Decision Fatigue Explained and How to Avoid It

Introduction

Your brain burns through mental fuel with every choice you make. The tenth email response drafts itself more slowly than the first. By evening, picking what to watch feels harder than the morning's project prioritization. This isn't laziness—it's decision fatigue, the measurable decline in judgment quality after sustained choice-making.

Physical exhaustion announces itself clearly. Mental depletion doesn't. A judge denying parole at 4 PM may feel no different than at 9 AM, yet data shows stark differences in outcomes based purely on time of day. Physicians prescribe unnecessary antibiotics more frequently in afternoon appointments. The executive who just closed a merger stands paralyzed before a dinner menu.

Here's what makes decision fatigue particularly insidious: you don't feel it happening. The degradation creeps in through shortcuts, defaults, and avoidance. Each choice taxes the same regulatory system responsible for overriding impulses, weighing trade-offs, and imposing deliberate control. That system recharges with rest but depletes predictably with use—and modern life demands thousands of micro-decisions daily.

Theoretical Foundations

The Ego Depletion Model

In the late 1990s, Roy Baumeister proposed a radical idea: self-control works like a muscle. Use it intensively, and it fatigues. Rest it, and capacity returns. All acts requiring willpower—resisting temptation, suppressing emotion, making choices—draw from one shared reservoir.

The evidence came from elegantly simple experiments. Baumeister, Bratslavsky, Muraven, and Tice (1998) placed participants in a room with freshly baked cookies and radishes. Some could eat cookies freely. Others had to resist the cookies and eat only radishes—an act of self-control. Then both groups worked on impossible puzzles.

Result? The radish-eaters quit far earlier. Resisting cookies had drained their persistence reserves. The cookie-eaters, who exerted no initial self-control, kept trying much longer.

This pattern held across domains. Vohs et al. (2008) showed that making repeated consumer choices—selecting products from catalogs, configuring options—impaired subsequent self-control on completely unrelated tasks:

Task Type Performance After Decision-Making
Physical stamina (handgrip test) Significantly reduced
Cognitive persistence (unsolvable problems) Quit earlier
Impulse control (temptation resistance) Weaker restraint
Unrelated decision quality Poorer outcomes

The mechanism transcended content. Choosing between jam flavors depleted the same resource needed for solving math problems or resisting chocolate.

The Glucose Hypothesis

Early researchers thought they'd found the smoking gun: literal fuel depletion. Gailliot et al. (2007) measured blood glucose before and after self-control tasks. Levels dropped. Participants who drank glucose-sweetened beverages recovered performance on subsequent tasks. Those who got artificially sweetened drinks didn't.

The brain runs on glucose. Executive function burns glucose faster than automatic processing. Deplete the fuel, impair the function. Simple, elegant, wrong—or at least incomplete.

Replication attempts produced messy results. Critics pointed out that the brain jealously guards glucose homeostasis even during intensive cognitive work. Kurzban (2010) offered a different interpretation: maybe the brain doesn't run out of fuel. Maybe it decides to redirect fuel elsewhere, signaling subjective fatigue as an opportunity-cost calculation.

Think of it this way—your brain constantly evaluates whether current cognitive effort justifies its metabolic cost compared to alternative activities. After sustained decision-making, it starts suggesting "maybe you should stop doing this and do something else instead."

The current scientific position? Multiple mechanisms probably operate simultaneously. Glucose depletion plays some role in extreme cases. Motivational shifts matter more in typical contexts. For practical purposes, the why matters less than the what: decision-making reliably degrades subsequent judgment.

Process Model of Self-Control

Inzlicht and Schmeichel (2012) challenged the resource depletion metaphor entirely. Their process model argues that ego depletion isn't about losing capacity—it's about shifting priorities. After exerting control, you don't can't keep going. You don't want to.

Depleted individuals show:

  • Decreased motivation for continued effortful processing
  • Increased sensitivity to immediately rewarding stimuli
  • Intact performance on tasks that don't require restraint
  • Strategic allocation toward activities with clear payoffs

This explains odd patterns the resource model struggles with. Offer someone meaningful incentives, and "depleted" performance improves dramatically. Frame a task as helping others rather than arbitrary, and depletion effects shrink. Tell someone their willpower is unlimited, and they show greater persistence.

