Near-misses sting even when they are uncontrollable
Desmond C. Ong (dco@stanford.edu)
Noah D. Goodman (ngoodman@stanford.edu)
Jamil Zaki (jzaki@stanford.edu)
Department of Psychology, Stanford University, Stanford CA, USA
Abstract
Observers often judge agents who miss a desired outcome by asmall, compared to a large, margin to be less happy. This nearmiss effect has typically been examined in situations where
the agents have control over outcomes (e.g., missing a flight).
Here, we extend this work in three ways. First, we showthat near-miss effects play into observers’ intuitive theories of
emotion even for randomly-determined outcomes over which
agents demonstrably have no control. Second, we find dataconsistent with a hypothesis in which—even in randomly determined cases—near-miss effects reflect an illusion of control
over those events. Finally, we integrate near-miss effects into a
broader model of affective cognition, and quantify the psychological cost of a missing a desired outcome by relatively little
distance, relative to winning or losing that outcome.
Keywords: Near-Miss; Counterfactual Distance; Lay Theories; Emotion; Illusory Control“Close only counts in horseshoes and hand grenades”— English Proverb
Observers typically infer that people feel more positively
when they succeed, rather than fail, at attaining desired outcomes (e.g., winning vs. losing a soccer game). Such intuitive reasoning about emotions comprises what we term affective cognition (Ong, Zaki, & Goodman, under review),
and forms an integral part of our social lives. Our intuitive
theories of emotion, however, are more nuanced than simple considerations of success and failure: If an agent fails toachieve an outcome by a small margin, such as losing a soccer
game by a single goal (a near-miss), people often infer thatthe agent would feel worse than if they had lost by a larger
margin. Penalty shootouts in soccer provides the most exaggerated of such near-miss scenarios: the losing team often
loses because of a single shot, sometimes an inch shy of escaping the goalkeeper’s hands. In cases like these, contrary
to the proverb above, close does count—emotionally.
Psychologists have long examined near-misses (Gleicher
et al., 1990; Johnson, 1986; Kahneman & Tversky, 1982;
Teigen, 1996), but have yet to factor them systematicallyinto theories of reasoning about emotions (Ong et al., underreview). Most of this work examines counterfactual thinking more broadly, or thinking about “what might have been”(Byrne, 2002; McMullen & Markman, 2002; Medvec & Savitsky, 1997; Roese, 1997). On most accounts, near-misses
increase counterfactual thinking, because alternate outcomesalmost occurred, and thus are easier to imagine.
Many open questions remain regarding the nature of nearmiss effects, and how they impact observers’ lay theories of
emotion. First, when and why do people infer that a near-misswill “hurt” an agent most? One explanation is that agents experiencing a near-miss could have done something different

to achieve the desired outcome, increasing their subsequent
experience of regret (e.g., Zeelenberg, van Dijk, Manstead, &der Pligt, 1998). Consider Kahneman and Tversky (1982)’s
classic example of Mr. Tees, who missed his plane by 5
minutes, and Mr. Crane, who missed his plane by 30 minutes. People reliably judge Mr. Tees, who narrowly missed
his plane, to feel worse than Mr. Crane. Here, Mr. Tees
could have left his home slightly earlier in order to catch hisplane, and Mr. Tees’ resulting responsibility for his misfortune might drive his near-miss negative affect. Consistent
with this idea, the controllability of outcomes affects counterfactual thinking in non-near-miss contexts (Roese & Olson,
1995). If people infer near-misses to be painful because ofan agent’s control over their outcomes, then observers should
only attribute near-miss-related negative emotion to agents
who have such control. By contrast, observers should notrate near-misses as worsening an agent’s emotional state ifthat agent had no control over the outcome. For instance, anobserver who sees an agent miss a randomly determined positive outcome (e.g., a win in roulette instead of soccer) by asmall distance should not rate that agent as feeling more negative emotion than an agent who misses by a large distance.
However, emotions may not always adhere to this rule.
For instance, gamblers show increased motivation and persisted in gambling more after near-misses, even though theoutcomes are random and thus independent of the gambler’s
actions (Kassinove & Schare, 2001; Reid, 1986). This behavior may very well be incorporated into observers’ lay theory of emotion. In Experiment 1, we tested the prediction of
the controllability account further by examining whether observers incorporate near-misses in judgments of others’ emotions even in scenarios with random outcomes. We show thatobservers readily judge an agent who “narrowly misses” on atask with randomly-generated outcomes (by rolling a number
on a die that is close to a target number) as feeling worse than
one who misses by a larger amount.
Second, why might observers factor near-misses into affective cognition for random outcomes over which agentsclearly have no control? One possibility is that in such situations, observers might irrationally believe that agents do exert
some control over their outcomes, or believe that agents believe they do. Such “illusions of control” over random events
are, in fact, common: People are more confident that they
will win a raffle if they chose their ticket numbers themselves
than when they are assigned numbers (Langer, 1975; Taylor& Brown, 1988). If observers do indeed ascribe illusory control to agents, observers should include near-misses into theiremotion attributions even in demonstrably random situations.

