Emotional reasoning and other cognitive distortions

It’s always interesting coming back to pieces that I’ve covered in the past and seeing how a few years later, I’m engaging with them in a different way.

Recently, this has happened to me with:

The Coddling of the American Mind

Which I was inspired to re-read after hearing Jonathan Haidt’s interview on the Sam Harris’ Waking Up podcast promoting his new book of the same name.

Two things struck me reading the post I wrote about it 3 years ago:

1. I rarely make predictions in this blog, but boy I got this one right

Here’s an excerpt from my old post explaining my “odd” decision to cover a piece like that in an organization-oriented blog:

But why am I covering a piece about campus culture in a blog about business organizational effectiveness? I’m glad you asked:

*) Today’s college culture problems are tomorrow’s business culture problems, as current students leave college and join the workforce, with this cultural indoctrination in mind.

*) Looking at the direction that typical “office sensitivity training programs” are headed, and the way that some related incidents are handled, some may argue that this culture has already started trickling into the work place.

*) Tech companies will be affected first as their demographic tends to skew young.

*) No matter on which side of the academic debate on “whether it’s the colleges’ job to prepare students for post-college life” you fall, this piece suggests that the skills/culture gap is widening. If colleges are not stepping up to address it (and some may argue, are making it worse), workplaces will have to.

Since then, interest in dealing with matters of Diversity, Inclusion & Belonging (DIB) in business has grown exponentially…

2. While back then I was intrigued mostly by the marco-pattern, nowadays I’m interested a lot more in the nuanced cognitive distortions that are covered in the article

I’m curious about those because they are part of the “so what?”, a piece of the puzzle that is a way to address this rising organizational challenge/opportunity.

So what are “cognitive distortions”? you may ask. Well, here’s a rather pithy definition:

Tendencies or patterns of thinking or believing, that are false or inaccurate, and have the potential to cause psychological damage.

It’s that latter piece about the psychological damage that sets them apart, in my mind at least, from the broader category of cognitive biases that they are part of.

Cognitive distortions are the foundation of a psychological therapy approach known as Cognitive Behavioral Therapy (CBT). CBT has identified these thinking patterns as highly correlated with disorders like depression, anxiety, and other mental illnesses and developed protocols for addressing these disorders through changing these patterns of thought.

In the lightweight research that I’ve done on this topic, I couldn’t find a list of cognitive distortions that is as MECE as I would have liked. But the one that Haidt and Lukianof included in their original article, is a decent reference list that I’m going to try and keep closer by from this point on:

  • Emotional reasoning. You let your feelings guide your interpretation of reality. “I feel depressed; therefore, my marriage is not working out.”
  • Mind reading. You assume that you know what people think without having sufficient evidence of their thoughts. “He thinks I’m a loser.”
  •  Fortune-telling. You predict the future negatively: things will get worse, or there is danger ahead. “I’ll fail that exam,” or “I won’t get the job.”
  • Catastrophizing. You believe that what has happened or will happen will be so awful and unbearable that you won’t be able to stand it. “It would be terrible if I failed.”
  • Labeling. You assign global negative traits to yourself and others. “I’m undesirable,” or “He’s a rotten person.”
  • Discounting positives. You claim that the positive things you or others do are trivial. “That’s what wives are supposed to do — so it doesn’t count when she’s nice to me,” or “Those successes were easy, so they don’t matter.”
  • Negative filtering. You focus almost exclusively on the negatives and seldom notice the positives. “Look at all of the people who don’t like me.”
  • Overgeneralizing. You perceive a global pattern of negatives on the basis of a single incident. “This generally happens to me. I seem to fail at a lot of things.”
  • Dichotomous thinking. You view events or people in all-or-nothing terms. “I get rejected by everyone,” or “It was a complete waste of time.”
  • Blaming. You focus on the other person as the source of your negative feelings, and you refuse to take responsibility for changing yourself. “She’s to blame for the way I feel now,” or “My parents caused all my problems.”
  • What if? You keep asking a series of questions about “what if” something happens, and you fail to be satisfied with any of the answers. “Yeah, but what if I get anxious?,” or “What if I can’t catch my breath?”
  • Inability to disconfirm. You reject any evidence or arguments that might contradict your negative thoughts. For example, when you have the thought I’m unlovable, you reject as irrelevant any evidence that people like you. Consequently, your thought cannot be refuted. “That’s not the real issue. There are deeper problems. There are other factors.”
Emotional reasoning and other cognitive distortions

Sacrificing or Suffering? [Petriglieri]

If you’ve been following this publication for a while, you should know by now that I love distinctions.

Our lives our nuanced and subtle, but we often seek to make generalizations and abstractions that help us reduce the complexity and see the bigger picture better. However, sometimes going the other way and adding back that nuance helps just as much. Which is why I love distinctions.