The shift matters for intervention design. If depletion reflects pure capacity loss, breaks and glucose are your only tools. If it reflects motivational reorientation, then meaning, autonomy, and reward structures become manipulable variables. You can't will yourself into having more fuel, but you can restructure when and why you deploy effort.

Mechanisms of Decision Fatigue

Decision Complexity and Cognitive Load

Not all choices drain you equally. "Coffee or tea?" barely registers. "Which health insurance plan maximizes expected value given my risk profile and projected medical needs?" That's different.

Reutskaja and Hogarth (2009) mapped the relationship between choice complexity and depletion. Simple binary decisions impose minimal load. Multi-attribute selections requiring trade-off evaluation hit much harder.

Complexity multipliers include:

Factor Impact
Number of alternatives Each additional option compounds comparison demands exponentially
Attribute dimensions Products varying on price, quality, features, aesthetics require integration across incommensurable scales
Uncertainty Ambiguous outcomes force probabilistic reasoning and risk assessment
Conflicting values Hard trade-offs between important goals demand emotional regulation
Irreversibility High-stakes, lasting consequences trigger additional scrutiny loops

Iyengar and Lepper (2000) ran the famous jam study: grocery store displays offered either 6 or 24 jam varieties. The extensive display attracted more initial attention but produced fewer purchases and lower satisfaction among buyers. Beyond some threshold, additional options impose cognitive costs exceeding potential benefits.

The curve isn't linear. A few options beat one option. Many options beat a few. Too many options perform worse than moderate selection—people choose nothing or choose poorly, then regret it.

The Sequence Effect

Levav et al. (2010) analyzed 1,067 car purchases at German dealerships, tracking every optional feature selected during vehicle configuration. Early in the customization sequence—exterior color, engine type—buyers deliberated carefully and showed diverse preferences. By the end—stereo systems, upholstery materials—they increasingly accepted default options or dealer recommendations.

This wasn't because later features mattered less. It happened because capacity had depleted.

The sequence effect produces systematic drift toward:

  • Status quo acceptance → Default options increasingly win
  • Cognitive shortcuts → Later decisions receive less thorough analysis
  • Passive deferral → Following recommendations rather than independent evaluation
  • Complete avoidance → Skipping optional decisions entirely

Order matters enormously. Place critical choices early when capacity peaks, and you get better outcomes. Bury significant decisions in long sequences, and watch quality collapse predictably.

Consider a medical consent form. Put the genuinely consequential decision (surgery yes/no) at the end after dozens of administrative questions, and you've architected poor judgment. Put it first, and the patient engages it properly.

Emotional and Social Dimensions

Decision fatigue interacts problematically with emotional regulation demands. Muraven, Tice, and Baumeister (1998) demonstrated that suppressing emotional responses depletes self-control for subsequent tasks. In naturalistic settings, decisions rarely occur in emotionally neutral contexts. Customer service roles, medical consultations, and parenting involve simultaneous demands for emotional regulation and decision-making, creating compounded depletion.

Social contexts add additional complexity. Explaining and justifying decisions to others imposes cognitive burden beyond the choice itself. Navigating disagreement, managing others' expectations, and maintaining relationships while making decisions all draw from the same regulatory resource pool. This explains why group decision-making, despite potentially improving information aggregation, often produces greater fatigue than individual choices.

Manifestations and Consequences

Judicial Decision-Making

The data from Israeli parole boards is stark enough to make you question the entire justice system. Danziger, Levav, and Avnaim-Pesso (2011) analyzed 1,112 parole decisions across 10 months. Pattern observed:

  • Beginning of session: 65% favorable rulings
  • Before break: Approval rate approaches 0%
  • Immediately after break: 65% again
  • End of session: Back down toward 0%

Graph it, and you get a sawtooth pattern. Defendants appearing right after breaks got parole at roughly 12× the rate of those appearing right before breaks. Same judges, same types of cases, wildly different outcomes based purely on when you happened to be scheduled.

The judges weren't consciously biased. They were depleted. Granting parole requires:

  1. Evaluating rehabilitation evidence
  2. Assessing recidivism risk
  3. Justifying the decision publicly
  4. Accepting potential blame if things go wrong

Denial requires none of that. It's the path of least resistance—safer, easier, requiring minimal justification. As mental resources drain, the easier option wins more often.

