Emotions in lay explanations of behavior
Desmond C. Ong (dco@stanford.edu)
Jamil Zaki (jzaki@stanford.edu)
Noah D. Goodman (ngoodman@stanford.edu)
Department of Psychology, Stanford University, Stanford CA, USA
Abstract
Humans use rich intuitive theories to explain other people’s
behavior. Previous work in lay psychology of behavior havetended to treat emotion as causing primarily unintentional behavior (e.g., being sad causes one to cry), neglecting how people incorporate emotions into explanations of rational, intentional actions. Here, we provide preliminary explorations into
integrating emotions into a theory of folk psychology. Specifically, we show that in the lay theory, people are willing toendorse emotions as causes of intentional actions. Moreover,people readily attribute beliefs and desires as explanations foremotional expressions. This work provides a first step in elaborating people’s rich understanding of emotions as an importantcomponent of intuitive social cognition.
Keywords: Intuitive Psychology, Emotions, Affective Cognition, Explanations“... men in rage strike those that wish them best” —Iago (Shakespeare, trans. 1996, 2.3.205)
In his plays, the Bard of Avon makes masterful use of thelayperson’s understanding of emotion: in Othello, the audience is privy to Iago’s deliberate manipulation of Othello’s
jealousy and rage, and can effortlessly predict Othello’s murder of Desdemona before it happens. Consider the following
counterfactual scenario: would Othello still have killed Desdemona had he not been feeling those emotions? This seems
unlikely. Intuitively, emotions played a key, irreplaceable rolein Othello’s decision making process.
We have a rich intuitive understanding of others and theiremotions (Ong, Zaki, & Goodman, 2015), and extend thisunderstanding to explain others’ behavior. Reasoning aboutothers’ mental states and behaviors is often called folk, intuitive, or lay psychology (e.g., Heider, 1958; Malle, 2011).
Many modern theories posit a “belief-desire psychology”(e.g., Bartsch & Wellman, 1995; Dennett, 1989; Gopnik &Meltzoff, 1997; Malle, 1999, 2011), in which an agent has a
set of goals (desires), and a set of ideas about the world that
help them understand how to achieve those goals (beliefs).
The agent then forms an intention to act upon his beliefs toachieve his desires, resulting in intentional action. Whenlaypeople are asked to explain an agent’s behavior, they often appeal to both the beliefs and desires of the agent: “Suewent to the store, because she wanted a drink, and she thinksthat the store sells drinks”. Unintentional behavior, on theother hand, are often described as a result of situational factors via physical causality (or impersonal causality; Heider,1958): “Sue slipped and fell because there was ice on the floor(not because she intended to)”. This taxonomy1 has provenfruitful in describing how laypeople explain behavior.
1 There are also other explanations that people give for inten-

tional actions, such as upstream events that result in beliefs or de-Previous theories relegate emotions to the bin of other, situational causes—feeling sad simply makes one cry. There is an
implicit assumption that emotion-driven behavior is unintentional, or otherwise “irrational”. See Figure 1a for an illustration, where we specifically distinguish Emotions (e.g., sadness) from Situational Factors (e.g., the ground is icy). Additionally, although most theories do not differentiate Emotional Expressions (e.g., crying, laughing) from Unintentional Behavior (e.g., slipping on ice, snoring), we separate
them in this model. This model is likely an insufficient description as it does not account for how lay people use emotions in causal explanations of intentional behavior, such as
how an agent’s emotional state might influence the formationof intentions. Returning to the example of Othello, we argue that even his (false) belief (that Desdemona cheated onhim) and his desire (to seek justice by punishing her) alone,
without any rage, would not have led him to murder his wife.
How can we incorporate emotions into an intuitive psychology of behavior? In this paper, we explore two questions. First, are intentional actions actually independent ofemotions? It is worth noting that many scientific theories of
emotion include both “automatic” and “intentional” behavior as crucial parts of their definitions of emotion. On one
hand, emotions cause characteristic “automatic” behavioral
responses like facial expressions and vocalizations (e.g., Ekman, 1992). On the other hand, emotions also bias agents
towards certain types of actions via action tendencies (Frijda,
Kuipers, & Ter Schure, 1989; Fontaine, Scherer, Roesch, &Ellsworth, 2007) or approach/avoid motivations (e.g., Carver& Scheier, 2004). For example, being in a state of happinesspredisposes one towards helping others (Isen & Levin, 1972)
and taking risks (Isen & Patrick, 1983). Emotions have been
incorporated into psychological (e.g., Schwarz, 2000), economic (e.g., Loewenstein & Lerner, 2003), and philosophical(e.g., Zhu & Thagard, 2002) theories of behavior, but they
are still lacking from theories of lay psychology. Consider
Figure 1b, which posits an intuitive theory in which emotions
directly influence the intentional decision making process—in the Discussion, we return to how exactly this might occur.
Second, in the intuitive theory, are emotion-caused
actions—specifically, emotional expressions—independentof beliefs and desires? Intuitively, this seems unlikely. For
one, emotional display rules dictate what expressions are
more appropriate in some contexts rather than other (e.g.,
Matsumoto, 1990). Display rules also subsume prosocial
sires (Malle, 1999’s “Causal History of Reasons”) and events thatfacilitated but not instigated the action (often called Enabling Factors). We do not discuss them in this paper.

