REPORT

Biology, Society, or Choice: How Do Non-Experts
Interpret Explanations of Behaviour?

Daniel Nettle1,2, Willem E. Frankenhuis3,4, and Karthik Panchanathan5

1Institut Jean Nicod, Département d’études cognitives, Ecole Normale Supérieure, Université PSL, EHESS, CNRS, Paris, France
2Population Health Sciences Institute, Newcastle University, Newcastle, ROYAUME-UNI
3Département de psychologie, Utrecht University, Utrecht, The Netherlands
4Max Planck Institute for the Study of Crime, Security and Law, Freiburg, Allemagne
5Department of Anthropology, University of Missouri, Columbia, MO, Etats-Unis

Mots clés: core cognition, intuitive psychology, intuitive biology, explanation, science
communication

ABSTRAIT

Explanations for human behaviour can be framed in many different ways, from the
social-structural context to the individual motivation down to the neurobiological
implementation. We know comparatively little about how people interpret these explanatory
framings, and what they infer when one kind of explanation rather than another is made
salient. In four experiments, UK general-population volunteers read vignettes describing the
same behaviour, but providing explanations framed in different ways. In Study 1, we found
that participants grouped explanations into ‘biological’, ‘psychological’ and ‘sociocultural’
clusters. Explanations with different framings were often seen as incompatible with one
another, especially when one belonged to the ‘biological’ cluster and the other did not. Dans
Étude 2, we found that exposure to a particular explanatory framing triggered inferences
beyond the information given. Spécifiquement, psychological explanations led participants to
assume the behaviour was malleable, and biological framings led them to assume it was not.
In Studies 3A and 3B, we found that the choice of explanatory framing can affect people’s
assumptions about effective interventions. Par exemple, presenting a biological explanation
increased people’s conviction that interventions like drugs would be effective, and decreased
their conviction that psychological or socio-political interventions would be effective. These
results illuminate the intuitive psychology of explanations, and also potential pitfalls in
scientific communication. Framing an explanation in a particular way will often generate
inferences in the audience—about what other factors are not causally important, how easy it is
to change the behaviour, and what kinds of remedies are worth considering—that the
communicator may not have anticipated and might not intend.

INTRODUCTION

In the social and behavioural sciences, and indeed in everyday talk, we explain human behav-
iour in a number of different ways. We might for example say that the actor wanted to do it;
that they chose to do it; that they did it because of what it meant to them; that they had a
predisposition to doing it; that they were constrained to do it by social factors or cultural
expectations; that the behaviour was driven by hormonal or physiological mechanisms; or that

un accès ouvert

journal

Citation: Nettle, D., Frankenhuis, W. E.,
& Panchanathan, K. (2023). Biology,
Society, or Choice: How Do Non-
Experts Interpret Explanations of
Behaviour? Open Mind: Discoveries
in Cognitive Science, 7, 625–651.
https://doi.org/10.1162/opmi_a_00098

EST CE QUE JE:
https://doi.org/10.1162/opmi_a_00098

Reçu: 7 Novembre 2022
Accepté: 26 Juillet 2023

Intérêts concurrents: The authors
declare no conflict of interests.

Auteur correspondant:
Daniel Nettle
daniel.nettle@ens.psl.eu

droits d'auteur: © 2023
Massachusetts Institute of Technology
Publié sous Creative Commons
Attribution 4.0 International
(CC PAR 4.0) Licence

La presse du MIT

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Biology, Society, or Choice Nettle et al.

it promoted the person’s survival or reproduction. The topic of the present study is the way
non-experts interpret these different ways of explaining. Which ones are perceived as meaning
the same thing? Are they perceived as compatible or not? And what does the audience infer
about the behaviour from the fact that the speaker used one way of talking rather than a
different one?

We will refer to the different ways of talking about behaviour as explanatory framings,
related to the notion of frame or framing employed in communication studies (Entman,
1993). An explanatory framing is defined by the kind of process or entity offered as the
explaining factor. We use the term explanatory framing rather than explanation because
explanations are tokens, whereas explanatory framings are types. Par exemple, ‘Paul smokes
because of a lack of inhibitory activation in the dorsolateral prefrontal cortex’ and ‘Paul
smokes because of a strong reward signal in the amygdala in response to tobacco cues’ are
different explanations, but the same explanatory framing (the explaining factor is a process in
the brain).

The explanatory framings (henceforth, for brevity, framings) that we study here are those we
encounter commonly in reading the literature in the social and behavioural sciences, and pop-
ular discussions of that literature. Our set of framings cross-cuts typologies of explanations that
have been offered by philosophers. In some cases, the framings differ in level of explanation
(Marr, 1982). Par exemple, an explanation with a brain-based framing might represent the
same facts as one with a cognitive framing, but at a lower or more reductive level (as discussed
by Hopkins et al., 2016). Dans d'autres cas, the framings differ in stance (in the sense discussed by
Dennett, 1987). Par exemple, desire or choice framings take the intentional stance; brain or
hormonal-based framings take the physical stance; and adaptive evolutionary explanations
take the functional stance. But in some cases, two framings share the same level and stance
but we nonetheless characterize them as different: Par exemple, a hormonal and a brain-based
explanation; or an explanation in terms of choice and one in terms of desire. People might
interpret such pairs as near-synonyms, but this is something we wish to discover rather than
assume.