Consider the implications: legal fairness partially determined by meal breaks. The defendant lucky enough to draw a 10 AM slot gets meaningfully different treatment than the one at 11:45 AM. This prompted serious discussions about mandatory breaks, decision load limits, and randomized scheduling to distribute fatigue effects fairly.

Medical Decision-Making

Primary care physicians face relentless choice demands: diagnostic evaluations, treatment selections, test ordering, medication adjustments, procedure determinations. Linder et al. (2014) tracked antibiotic prescribing patterns across 23,000 visits.

Time of Day Inappropriate Antibiotic Prescriptions
Morning (8 AM - 12 PM) Baseline rate
Afternoon (1 PM - 5 PM) +26% increase

These are prescriptions for viral infections—where antibiotics don't work and contribute to resistance. Why prescribe them? Because it's easier. Writing the prescription satisfies patient expectations, ends the encounter quickly, avoids the cognitive effort of explaining microbiology and immunology to a frustrated parent whose kid has a cold.

Morning: enough mental reserves to have that conversation. Afternoon: not so much.

Emergency departments show even more dramatic patterns. Kahol et al. (2011) documented surgeons' performance across extended shifts. Complex diagnostic reasoning—integrating multiple information sources, considering alternative explanations, updating priors—deteriorates substantially. Simpler pattern-matching judgments ("this looks like X") dominate increasingly.

The consequences? Missed diagnoses. Inappropriate treatments. Medical errors that wouldn't occur if the same physician saw the same patient four hours earlier.

Consider the scale: millions of unnecessary antibiotic prescriptions annually, contributing to one of the major public health threats of the 21st century, driven partially by the banal fact that doctors get tired and decision-fatigued like everyone else.

Consumer Decision-Making

Retail environments deliberately exploit decision fatigue. Baumeister (2002) identified "shopping momentum"—initial purchases lower resistance to subsequent ones. Stores don't put impulse items near checkout by accident. They position them exactly where shoppers have depleted regulatory resources making dozens of product selections, price comparisons, and budget calculations.

Online environments amplify the exploitation through "dark patterns":

  • Lengthy checkout processes with multiple upsell opportunities
  • Optional insurance at step 7 of 9
  • Warranty offers at step 8 of 9
  • Premium shipping as default at step 9 of 9

By final confirmation, you've made 30+ micro-decisions. Your resistance to that $4.99 premium shipping—which you'd definitely decline fresh—collapses.

The subscription economy runs partially on decision fatigue. Free trials convert to paid subscriptions at high rates not because people carefully evaluated value and decided "yes, this is worth $12/month to me ongoing." They convert because cancellation requires active decision-making. The depleted default to continuation.

Consider your own subscriptions. How many would you actively choose to purchase today if they weren't already auto-renewing? That gap between "active purchase decision" and "passive continuation" represents decision fatigue captured as revenue.

Willpower and Self-Control

Decision fatigue directly impairs behaviors requiring impulse control. Vohs and Faber (2007) showed that making preliminary consumer choices increased subsequent impulse purchases, even of unrelated products. The regulatory resources depleted by initial decisions left less capacity for restraining impulses later.

This explains puzzling patterns in personal behavior management. People maintain healthy eating throughout the workday, making numerous dietary decisions, then overeat at dinner—not from lack of knowledge or commitment, but from depleted decision-making capacity. The cumulative effect of resisting temptations, making food choices, and regulating eating behavior throughout the day leaves minimal resources for continued restraint.

The implications for habit formation prove significant. Neal, Wood, and Quinn (2006) demonstrate that behaviors performed when decision-making resources are depleted become increasingly automatic and habit-based. This can work favorably (evening exercise routines become automatic, bypassing decision fatigue) or unfavorably (stress eating becomes an automatic response when depleted).

Individual Variation and Moderating Factors

Trait Differences

While decision fatigue affects everyone, substantial individual variation exists in susceptibility. Muraven, Baumeister, and Tice (1999) found that trait self-control—measured through validated personality assessments—predicts resistance to depletion effects. Individuals scoring high on conscientiousness and self-discipline show smaller performance decrements after ego-depleting tasks.