In Experiment 2, we explored this possibility by manipulating the rules of a game with random outcomes, producing two
situations that we believed would induce illusions of control
in different ways. In Version A of the game, observers saw
an agent guess the location of a single “target” card with aknown number (“Guess where the 10 card is”) amidst a large
array of cards. In Version B, agents had to guess the number
behind a target card at a fixed location (“Guess what number is behind this card”). We crossed this manipulation withwhether agents’ choices were spatially and / or numericallyclose to / distant from the actual target. In all cases, observers
believed that the location (in Version A) or number (in Version B) of the target card was chosen at random, and thus
agents had no actual control over their chances of winning.
We reasoned that in Version A—when agents chose the locations of their cards—observers might nonetheless ascribe
illusory control over whether agents chose the location of thetarget card1 . Hence, observers would be more likely to judge
that an agent whose choice was spatially close to, as compared to distant from, the target card would feel worse. By
contrast, in Version B—in which agents guessed the numberof a fixed location card—observers might imbue that agent
with illusory control over guessing the correct number. If thisis the case, observers should attribute more negative affect toagents when they are numerically close, versus far, from thetarget answer. The results of Experiment 2 support this hypothesis of illusory control impacting near-miss judgments.
Finally, how much does a near-miss cost psychologically?
That is, what is the size of the near-miss effect relative tothe overall effect of an agent’s win or loss on an observer’s
attribution about that agent’s emotion? In a meta-analysis,
we build upon a previous model of affective cognition (Onget al., under review). This previous work used a gambling
paradigm that allowed us to parametrically vary features ofthe situation that would impact judgment of emotions. We explicitly incorporate near-miss effects into our existing quantitative model, which allowed us to estimate the extent to whichobservers believe agents who were close, as compared to far,from desirable outcomes felt negative emotion. We could further compare the effect size of near-misses to those associatedwith achieving relatively favorable or unfavorable outcomes,
thus quantifying near-miss effects and integrating them into a
more comprehensive models of affective cognition.
In sum, this paper explored (i) whether people incorporatenear-misses in their lay theories of emotion in scenarios withrandomly-determined outcomes; (ii) whether people might doso because of illusory control agents may feel over these outcomes; and (iii) how much near-misses “cost,” in emotionalterms relative to winning and losing.
1 Note that agents do have real control over their picked card’s location, just like people buying lottery tickets have control over what
number they bought. However, agents have only illusory control
over how close their cards’ location came to the target card’s location, just like lottery ticket buyers have only illusory control over
how close their number came to the winning number.