or politeness considerations: presumably, one might want toforce a laugh at a boss’ joke, or hide their joy from a recent promotion when comforting a friend who has just losttheir dog. Alternatively, crocodile tears and other deliberately
deceptive strategies (e.g., DePaulo et al., 2003) are a common, if distasteful, intentional use of emotional expressions
as a means to obtain some desired outcome. Indeed, emotional expressions form an interesting category of behavior
that does not fall neatly into the “Intentional” vs. “Unintentional” dichotomy, as there are clear examples of both. Figure
1c allows for the former possibility of an agent’s beliefs anddesires influencing their emotional expressions. Under thismodel, beliefs and desires can impact expressions (thus, expressions do not fall into “Unintentional”), but at the sametime are not required for expressions (thus, they do not fall
into “Intentional” either). The place of emotional expressions
in the lay theory deserves further attention, and Figure 1c provides one possible interpretation.

a)Beliefs

EmotionSituational
factors

Emotional
Expressions

Unintentional
Behavior

EmotionSituational
factors

Emotional
Expressions

Unintentional
Behavior

EmotionSituational
factors

Desires

Intentional
Actionb)Beliefs

Desires

Intentional
Actionc)Beliefs

Desires

Intentional
ActionEmotional
Expressions

Unintentional
Behavior

Figure 1: Possible lay theories of behavior. (a) Two distinct
types of behavioral responses. Left: beliefs and desires influence an intentional decision-making process, resulting in intentional action. Right: unintentional behavior and emotional
expressions are simply caused by situational factors and emotions, without the need for an agent’s intentionality. (b) Emotions influence the intentional decision making process. (c)Beliefs and desires also influence emotional expressions.
In this paper we provide some preliminary explorationsof how emotions are incorporated into intuitive belief-desire
psychology, and specifically, how emotions are used or
judged as explanations of intentional actions. The three possible models that we discussed in Fig. 1 each provides distinct,
testable predictions. In Study 1, we study the types of actions that laypeople think emotions cause, and show that the
top emotion-caused actions are judged to have been causedby emotions only about half of the time. In Study 2, we showthat laypeople are willing to endorse emotions as causes ofintentional actions. We also find that people are willing to endorse beliefs and desires as likely causes of emotional expressions (e.g., smiling), which have been treated as unintentional
behavior in previous lay theories. This paper provides a firststep in exploring how emotions are used by laypeople in explaining behavior, and we end by discussing future directions
that are inspired by this research.