And Gianpiero Petriglieri introduces us to a very powerful distinction in:

Are You Sacrificing for Your Work, or Just Suffering for It?

The core distinction is captured in the following paragraph (emphasis mine):

Not all pain and suffering, however, amount to sacrifice. The difference is not just philosophical. It is practical. Sacrifice might be hurtful and exhausting, but it is a conscious choice. Suffering is the result of feeling that we cannot slow down or else we will be shamed and lose control. Sacrifice makes us who we are. Suffering keeps us captive. When putting our bodies through hell at work, at least for a while, is worth the rewards we get and the contribution we make, it is sacrifice. But if you can come up with many reasons for hurting at work, but see little purpose in it, then it is not.

Petriglieri argues that suffering is more pervasive than sacrifice in the business realm, and hypothesizes the causes for it by contrasting the dynamic in it and those in the realm that’s often cited as having mastered sacrifice — elite athletes:

  1. Lack of discipline in seeking of and working on our limits
  2. Insufficient respect for the criticality of pace
  3. Underinvestment in seeking out the help and support needed to improve

This ponderous note is a good place to pause and reflect…

Sacrificing or Suffering? [Petriglieri]

People as Vectors [Fishkin]

Source: SparkToro

A neat post by Rand Fishkin, expanding on an analogy from a conversation between Dharmesh Shah and Elon Musk:

Why Elon Musk’s “People as Vectors” Analogy Resonates

The most useful insight from the post is explaining what we mean by the word “alignment” using this (overly-)simplistic analogy:

We can think of the people on any given team as vectors

People vectors have both direction and magnitude

When people all work in precisely the same direction, their magnitudes are added to each other

When people have any degree of deviation, the varying directions subtract (at least somewhat) from the maximum amount of productivity that could be achieved

The original talk covered alignment in three levels:

  1. Align people with the organization’s goals.
  2. Aligning individual teams (product, marketing, sales, service, etc.) with the organization’s goals.
  3. Aligning the organization’s goals with the needs of the customer.

And Fishkin offers decomposing each further to assess the level of agreement on the following questions:

  • Why are we on this team together? (motivation)
  • For whom are we building this? (customer)
  • What are we creating? (product/service)
  • Do we have shared respect, trust, and empathy for one another?

The last one seems a bit like the odd-man-out and ties better to the next section where Fishkin lists out the things that drive/hinder getting aligned:

  • Trust
  • Agreement on values>goals or goals>values. This one was not easy for me to understand, but it seems to have to do with deeper shared beliefs that the participants have on whether “where are we going?” (goals) is more important than “how are we going to get there?” (values), or the other way around.
  • Psychological safety

Lastly, Fishkin covers the importance of emotional buy-in and why he believes “disagree & commit” hurts alignment. This is the piece on which Fishkin and I are not aligned 🙂 This may be because I support a narrower definition of “disagree & commit” which may mitigate most of the down-side that troubles Fishkin.

People as Vectors [Fishkin]

Pitfalls in Performance Feedback [Sinofsky]

Steven Sinofsky is crushing it this year penning several thoughtful posts on the performance management process that’s at the heart of so many organizations.

His latest is

Pitfalls in Performance Feedback

Where he covers, well, the common pitfalls in giving performance feedback.

The first part of the post calls out behaviors that in his opinion tend to be (wrongfully) rewarded in performance feedback:

  • Visibility of the person
  • Self-promotion
  • Handling real-time exchanges or “thinking on your feet” well
  • Heroics. Over-promising
  • Goal-setting gymnastics. Over-delivering
  • Arguing (as opposed to debating)
  • Writing skills
  • Presentation skills
  • Confrontation (as opposed to providing feedback)

The second part makes a broader observation, that a common pitfall in performance feedback has to do with its focus. Sinofsky discerns between three aspects of a work an employee does:

  1. Deliverables
  2. Process — which is poorly worded, in my opinion, and actually refers to teamwork, collaboration and overall good corporate citizenship
  3.  Style and Technique

He then argues that the rough focus of performance feedback should be ~80% deliverables, 15% process and 5% style and technique.

This deeper insight contextualizes the list in the first part and refines the argument there a bit, since it’s clear that most of the pitfalls listed in the first part have to do with “style and technique” and a handful has to do with “process”. So even if the feedback there is valid (some roles do require strong writing skills) — it should not be the primary focus of the feedback.

Pitfalls in Performance Feedback [Sinofsky]

Affirmative feedback

When we give someone constructive feedback, we typically call attention to the negative impact that their behavior had on us, and invite them to change their behavior (and if we’re really doing a good job, we also commit to helping them do so). And that makes a lot of sense. Our brain makes sense of the world around us through the differences between what we expect will happen and what actually happen. So when someone behaves in a way that’s different than the way we’ve expected them to behave, it’s easy for us to notice that and call attention to it.