Experiment 1: Near-Misses in a random event
In Experiment 12 , we tested if participants would incorporatenear-miss effects in their judgment of emotions when agentsplayed a luck-based gamble. Importantly, we varied nearmisses along numerical closeness, and because the outcomesfrom rolling dice are completely random, the agent demonstrably had no control over the outcome.
Participants and Procedures. We recruited 150 participants through Amazon’s Mechanical Turk (AMT). Participants read about two characters, Jacob and Alex, playing agambling game. Both needed to roll a 6 on a die to win. Jacob rolled a 1, whereas Alex rolled a 5. Participants then answered attention check questions (“what did X roll?”) before
attributing emotions along six categories (happiness, sadness,relief, regret, contentment and disappointment) to each character. Finally, they answered a three-alternative forced choicequestion: “Who felt worse?”, and were allowed to endorse“They both felt equally bad” as an option. Participants were
prompted for a free-response justification for their choice.
Results. Three participants were excluded for failing the attention check. Participants rated the near-miss character (thecharacter who rolled the 5) as feeling significantly more disappointed (t(146) = 2.17, p = 0.03), but no different on theother emotions. In the forced-choice question, a large majority (107/147 = 73%) rated both characters as feeling equally
bad. Among the remaining participants, significantly moreparticipants rated the character who rolled the 5 (the nearmiss character) as feeling worse (N=30) compared to thecharacter who rolled a 1 (N=10; bootstrapped simulation with
10,000 iterations on full sample, p = 0.0007) (See Fig. 1.)

Figure 1: Expt 1 Results. Proportions of forced choice response. Error bars indicate standard errors. The goal was toroll a “6”. More people judged the character who rolled a “5”as feeling worse than the character who rolled a “1”.
Next, we coded participants’ free-response justifications
into three categories. 84 (57%) participants made judgments
based on equal outcomes (“they both lost so they should feel
equally bad”), 40 (27%) participants made reference to closeness (“he was soooo close”), and only 22 (15%) participants
made an explicit reference to there being no closeness differences (“it’s a 1/6 chance for both of them”; “the numbers aremeaningless”). One participant did not give a justification.
2 https://cocolab.stanford.edu/cogsci2015/nearmiss/expt1/

Thus, we find that whereas a large majority of participants
said that both characters felt equally bad, this is primarily
due to the fact that both characters lost. This suggests thatnear-miss effects on emotion attribution are smaller in magnitude than those associated with winning or losing in general, though they contribute a significant additional influence.
Thus, the results provide evidence that observers are sensitive to near-miss effects in this scenario and they (irrationally)judge near-misses based on a randomly-determined distancethat the agent has no control over.

Experiment 2: Manipulating illusory control
over random events
We designed Experiment 23 to manipulate the illusion of control that agents have over different dimensions in a randomgame. Using a card guessing task, we manipulated the dimension (spatial position or numbers) along which agents madeguesses in a random task, as well as the spatial or numericaldistance by which they missed the desired outcome.
Participants. We recruited 200 participants through AMT,
and assigned them to either a Choose-Position (N=100) or
Choose-Number (N=100) condition.

Figure 2: Expt 2 Paradigm, Position condition. Characters’
goal is to pick the card with 10 on it. In the “Close Distancevs. Close Number” trial, one character picks the card with19 on it, outlined in red (Close in physical Distance, far in
number), and another character picks the card with 11 on it,
outlined in green (Close in Number, far in physical distance).
The target card is then revealed, outlined in purple. In other
trials, one of the characters picks 1 (indicated in blue; Far in
Both physical distance and number).
Near-Miss
Close Distance
far number
Close Number
far distanceFar both(distance, number)