However, even high-trait self-control individuals experience fatigue; they simply exhibit greater capacity before performance deteriorates. The difference resembles physical fitness: trained athletes fatigue more slowly and recover faster, but still experience exhaustion. No amount of trait advantage provides immunity from depletion under sufficient demand.

Motivational factors significantly moderate depletion. Muraven and Slessareva (2003) demonstrated that participants who believed their depleting task would help others or contribute to valued goals showed reduced depletion effects. Perceived meaning and importance of decisions partially offset fatigue, suggesting that pure willpower isn't the only relevant resource—motivation serves as an independent factor.

Glucose and Physical State

While the glucose hypothesis as a complete explanation fell from favor, physical state clearly influences decision capacity. Sleep deprivation dramatically amplifies decision fatigue. Harrison and Horne (2000) found that sleep-restricted individuals showed impaired decision-making particularly on novel, complex tasks requiring flexible thinking. The combination of sleep loss and accumulated decisions proves especially detrimental.

Gailliot and Baumeister (2007) demonstrated that consuming glucose-containing beverages did improve performance on self-control tasks after depletion, though subsequent research complicated interpretation. The practical implication remains relevant: physical state—nutrition, hydration, rest—affects decision-making capacity. Organizations ignoring these biological factors when structuring decision demands invite systematic judgment errors.

Experience and Expertise

Domain expertise provides protection against specific types of decision fatigue. Klein (1998) describes how expert pattern recognition enables experienced professionals to make rapid, accurate decisions without extensive deliberation. Expert firefighters, chess players, and physicians often make superior decisions more quickly than novices, drawing less on deliberate analytical resources.

However, expertise protects only within the domain of competence. The experienced surgeon who makes hundreds of skilled procedural micro-decisions during operations still experiences decision fatigue affecting unrelated choices. Expertise reduces cognitive load for domain-specific decisions but doesn't increase general decision-making capacity.

Practical Mitigation Strategies

Strategic Decision Scheduling

The simplest intervention: put important decisions early. Decision quality declines predictably across sequences and time. Schedule critical choices when capacity peaks—for most people, morning hours after rest and before daily decision accumulation.

Practical implementation:

Principle Application
Front-load critical decisions Morning strategy sessions, not afternoon. First agenda item, not last.
Cluster trivial choices Batch administrative decisions during lower-priority time blocks
Insert decision-free intervals Breaks between demanding sequences restore capacity measurably
Recognize decision-dense periods Project launches, fiscal year-end, major transitions—protect capacity deliberately

Some judicial systems now mandate breaks after specific case counts or time intervals—direct response to the parole board research. Meeting design placing critical decisions early in agendas produces measurably better outcomes than traditional "save the big stuff for end" approaches.

Warren Buffett famously leaves his calendar mostly empty. He's protecting decision capacity for the few choices that actually matter. Most executives do the opposite—pack schedules dense with decisions, then wonder why judgment quality degrades.

Decision Elimination and Automation

The best strategy for avoiding decision fatigue: make fewer decisions. Not avoidance—strategic allocation of decision-making capacity toward choices that genuinely matter.

Steve Jobs wore the same outfit daily. Mark Zuckerberg does too. Barack Obama wore only gray or blue suits as president, stating explicitly: "I'm trying to pare down decisions. I don't want to make decisions about what I'm eating or wearing. Because I have too many other decisions to make."

These aren't eccentric quirks. They're capacity protection strategies.

Standardization approaches:

  • Routine decisions → Establish default rules (meal planning, clothing, meeting structures)
  • If-then planning → "If situation X occurs, then I automatically do Y" (no deliberation required)
  • Wardrobe simplification → Eliminate daily appearance decisions entirely
  • Delegation protocols → Systematize which decisions require personal judgment vs. automatic routing

The key distinction: decisions that benefit from deliberation versus those where the cost of deciding exceeds potential value gained. Most people dramatically overestimate how much their lives improve from deliberating over trivial choices.

Do you need to decide what to wear every day? Or could you precommit to a rotation and use that mental energy elsewhere? Do you need to browse 40 lunch options, or could "Tuesday = sandwich place" work fine? The marginal quality gain from extra deliberation often doesn't justify the capacity cost.

