Snowy mountain ridgeline against a bright blue sky, with a terracotta distribution plot overlaid and the words "Developers deserve science." set in white serif type.
The Psychology of Software Teams, cover art: mountain photography in blues, whites, and autumn golds layered against a brick-red background, with white ridgeline distribution plots overlaid across the composition, and the author name Cat Hicks, PhD set in serif type below.

A Book Club Guide to
The Psychology of Software Teams

by Cat Hicks, PhD
For buying options, see drcathicks.com/#book
Buy the book

Book Synopsis

To build the future, we need new ways of supporting software teams. This book will give you a secret weapon: the psychology that creates resilience for developers, sustainable practices for software teams, and innovation for organizations. You’ll learn from rigorous empirical evidence gathered from top engineering organizations and thousands of developers around the world, revealing powerful principles software teams can use to guard against failure and drive cultures of collaboration and problem-solving.

Making incredible software doesn’t have to be a death march. This book presents a humane alternative for software teams looking to use the transformative power of behavioral science to understand what drives technology businesses forward.

This book is for the developers and builders of the future. Bringing science and heart together, The Psychology of Software Teams will teach you how to untangle your thinking from pervasive myths about software work and harness the superpowers of psychology to create more joyful, innovative, and thriving environments for software.

Chapters
  1. The Brains and Hearts That Build the World
  2. Breaking Up with Brains-in-Jars
  3. More Bands and Fewer Rockstars
  4. The Performance Paradox
  5. Conflict or Coalitions?
  6. Becoming an Organization That Wants to Understand Itself
  7. Fighting Dirty for Good Culture

About the Author

Cat Hicks, PhD, is a psychological scientist who studies software teams and technology work. She holds a PhD in Quantitative Experimental Psychology from UC San Diego, where her cross-national dissertation examined belief formation and how differences in how people think about ability and performance can drive more sustainable thriving (a thread that runs through all of her later work). She was an inaugural postdoctoral fellow in the UC San Diego Design Lab, bringing cognitive science and data science to the design of large-scale digital learning interventions. She went on to drive real world empirical research identifying levers to move human outcomes at scale across millions of learners at organizations such as Google, Age of Learning and Khan Academy.

Cat is the founder of Catharsis, an evidence science consultancy with an open-science arm that helps technical people and organizations understand and protect meaningful work. She previously founded the Developer Success Lab, an empirical research lab studying developer wellbeing at scale, and Travrse, a developer tool startup focusing on how IDE design can help developers do more strategic just-in-time learning in their moments of navigating and drafting code. Her research influences practice at organizations worldwide. She is an international keynote speaker, writes the newsletter Fight for the Human, and co-hosts the podcast Change, Technically. She can be generally found sharing psychology thoughts and making bad jokes on microblogging sites under the handle grimalkina.

How to Use This Guide

This guide was made to help any individual or group that wants to dive into the book together and connect its research and ideas to their own lives. You are free to take, share, use and remix this guide and its content in any way that helps you. Each chapter section includes discussion questions, and one or more “try it” prompts you can use to apply a concept to your own experiences and teams, past or present.

You don’t need any particular background to lead or join this discussion, nor do you need to have a certain role. The book (and this guide) are written for anyone who works with, manages, or cares about the people who build software and the psychological experience of technical communities.

However, the book’s ideas can play out differently or feel more relevant depending on scale and the context of your guide use and group. Some things are within a team’s own reach (such as norms, habits, how a manager runs a retro); others require company-level authority, budget, or policy to shift (such as hiring rubrics, promotion criteria, org-wide metrics). Some questions ask you to explicitly split these apart. If you are using the guide for individual reflection, feel free to re-draft items to something more relevant to you, or focus primarily on the reflection and try-it exercises. If you are leading a group where you are acting as a facilitator, you may wish to steer questions more towards the individual or company level depending on your group’s context, and focus on the try-it exercises most relevant to your group's challenges.

Consider assigning one chapter per meeting, or grouping chapters into two or three longer sessions if your club prefers fewer, deeper conversations. Or, use the “deep dive” suggestions to focus on a few thematic sections instead. It's not necessary to work exhaustively through this entire guide. Treat this as a pool of possible questions to discuss!