Illusory control over Position

Illusory control over Number

XX

X

X

XX

-

-

Procedures. In the Choose-Position condition, participants
saw a 5x4 array of cards face down. They were told that two
characters were playing a game: the cards were numbered
1-20, and the characters had to pick the card with the number 10 to win. There were three types of trials, which were
all between subject manipulations, and each participant only
saw one trial. In the “Close Distance vs. Close Number” trial(depicted in Fig. 2), participants were told of two characters, Scott, who picked a card with 19 on it (Close Distance),and Frank, who picked a card with 11 on it (Close Number). After the characters picked their cards, the winning
number 10 was revealed. Participants saw the locations of thetwo cards the characters chose, as well as the winning card.
The close distance character’s card was only 1 card away (inphysical distance) from the winning card but it was far in numerical distance, whereas the close number character’s card
had a number that was only 1 away (in numerosity) from thewinning card, but far in physical distance. Participants thenrated the emotions of the two characters they saw (along thesame six emotions as Experiment 1), and rated how close thecharacters came to winning. Finally, participants answered aforced-choice question, “Who felt worse?”, with a possible
option “Both felt equally bad.”
For comparison, we included two other trials with a character who was far on both distance and numerosity. In the“Close Distance vs. Far Both” comparison, participants saw
Scott (who picked the 19 card) and David, who picked a cardwith 1 on it, which is far along both physical distance andnumerosity. In this case, David’s card is 9 numbers awayfrom the winning card, which is the same numerical distanceas Scott’s card. In the “Close Number vs. Far Both” comparison, participants saw Frank (who picked the 11 card) andDavid. In this case, David’s card is just as far in physicaldistance from the winning card as Frank’s card.
In the Choose-Number condition, participants were presented with a game with slightly different rules. There were
the same 20 cards, and a target card (circled in purple), allface-down. Characters had to guess the number behind thetarget card. After picking a number, the card with the picked
number was revealed. The characters were the same as in the
Position condition, i.e., on “Close Distance vs. Close Number” trials, Scott picked the number 19 (Close distance) andFrank picked 11 (Close Number). After the character madetheir guesses, the winning number behind the target card isrevealed to be 10. Similar to the Position condition, the close
distance character’s card was close in physical distance, but
far in numerical distance; whereas the close number character’s card was close in numerical distance, but far in physical distance. Participants then attributed emotions to the two
characters, and made a forced-choice judgment about who
felt worse. There were similar “Close Distance vs. Far Both”
and “Close Number vs. Far Both” trials.

3 https://cocolab.stanford.edu/cogsci2015/nearmiss/expt2/Position,
https://cocolab.stanford.edu/cogsci2015/nearmiss/expt2/Number

Predictions. To reiterate, in the Position condition, thenumber of the goal was known (10) whereas the position was

Table 1: Predictions for Experiment 2. Near-miss effects are
predicted to be strongest when agents are close on dimensions
over which they have illusory control.

unknown – characters picked a position and were assigned
a number (based on their choice). In the Number condition,
the position of the goal was known, but the number was unknown – characters picked a number and were assigned a position. The two conditions differed in which dimension (position or number) the characters had control over. Importantly,
the games were still random events, and the agents’ control
is merely illusory. For example, consider the Position condition. One can imagine a possible counterfactual statement
generated by Scott, the close distance character: “if only I had
chosen the card one position down”. However, if the games
were truly random, his re-picking a different card position
would still have given him the same 1/20 chance of winning.
Our predictions are summarized in Table 1. In the Position
condition, the characters chose the physical position of a card,
and we predicted that near-misses along physical closenesswould be weighted more than near-misses along numericalcloseness. In other words, the close distance character would
be judged to feel the worst, then the close number character, then the far character. On the other hand, in the Number condition, the characters chose the number, and so we
predicted that near-misses along numerical closeness would
be weighted more than near-misses along physical closeness:the close number character would be judged to feel the worst,
then the close distance character, then the far character.
Results. Nine participants were excluded for failing the attention checks. When we examined individual emotion ratings, none of the comparisons in the Position condition cameout significant. For the Number condition, as compared to
the far character, the close number character was judged tofeel more disappointed (t(43) = 2.81, p = .007), more regret (t(43) = 2.44, p = .02), more sadness (t(43) = 2.54, p =.015) and less relief (t(43) = 2.12, p = .04). For closeness judgments, in the Position condition, both the close distance and close number characters were judged to be closer
to winning than the far both character (t(20) = 3.5, p =.002;t(41) = 4.1, p < .001 respectively), and in the Number
condition, the close number character was judged to be closer
than the close distance (t(22) = 4.4, p < .001) and the far bothcharacters (t(43) = 8.2, p < .0001).
The results for the forced-choice ratings are in Fig. 3. Inline with our predictions, in the Position condition, the close
distance character was judged to feel worse than the close
number (bootstrap, 10,000 iterations, p = .023) and the far
characters (p = .003). To a smaller extent, the close number character was judged to feel worse than the far character(p = .016). This, together with the closeness judgment result
above, suggests that participants might still be sensitive tonumerical closeness even when it is task-irrelevant, perhaps
because comparing numerical differences is often importantin daily life. By contrast, we see the opposite pattern in the
Number attributions: The close number character was judged
to feel worse than the close distance character (p = .002) andthe far character (p < .0001), and there was no difference between the close distance and far characters (p = .17).