Choice Architecture and Pre-Commitment

Thaler and Sunstein's (2008) nudge framework provides tools for structuring choices to minimize depletion while preserving autonomy. Well-designed choice architecture reduces cognitive burden without eliminating options.

Effective techniques:

  • Defaults with opt-out: Pre-select reasonable options while allowing changes for those willing to expend effort
  • Progressive disclosure: Present complex choices in stages rather than overwhelming simultaneous evaluation
  • Attribute prioritization: Guide attention toward most relevant dimensions, reducing comparison complexity
  • Temporal separation: Distribute multi-attribute choices across time rather than requiring simultaneous evaluation

Pre-commitment strategies leverage present-state decision-making to constrain future choices when capacity will be depleted. Ariely and Wertenbroch (2002) demonstrated that people voluntarily impose deadlines and penalties on themselves, recognizing that their future depleted selves will require external constraints. Applications include:

  • Automatic savings deductions that bypass monthly spending decisions
  • Pre-scheduled exercise commitments that remove daily motivation decisions
  • Meal-kit subscriptions that eliminate daily food choice demands
  • Pre-determined decision criteria for routine purchases

Environmental and Social Support

Decision fatigue rarely occurs in isolation from social and environmental contexts. Creating supportive structures reduces individual burden.

Social distribution of decisions:

  • Role specialization: Within households or teams, assign decision domains to specific individuals rather than requiring everyone to engage with everything
  • Consultation protocols: Establish when decisions genuinely benefit from group input versus when individual choice suffices
  • Decision support systems: Technological tools that filter options, provide recommendations, or automate routine determinations

Some couples deliberately divide household decision domains: one partner handles financial planning while the other manages social scheduling. This isn't shirking responsibility—it's strategic capacity allocation that reduces total decision burden for both partners.

Environmental simplification:

  • Reduce choice exposure: Limit environments presenting constant decision demands (unsubscribe from marketing emails, reduce shopping frequency)
  • Physical organization: Structured environments with clear systems reduce micro-decisions about object location and task sequencing
  • Digital hygiene: Notification management, app organization, and information diet all reduce trivial decision accumulation

Organizational Implications

High-Stakes Decision Contexts

Organizations making consequential decisions—investment firms, government agencies, healthcare systems—must systematically address decision fatigue in their processes and structures.

Structured decision protocols that external frameworks reduce reliance on depleted individual judgment. Kahneman, Lovallo, and Sibony (2011) advocate for decision hygiene practices including:

  • Sequential evaluation: Team members independently assess options before group discussion, preventing early anchoring and cascade effects
  • Reference class forecasting: Systematic comparison to similar past situations reduces over-reliance on case-specific intuition
  • Premortems: Imagining future failure and working backward surfaces concerns that optimism and fatigue suppress
  • Red team analysis: Designated critics who actively challenge prevailing views

Rotation and shift structure prove critical in roles involving sustained decision demands. Emergency departments, air traffic control, and similar high-stakes environments establish shift length limits and mandatory breaks explicitly to prevent fatigue-induced errors. The operational cost of having positions temporarily unstaffed far outweighs the risk of impaired judgment from exhausted personnel.

Leadership and Strategic Decisions

Executives face asymmetric decision demands—their choices affect thousands of people and millions of dollars. Yet organizations often maximize their leaders' decision burden rather than protecting decision-making capacity.

Buffering strategies:

  • Decision delegation frameworks: Clear criteria for what requires executive judgment versus what subordinates handle autonomously
  • Prepared options: Staff work that presents pre-vetted alternatives rather than requiring leaders to generate options
  • Meeting design: Structured agendas that batch similar decisions, present information concisely, and respect cognitive limits
  • Recovery time: Explicitly scheduled periods without decision demands following high-intensity decision periods

Bezos described Amazon's approach: "I make a small number of high-quality decisions. I think about things that are very hard to reverse... I'm not interested in making thousands of decisions a day." This reflects recognition that leadership effectiveness depends on preserving capacity for consequential choices rather than demonstrating involvement in everything.