Study 1a: Emotions to Actions
In Study 1a, we asked participants to freely generate actions
that would be likely caused by different emotions. We thenobtained judgments of the counterfactual likelihood (i.e., howlikely were the generated actions if the agent had not beenfeeling the given emotion). This allowed us to find the actions
most likely to be “emotion expressions” (as described above)and to explore whether the emotions are causally necessary tobring about these actions (via the counterfactual judgments).2
Participants and Procedures. We recruited 100 participants (99% had English as their native language) throughAmazon’s Mechanical Turk (AMT). Participants saw statebecause he was [emotion]”,ments of the form “Boband were asked to give sentence completions. The presented
emotion was one of: {happy, calm, angry, sad, surprised}3 ,presented in a randomized order. Participants gave 5 different
completions per emotion, for a total of 25 completions.
After participants had given completions to all 5 emotions,they were presented again with their answers, and asked torate the likelihood of the counterfactual: “You wrote that ‘Bob
[cried] because he was [sad]’. If Bob was not feeling [sad],would he still have [cried]?” Participants gave responses on a
7 point Likert scale from “Very Unlikely” to “Very Likely”.
Results. Two coders (the first author and a naive coder)independently coded the free-responses into prototype actions, by removing adverbs and modifiers, and grouping bysynonyms (e.g., “smiled”, “smiled widely”, and “beamed”).
There was agreement on 99.6% of the responses; disagreements were resolved by consensus. The top 5 response groups
for each emotion is given in Table 1. The 900 responses inthis Table make up 36% of all responses. As expected, themajority of these modal responses would easily be judged
to be emotional expressions: some notable exceptions are“killed himself”, “punched the wall”, “slept”, and “sat down”.
We turn next to the counterfactual rating task, to investigate
necessity of emotion for the elicited actions. First, we notethat the mean counterfactual ratings (i.e., how likely is theaction to occur if the emotion was absent, P(A|¬E)) for themodal responses (M(SD)=3.08(1.92)) was significantly lower
2 All studies, data, and coding analyses are available at:https://github.com/desmond-ong/shakespeare/
3 We chose a high-arousal positively valenced (“happy”), a lowarousal positively valenced (“calm”), a high-arousal negatively valenced (“angry”), a low-arousal negatively valenced (“sad”), and ahigh-arousal neutral valenced (“surprised”) emotion.

Happysmiled: 73
laughed: 52
jumped: 42

Calmrelaxed: 52
slept: 46
sat down: 34

danced: 23

smiled: 26

cried: 21

sighed: 10

Sad
cried: 97
frowned: 17
slept: 14
killedhimself: 13
yelled: 13

Anger“hit X”: 69
yelled: 56
screamed: 27

Surprisedjumped: 65
laughed: 41
screamed: 30

cried: 16

yelled: 26

cursed: 14

smiled: 23

Table 1: Study 1a Results: Top 5 responses for each emotion, with frequency counts. The most common responses foranger were variants of “hit X”, where X is an object or person(of these, the modal was: “punched the wall”).

Counterfactual judgment P(A | not E)

than that of the non-modal responses (M(SD)=3.32(1.85);
t=3.14, p < .001): people judged the modal responses tobe less likely to occur in the absence of the emotion, than
the non-modal actions. Additionally, we hypothesized thatthe more often a particular action a is generated across all
participants for emotion e, the more necessary is emotion
e for action a to occur. To test this, we regressed participants’ counterfactual likelihood ratings against the frequencyof that action being generated across the sample, with random intercepts by participant and emotion. We find that,across all emotions, the more frequently an action is generated by all participants, the less likely participants rate
it to occur in the absence of the corresponding emotion(b = −0.0083, 95% CI : [−0.0106, −0.0060],t = −7.15, p <0.001; See Fig. 2). Thus, in participants’ lay theories, thecorresponding emotions are more necessary for these modal
actions to occur: we shall use this result in Study 1b.

6

4

2

0

25

50

75

100

Frequency of response across sample

Figure 2: Study 1a Results. Individual counterfactual likelihood ratings (P(A|¬E)) against frequency of an action’s generation across the sample. Data points are rendered transparent and jittered for clarity.