Figure 3: Expt 2 Results. Proportions of forced choice response. Error bars indicate standard errors. Top row: Choose
Position condition. Bottom row: Choose Number condition.
The results suggest that participants are sensitive to nearmisses along multiple dimensions, in this case, both physical closeness and numerical closeness. When given multiple dimensions, participants weight the dimension on which
the characters would likely feel more illusory control (physical closeness in the Position condition and numerical closeness in the Number condition) more than the other dimension.
When characters have to pick the physical location of thecard, participants attribute more near-miss effects along physical closeness, and when characters have to choose a number(rather than a location), participants attribute more near-misseffects along numerical closeness. Again, it is worth reiterating that characters only have an illusion of control along these
dimensions, yet this illusion of control, much like actual control, affects participants’ judgments of emotions.

Quantitative modeling via meta-analysis
Next, we performed a meta-analysis of three prior experiments designed to examine the features underlying affective
cognition in a gambling paradigm. We explicitly model thenear-miss effect in a quantitative model of affective cognition.
Participants and procedures. 690 participants were recruited across 3 different experiments4 previously reported in
Ong et al. (under review). The trial involved watching a character spin a wheel and win the amount the wheel landed on(Fig. 4). Participants then attributed 8 emotions (happy, sad,anger, surprise, disgust, fear, content and disappointment) tothe character after the outcome, using 9 point Likert scales.
Each participant saw 10 trials, and the payoff and probability
structure of the wheels were varied systematically to decorrelate the amount won with the expected value of the wheel.
The first experiment only had these wheel trials: the second
and third had wheel trials intermixed with emotion attribution trials given other stimuli (faces and utterances) insteadof wheels. We took the subset of wheel trials from the second and third experiment, and the whole first experiment, to
4 https://cocolab.stanford.edu/cogsci2015/nearmiss/metaAnalysis/

amass a dataset of 3048 observations from 690 participants.
These experiments were designed to test how different features of the situation, namely the amount won and the prediction error (the amount won relative to the expected value ofthe gamble), affected observers’ attribution of emotion to thecharacter. Yet, because we randomized the position on which
the spinner lands, these experiments incidentally provided avaluable dataset to test for near-miss effects.

Figure 4: Paradigm for the meta-analysis. Participants attribute emotions to an agent after the outcome of a spin. After
this spin (right), the agent won $60 (as indicated by the black
pointer), but almost won a higher amount of $100.
Previous model. The model in Ong et al. (under review)
incorporated three important features: the amount won (win),
the prediction error (PE), and the absolute value of PE (absPE). The emotion E attributed to the agent after event X was:
E(X) = b0 + b1 win(X) + b2 PE(X) + b3 absPE(X) + ε, (1)a linear combination of win, PE and absPE. The absolutevalue of the PE was added to test for nonlinearities, namely,
loss aversion, whereby agents will be more sensitive to negative PE values and weigh them more than positive PE values.
Separate models were fit for each emotion. More discussion
can be found in the paper. Eqn. 1 provided the starting point
for the model in the following analysis.
Adding Near-Miss to the model. Next, we proceeded todefine a near-miss distance. We calculated a normalized “distance from the edge” which ranged from 0 to 0.5, with 0 being
the boundary edge between the current and closest sector and
0.5 indicating the exact center of the current sector. We thentook a reciprocal transform (1/x) to introduce a non-linearitythat favors smaller distances. Finally, we multiplied the transformed distance with the difference in payment amounts fromthe current sector to the closest sector. This last component
was to account for the difference in utility in the two payoffs.
Hence, the near-miss term we defined was:
NM(X) =