Explained though the Johari Window, we assume that the negative impact of their behavior falls in their “blind area” — know to us (others) but unknown to them. By disclosing it, we bring that insight into the open and enable growth and development.

Johari Window

But there’s another, hidden opportunity that we tend to miss. We incorrectly assume that any positive implications of their behavior are fully known to them and that these aspects of their behavior are fully conscious, deliberate and intended.

That is often not the case.

Just like behaviors with negative impact, behaviors with positive impact regularly fall in one’s blind area, and are unconscious or unintended. Therefore, there is a significant developmental benefit in bringing them into the open through affirmative feedback —  feedback that’s meant to reinforce a particular behavior pattern rather than encourage the changing of one.

Compounded by the fact that we usually find it easier to do more of something that we already rather than stop something we’re already doing or start doing something totally new, the impact of affirmative feedback can be much higher than that of constructive feedback.

Note that there’s an important distinction between affirmative feedback and praise: the former is still intended to serve a developmental purpose (just like constructive feedback), while the latter is intended more to demonstrate situational empathy, gratitude, and recognition of the actions taken.

Affirmative feedback

Learning from failure #3 [Mgmt 3.0]

The relationship between learning and failure is topic that I’ve covered a few times on this publication:

This week’s post is this simple diagram from the folks at Mgmt 3.0:

Celebration Grids

While calling it a “celebration grid” is a bit over-the-top in my mind, this nifty diagram packs a lot of insight into a simple visual

I like the behavioral progression from mistakes through experiments to practices, with an increased likelihood of the outcome beings a success and failure being treated differently under each behavioral phase. All the while, learning follows an inverted U shape. We learn through experiments, regardless of whether the outcome was success or failure.

Learning from failure #3 [Mgmt 3.0]

Surveys: exploring statistical significance

WARNING: Some stats and math ahead. Mostly based on this lovely post: T-test explained: what they mean for survey analysis

Who doesn’t like surveys?

Well, most people. And yet, we love using them in organizational contexts for various purposes.

One big challenge in using them in that context is that they are a one-sided exchange of information. And while that makes sense in many other contexts, for example, when asking customers for feedback about a product; inside the organization, what we’re really trying to create is dialogue, since survey “takers” have a big part to play in addressing any insights that may come up from the survey. But that’s a topic for a different post. Today, I want to focus on something a lot more concrete.

We like using surveys because they can provide us with a quantitative assessment of a situation. For example, to measure “how are we doing?” in a particular area and to track it over time or across different organizational demographics. But sometimes, if we’re not analyzing the data carefully enough, over-reliance on surveys can lead us to over-react.

Let’s say that we ran an inclusion survey in which participants were asked to respond to the following statement using our beloved 5-point Likert scale: “When I speak up, my opinion is valued”. When analyzing the survey results we discovered that women, on average scored a 4.5, while men, on average scored a 4.3. Can we say based on the survey data that men and women in our organization are not given an equal voice?

The answer, as always, is: “it depends”. Depends on what? Glad you asked! It depends on the following things:

  1. The size of our organization and the participation rate in our survey
  2. The confidence level we want to have in our answer. The standard 95% confidence level means that if we ran the survey again, we’ll reach the same conclusion 95% of the times.
  3. The difference in the means between the two groups
  4. The standard deviation of the responses in each of the group

1–3 are fairly straight forward. The standard deviations is the least intuitive of the bunch so we’ll focus on it and say that: assuming an organization of a certain size, in order for a certain difference in means to be statistically significant at a certain confidence level, the standard deviation of the results in each group must fall below a certain maximal threshold.

More so: the smaller the org (or the lower the participation rate), the smaller the difference in means and the higher the confidence level required— the lower the standard deviation threshold will be.

Let’s make this a bit more concrete: assuming a best case scenario in which there’s full participation in the survey and the groups are of equal size — these would be the standard deviation thresholds for various combinations of org size (n), confidence levels, and difference in means:

Standard deviation thresholds for statistical significance of difference in means at varying confidence levels and org sizes

So in a 100-person organization, in order for a 0.1 difference in means to be statistically significant at a 95% confidence level, the standard deviation of both groups must be below 0.25. Keep in mind that this is the best case scenario, so if participation was lower or the groups were not equal in size, that threshold will be even lower.

Which leads us to the next question: what does a 0.25 standard deviation look like? Sure we can do the math and crunch the numbers, but for those of us (yours truly included) who don’t have a strong statistical intuition this may help:

Distribution of n=100 results on a 1–5 scale with standard deviations of 0.3, 0.6, 0.9, 1.2

The next time I’m running a survey, before jumping to action simply by looking at the means, I plan to look up my standard deviations at the table above and figure out whether action is truly needed. I’d encourage you to do the same 🙂

Surveys: exploring statistical significance