Study 1b: Causes of Actions
In Study 1b, we recruited a separate group of participants togive explanations for the modal actions generated from Study
1a. This allows us to explore the sufficiency of emotions tocause the actions elicited above, and to test among causal

models of “emotional expressions.” If Model 1 (Fig. 1a) iscorrect then in Study 1b we should only find explanations due
to emotions (and perhaps, situational factors), and not explanations that appeal to beliefs or desires.
Participants and Procedures. We recruited 100 participants through AMT (98% had English as their native language). Participants saw statements of the form “Bob”, and were asked to complete the sen[action] because
tence. The presented action was one of the fifteen actions
drawn from the most popular responses from Study 1a (the15 unique actions in Table 1, with “punched the wall” usedin place of “hit X”). Participants saw the actions in a random
order and gave 1 completion per action.
Results. Two coders classified the free-response completion into one of five categories. The two main categories ofinterest are: (a) “Emotion” (if the explanation contained an
explicitly mentioned emotion), and (b) “Cause of Emotion”(if there was a mention of an event that is very likely to cause
an emotion, e.g., “his dog died”). We explicitly added a category of coding for Causes of Emotion, because laypeople
often give events that caused emotions as explanations foremotional displays. For example, if someone asks, “why is
he crying?”, “because he is sad” is a somewhat unsatisfying
explanation (because presumably, laypeople find it obvious),as compared to, “because his dog died (and hence he is sad)”.
We also coded for (c) “Physical state” (if the explanation referenced a physical state like tiredness or pain), (d) “Mental
state” (if the explanation referenced a desire or a belief); (e)“Situation” (for other situation factors that does not cause
emotions). The two coders achieved a high agreement (Cohen’s κ = 0.935 over 1500 responses), and the responses withdisagreements were discarded (< 5%).
The distribution of coded free-responses is given in Figure
3. If the actions generated from Study 1a were characteristicof those emotions, and if they could only have been caused byemotions (i.e., Fig. 1a), then we should expect the vast majority of the explanations to be due to emotions or to upstream
causes of emotions. However, only 39.8% of explanations appeal to an Emotion, and 50.9% appeal to either an Emotion or
to a Cause of Emotion. There are many references to Physical states (17.7%; e.g., physical pain as a cause for “cursing” or “yelling”; fatigue as a cause of “sat down” or “slept”)
and Mental states (10.6%; e.g., a belief or a desire). Thus,
it seems very likely that lay people’s judgments of emotioncaused actions are not, in fact, predominantly caused only byemotions. The results of Studies 1a-b provides preliminary
support for the model in Figure 1c, which does not distinguish intention-caused from emotion-caused actions. We test
this model more precisely in Study 2.

Study 2: Rating explanations for actions
In order to further test the models in Figure 1, we exploreda broader set of actions in Study 2. We chose a set comprising Intentional Actions, Emotional Expressions, and Unin-

Type

Cause of
EmotionEmotionPhysicalstate

Mentalstate

Situation

100

Proportion

75

50

25

slept

sat down

relaxedcried
killedhimself
laughedsigheddancedsmiledjumpedyelledcursedscreamed

punchedthe wallfrowned0

ActionFigure 3: Study 1b Results: Distribution of explanation types.

tentional Behavior, and asked participants to rate how likely
it was that each action was caused by Belief, Desire, Emotion,and Situational Factor explanations4 . First, we aimed to determine if people judge emotions as suitable explanations forintentional behavior. Second, we aimed to confirm the finding from Study 1 that people will judge beliefs and desires assuitable explanations for emotional expressions. Specifically,
we would predict, under the model in Figure 1c, that for Intentional Actions, beliefs and desires will be rated as the most
likely causes, followed by emotion. For Emotional Expressions, emotion explanations will be rated as the most likely
causes, followed by ratings of beliefs and desires. For Unintentional Behavior, we predict high ratings for situational
factor causes and low endorsements for other causes.
Stimuli Selection. We selected a set of 20 actions, comprising 6 intentional actions, 6 emotional expressions, 5 unintentional behaviors, and 3 ambiguous behaviors (given in
Fig. 4.) Of the 6 intentional actions, we chose 3 (“stole apound of peaches”; “invited Sue to have lunch”; “watered his
new plants”) from Malle (1999) as they had been previously
rated as highly intentional.We chose 6 emotional expressions
from the modal responses of Study 1a. We used two unintentional behaviors (“yawned during a lecture”, “won the lottery”) from Malle (1999), as they had been rated as being low
on intentionality. Finally, we chose 3 “ambiguous” behaviors (“interrupted his mother”; “ignored Greg’s arguments”;“drove above the speed limit”) from Malle (1999), which had
been rated by some participants as being intentional, and others as being unintentional. We predicted that ambiguous behaviors would elicit uniform endorsements across all explanation types.
4 Although the three models do not differentiate between thecauses of Unintentional Behavior, we included Unintentional Behavior and Situational Factors in order to calibrate against baseline
predictions that all three models should make.