Performance Management and Well-Being

Organizations routinely measure output but rarely assess decision quality or monitor conditions producing decision fatigue. Performance management systems that ignore cognitive load create perverse incentives—rewarding quantity of apparent productivity while degrading judgment quality.

Indicators of systematic decision fatigue problems:

  • High error rates during specific shifts or times
  • Quality variations across decision sequences
  • Increased defaults, conservative choices, or decision avoidance over time
  • Employee reports of exhaustion, burnout, or feeling overwhelmed

Interventions extend beyond individual strategies to systemic design:

  • Task analysis identifying decision demands in roles
  • Workflow redesign to distribute cognitive load more evenly
  • Technology systems that automate routine determinations
  • Training on decision-making strategies and fatigue recognition
  • Cultural norms that legitimize selective engagement rather than constant availability

Limitations and Ongoing Debates

The Replication Crisis

The broader replication crisis in psychology has affected ego depletion research. Carter and McCullough (2014) conducted a meta-analysis finding that the effect size for ego depletion has declined over time, with more recent studies showing smaller effects than earlier research. Some replication attempts failed entirely to demonstrate depletion effects.

Hagger et al. (2016) conducted a large registered replication report across 23 laboratories worldwide. While the aggregate result showed a significant depletion effect, the effect size was considerably smaller than original studies reported, and substantial variation existed across laboratories. This suggests that depletion effects are real but potentially more context-dependent and smaller than initially believed.

The debate highlights important considerations:

  • Publication bias likely inflated early effect sizes
  • Experimental paradigms may not generalize to naturalistic decision-making
  • Individual and contextual moderators prove more important than initially recognized
  • Subjective beliefs about depletion may themselves affect performance

Theoretical Alternatives

While decision fatigue as a phenomenon proves robust, the underlying mechanisms remain contested. Alternatives to the resource depletion model include:

Process model: Depletion reflects shifts in motivation and attention rather than capacity exhaustion. After exertion, people become less willing to expend effort and more attracted to rewarding stimuli.

Opportunity cost model: Subjective fatigue represents adaptive resource allocation. The brain signals depletion to redirect resources toward potentially more valuable activities.

Mental effort as aversive: People avoid mental effort not because capacity is depleted but because such effort is inherently unpleasant, with tolerance determined by motivation and expected payoff.

These alternatives share the prediction that continued decision-making impairs judgment quality, differing primarily in explanatory mechanism. For practical purposes, the robust behavioral pattern matters more than the precise underlying cause.

Boundary Conditions

Decision fatigue effects vary substantially across contexts and individuals. Important boundary conditions include:

  • Motivation and perceived importance: Highly motivated individuals show reduced depletion
  • Autonomy: Choices aligned with personal values deplete less than coerced decisions
  • Expertise: Domain knowledge reduces cognitive load for familiar decisions
  • Cultural factors: Individualistic versus collectivistic cultures may show different patterns
  • Beliefs about willpower: Individuals who view willpower as unlimited show smaller depletion effects

These moderators suggest that decision fatigue isn't a fixed constraint but rather varies with psychological and contextual factors. Interventions addressing these factors—enhancing meaning, increasing autonomy, building expertise—may prove as important as decision reduction strategies.

Conclusion

Decision fatigue constrains human judgment fundamentally. Every choice burns mental fuel needed for subsequent choices—producing predictable, measurable deterioration in judgment quality.

The depletion operates through multiple pathways simultaneously. Genuine cognitive load from evaluating options. Motivational shifts away from continued effortful processing. Increased reliance on heuristics, defaults, and whatever path offers least resistance. The mechanisms matter less than the pattern: sustained decision-making degrades judgment.

Effective responses don't rely on willpower. They rely on system design:

  • Temporal strategies → Schedule critical decisions during peak capacity periods
  • Elimination → Automate and standardize routine choices ruthlessly
  • Architecture → Design environments that reduce decision exposure
  • Distribution → Spread decision demands sustainably across people and time

The executive making three high-quality strategic decisions daily outperforms the one making fifty mediocre choices. The parent automating routine household decisions maintains capacity for responding thoughtfully when kids need real guidance. The professional structuring their environment to minimize trivial choices performs more reliably when consequential judgment matters.

