A behavioral scientist’s take on providing effective instructions for optimal learning.

A behavioral scientist’s take on providing effective instructions for optimal learning.

Teaching is a large part of any leader’s position. Whether academic or business, most leader’s are promoted due to others’ perceptions that they have mastered a domain of knowledge. Unfortunately, most leaders are simply assumed to then know how to impart this knowledge on their supervisees/mentees.

Effective teaching is an art and science. Here we’ll discuss guidelines for understanding, breaking, up and imparting your knowledge of complasks for others.

The first step is to understand the task:

Task analysis (TA) is a method for identifying and documenting the process of task completion. Many different methods exist for documenting and analyzing task completion (Tofel-Grehl & Feldon, 2012).

Minimalist Instruction
In its most fundamental form, TA instruction is merely a list of steps.

    1. Read task analysis (TA) article
    2. Do your own task analysis (TA) of a complex task
    3. Present simple step-by-step instructions for task to learnerSimple is important because new learners to a complex task will tend to become frustrated and give up.In addition to helping you impart skills to learners, task analysis (TA) can be a way of discovering how to optimize the efficiency, productivity, or safety of a task

Interested in learning more about Behavioral Science?

Task Analysis for Instruction Optimization

Instruction optimization involves a data-based assessment of instructional effectiveness. Any material or stimulus intended to prompt, guide, or teach a response can potentially be improved if you take the time to identify what components promote the effective behavior.



From an operations perspective, optimally effective instructions have many benefits such as:

  • Saving time during onboarding and training
  • Reducing the frequency of errors
  • Maintaining knowledge retention over time

Best Practices in Task Analysis Instruction
Learning outcomes and performance can generally be enhanced for most learners by:

  • Presenting steps in smaller pieces (Crist, Walls, & Haught, 1984)
  • Limiting jargon, presenting images, and providing examples (Graff & Karsten, 2012)
  • Presenting images of and describing stimuli that should trigger the response and the expected outcome(s) of a correct response (Tyner & Fienup, 2015)
  • Updating instructions frequently to match task changes (Dixon et al., 2007)

Though these general guidelines should improve the effectiveness of your instructions, optimizing ultimately requires measuring and adapting to what creates the best outcome for your learners.

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Remember, though examining the effectiveness of your instruction formally may initially require some time investment this cost should be offset by improvements in efficiency and reduction of errors.

Other considerations may also inform whether the expected ROI justifies the cost, including the risk or danger of the task, the criticality of accuracy, the acceptable threshold for errors, the cost and time required to correct errors, and the qualitative experience of those who are performing the task.


Task analysis is a method for investigating and documenting the process of completing a task that is prevalent across diverse industries. TA instruction is robust for training and guiding task performance, and many best practices have been published to improve its effectiveness. Robust methods from behavioral science are also available to further enhance instructional effectiveness and efficiency.

Bryan Tyner, PhD

Bryan Tyner, PhD

Optimized Behavior Technology

Bryan Tyner is a behavioral scientist and research-strategy consultant. He has a PhD in behavioral psychology from The Graduate Center at the City University of New York (CUNY). His research on instructional design, assessment, and optimization has been published in the Journal of Applied Behavior Analysis and the Journal of Behavioral Education. Bryan is the founder of Optimized Behavior Technology, an independent research agency that consults on the use of research methodology and data analytics to inform business operations, strategy, and product development. More information about his services is available at www.optimizedbehavior.technology 


Crist, K., Walls, R. T., & Haught, P. A. (1984). Degrees of specificity in task analysis. American Journal of Mental Deficiencies, 89, 67-74.

Dixon, M. R., Jackson, J. W., Small, S. L., Honer-King, M. J., Mui Ker Lik, N., Garcia, Y., & Rosales, R. (2009). Creating single-subject design graphs in Microsoft Excel 2007. Journal of Applied Behavior Analysis, 42, 277-293.

Gero, J. S. & Mc Neill, T. (1998). An approach to the analysis of design protocols. Design Studies, 19, 21-61.

Graff, R. B. & Karsten, A. M. (2012). Evaluation of a self-instruction package for conducting stimulus preference assessments. Journal of Applied Behavior Analysis, 45, 69-82.

IBM SPSS. (2016). Multivariate linear regression in SPSS. IBM Support. Accessed April 19, 2017 from http://www-01.ibm.com/support/docview.wss?uid=swg21476743

May, J. & Barnard, P. J. (2004). Cognitive task analysis in interacting cognitive subsystems. In Diaper, D. & Stanton, N. A. (eds.) The Handbook of Task Analysis for Human-Computer Interaction (pp. 295-325). New Jersey: Lawrence Erlbaum Associates, Publishers.

Tofel-Grehl, C. & Feldon, D. F. (2012). Cognitive task analysis-based training: A meta-analysis of studies. Journal of Cognitive Engineering and Decision Making, 7, 293-304.

Tyner, B. C. & Fienup, D. M. (2015). The effects of describing antecedent stimuli and performance criteria in task analysis instruction for graphing. Journal of Behavioral Education, 25, 379-392.