1
∗ ∆Payoffs(X)distanceToEdge(X)(2)

which we added to the model in Eqn. 1. To illustrate, for the
result shown in Fig. 4, the distance is .05 (5% of the sector
size away from the $60-$100 boundary), and the ∆Payoff is60-100 = -40, as $100 is the next nearest sector.
Meta analysis results. We fit a linear mixed-effects modelwith the amount won, PE, absPE, and the Near-Miss (NM)term (Eqn. 1, 2) as fixed effects, and random intercepts byparticipant, wheel, and experiment. There is a significant
slope on the NM term (b = -3.5 ∗ 10-5 ,t(682) = -2.80, p =0.005) on happiness. There was no significant differences
between NM terms when the next-nearer outcome was positive or negative (χ2 (2) = 0.13, p = 0.94). To understand themagnitude of the NM term, consider the slopes on win (b =0.0405,t(682) = 7.08, p < .0001), PE (b = 0.036,t(682) =5.86, p < .0001), and absPE (b = -0.015,t(682) = -2.83, p =.005), and the example in Figure 4. Not considering the nearmiss effect and all else being equal, if the result had changed
from $60 to $100, there will be an increase in happiness due
to win, PE and absPE of 40 ∗ (.0405 + 0.036 + (-0.015)) =2.46 points on a 9 point Likert scale. By contrast, if wemoved from the center of the $60 sector to a distance of.01 (1% of the sector size) away from the $60/$100 boundary, there would be a decrease in happiness of 40 ∗ (1/0.5 −1/0.01)∗(-3.5∗10-5 ) = 0.137 points on a 9 point scale. Thus,in this gambling scenario, the effect of a near-miss on subjective happiness attributed is on the order of 5% of the relative
happiness of winning the next higher amount. Getting a nearmiss on the $60 wheel in Fig. 4 and narrowly missing the$100 sector (narrowly missing winning $40 more) has a subjective cost equivalent to losing about $2, compared with a
far-miss (landing in the center of the sector).
This is a small effect relative to actually winning, yet it is alarge and not insignificant effect considering that it does notdepend on changing actual payoffs, but relative closeness.

Discussion
Near-misses matter emotionally; here we produce novel insights about how observers’ incorporate near misses into their
lay theories of emotion. First, people incorporate near-misses
in situations even in random situations under which agents
have no direct control over outcomes. Second, this effectmight reflect observers’ tendency to imbue agents with illusory control over even demonstrably random outcomes. Consistent with this idea, observers factored near-misses moreheavily into their emotion attributions when near-misses occurred within a dimension (e.g., location or number) overwhich agents made a choice on, even though agents could
not actually control their ability to win or lose based on theirchoice. Third, using a meta-analysis of three previous experiments, we modeled near-misses and compared their effect size to emotions associated with actual wins and losses.
Crucially, this allowed near-misses to be incorporated, for the
first time, into a quantitative model of affective cognition.
Many open questions concerning near miss effects remain.
For instance, how does a near-miss compare with its positive
counterpart, the relatively-less-studied “just-hit” (when an
agent narrowly wins by a small margin)? Although there aresome qualitative differences between near-misses and justhits (e.g., the frequency of downward and upward comparison, Roese, 1997; Sweeny & Vohs, 2012), our meta-analysis
revealed no difference between positive and negative closecomparisons (near-miss and just-hits). This suggests that outcome closeness works similarly in positive and negative domains, but future work should investigate this further.
Our results suggest some interesting properties about nearmiss judgments in scenarios where the outcome is randomlygenerated. Future work will have to unify this and previous,
more general work on counterfactual judgments into quantitative models of affective cognition. The model in our metaanalysis already had consideration of counterfactual judgment with its prediction error terms, yet it still does not capture the complete story. For example, related work in counterfactuals more generally has shown that the temporal recencyof the counterfactual event (Miller & Gunasegaram, 1990),
and whether the outcome resulted from an act of omission oran act of commission (Kahneman & Tversky, 1982; Landman, 1987) both affect judgments of emotion. How mightthese features of counterfactuals interact with near-misses
and fit into a computational model of affective cognition?
Third, it is interesting to consider whether or not near misseffects in observers’ theory of emotion are rational. On its
face, ascribing more negative emotion to an agent who rolls a
5, as compared to a 1 (given the goal of rolling a 6), on a die
appears demonstrably irrational, as both outcomes are random and neither is “closer,” strictly speaking, to the desired
outcome. However, agents likely do feel closer after rollinga 5, rather than a 1, do ascribe illusory control to themselves,and hence do feel more negatively following near-, versus
far-misses over random outcomes. Thus, observers’ use ofnear misses are actually functional in helping observers understand agents’ experiences. This suggests that the near misseffects that we observed could reflect some combination of
two sources: (i) observers own inaccurate belief that agents
control random outcomes, or (ii) their accurate understandingthat agents react emotionally as though they controlled thoseoutcomes. Further work should unpack this question.
Emotion and reason are often treated separately, and contrasted. Yet emotion can be reasonable and reason can beapplied to emotion, as exemplified by work on appraisal theories of emotion (see Scherer, Schorr, & Johnstone, 2001). By
exploring the effects of near-misses on emotion attribution in
random situations we have exposed a fascinating twilight areaof the mind, where observers make reasoned judgments about
the less rational emotional reactions of others.