Explanations for the same phenomenon with different framings are often, in fact, mutually
compatible, in the sense that one being true does not necessarily make the other false. Le
causal chains leading to specific behaviours have many links. Par exemple, social-structural
factors can exert properly causal influence on behaviour, but they generally do so by con-
straining the choices and consequences for individual actors. The individual actors still have
to make choices within the constraints, so their agency is also causal (Haslanger, 2016; Ross,
2023). And the choices and agency of an individual actor can be unpacked at lower mecha-
nistic levels, such as that of neural implementation (Marr, 1982). That implementation may be
structured the way it is because of past consequences for survival and reproduction (Tooby &
Cosmides, 1989). The veracity of such evolutionary explanations in terms of adaptive function
does not make explanations for the same phenomenon in terms of mechanism false
(Tinbergen, 1963). En effet, much of the work in behavioural biology resides in integrating
the two. Ainsi, while not every pair of explanations is mutually compatible, having very dif-
ferent framings does not necessarily rule compatibility out.

Cependant, the fact that explanations with different framings can be compatible does not
mean that they are cognitively equivalent. Explanation-giving is a communicative activity
(Hilton, 1990; Kirfel et al., 2022), and follows the pragmatic and cooperative principles com-
mon to human communication in general (Sperber & Wilson, 1995). C'est, if a speaker offers
an explanation with framing X, where other framings would be possible, the audience may

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infer that the speaker believes that framing X is not just a true explanation, but the most rel-
evant one. Explaining is closely tied to offering causes, and the causal explanations that people
prefer are generally those that identify difference-making factors (Hitchcock & Knobe, 2009;
Lombrozo, 2010; Quillien, 2020). C'est, an explanation for X is a useful one if it makes
salient the factor that makes the difference between X happening and not happening. Put
another way, good explanations economically characterize a possible intervention on the
world that would change the outcome (Hitchcock & Knobe, 2009; Woodward, 2003). If a
conférencier, asked why a recent car crash happened, gives a detailed physical account of the
kinetics of the cars, hearers will assume that they do not know that one of the drivers was
asleep, or they would have mentioned that first. Ainsi, the choice of one framing over another
logically compatible one may make a considerable difference to what hearers infer about
where the key difference-makers lie, and hence whether or how it would be possible to
intervene.

Previous research on framings has tended to assume higher-order groupings: Par exemple
‘biogenetic’ versus ‘psychological’ (Haslam & Kvaale, 2015); ‘individualistic’ versus ‘societal’
(Cozzarelli et al., 2001); ‘dispositional’ versus ‘situational’ (Piff et al., 2020); or ‘functional’
versus ‘mechanistic’ (Lombrozo & Gwynne, 2014). Because each of these studies focuses
on one of these dichotomies, chosen a priori, each study uses only a subset of the framings
routinely encountered in the social and behavioural sciences. De plus, they assume that the
higher-order groupings are psychologically real for their participants. The innovation of our
studies is to use a larger set of framings, and to allow the higher-order grouping to emerge from
the participants’ responses, rather than assuming it.

In what follows, Étude 1 examines which framings are judged as more and less similar to
one another, and more and less compatible. Étude 2 examines the inferences people make
beyond the information given when explanations with different framings are used, particularly
about the malleability of the behaviour and the locus of causal responsibility. Studies 3A and
3B examine inferences about interventions: what differences does it make to people’s views
about how a behaviour could change, when an explanation is framed one way rather than
another?

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STUDY 1

Étude 1 explored perceived similarity and compatibility of explanations for a behaviour. Nous
sought to determine which kinds of explanations were perceived as meaning roughly the same
chose, and which ones were different. De même, we wished to know to what extent having an
explanation with one framing ruled out the truth of an explanation with a different framing.

As for compatibility, we can define two possible extreme scenarios. At one extreme, people
could be total ‘compatibilists’, believing that the truth of an explanation with one framing
never rules out the truth of explanations with any different framing. At the other extreme, ils
could be hard ‘incompatibilists’, believing that the acceptance of the truth of one framing nec-
essarily means rejecting the truth of explanations with any other framing. Previous research
suggests an intermediate situation: different types of explanation for the same phenomenon
do often coexist in people’s minds (Blanchard et al., 2022; Legare & Visala, 2011; Shtulman
& Lombrozo, 2016), but ‘explanation discounting’, whereby the availability of one explana-
tion makes other explanations seem less plausible, has also been documented (Kelley, 1973;
Sloman, 1994). Explanation discounting can also happen at the level of framings (Heussen,
2010). Par exemple, among both lay people and physicians, the more someone believes a
disorder has biological causes, the less they believe it has psychological causes, and vice versa

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Biology, Society, or Choice Nettle et al.

(Ahn et al., 2009; Marais & Romano, 2016). Ainsi, it is likely that certain combinations of fram-
ings will discount one another more strongly than other combinations do.

UK general-population adults read vignettes describing a behaviour and were then asked to
consider different explanations. They rated either how similar those explanations were to one
another (half the participants), or how compatible they thought they were (the other half ). Ce
design allowed us to map the landscapes of perceived similarity and compatibility, et investit-
tigate how similarity and compatibility related to one another. We also used cluster analysis to
determine how many types of framing there were.