Participants and Procedures. We recruited 100 participants through AMT (98% had English as their native language; 1% did not report). First, participants were told about
different types of explanations, with examples of each. (1)People have thoughts or beliefs about the way the world isthat make them behave so (“Bob moved to Iowa because hethinks people are nice there”); (2) People feel certain Emotions that make them behave so, (“Bob ran away because he
was feeling scared”); (3) People behave that way to achievecertain Aims (“Bob kicked the ball because he wanted to
win the game”); and (4) People behave that way because of
the Situation (“Bob shivered because it was cold outside”).
Next, participants saw statements of the form “Bob[action] because ...”, and descriptions of four explanation
types: (“... he was motivated by some thoughts or beliefs
about the way the world is”; “... he felt some emotions”; “...
he wanted to achieve some aims of his”; “... of some situational causes”). They then used continuous 100 point sliders
to rate how likely it was that the behavior was caused by eachtype of cause, from “Not at all likely” to “Extremely likely”.
Participants saw the 20 actions in a random order.
Results. We used k-means clustering to cluster participants’
responses into “explanation profiles”. We took each participant’s response to each action as a 4-dimensional vector corresponding to the 4 ratings they gave (belief, desire, emotion,
situation). Although we had an a priori prediction that there
would be k = 4 clusters, we ran bootstrap clustering stability
analyses (Hennig, 2007) that revealed that solutions with 5
or more clusters were unstable across nonparametric resampling. Further analyses confirmed that k = 4 was the maximal
stable solution. Indeed, visual inspection of the centroids ofthe 4 clusters (Fig 4, left column) verified that the stable cluster solutions correspond to our predicted explanation profiles.
The 4 cluster profiles look similar to our predictions: an Intentional profile (b=26.7, d=79.5, e=20.0, s=38.7) predominantly driven by Desire ratings, an Emotion Expressions profile (23.6, 20.5, 83.8, 56.1) predominantly driven by Emotions, an Unintentional profile (4.8, 6.9, 7.2, 87.7) driven by
Situation causes, and an Ambiguous profile (57.1, 63.6, 66.6,
61.1) resulting from uniform ratings across all explanation
types. First, we note that the Intentional profile seems to be
driven primarily by only Desire, and that Belief seems to be
only weakly associated with it. Second, we note that there
is a sizable amount of rating of Emotions in the profile forIntentional Actions, with an average of e=20.0 points on a
100 point scale (just slightly behind Beliefs at b=26.7). We
also note that there is a sizable endorsement of Beliefs (23.6)
and Desires (20.5) in the Expressions profile. The Ambiguous profile is an interesting case that result from variations inparticipants’ appraisal. Some participants may judge a given
action as Intentional; others, Unintentional; and yet others, as
Expressions.
Participants’ ratings for the causes of different actions areshown in Figure 4. In each row, we show the actions that wereassigned to the prototype cluster on the left. While the un-Explanation Type