Facilitator note.

If your group is short on time, or you’re running this in a workplace format like a lunch-and-learn or a single afternoon workshop, you can use one of these themes for a whole discussion rather than working chapter by chapter. Pick the theme that best matches what your team is currently wrestling with. Themes give you a broader, whole-book conversation compared to a chapter walk-through, and may suit if you can only get people together once.

Key Themes to Explore

Before diving into individual chapters, it can help to name some of the major themes across the book. Here are a few suggestions for the group to discuss:

The “Brains-in-Jars” myth.

Software work often gets treated as an isolated, individual, purely cognitive act, as though developers are disembodied, fungible floating brains rather than whole people embedded in teams, histories, and psychological environments. POST argues this framing can actively damage teams, but that it’s sticky because it gets us some short-term outcomes. It is reinforced by common stereotypes about what drives technical work.

Thriving and long-term problem-solving over production/productivity.

Rather than asking just “how do we make developers produce more,” POST reframes a central question for software as “what makes developers resilient, engaged, and able to do good problem-solving over time.” The book explores how these two frames lead to different, sometimes conflicting, answers, and makes the case that lasting technology depends on high-quality problem-solving.

Evidence over assumptions.

POST repeatedly asks readers to notice when a belief about software teams (the lone genius, believing that technical work operates as a meritocracy, us vs. them stories) is really an untested folk theory, and what it would take to actually test it. Nuances and gaps in our evidence are to be acknowledged honestly, and POST argues that technical communities and scientists need to work together to build understanding past those gaps.

Culture as infrastructure.

Psychological safety, learning environments, and collaborative groups that can solve conflict aren’t perks, but affordances that determine whether teams succeed or fail, especially under pressure.

Chapter-by-Chapter
Discussion Questions

Chapter One

The Brains and Hearts That Build the World

This opening chapter makes the introductory case that software is built by whole humans, with histories, emotions, and social needs, and that technical people do vital work in environments that often fail to listen to and understand them.

Companion resources
  • Watch: The Psychology of Technologists. Cat’s posit::conf(2025) keynote (1:02:43) is an overarching exploration of a developer science research agenda in one talk. Watch to learn about our mental models for the environments around us, hear a live version of some of the stories in the book, and hear background details about some of the studies in the book.
  • Read: Hicks, Lee, & Ramsey (2024), Developer Thriving, a short-form, practical IEEE Software paper on the four sociocognitive factors of the LABS model for resilient teams. Read to learn about the methodology details of the study and use the specific measures in the study.
Facilitator note.

If your group has both managers and developers, the “visible and valued” question can surface disagreement between the two. While this might feel scary, it could also be tremendously valuable for your group. Spend a few minutes thinking about how you as a facilitator plan to respond if a tension emerges. It might be helpful to establish expectations (e.g., mutual listening before response). Directing attention to shared goals can go a long way toward helping people feel like different viewpoints are tolerable, and can help your group find resolution.

Try it

Think of one moment at work where your emotional state visibly shaped your technical output (a good day or a bad one). What would it have looked like for your team to account for that? Make an if-then plan (“if I have a bad day, then I will…”) to take care of yourself on a bad day.

Try it

Think of a moment where you’ve seen a technical person or team successfully make their work more “visible and valued.” What barriers did they face in doing so? Is there any wisdom they would pass along to others? If you are that person, what advice would you give your younger self? To make it tangible, write down one concrete piece of advice, or even a whole letter, to your younger self.

Chapter Two

Breaking Up with Brains-in-Jars

In Chapter 2 we dig further into the “Brains-in-Jars” metaphor and the myths it produces across the three “Traps” that describe the negative cycles running through tech communities.

Companion resources
Facilitator note.

The Chilly Climate topic can bring up experiences of exclusion tied to identity. Think carefully about whether your group is the right space to surface these, and be prepared to offer supportive facilitation.

Try it

Look at your team through the lens of all three traps (Brittle Productivity, Lone Genius, and Chilly Climate), then pick whichever one feels most salient right now. Name one concrete, observable sign of it (for example, a sprint that “succeeded” but left everyone wrecked; a story where one person is framed as the only source of breakthroughs; a cue about whose identity is really welcomed in the room). Then identify one small step you could take this week that would help break the thinking trap.