The study was exploratory rather than confirmatory. Cependant, we were guided by one pos-
sible hypothesis: explanations would be seen as similar, and compatible, to the extent to
which they activate the same core cognitive system. Some psychologists argue that humans
are endowed with a small number of separate core cognitive systems for dealing with different
classes of entity (Jeux & Kinzler, 2007). From early childhood onwards, these give rise to
several distinct systems of intuitive knowledge and spontaneous inference: intuitive physics,
which deals with objects and their motion (Jeux, 1990); intuitive biology, which deals with
species of plants and animals (Atran, 1998); intuitive psychology, which deals with the beliefs,
desires and goals of human agents (Kamps et al., 2017); et, peut-être, intuitive sociology,
which deals with social groups, group membership and social norms (Shutts & Kalish,
2021). Each of these systems produces a characteristic, and different, type of intuitive reason-
ing. En principe, human behaviour could activate any or all of these systems: humans are, after
tous, physical objects, animals, individual agents, and members of social groups. Which sys-
tem(s) are active might depend on the way the behaviour is framed. If this hypothesis is correct,
we would expect explanations to cluster for similarity and compatibility according to which
feature of humans—their animal-like embodiment, their psychological states, or their mem-
bership of social structures or networks—was made most salient.

Methods

Ethics. All studies received ethical approval from Newcastle University Research Ethics
Committee (approval 16509/2021).

Pre-registration. Although this study was exploratory, we pre-registered methods, materials,
and planned analyses at: https://osf.io/8ba5m.

Participants. Participants (200 UK resident adults with first language English) were recruited
via online platform Prolific (www.prolific.co). Mean age was 39.9 années (SD 13.0), avec 104
women and 96 men. Participants were mostly non-students (145 non-students, 22 students, 33
not stated), in employment (124 employed full or part-time, 32 not currently employed, 34 pas
stated). Participants received £2.50 for taking part. Six participants were excluded for failure to
fully complete the survey, leaving 194.

Design. Participants were allocated, in a between-subjects design, to one of four different
brief vignettes, and one of two different response conditions (similarity or compatibility). Chaque
vignette described a behavioural phenomenon that was common in a particular population,
respectivement: homicide, teenage parenthood, land diving (a risky display behaviour), et
blood blessing (a precautionary behaviour for dangers). At the end of the vignette, participants
saw 12 ‘explanations that have been offered for the behaviour’. Chiffre 1 shows the vignette
and explanations for one of the four cases. The wording for the other three vignettes is given in
the pre-registered protocol (https://osf.io/8ba5m). The explanations were framed in terms of:
meaning, choice, a psychological trait, culture, social structure, social roles, evolutionary

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Chiffre 1. Screen capture from the study, showing one of the brief vignettes and its 12 explanations.

advantage, physiology, childhood experience, genetic propensity, motivation, and social
pressure. From here on, we refer to the psychological-trait explanation as ‘trait’, because
‘psychological’ will be later used as a superordinate category. The precise wording of each
explanation was slightly adapted to the specific vignette.

Following the initial presentation of the vignettes and explanations, one explanation (le
focal explanation) was placed at the top of the screen. The participant was then asked to rate,
for each of the other 11 explanations, either ‘how similar you think it is to the explanation
listed at the top of the page’ (similarity condition; 0–100 slider anchored with ‘totally different’
and ‘totally similar’), or ‘how compatible you think it is with the explanation listed at the top of
the page’ (compatibility condition; 0–100 slider anchored with ‘totally incompatible’ and
‘totally compatible’). In the compatibility condition, the initial instructions further specified
that ‘by compatible, we mean that both explanations could be true at the same time’.

The rating procedure was then repeated a further 11 times with each of the explanations in
turn serving as the focal explanation. Ainsi, each participant completed a total of 132 ratings
(11 other explanations × 12 focals). Every possible pairing of explanations appeared twice;
once with the first explanation of the pair as the focal, and once with the second explanation
as the focal. As a robustness check prior to the main analysis, we examined the correlation
between the rated similarity or compatibility the first time the participant rated the explanation
pair, and the second time. In the pre-registration, we specified that we would exclude any
participant for whom the correlation between their first set of ratings and their second was less
que 0.7. Observed correlations for each participant were lower than anticipated (similarité
condition: mean r = 0.39; compatibility condition, mean r = 0.43). Cependant, the correlations
between the across-participants average rating the first time and the second time were
extremely high (similarity condition: r = 0.93; compatibility: r = 0.79). We therefore adjusted
our pre-registered exclusion criterion and excluded any participant who lacked a significant
positive correlation between their first set of ratings and their second. This excluded 58 par-
ticipants, leaving 136. In the post-exclusion dataset, the correlation between across-
participants average ratings the first and second time were r = 0.96 (similarity condition)

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and r = 0.89 (compatibility condition). All subsequent analyses use ratings averaged across
participants and across the first and second time of rating.

Data Analysis. Data and code for all studies are available at: https://osf.io/wte2c/. Similarity
ratings for pairs of framings were well correlated across vignettes (inter-vignette correlations
0.68–0.88), as were compatibility ratings (inter-vignette correlations 0.67–0.84). All subse-
quent analyses were performed on the data pooled across vignettes. D'abord, we used multidi-
mensional scaling to create a map of rated similarity, using R package ‘smacof’ (see Jones
et coll., 2018). We then used the configuration matrix from the multidimensional scaling output
to perform k-means clustering analysis to detect clusters in the similarity space. We determined
the optimal number of clusters to extract using the function fviz_nbclust() from the ‘factoextra’
R package (Kassambara & Mundt, 2020). We then repeated the multidimensional scaling and
clustering for rated compatibility. Enfin, we examined how rated compatibility related to
rated similarity. We examined how rated compatibility differed by the respective similarity
cluster membership of the two framings in question.