BeliefDesireEmotionSituation

Prototype:
Intentional

watered his
new plants

read a bookstole a poundof peaches

drove above
the speed limit

bought a drink

Prototype:
Expressions

laughedsmiledcried

jumpedup and downyelled loudly

Prototype:
Unintentional

stubbed his toe

snored inhis sleep

fell down

won the lottery

yawned during
a lecture

Prototype:
Ambiguousbought a round of
drinks for everyone

interrupted
his motherignored Greg's
arguments

invited Sueto have lunch

Likelihood Ratings

75
50
25
0
danced

75
50
25
0

75
50
25
0

75
50
25
0

Figure 4: Study 2 Results. Mean ratings, with 95% confidence intervals (CIs), of how likely each explanation type was to havecaused the action. Left most column: cluster prototypes from clustering analyses, with CIs obtained from 100 bootstraps.
supervised clustering for the Expressions and Unintentional
Behavior matched our a priori predictions exactly, we notethat there were a few actions that were not classified according to our initial hypothesized groupings. “Drove above thespeed limit” fit more closely with the Intentional profile thanthe Ambiguous profile, while “bought a round of drinks foreveryone”, and “invited Sue to have lunch”, more closely resembled the Ambiguous profile than the Intentional profile.
Overall, these results support our predictions of an intuitive theory with different types of actions, as evidenced bydifferent clusters of explanation profiles. Importantly, there
is a distinct profile for Emotional Expressions, and a distinctrole of Emotion causes in Explanations. Finally, we visualized the explanation profiles using multidimensional scaling
in two dimensions (Fig. 5). We see that the Intentional, Ambiguous, and Expressions clusters seem to fall along a spectrum (from top to bottom), suggesting some structure in ahigher-dimensional semantic space that future work shouldverify and explore.

watered his new plants
read a bookstole a pound of peaches
drove above the speed limit
invited Sue to have lunch
won the lotterybought a drinkbought a round of drinks for everyone
interrupted his motherignored Greg's arguments

snoredin hishissleep
stubbedtoefell down
yawned during a lecture

dancedjumped up and downyelled loudly

smiledcriedlaughedFigure 5: Multidimensional scaling of explanation profiles,
with cluster centroids marked with dots. Clockwise from top:
Intentional, Ambiguous, Expressions, Unintentional

Discussion
Laypeople have rich, intuitive theories of emotion that they
use to explain many types of behavior. By contrast, emotion
has tended to be neglected in work on intuitive theories. Using both an unstructured free-response, sentence completiontask (Study 1a/1b) and a structured explanation rating task
(Study 2), we show that people judge beliefs and desires to be

explanations of emotional expressions, and endorse emotions
as causes of intentional actions. Our results suggest the needto expand the belief-desire model of lay psychology to capture how people reason about emotion-driven and emotioninfluenced actions, and provides a first step in that direction.
Measuring lay theories is difficult, as one must elicit par-

ticipants’ judgments without imposing too much of the researcher’s own bias. We tried to mitigate this by using two
approaches, but limitations remain. The space of emotions
and the space of actions (intentional, emotional expressions,and unintentional) are both large; by focusing on a small setof emotions and actions, we risk the chance of conclusions
being driven by idiosyncratic emotion or action choices. We
elicited a wide variety of actions in Study 1a, but had to focus
on modal responses for analysis. Some of the modal actions
that we observed (e.g., anger / “punched the wall”) may be
driven by cultural tropes, and may not be representative of
real-life actions. The approach in Study 2 allowed us to examine what types of explanation profiles people attribute toa set of actions. Again, the set of actions was necessarily
restricted, this time by experimenter selection. Future work
should aim to achieve an unbiased yet representative sampling of actions. Extending the approaches taken here to maylead to new and more precise ways to measure lay theories.
Many questions remain about how exactly emotions affectintentional actions in the lay theory. Borrowing ideas from
affective science, emotions might impact an agent’s beliefs,
by biasing their subjective judgments of probability (Wright& Bower, 1992) or influencing the processing of novel information (Forgas, 1995). Alternatively, emotions might influence desires, by introducing new goals via approach/avoid
motivations (Carver & Scheier, 2004), or by introducing emotional states as regulatory goals in and of themselves (Gross,Richards, & John, 2006). Finally, emotions might have a direct impact on intentional action that is independent of beliefs
and desires; this possibility might be needed for lay explanations of “rash” decisions (such as crimes of passion, like Othello). Any of these causal pathways—or all of them—may
be part of the lay theory of psychology. Future work should
address these possibilities.
We have focused on lay explanations of behavior, especially those that go beyond beliefs and desires. This workbuilds towards a larger research program on how humans use
rich intuitive theories of emotion to reason about others—
what we call Affective Cognition (Ong et al., 2015). These
intuitive theories have broad and wide-reaching impact on all
forms of social cognition, from understanding family, friends
and colleagues, to making attributions in moral and legal
judgments. And of course, they help us enjoy a little theatre.

Acknowledgments
This work was supported by an A*STAR National Science
Scholarship to DCO, ONR grant N00014-13-1-0788, and aJames S. McDonnell Foundation Scholar Award to NDG.

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