Try it

Look at a tech community you’re part of (an open-source project, a meetup, a Discord or forum, a professional network) through the same three traps. Pick whichever trap feels most salient. Brittle Productivity might appear in unpaid maintainers burning out while everyone praises the project’s “amazing momentum.” Lone Genius might show up in who gets thanked in the release notes versus who did the coordination and community work. Chilly Climate might be present in a community’s inside jokes, its default assumptions about who’s “actually technical,” or who stops showing up to events. Name one concrete sign of the trap, and then write down one thing you personally could do this month to interrupt it.

Alternative deep dive into section

About the Evidence

Facilitator note.

This section is a bit different from the rest of Chapter 2. Instead of a research finding to apply, Hicks turns the lens on her own methodology, laying out exactly how she weighed and selected the evidence behind the whole book. For some groups, this can be worth its own deep dive because it is a chance to model what good “evidence citizenship” looks like, and because the same critical eye can be turned on any claim your own workplace relies on.

Companion resource
  • Read: Hicks (2026, May 21), Software research must become more reusable in the Fight for the Human newsletter. An essay from Cat on why community-based work coupled with methodological rigor can create new insights for the technical people that software research is about.
  • POST describes a knowledge gap: software research often fails to draw on rigorous theory about how our minds work, while psychology studies human behavior broadly but rarely studies developers specifically. Have you ever encountered the gap? What do you wish psychologists would study with developer populations? What do you wish software research would bring in from the behavioral sciences?
  • This section describes four families of methods and some of their trade-offs: surveys (self-report and memory bias), lab experiments (artificiality), observational studies (hidden confounds), and large-scale trace data like software metrics (variables that weren’t designed to answer human questions in the first place). Which of these does your own technical community or organization lean on most heavily when it makes decisions about people? What blind spot does that create?
  • Hicks flags the “WEIRD” sample problem: a long history in research of generalizing about all humans from participants who are Western, Educated, Industrialized, Rich, and Democratic. Has your team or company ever imported a “best practice” or intervention from research or a well-known book that may never have been tested on a workforce that looks like yours? What groups and experiences are particularly underrepresented in evidence about software teams?
  • POST lays out three criteria in the book for evaluating evidence: (1) understand how the evidence was gathered, not just the headline finding; (2) require an argument to be supported by multiple studies and multiple kinds of evidence, not just one; (3) treat findings as something to dialogue with and compare against your own experience, not something to passively accept. Pick a “best practice” claim you’ve heard repeated at work (a productivity habit, a hiring criterion, a team ritual) and run it through these three questions. What do you actually know about it, and what are you assuming?
  • The section closes with, “rigor is a form of care.” What does it mean to treat methodological rigor (sample size, replication, construct validity) as an act of care for the people a claim is about, rather than as a dry academic virtue? Does that reframe change how impatient you feel with “boring” evidentiary questions?
Try it

Pick one claim about “what works” for developers or software teams that gets repeated in your technical context without much scrutiny. Try to trace where it actually came from. Would it survive the three evidence criteria?

Chapter Three

More Bands and Fewer Rockstars

This chapter shifts the unit of analysis from the individual “rockstar” to a collaborative unit, the “band.” In this chapter, we trace the origins of a few myths about individual differences in programming ability, review the impact of metacognitive strategies, and look at the attribution errors that push our minds to explain software in terms of individuals.

Companion resources
Try it

Think of a recent technical win on your team. Trace how many people’s contributions actually fed into it, even indirectly. Who tends to get the credit, and does that match the trace?

Try it

Identify a hiring or performance review practice you’ve encountered that assumes individual, fixed ability. How might it look different if it assumed ability is shaped by environment and support?

Try it

Pick a recent piece of code or a solution you’re proud of and trace your thinking’s lineage. How much of it was genuinely built from scratch, versus adapted from documentation, Stack Overflow, a coworker’s earlier PR, or an open-source library? What does that trace tell you about how “individual” your work actually was?

Chapter Four

The Performance Paradox

Chapter 4 tackles how understanding our own motivational system, and how we respond to the social pressures around performance and disclosure, shapes performance. A quick frame for the group: expectancy-value theory says we can think about our motivation as riding on two separate ingredients: your expectancy (how confident you are that you’ll succeed) and the value you place on the work itself (how much you care about or enjoy it) or on the outcome (what it will bring to your life, and why that matters).