Yu, R., Gero, J., & Gu, N. (2015). Achitects’ cognitive behavior in parametric design. International Journal of Architectural Computing, 1, 83-101.

The paradox of power and the nature of human bias.

The paradox of power and the nature of human bias.

A behavior analyst’s notes on Uber, Tesla, Wells Fargo, and multi-systems change processes.

Orignal post to bSci21.org March, 2017

As a behavior analyst my attention is often drawn to cycles of behavior. Where there is a pattern, there can be prediction and change. In recent news, I have seen repeated criticisms of Uber, Tesla, and the “Bro-culture” of Silicone valley’s tech industry. These complaints of misogynistic culture are further situated in the wider news environment depicting large-scale divisions across racial, sexual preference, political, and socieoeconomic divides.

In most cases, we learn of these issues only when an incident arises that is of sufficient magnitude to serve as a catalyst. These incidents are generally quite costly for all involved; whether a lawsuit, a death, or a war – everyone pays. In this situation it may be easy to assign blame but I argue that for the most part businesses like Uber, Tesla, Wells Fargo, and the like, are simply experiencing the natural unaddressed impact of their contexts on their employees and organizations. These companies are not exceptions or ‘bad apples’, instead they are the natural result of unaddressed conflicting contingencies. For the Uber’s executives, board, and its employees these issues unaddressed will likely contribute to further financial losses, public relations nightmares, and continuing branding problems.

Let’s look in further detail at the example of Uber to further examine: 1) the nature of in-born human bias as it plays out on the interpersonal, organizational, and multi-systemic levels, and 2) what we can do to prevent harm and promote wellness on the interpersonal, organizational, and multi-systemic levels.

1) The nature of human bias:

            The nature of human thought, language, and behavior naturally pulls for bias (RFT; Hayes, Barnes-Homes, & Roche, 2001; Banaji & Greenwald, 2013). This can be observed on every level of human experience from our ‘internal’ worlds, to our interpersonal behavior, to our effect on our environments. Additionally, none of these exists in exclusion from the others. We are essentially bound environment-to-behavior – on multiple levels – in any moment, as well as to our past experiences through the quirks of human language/thought. This leads to loops of interacting behaviors so large that no human mind, alone, can track them.

This accounts for the vast majority of complex human behavior, and yet, our biases interact and cause literal reductions of awareness (Banaji & Greenwald, 2013). We are, essentially, unaware of our own blind spots as we meet them in daily life.

For clarity, here’s a list of a small number discussed in non-behavioral for the lay reader:

  1.  ‘Reward’, ‘punishment’, and ‘pairing’ – Though sacrificing a great deal of technical detail here; much of our response to our environment is a response to how certain behaviors have been rewarded, punished, or paired with other experiences that were experienced as rewarding or punishing.
  2. Coherence – This refers to our tendency to see, perceive, remember, and seek out what ‘makes sense’ based on our own experience.
  3. ‘Self-as-idea’ and ‘other-as-idea’ – This refers to our tendency to form internal ‘stories’ about who we, others, and even organizations ‘are’.
  4. ‘Rules’ – Humans are sense making, predictability seeking creatures, and must learn from each other. One primary way that we do this is through the description of if-then relationships and concepts. This is a huge advantage to human-kind, who can learn through instruction to reach ‘reward’ and avoid ‘punishment’ socially.
  5. ‘Believing’ the idea or rule – In our efforts for predictability and sense-making we tend to ‘believe’ thoughts, perceptions, and ideas that made impressions on us. This is necessary to organize our experience of the world and allow adaptive interaction; however, it often also creates emotional attachment, and paradoxically, blindness – to what is not described by the rule.
  6. ‘Difference bias’ – This is a collection of biases around how we perceive ‘similar’ and ‘different’. Humans are prone to categorizing, simplifying, and, unfortunately, fearing what is different.

The interaction of these biases play out in many ways interpersonally, organizationally, and multi-systemically:

Let’s return to the example of Uber’s most recent public relations conundrum. On Feb. 19, 2017, former Uber engineer, Susan Fowler, posted an account of sexual harassment she reportedly experienced by her direct supervisor on her blog. This material, still searchable by her name at this point, is of special interest as the writer focused on her experience with the organization and several layers of organizational process over time.

For Uber and any organization undergoing change in a competitive environment, what she describes should serve as a preventative guide and an action plan towards change. As Susan reports and Uber’s stock and business profiles confirm, Uber has been in a period of rapid development. Uber, founded only in 2009, is now worth approximately $68 billion dollars. This exceptional rate of growth, paired with the competitiveness of their business sector [tech, services, etc.] as well as their existence in technology development ‘hot-spots’ nearly assures both internal and external competition for ‘rewards’. As Susan describes, companies experiencing this rate of growth will generally experience competition between employees, shuffling of work assignments as market demands fluctuate, and political shifts. Organizationally, the natural tendency will be to respond to the chaotic competitive market and competitive interpersonal dynamics by rewarding behavior without attention to process and placement of rules meant to minimize risk. In organizations, this usually takes the form of actual organizational policies [rules], or stratification of power and access [organizational hierarchy; also functioning as a rule]. Organizational hierarchy thus amplifies comparison dynamics  [including power dynamics] that amplify further amplify any culturally-bound biases (e.g., socialized gender biases).