Results

The multidimensional scaling results for similarity are shown in Figure 2A. The stress metric
était 0.21. This value is considerably less than the metric for randomly generated networks of
this size, though there is no simple rule for what constitutes good fit of a multidimensional
scaling solution (Mair et al., 2016). The optimal number of clusters was three. Cluster mem-
bership is indicated by colour-coding on Figure 2A. One cluster consisted of explanations that
we can label ‘biological’ (genetic, hormonal, evolutionary); the second, ‘psychological’ states
or processes (trait, choice, motivation, meaning); and the third cluster, explanations that would
be described as ‘sociocultural’, though childhood experience was also included in this cluster
(constituent members were culture, social role, social pressure, opportunité, and childhood
experience).

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Chiffre 2. Visualization of multi-dimensional scaling and k-means clustering for (UN) similarity and (B) compatibility of explanations with dif-
ferent framings, Étude 1. Euclidean distance and line darkness represent similarity or compatibility. Networks have been subject to Procrustes
rotation for comparability. Node colour represents cluster membership in k-means clustering. Abbreviations: HOR: hormonal; GEN: genetic;
EVO: evolutionary; TRA: (psychologique) trait; MOT: motivational; CHO: choice; MEA: meaning; CUL: culture; ROL: social roles; PRE: sociale
pressure; CHI: childhood experience; OPP: opportunité.

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The multidimensional scaling map for compatibility (Figure 2B, stress metric 0.20) was very
similar to that for similarity (coefficient of congruence after Procrustes rotation, 0.98). The opti-
mal number of clusters for compatibility was two rather than three. The clusters extracted for
compatibility were identical to the ‘biological’ similarity cluster (genetic, hormonal and
evolutionary), plus the other two similarity clusters combined. Within the large ‘non-biology’
cluster for compatibility, the relative distances of the explanations were much as for similarity,
with the more psychological framings perceived as more compatible to one another than they
were to the more sociocultural ones. Cependant, culture and childhood in particular were rated
as more compatible with ‘psychological’ explanations than they were similar to those expla-
nations. This is why, in the compatibility clustering, the ‘psychological’ and ‘sociocultural’
clusters did not separate.