Companion resources
Try it

Take a piece of praise you’ve recently given or received that emphasized talent or outcome (“you’re a natural at this,” “great job hitting the deadline”). Rewrite it to emphasize effort, strategy, or improvement instead.

Facilitator note.

The next Try it is more personal than a typical discussion prompt. Depending on your group’s comfort, consider inviting the group to think about it privately, then asking only whether it felt useful, not what came up.

Try it

Bring to mind a recent moment when your technical work did something negative you didn’t expect, like created a bug in production, put you through a review that went badly, or locked you into a design you now regret. Instead of moving past it, sit with it for a minute. Try to recreate the feeling you had when you realized that negative thing was happening. Now, shift your attention to how you're thinking about yourself, and try saying these three sentences about that moment. This might feel a little cheesy at first, but notice if it has an impact as you try on these thoughts:

“This is a hard moment.”
Try to name what you feel without analyzing it.
“Technical work goes wrong for every person who takes on substantive problems. This is part of the work I have chosen.”
Situate yourself in a shared reality instead of an isolated failure.
“Can I offer myself the calm and empathy I would offer a colleague who told me this same story?”
Extend to yourself the professional respect you already give to others.
Try it

Think back to a version of yourself before your current skills became tied to evaluation, your own equivalent of Shaun’s “motivated kid” who loved the puzzle before it became a performance. Pick one piece of technical work this week with nothing riding on it (a side project, an unfamiliar language, a bug with no deadline attached). Deliberately set a mastery goal for it instead of a performance goal: define success as “what did I learn” or “how did my strategy improve” rather than “did this look impressive” or “did I get it right on the first try.”

Chapter Five

Conflict or Coalitions?

This chapter looks at common ways that disagreement and tension play out on technical teams, what separates destructive conflict from productive coalition-building, and how understanding group identities and group psychology can help us steer our groups toward better.

Companion resources
Facilitator note.

If your group has a history of working together, this chapter’s questions about outsiders and coalitions can potentially raise old tensions. On the first pass, consider framing examples around past teams or third parties rather than the current room. Consider setting a group intention to find the superordinate goal (a shared objective that even two different, opposing sides can agree matters to them).

Try it

Think of someone on a past team you were in coalition with, even if you didn’t always agree with them. What made that relationship work? How did it change your thinking? If you’re still in contact, make a plan to reach out to that person and thank them.

Try it

Think of a disagreement with a group that you’re experiencing or seeing right now. Whether or not you agree, take two minutes to experiment with reframing the conflict as coming from loyal dissent, putting it into the context of a push for better coming from someone committed to the team’s success. Try writing a few sentences from the dissenter’s point of view.

Chapter Six

Becoming an Organization That Wants to Understand Itself

Chapter 6 makes the case for organizations building self-understanding and self-correcting processes through developing an evidence strategy. This requires actually wanting to know what’s true about our teams, not just wanting to confirm what we already believe. It also requires creating a measurement plan that technical people can trust, give feedback on, and believe in.

Companion resources
  • Watch: Measuring Cycle Time with Dr. Cat Hicks (44:53). A deep dive conversation about the complexities of measurement, where Cat describes the challenges and translating her empirical work on cycle time into advice for leaders.
  • Read: Flournoy, Lee, Hicks, & Wu (2025), No silver bullets: Why understanding software cycle time is messy, not magic also published in Empirical Software Engineering. Read for our evidence practice in action, including an analysis that questions the predictive value of a software metric widely perceived to be predictive. If you are tasked with handling software metrics data yourself, you can find and build on our statistical analysis code available at the paper’s repo.
Facilitator note.

This chapter asks questions that can veer into critique of specific leaders or decisions “when has leadership ignored inconvenient data,” for instance, could call up real memories. If this is a concern in a workplace group, consider asking participants to frame the discussion around patterns rather than people (“we’ve seen this happen when…”) and around past examples rather than current tensions.

Visual reference

The Evidence Readiness Levels shows nine different levels of evidence to consider when moving interventions from “lab” to “real world ready.” Where have you seen software team evidence used to drive decisions around you, and which level does it match?