The paradox of power then – is that the more access and control we have, the less we can clearly see the problems relevant to our success.

Rules, and unfortunately, punishment are our natural response to not being able to track all important outcomes. These well-intentioned rules, including Uber CEO Travis Kalanick’s recent announcement that any sexual harassment is considered to be a fire-able offense – often malfunction. Clearly sexual harassment should not be overlooked; however, the way in which we speak ‘rules’, in-context, change their overall function. The likely function of CEO Travis Kalanick’s statements on Uber employees, as well as the upcoming ‘investigations’ into Uber’s ‘Bro-Culture’, will almost certainly set the stage for further volatility, turn-over, and costly complaints for the company.

If the company culture was one of fear, competitiveness, change, and division both on performance status and gender – than we can only expect that punishment-based change programs will amplify these behaviors as employees try to preserve their employment. This will cause less transparency in operations as it further amplifies the ‘everyone for himself’ context. This is a real shame, as based on what Susan Fowler discloses in her blog, there is a significant but dwindling group of employees who feel great loyalty to the organization and vitality in their work.

2) What we can do to prevent harm and promote wellness:

What is called for now, based on the context and contingencies Uber finds itself in, is an intervention that embraces paradox. It is one that examines the context and contingencies that result behaviors at the personal, interpersonal, and multi-systemic levels that damage the overall organization. It calls for a focus on ‘rewarding’ adaptive behavior and creating systems where competition and change can exist in a psychologically safe context. Recent developments in assessment and intervention in organizational behavior management (OBM) better support the measurement of contingencies system and contexts system wide. With the use of Natural Language Process (NLP) to measure verbally described contingencies on mass and the ease of system wide feedback systems (e.g., normalized 360 degree feedback) to promote multi-level transparency – organizations such as Uber stand to benefit greatly from evidence-based ‘reward’ driven systems that increase stability, performance, and employee satisfaction. These interventions allow behavior analysts to functionally view the contingencies of an organization over-time to cater and refine interventions. Additionally, for the employees – these interventions guided by external parties can serve as a safe way to anonymous way to provide needed feedback to CEOs, boards, and consultants who can redirect contexts towards growth and stability.

 “The sailor cannot see the North but knows the Needle can.” – Emily Dickenson

For organizations like Uber, for our culture, and our own psychological health – a needle that points north is exceedingly rare. With the ability and willingness of organizations, such as Uber, to respond to the contingencies of employees as described organizations have the opportunity to follow the “North”  determined by an awareness of the contexts and behaviors driving each department and levels of their organization. This ability to listen to the collective wisdom of their own employees may mean the difference between disaster and growth in today’s market.

References and recommended reading:

CNN Money on investigation:


Allen, T. D., & French, K. A. (2016). Women and career advancement: Issues and opportunities. Organizational Dynamics, 45, 206-216.

Bear, J. B., Cushenbery, L., London, M., & Sherman, G. D. (2017). Performance feedback, power retention, and the gender gap in leadership, The Leadership Quarterly

Banaji, M. R., & Greenwald, A. G. (2013). Blindspot: Hidden biases of good people. New York, NY: Delecorte Press.

Biglan, A. (1995). Changing cultural practices: A contextualist framework for intervention research. Reno, NV: Context Press.

Guerin, B. (1994). Analyzing social behavior: Behavior analysis and the social sciences. Reno, NV: Context Press.

Hayes, S. C., Barnes-Holmes, D., Roche, B. (2001). Relational Frame Theory: A Post-Skinnerian account of human language and cognition. New York, NY: Kluwer/Academic/Plenum Publishing.

Hayes, S. C., Bunting, K., Herbst, S., Bond, F. W., & Barnes-Holmes, D. (2006).

Expanding the scope of organizational behavior management: Relational Frame Theory and the experimental analysis of complex human behavior. Hasworth Press.

Maraccini, A. M., Houmanfar, R. A., and Szarko, A. J. (2016). Motivation and complex verbal phenomenal: Implications for organizational research and practice. Journal of Organizational Behavior Management, 36 (4), 282-300.

Pela’ez, M. & Moreno, R. (1999). Four dimensions of rules and their correspondence to rule-governed behavior: A taxonomy. Behavioral Development Bulletin, 8 (1), 21-27.



Angela Cathey, MA

Angela Cathey, MA

Founder, Partner, Consultant, Data Scientist

Angela is experienced in leading and coordinating the operations of research and intervention teams. She has a master’s in Clinical Psychology from the University of Houston – Clear Lake. She has specialty training in measurement, intervention, People Analytics, natural language processing, and data science. Angela was the entrepreneurial lead in the National Science Foundation i-Corps customer validation program for Enso’s key products. She has a background in innovative technology problem solving, technology development, and resulting market-ready product development.