routines and patterns. Some of these routines are conscious or learned, like how to make a sandwich. Many of these routines are subconscious or natural, like chewing and digesting the sandwich, dictated by systems operating automatically in our bodies. There are also some routines that are somewhere in between, like recognizing that we are hungry. These routines combine to ultimately create patterns: our blood sugar drops and triggers notifications that we recognize as hunger, we decide to get up and make a sandwich, we eat until we are no longer hungry, and our body processes the food until it runs out and the process starts anew.

In sum, we can reduce most of our daily existence as experiencing an emotion triggered through a natural reaction, our personality and motivations are the components that interact with the emotion to dictate a response, and we then behave accordingly until another emotion is realized. While at first it might seem a bit cold and depressing to recognize that humans - just like all animals - can be broken down into these patterns, there is a silver lining. Because we are advanced enough to recognize these patterns, we can not only assess the patterns to determine a likely behavior but can intervene to disrupt patterns and change the behaviors we don't like.

The challenge, as always, is to find ways of impartially conducting pattern analysis on the trickiest subject possible: ourselves. Most people don't like discovering they are more concerned with what other people think of them than they would care to admit, for instance, or that they could stand to be a bit more persistent in completing tasks.

It turns out computers are an excellent means of impartial pattern identification, particularly in language and learning. The earliest automated processors - the US's ENAIC computer, Alan Turing's Enigma codebreakers, Bomba and Colossus, and other wartime machines - were created specifically to perform rapid, complex pattern identification and decryption. It is hardly surprising that natural language processing NLP - the pattern analysis of syntax and word usage in order to, among other things, identify human emotions and personality - followed less than half a century later. And as these processes are continually refined it is repeatedly verified through a plurality of studies that NLP is an unbiased means of determining a variety of personality traits and emotional indicators.

NLP can identify the components of our internal routines, and even accompanying emotions that trigger the processes. What about the next steps, where we decide what to eat and get up to make the sandwich? These are more complex and combine our past experiences, our current situational processing, and our motivation for the future. Resilience, Toughness, even Optimism or Positive Reinterpretation live in this realm of not being solely composed of personality, emotion, or behavior. But these attributes are the key to understanding our responses and actions, and are often what dictate our decision-making processes.

Luckily, computers can assist here as well. For example, positive reinterpretation is the ability to examine a previous negative experience and extract whatever benefits and good outcomes may have arisen from an otherwise bad situation. To determine if a person does reframe a past event is not a part of personality, but the likelihood of the attribute can be calculated in looking at other NLP results. For example, does the person have a cheerful demeanor or are they very serious? Do they have a strong motivation to learn? Do they believe in their own abilities? Examining coping mechanisms and resulting positive reinterpretation through scientific studies identifies sub-components of the combined attribute.

How much of each sub-component factors into the attribute is another step for computational analysis. Weights for each are found through regression analysis and statistical comparisons, which give clues to the relevance of each component. If cheerfulness is highly correlated in repeat studies of positive reinterpretation, for example, it will have a higher weight relative to the other components. These are the parts to a sandwich: if they are not all present when we open the fridge, we might need to make a different decision on what to eat. If we are out of cheerful demeanor because we tend to be very serious, we might not be able to form strong positive reinterpretation. If the attribute is highly correlated so the weight is high, that might make cheerfulness the equivalent of bread. Without the bread, we might not be able to make any sandwich at all and continue to be hungry. But this does identify a needed change: we should go buy bread, i.e. we should find ways to relax our outlook.

The component measurements - how much we have of each item - is calculated by the NLP and can be relied upon as a usable metric with a fairly high degree of accuracy. But the weights are taken from studies that may be conducted with smaller sample sizes and in all likelihood personality components came from self reported surveys containing natural bias. This is important, since these weights not only calculate the accurate measurement of the attribute, but they also set the priority for how we need to focus our efforts to change. We don't want to assume the wrong weight that leads us to focus on buying more mustard before we buy more bread, after all.

In order to refine the weights of each component within an attribute we again turn to technology, this time through machine learning. Having calculated our best guess from correlations and comparative data, we set up a system that evaluates the weighting of each piece using historical texts and known results for the attribute. This means if an individual had an XX Cheerfulness score and a YY Learning score leading to a weighted Positive Reinterpretation score of ZZ, when they next encountered a challenge how did they react? If they showed Positive Reinterpretation - relative to all of the other examples entered into the same analysis - the weights do not adjust, if they did not the weights may adjust. As more text, metrics, and verification data is entered into the system the machine can compute any weight adjustment; in effect, it "learns" what is going right or wrong and how to adjust the weights so it can better estimate the probability of a person positively reinterpreting an event.

Additionally, the computer can assess for differences in weights given patterns in different demographics: does cheerfulness matter more in men than women, for instance, or in programmers more than operations personnel? Over time, the system can refine to become more and more precise. And more precision leads to better gauges of a particular attribute, which in turn creates a better understanding of probable behavior and faster, more effective ways to become better individuals and teammates. And make some killer sandwiches, too.