Try it

Pick one causal claim currently floating around your technical community or workplace (“X practice makes us more productive,” “Y tool improves quality”). Write out the claim. Then, see if you can answer these questions for it: what specific effect is this, over what timeframe? What evidence could you realistically get to assess it? What would a counterfactual comparison look like? Is there an important confound (a third variable that changes the relationship) that might change whether you get this effect?

Try it

If you could run one evidence project inside your own team or org, what question would you actually want answered?

Chapter Seven

Fighting Dirty for Good Culture

The closing chapter is a call to action: protecting good culture takes deliberate, sometimes uncomfortable effort. But POST argues that this effort is worth it, because it brings more meaning and agency to our lives.

Companion resources
Facilitator note.

When talking about culture change, people often express frustration about workplaces that feel unmovable. Venting and validating frustration may be a valued use of time for your group, but the closing prompt suggests pushing for action to avoid discussions getting too mired in pessimism. If the discussion tilts into venting, consider gently redirecting toward the one small thing each person is actually willing to try.

Try it

Write down one sentence you could say in your next team meeting that puts one of this book’s ideas into practice.

Try it

Take two minutes to write down a concrete value that matters to you. It can be broad, but should be personal to you. For instance, you might consider family, friendships, craft, honesty, care for other people, learning as a lifelong value, or intellectual fulfillment. Don’t necessarily try to connect it to your work; just write about why the value matters to you and one specific way it has shown up in your life. Then think about your technical work. How might you show up for and demonstrate this value in your specific role, workplace, or life in technical communities?

Alternative deep dive into section

The Culture Cycle

Facilitator note.

This section is a bit different from the rest of Chapter 7 as it offers a working group framework that maps culture across four interlocking levels. For some groups, this can be worth its own deep dive because it is a chance to move from discussion to concrete planning, and because the same framework can be applied to any change your group is trying to make.

This section draws on the “culture cycle” framework from Hamedani, Markus, Hetey, and Eberhardt’s (2024) American Psychologist article, “We Built This Culture (So We Can Change It).” The model maps culture across four interlocking levels: ideas (shared narratives and assumptions about what’s good, right, and effective), institutions (for example, an organization’s formal laws, policies, and practices), interactions (the everyday exchanges among people, groups, and tools), and individuals (people’s own beliefs, identities, and behaviors). The four levels continuously shape one another, and change is most durable when it happens at multiple levels at once, rather than being left to a single “culture initiative.”

Visual reference

A chapter 7 table proposes contrasting ways that Brains-in-Jars and Thriving organizations take up beliefs and norms about ideas, institutions, interactions and individuals. Do any of these resonate in particular with this group?

  • Map your own team or org onto the four levels: What’s an idea about “good developers” or “good code” that seems to circulate unquestioned? What institutional policy operationalizes it (hiring rubrics, promotion criteria, review templates)? What interactions reinforce it day to day (standups, code review comments, Slack norms)? How do individuals internalize it, in their own self-talk or self-doubt?
  • The article argues culture change sticks best when the four levels are in alignment, and that misalignment (“culture eats policy for lunch”) is why well-intentioned policies often fail. Can you think of a policy at your workplace that didn’t “take” because it wasn’t backed up at the interactions or individuals level? What would alignment have looked like?
  • The framework distinguishes top-down change (initiated by people with power or authority) from bottom-up change (initiated by people with less power, often because they’re the ones most affected by the status quo). Where have you seen each type play out in a software organization, and which tends to be trusted more?
  • One principle from the paper: culture change is easier when it leverages existing core values, and harder when it challenges deep-seated defaults and biases. What’s a “default” in software culture (about who counts as technical, whose feedback carries weight, what “10x” looks like) that is hard to dislodge?
  • The framework argues that culture change often involves power struggles and identity threat, and that resistance or backlash is a normal part of the process rather than proof that the change has failed. Has your team ever mistaken pushback for failure and abandoned a change too early? What would it have looked like to expect and plan for that resistance instead?
  • Timing and readiness matter, and change attempted in a moment of crisis or exhaustion can land very differently than the same change attempted when a team feels some stability. Have you seen a “fight for good culture” that failed mainly because of bad timing, not a bad idea?
Try it

Pick one small change you’d want to see in how your team works. Using the four levels, sketch one concrete strategy you could try at each: an idea to name and question, an institutional practice to adjust, an interaction to change, and something for yourself as an individual to try differently.