Acknowledgments
This work was supported in part by an A*STAR National
Science Scholarship to DCO and by a James S. McDonnell Foundation Scholar Award and ONR grant N00014-131-0788 to NDG.

References
Byrne, R. M. (2002). Mental models and counterfactual
thoughts about what might have been. Trends in Cognitive
Sciences, 6(10), 426-431.
Gleicher, F., Kost, K. A., Baker, S. M., Strathman, A. J., Richman, S. A., & Sherman, S. J. (1990). The role of counter-factual thinking in judgments of affect. Personality andSocial Psychology Bulletin, 16(2), 284-295.
Johnson, J. T. (1986). The knowledge of what might havebeen: Affective and attributional consequences of near outcomes. Personality and Social Psychology Bulletin, 12(1),51-62.
Kahneman, D., & Tversky, A. (1982). The simulation heuristic. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under uncertainty: Heuristics and biases (p. 201208). Cambridge University Press.
Kassinove, J. I., & Schare, M. L. (2001). Effects of the“near miss” and the “big win” on persistence at slot machine gambling. Psychology of Addictive Behaviors, 15(2),155.
Landman, J. (1987). Regret and elation following action
and inaction: Affective responses to positive versus negative outcomes. Personality and Social Psychology Bulletin,
13(4), 524-536.
Langer, E. J. (1975). The illusion of control. Journal of
Personality and Social Psychology, 32(2), 311-328.
McMullen, M. N., & Markman, K. D. (2002). Affective
impact of close counterfactuals: Implications of possible
futures for possible pasts. Journal of Experimental Social
Psychology, 38(1), 64-70.
Medvec, V. H., & Savitsky, K. (1997). When doing better means feeling worse: The effects of categorical cutoff
points on counterfactual thinking and satisfaction. Journalof Personality and Social Psychology, 72(6), 1284.
Miller, D. T., & Gunasegaram, S. (1990). Temporal order andthe perceived mutability of events: Implications for blame
assignment. Journal of personality and social psychology,59(6), 1111.
Ong, D. C., Zaki, J., & Goodman, N. D. (under review).
Affective cognition: Exploring lay theories of emotion.
Reid, R. L. (1986). The psychology of the near miss. Journalof Gambling Behavior, 2(1), 32-39.
Roese, N. J. (1997). Counterfactual thinking. Psychologicalbulletin, 121(1), 133.
Roese, N. J., & Olson, J. M. (1995). Outcome controllability and counterfactual thinking. Personality and Social
Psychology Bulletin, 21, 620-628.
Scherer, K. R., Schorr, A. E., & Johnstone, T. E. (2001). Appraisal processes in emotion: Theory, methods, research.
Oxford University Press.
Sweeny, K., & Vohs, K. D. (2012). On near misses and completed tasks: The nature of relief. Psychological Science,23(5), 464-468.
Taylor, S. E., & Brown, J. D. (1988). Illusion and wellbeing: A social psychological perspective on mental health.
Psychological Bulletin, 103(2), 193-210.
Teigen, K. H. (1996). Luck: The art of a near miss. Scandinavian journal of psychology, 37(2), 156-171.
Zeelenberg, M., van Dijk, W. W., Manstead, A. S. R., & der
Pligt, J. (1998). The experience of regret and disappointment. Cognition and Emotion, 12(2), 221-230.