Modern life drowns you in choices. Breakfast options. Email responses. Meeting attendance. Grocery selection. Entertainment. Wardrobe. Route planning. Notification responses. The cumulative load exceeds what human decision-making capacity evolved to handle.

You can't expand capacity much—it's biologically constrained. But you can ruthlessly protect how you spend it. That's the practical insight: optimal decision-making isn't about deciding more or deliberating longer. It's about preserving cognitive resources for choices that genuinely matter and refusing to waste them on choices that don't.

References and Further Reading

Foundational Research:

  • Baumeister, R. F., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). "Ego Depletion: Is the Active Self a Limited Resource?" Journal of Personality and Social Psychology, 74(5), 1252-1265. https://doi.org/10.1037/0022-3514.74.5.1252

  • Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M., & Tice, D. M. (2008). "Making Choices Impairs Subsequent Self-Control: A Limited-Resource Account of Decision Making, Self-Regulation, and Active Initiative." Journal of Personality and Social Psychology, 94(5), 883-898. https://doi.org/10.1037/0022-3514.94.5.883

  • Danziger, S., Levav, J., & Avnaim-Pesso, L. (2011). "Extraneous Factors in Judicial Decisions." Proceedings of the National Academy of Sciences, 108(17), 6889-6892. https://doi.org/10.1073/pnas.1018033108

Theoretical Development:

Applied Research:

  • Levav, J., Heitmann, M., Herrmann, A., & Iyengar, S. S. (2010). "Order in Product Customization Decisions: Evidence from Field Experiments." Journal of Political Economy, 118(2), 274-299. https://doi.org/10.1086/652463

  • Linder, J. A., Doctor, J. N., Friedberg, M. W., Reyes Nieva, H., Birks, C., Meeker, D., & Fox, C. R. (2014). "Time of Day and the Decision to Prescribe Antibiotics." JAMA Internal Medicine, 174(12), 2029-2031. https://doi.org/10.1001/jamainternmed.2014.5225

  • Iyengar, S. S., & Lepper, M. R. (2000). "When Choice Is Demotivating: Can One Desire Too Much of a Good Thing?" Journal of Personality and Social Psychology, 79(6), 995-1006. https://doi.org/10.1037/0022-3514.79.6.995

Practical Applications:

  • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press. [Choice architecture framework]

  • Kahneman, D., Lovallo, D., & Sibony, O. (2011). "Before You Make That Big Decision." Harvard Business Review, 89(6), 50-60. [Decision quality in organizations]

  • Ariely, D., & Wertenbroch, K. (2002). "Procrastination, Deadlines, and Performance: Self-Control by Precommitment." Psychological Science, 13(3), 219-224. https://doi.org/10.1111/1467-9280.00441

Replication and Critique:

  • Carter, E. C., & McCullough, M. E. (2014). "Publication Bias and the Limited Strength Model of Self-Control: Has the Evidence for Ego Depletion Been Overestimated?" Frontiers in Psychology, 5, 823. https://doi.org/10.3389/fpsyg.2014.00823

  • Hagger, M. S., et al. (2016). "A Multilab Preregistered Replication of the Ego-Depletion Effect." Perspectives on Psychological Science, 11(4), 546-573. https://doi.org/10.1177/1745691616652873

Individual Differences:

  • Muraven, M., Baumeister, R. F., & Tice, D. M. (1999). "Longitudinal Improvement of Self-Regulation Through Practice: Building Self-Control Strength Through Repeated Exercise." Journal of Social Psychology, 139(4), 446-457. https://doi.org/10.1080/00224549909598404

  • Muraven, M., & Slessareva, E. (2003). "Mechanisms of Self-Control Failure: Motivation and Limited Resources." Personality and Social Psychology Bulletin, 29(7), 894-906. https://doi.org/10.1177/0146167203029007008

Related Concepts:

  • Neal, D. T., Wood, W., & Quinn, J. M. (2006). "Habits—A Repeat Performance." Current Directions in Psychological Science, 15(4), 198-202. https://doi.org/10.1111/j.1467-8721.2006.00435.x

  • Klein, G. (1998). Sources of Power: How People Make Decisions. Cambridge, MA: MIT Press. [Expert decision-making and pattern recognition]

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