Big Picture Discussion Questions

Facilitator note.

This is the closing conversation, and it invites more personal reflection than earlier questions did, especially the two questions about naming an experience the book helped you understand and identifying one idea to act on. You might consider giving people permission to answer briefly or to pass. You might also consider ending with a round-robin where each person briefly names one thing they’re taking from the book with no discussion. That structure gives everyone a way to be heard but manages time and energy at the end of a session.

Further Reading & Resources

The book

Hicks, C. (2026). The psychology of software teams. Routledge. https://doi.org/10.1201/9781003589112

Research

Flournoy, J. C., Lee, C. S., Hicks, C. M., & Wu, M. (2025). No silver bullets: Why understanding software cycle time is messy, not magic. Empirical Software Engineering, 30, Article 174. https://doi.org/10.1007/s10664-025-10735-w. Also available on arXiv (arXiv:2503.05040): https://arxiv.org/abs/2503.05040

Hamedani, M. G., Markus, H. R., Hetey, R. C., & Eberhardt, J. L. (2024). We built this culture (so we can change it): Seven principles for intentional culture change. American Psychologist, 79(3), 384–402. https://doi.org/10.1037/amp0001209

Hicks, C. M. (2024). Psychological affordances can provide a missing explanatory layer for why interventions to improve developer experience take hold or fail [Preprint]. PsyArXiv. https://osf.io/preprints/psyarxiv/qz43x

Hicks, C. M., & Hevesi, A. (2024, November 21). A cumulative culture theory for developer problem-solving [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/tfjyw

Hicks, C. M., Lee, C. S., & Foster-Marks, K. (2025, March 15). AI skill threat: How the structure of developers’ beliefs about software development ability impacts their psychological resilience during rapid technology shift [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/2gej5_v2

Hicks, C. M., Lee, C. S., & Ramsey, M. (2024). Developer thriving: Four sociocognitive factors that create resilient productivity on software teams. IEEE Software. https://ieeexplore.ieee.org/abstract/document/10491133

Lee, C. S., & Hicks, C. M. (2024). Understanding and effectively mitigating code review anxiety. Empirical Software Engineering, 29(6), Article 161. https://doi.org/10.1007/s10664-024-10550-9

Lee, C. S., Hicks, C. M., & Foster-Marks, K. (2024). “My code is shit”: Negative automatic thoughts and outcomes of a behavioral experiment for code review anxiety [Preprint]. PsyArXiv. https://osf.io/preprints/psyarxiv/hz3et_v1

Find Cat at…

Invite Cat to your book club

Cat is glad to visit book clubs and technical communities that are using this guide for a short virtual Q&A. If your group would like her to join a session, reach out through drcathicks.com.

Research measures and additional open-science materials are available through the Research and Catharsis links above.

License & attribution

To facilitate broad use (including inside of your organization), this Book Club Guide to The Psychology of Software Teams by Cat Hicks, PhD is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share, adapt, and remix this guide for any purpose, including commercially, provided you give appropriate credit and note what changes you made.

Please cite as: Hicks, C. (2026). A Book Club Guide to The Psychology of Software Teams. drcathicks.com/#book. Based on The Psychology of Software Teams by Cat Hicks, PhD (Routledge, 2026, ISBN 978-1-032-96338-9).

Other citation formats
MLA
Hicks, Cat. A Book Club Guide to The Psychology of Software Teams. 2026, drcathicks.com/#book.
Chicago
Hicks, Cat. 2026. A Book Club Guide to The Psychology of Software Teams. https://drcathicks.com/#book.
BibTeX
@misc{hicks2026guide,
  author = {Hicks, Cat},
  title  = {A Book Club Guide to The Psychology of Software Teams},
  year   = {2026},
  url    = {https://drcathicks.com/#book}
}

However, the book The Psychology of Software Teams and its cover art are separately copyrighted; the CC BY 4.0 license applies to this guide, not to the book.