The reason for the close congruence of the similarity and compatibility spaces shown in
Chiffre 2 is that compatibility was treated, in our sample, as a near-synonym for similarity
(Figure 3A). The correlation, across pairs of explanations, between their rated compatibility
and their rated similarity was r = 0.96. Pairs of explanations belonging to different similarity
clusters were perceived as much less compatible than pairs of explanations belonging to
different similarity clusters (same clusters: mean compatibility 64.1, SD 11.8; different
clusters: mean compatibility 42.7, SD 12.8; t (64) = −6.30, p < 0.001). More specifically, a pair of explanations was perceived as much more incompatible if one member belonged to the ‘biological’ similarity cluster and the other did not than in any other scenario (Figure 3B). In general, our participants viewed explanations with different framings as neither wholly compatible nor wholly incompatible. Averaging across participants, the most compatible pair of framings (social role and social pressure) had a mean compatibility of 87.5, whilst the least compatible (hormonal and childhood) had a mean compatibility of 26.1. Of the 12,672 com- patibility ratings given in total, 5677 (45%) were lower than the mid-point of the scale (i.e., l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / . / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 (A) Relationship between rated compatibility and rated similarity, Study 1. Each point represents a pair of explanatory framings. Figure 3. Horizontal and vertical error bars represent ± 1 standard error of the mean in the similarity and compatibility dimensions. (B) Boxplot of rated compatibility of pairs of explanations, by the clusters they respectively belong to in the similarity space, Study 1. PP: both ‘psychological’; SS: both ‘sociocultural’; BB: both ‘biological’; PS: one ‘psychological’ and one ‘sociocultural’; PB: one ‘psychological’ and one ‘biological’; SB: one ‘sociocultural’ and one ‘biological’. OPEN MIND: Discoveries in Cognitive Science 631 Biology, Society, or Choice Nettle et al. more towards incompatible than compatible), with the remaining 6995 (55%) at or above the mid-point. There were 1114 (9%) ratings of 0 (i.e., the totally incompatible endpoint of the scale), and 727 (6%) of 100 (totally compatible). Of the 96 participants who rated for compat- ibility, 51 (53%) gave at least one rating of zero, and 28 (29%) gave at least 10 ratings of zero. All but 3 participants (97%) gave 10 or more compatibility ratings that were below the mid- point of the scale. Discussion The main findings of Study 1 can be summarised as follows. In terms of similarity to one another, explanations fell into three clusters, a ‘biological’ cluster (framed in terms of hormones, genes and evolutionary advantage); a ‘psychological’ cluster (choice, meaning, motivation and psychological traits); and a ‘sociocultural’ cluster (culture, social role, social pressure, opportunity and childhood experience). The three clusters plausibly correspond to the domains of intuitive biology, intuitive psychology, and intuitive sociology respectively. Cluster membership seems to have been driven primarily by explanation content (does the explanation mention bodily or animal features; or mental states; or social networks and expec- tations that constrain the individual’s choices). Our results recover some of the higher-order dichotomies assumed in previous research, such as ‘biogenetic’ versus ‘psychological’ (Haslam & Kvaale, 2015), which corresponds to our biological versus psychological clusters; and ‘individualistic’ versus ‘societal’ (Cozzarelli et al., 2001), which corresponds to our psy- chological versus sociocultural clusters. In terms of compatibility, ratings were typically around the middle of the scale, but with some pairs of explanations much less compatible than others. Compatibility and similarity judgements were strongly concordant: explanations were perceived as more compatible with one another exactly when they were perceived as more similar to one another. This is a non- obvious and possibly disturbing result from a scientific point of view. For example, a scientist might view a social role explanation and a hormonal explanation as dissimilar, but compatible (e.g., the hormonal changes provide the proximal pathway through which social expectations are internalised and expressed). It is therefore an important discovery that among these non- experts, dissimilar explanations are experienced as rather incompatible with one another. Since explanations with ‘biological’ framings were considered the most dissimilar from other kinds of explanation, they were also considered to be the most incompatible with other kinds of explanation. This cognitive clash may represent the different core procedures of intuitive biology from those of intuitive psychology and sociology (although, this is not the only possi- ble explanation, see General Discussion). A limitation of Study 1 is that similarity and compatibility judgements were made about particular tokens of explanations, in the context of specific behaviours. Generalizability to other kinds of behaviours and other tokens of explanation for them cannot be assumed. We aimed to minimize this concern by averaging across four different vignettes, and the similarity and compatibility judgements about the framings were similar in the four cases. However, judgements about causes can be influenced by what is perceived to be normal, and also by valence (Kirfel et al., 2022; Knobe & Samuels, 2013). Our behaviours would probably be con- sidered to have negative (killing, teenage parenthood) or at best neutral (blood blessing) valence. Moreover, all of them would be considered uncommon or atypical (killing and teen- age parenthood being atypical individual behaviours, and land diving and blood blessing being atypical group behaviours). The patterns could look different for cases of typical and/or positively valenced behaviours. OPEN MIND: Discoveries in Cognitive Science 632 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / . / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Biology, Society, or Choice Nettle et al. STUDY 2 Study 1 described the mental map of explanation similarity in our study population. It sug- gested a three-way clustering of ‘biological’, ‘psychological’ and ‘sociocultural’ explanations. If these three clusters stem from the predominant activation of different core cognitive domains (intuitive biology, intuitive psychology and intuitive sociology), then people should make dif- ferent patterns of inference according to which core domain the explanation activates most strongly. These inferences will follow the priors and principles of intuitive cognition in that domain. For example, intuitive biology characterizes organisms as owing their properties to an inner essence that is fixed and transmitted through reproduction (Atran, 1998; Linquist et al., 2011; Machery et al., 2019). Thus, to the extent that the explanation activates intuitive biology, people should infer fixity. There is prior evidence that this is the case. For example, when the explanation evokes a bodily basis, presumably activating intuitive biology, psycho- logical disorders are seen as more immutable and transmissible (Berent & Platt, 2021a, 2021b; Haslam & Kvaale, 2015). Likewise, framing explanations for gender differences biologically makes people judge that the gender differences would be hard to change (Brescoll & LaFrance, 2004). Our general expectation is, therefore, that choosing one type of framing over another, without providing further specification, will trigger different inferences about the behaviour under description. In Study 2, we used the same vignettes and explanations as in Study 1. Instead of asking participants to rate similarity and compatibility, we asked them to make judgements about what would be true of the behaviour if each of the 12 explanations were in fact correct. Spe- cifically, we asked about malleability (how easily each behaviour could change); externality (the extent to which responsibility for the behaviour lies out in society rather than internally to individuals); and simplicity (whether the causes of the behaviour are simple or complex). As in Study 1, our aims were exploratory. We expected different explanations to have different pro- files of malleability, simplicity and externality. We tested this explanation both by comparing ratings of every explanation to every other, and also by grouping the explanations into the similarity clusters discovered in Study 1, and examining how malleability, simplicity and exter- nality vary between clusters. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / / . 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i Methods Pre-registration. We pre-registered methods, materials and planned analyses at: https://osf.io /a7s45. Study 2 was conducted concurrently with Study 1 (although with different partici- pants). Hence, the three-way similarity clustering of framings observed in Study 1 was not known. Analyses reported below that use the three clusters were therefore not pre-registered. There was also a pilot study for Study 2 whose results informed the design and are presented in the pre-registration of the main study. f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Participants. A total of 400 participants (200 male, 200 female; UK resident adults with first language English who did not participate in Study 1) were recruited via online recruitment platform Prolific (www.prolific.co). Mean age was 41.89 years (SD 13.28). Participants were mostly non-students (299 non-students, 25 students, 76 not stated), in employment (218 employed full or part-time, 83 not currently employed, 99 not stated). Participants received £2.20 for taking part. For 10 surveys, the final submit button was not selected. The study contained a total of 12 attention checks (see below). 55 participants failed at least one of these and were excluded, leaving a final sample of 335. OPEN MIND: Discoveries in Cognitive Science 633 Biology, Society, or Choice Nettle et al. Design. We used the same four vignettes as Study 1, with approximately equal numbers of participants seeing each vignette. At the end of the vignette, respondents saw the list of 12 explanations that have been offered for the behaviour described in the vignette (the same list as in Study 1). Participants were then taken through each explanation in turn, in random order. Assuming the given explanation to be true, they were asked to indicate their agreement with six statements. Instructions made clear that participants were not rating their degree of belief that the explanation was true, but rather what would follow if the explanation were true. Dependent Variables. The six rating measures, all assessed on a 100-point slider anchored with ‘disagree’ and ‘agree’, were as follows. 1. 2. 3. 4. 5. 6. This behaviour could change easily. All of society is responsible for this behaviour. This behaviour has a simple cause. This behaviour is inevitable. Responsibility for this behaviour lies within the individuals that do it. The causes of this behaviour are complex. These ratings were intended to give two-item measures of malleability (items 1 and 4), exter- nality (2 and 5) and simplicity (3 and 6). We scaled each rating within subjects, as we were primarily interested in the within-subjects covariation of ratings across explanations, rather than the between-subjects covariation of ratings. Results were very similar without applying within-subjects scaling. We tested whether the six items covaried as intended by fitting a three- component PCA with oblimin rotation. The KMO statistic for the correlation matrix of the six items was 0.61, which is adequate for PCA but not excellent. The three components extracted were essentially uncorrelated with one another, and accounted respectively for 26%, 26% and 19% of the variation. The first component had loadings of 0.83 on item 1 and −0.82 on item 4. The second component had loadings of 0.90 on item 3 and −0.74 on item 6. The third component had loadings of 0.96 on item 2, but only −0.29 on item 5 (this item also loaded on both other components with 0.44 and 0.31 respectively). Thus, the pairs of items covary as intended for malleability and simplicity, but not well for externality, and the component for externality primarily reflects just one of the items. The scores from the PCA were used as the three dependent variables. Attention Checks. After completing the ratings for each explanation, respondents were shown one of the 12 explanations and asked (True/False) whether this was the explanation they just provided ratings about. Six of the attention checks show the right explanation (True), and six showed a different one (False). Data Analysis. As in Study 1, we pooled results across the four vignettes. Our first set of anal- yses used individual participant ratings as the unit of analysis. The within-subjects scaling removed the need for random intercepts by participant (since every participant had a mean rating of 0 with a standard deviation of 1). Using MANOVA and general linear models, we examined whether ratings differed by framing. For our second set of analyses, we calculated the mean malleability, externality and simplicity per framing; hence, the framing is the unit of analysis for these models. We grouped the framings into the similarity clusters observed in Study 1. We used MANOVA and linear mixed models (with a random effect of framing) to examine whether the mean malleability, externality and simplicity differed by framing cluster. OPEN MIND: Discoveries in Cognitive Science 634 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / / . 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Biology, Society, or Choice Nettle et al. Results The 12 framings received significantly different ratings from one another across the set of three dependent variables (F(33, 11550) = 108.64, p < 0.001). Specifically, the framings differed significantly from one another in terms of malleability (F(11, 3850) = 160.62, p < 0.001), externality (F(11, 3850) = 148.26, p < 0.001)) and simplicity (F(11, 3850) = 61.93, p < 0.001). Framings belong to different clusters (from Study 1) received different average ratings across the set of three dependent variables (F(2, 9) = 9.06, p = 0.007); specifically, they received significantly different average ratings for malleability (F(2, 9) = 7.02, p = 0.015), exter- nality (F(2, 9) = 14.29, p = 0.002) and simplicity (F(2, 9) = 5.25, p = 0.031). Figure 4 visualizes these results. Explanations belonging to the ‘biological’ cluster were rated as relatively unmalleable; those belonging to the ‘psychological’ cluster as relatively mal- leable; and those belonging to the ‘sociocultural’ cluster generally intermediate. There was also variation within clusters. For example, within the ‘biological’ cluster, genetic was the least malleable, and hormonal the most; within ‘psychological’, choice was extremely malleable and meaning sat closer to the mean for ‘sociocultural’ explanations; and within ‘sociocultural’, opportunity was fairly malleable whilst culture was very non-malleable. As for externality, the ‘sociocultural’ explanations were relatively external, whereas the ‘biological’ and ‘psycholog- ical’ framings were relatively internal. In the case of simplicity, differences across clusters were the least marked, but ‘psychological’ explanations were the simplest on average, and ‘socio- cultural’ the least simple. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / Discussion Different framings provoked different judgments in our participants. Sociocultural explana- tions were perceived as locating the causality out in society rather than within the individual, whereas both biological and psychological explanations were rated as locating causality within individuals. Partial exceptions to this generalization were evolutionary explanations and explanations in terms of meaning, which were perceived as intermediate between inter- nal and external. These findings suggest our participants did attend to the material. There is an obvious sense in which psychological and biological explanations do concern intra- personal processes, and in which evolutionary and meaning explanations are atypical. i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 4. Ratings of malleability, externality and simplicity across framing clusters, Study 2. Grey points and whiskers show estimated marginal mean and 95% confidence interval at the cluster level. Superimposed black points represent means for individual explanations. Note that since var- iables are scaled, zero always represents the mean across all explanations. OPEN MIND: Discoveries in Cognitive Science 635 Biology, Society, or Choice Nettle et al. Evolutionary explanations concern the fit between organism and environment, and explana- tions in terms of meaning concern processes that are internal to the mind but also shared with others in social networks. We also found differences in how simple the explanations were perceived as being: psychological trait, motivational, choice and hormonal explanations were perceived as relatively simple. All other explanations were perceived as complex compared to these. The finding with the most important implications is that explanations with different framings were perceived to imply different levels of malleability of the behaviour. Making reference to biology, in the absence of any other information, triggered the inference that the behaviour would be hard to change. Whilst this inference might be correct for certain cases of genetic explanation, it need not be true for all biological explanations. For example, hormone levels are highly dynamic, and can change rapidly in response to environmental inputs. Moreover, adaptive evolutionary explanations really concern the consequences of the behaviour for survival and reproduction. How rapidly the behaviour might change if the context changes will depend on the proximal mechanisms that deliver the adaptive behaviour. Thus, merely demonstrating that there is a ‘biological’ basis does not necessarily mean that the behaviour could not change easily. However, for our participants, all ‘biological’ explanations led to the inference of below-average malleability. Possibly this is due to our ‘biological’ explanations cueing intuitive biological cognition, under whose logic creatures follow inner, immutable, transmissible essences (Atran, 1998). For this reason, actors who find implications of non- malleability congenial may be particularly drawn to transmitting explanations with biological framings (as Brescoll and LaFrance (2004) found for politically conservative newspapers and research on gender differences). However, it is a hazard for any researcher who is interested in biological mechanisms or evolutionary adaptiveness, but does not believe, or wish to be taken as implying, that the behaviour is not malleable (see also Dar-Nimrod & Heine, 2011). By contrast, ‘psychological’ explanations (especially psychological traits, motivation, and choice) produced the highest inferred malleability. This may stem from their engagement of intuitive psychology, whose function is to predict and intervene in individuals’ behaviour as time and context changes (Ho et al., 2022). In line with this function, intuitive psychology sees behaviour as stemming from transient and reversible inner states such as beliefs and desires. The researcher who frames their results at this level of the causal chain may thus be taken to imply—whether they will it or no—that changing beliefs and desires is relatively easy and sufficient to produce behavioural change. However, this will not necessarily be the case if individual agency is heavily constrained by social-structural factors (Chater & Loewenstein, 2023; Haslanger, 2016; Ross, 2023; Vasilyeva & Lombrozo, 2020). The sociocultural explanations were markedly heterogeneous in terms of inferred mallea- bility. Explanations in terms of opportunity produced fairly high ratings of malleability. Expla- nations in terms of culture, on the other hand, led to the inference that behaviour change was almost as difficult as in cases of genes and evolution. The difference between genes and culture in the minds of our participants was not that they implied different levels of malleability—these were about the same—but that genes locate the causality within the indi- vidual, and culture locates it out in society. These findings are interesting in light of the way culture is often described in academic literature, as an inheritance system at least partly anal- ogous to genes, with a fair degree of historical persistence. It also relates to critiques of the use of the culture concept (for example, in the case of ‘the culture of poverty’, Black and Dolgon (2021)). These critiques see the term as essentializing the current behaviours of people in pov- erty, obscuring the extent to which those behaviours may in some cases be dynamic, rational, responses to very specific structural factors or policy measures. OPEN MIND: Discoveries in Cognitive Science 636 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Biology, Society, or Choice Nettle et al. STUDY 3A Studies 1 and 2 found evidence that people group explanations of behaviour into ‘biologi- cal’, ‘psychological’ and ‘sociocultural’ clusters. Further, people assume differential mallea- bility according to which framing is invoked. Study 3 (A and B) extends Studies 1 and 2 by focusing on how the choice of framing affects the preferred choice of intervention. That is, what kinds of measures are plausible candidates for making the behaviour change? There are close connections between explanations (why did X happen?), counterfactuals (what would have to have gone differently for X not to happen?), and intervention strategies (how can X be prevented from happening?) (Gerstenberg et al., 2017; Halpern & Pearl, 2005; Hitchcock & Knobe, 2009; Quillien, 2020; Woodward, 2003). Pragmatically, the fact that someone has adopted a particular framing suggests that they think it is the optimally relevant one, and therefore that the most promising intervention strategies reside at this level (Hilton, 1990). We therefore expect the choice of framing to guide cognition about effective interventions. In communication studies, one of the functions attributed to framing is to guide the search for remedies (Entman, 1993). Previous research suggests that there are effects of this kind. Physicians intuit that pharmacological treatment will be more effective for disorders they consider biogenetic in origin, and talking cures for disorders they consider of psychological origin (Ahn et al., 2009; Marsh & Romano, 2016). Adopting a ‘biological’ framing might not just increase the perceived effectiveness of pharmacological interventions, but also decrease the perceived effectiveness of other kinds of interventions, such as political or socioeconomic reform. The reason for this would be that if the speaker had believed those kinds of inter- ventions to be the right ones, she would have framed the explanation in a different way (Hilton, 1990). In Study 3A, we used two of the vignettes presented in Studies 1 and 2, with slight wording modifications. In a between-subjects design, participants saw the description of the behaviour, plus one explanation, or no explanation. They were then asked how much difference could be made to the behaviour by changing biology (for example through drugs), changing psychology (for example through thinking training), and changing society (for example through political reform). Since every participant was asked about the effectiveness of all three types of inter- vention, the design potentially allowed us to detect multiple effect patterns. For example, an explanation with a certain framing might be perceived as making all interventions more effec- tive (relative to no explanation), all interventions less effective, or some interventions more effective and some interventions less so. We pre-registered three predictions (see pre-registration link below), using the explanation cluster (rather than the individual explanation) as the explanatory variable: P1. The cluster of explanation offered will affect the rated effectiveness of the three types of interventions. P2. Offering an explanation belonging to a particular cluster will increase the rated effec- tiveness of interventions belonging to that cluster (relative to ‘no explanation’). P3. Offering an explanation belonging to a particular cluster will decrease the rated effective- ness of interventions belonging to the other two clusters (relative to ‘no explanation’). We also performed exploratory analyses at the individual explanation level rather than the level of the explanation cluster. Participants of Study 3A who were in the ‘no explanation’ group additionally completed Study 3B (see below). OPEN MIND: Discoveries in Cognitive Science 637 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i . / / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Biology, Society, or Choice Nettle et al. Methods Pre-registration. We pre-registered methods, materials, and planned analyses at: https://osf.io /aqsz3. Participants. Participants were recruited via Prolific as per Studies 1 and 2. We pre-registered a sample size of 320. However, due to an error, the participants in the ‘no explanation’ condi- tion were not shown the Study 3B questions (for Study 3B, see below), leading us to add 80 more participants in the ‘no explanation’ condition. Preliminary inspection of the data after the 320 + 80 participants suggested we were under-powered to determine what was happening at the individual explanation level. We therefore added another 320 participants as per the orig- inal design. Post-hoc increases in sample size inflate type-I error rates. We therefore recalcu- lated the critical p-values required in the final sample to keep overall type-I error rates at 0.05, using the pcrit formula (Sagarin et al., 2014), for one additional round of data collection, and assuming that any p-value less than 0.2 would have led us to collect more data. This corrected α was 0.04, which should therefore be considered the appropriate α for the results presented below. Design. We used vignettes from Studies 1 and 2, homicide and teenage parenthood. Each participant saw one vignette. In Study 1, there were unequal numbers of framings in the three clusters: three ‘biological’, four ‘psychological’ and five ‘sociocultural’. To equalize the repre- sentation of clusters, we added an additional biological framing (‘brain circuits’), and omitted the ‘childhood’ explanation (from ‘sociocultural’), producing four framings per cluster. The probability of being assigned to the ‘no explanation’ condition was four times that for every other condition. This is so that when explanations are aggregated to the cluster level, the num- bers of observations in the ‘no explanation’, ‘biological’, ‘psychological’ and ‘sociocultural’ clusters were approximately equal (see Table 1 for achieved sample sizes). Vignettes. The vignettes describe the behaviour (homicide or teenage parenthood respec- tively) followed by the statement: ‘scientists have discovered that …’ and one of the expla- nations (for example, “In the town of XXXX, young men commit a large number of murders each year. Scientists have discovered that young men in XXXX kill because of low levels of activity in a particular brain region”). The vignettes and explanations had slight wording dif- ferences from Studies 1 and 2, particularly to make all options more grammatically similar Table 1. Design summary, with realised numbers of participants in each cell. Vignette Homicide No explanation 122 Biological Psychological Sociocultural l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / . / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Brain Evolutionary Genetic Hormonal 27 23 20 18 22 17 19 20 Choice Meaning Motivation Trait Choice Meaning Motivation Trait 15 16 14 25 23 20 23 22 Culture Opportunity Pressure Role Culture Opportunity Pressure Role 20 21 17 21 16 21 24 19 638 Teenage parenthood 115 Brain Evolutionary Genetic Hormonal OPEN MIND: Discoveries in Cognitive Science Biology, Society, or Choice Nettle et al. and all contain an explicitly causal ‘because’ (see pre-registration at https://osf.io/8ba5m for details). In the no-explanation condition, we omitted the ‘scientists have discovered that …’ statement. Dependent Variables. After reading each vignette and any explanation, participants responded by answering three questions on a 100-point scale anchored with ‘none’ and ‘a great deal’. The wording for the homicide vignette was: (cid:129) How much could the rate of homicide be decreased by changing biology, for example through tablets, injections, or brain stimulation? (cid:129) How much could the rate of homicide be decreased by changing psychology, for example through education, persuasion, or thinking training? (cid:129) How much could the rate of homicide be decreased by changing society, for example through better political representation, increasing incomes, or better jobs? The wording for the teenage parenthood vignette replaced the words ‘the rate of homicide’ in the question with ‘the rate of teenage parenthood’). Predictions and Analysis Plan. Pre-registered predictions were as listed in the Study 3 introduc- tion. In the pre-registration, we acknowledged the possibility that P2 and P3 might hold for some types of framing but not others. For example, biologically-framed explanations might reduce the rated effectiveness of societal interventions, but not vice versa. We made no a priori predictions about this heterogeneity. We fitted linear mixed models with the predictors: intervention type, framing cluster, and their interaction; and rated effectiveness as the outcome. P1 implies effects of framing cluster, either as main effects or interactions with intervention type. P2 and P3 imply significant inter- actions between intervention type and framing cluster. We included random effects of individ- ual framing, and participant. We also ran the same analyses with individual framing as the independent variable, and these results are superimposed on Figure 5. Data from the two vignettes were pooled. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / . / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d / . i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Results In a mixed model with effectiveness as the outcome and intervention, framing cluster, and their interaction as predictors, there was a significant effect of intervention (F(2, 1431.77) = 207.35, p < 0.001). This was driven by an overall endorsement of psychological and social interventions as more effective than biological ones (estimated marginal means: biological, 38.5 (SE 1.55); psychological 60.9 (1.55); social 59.4 (1.55); see Figure 5A). The main effect of framing cluster was not significant (F(3, 6.43) = 0.44, p = 0.73), but there was a significant interaction between intervention and framing cluster (F(6, 1431.74) = 23.33, p < 0.001). To examine this interaction, we broke the data down into the three types of intervention. We ran separate models to examine the effect of each type of framing (relative to the reference category of ‘no explanation’) on rated effectiveness for that type of intervention. Results are shown in Figure 5B. Providing a biologically-framed explanation both increased the rated effectiveness of biological interventions, relative to no explanation, and decreased the rated effectiveness of psychological and social interventions, relative to no explanation. By contrast, providing a psychologically- or socially-framed explanation had no systematic effect of the rated effectiveness of any type of intervention. Thus, we found support for P1 (choice of OPEN MIND: Discoveries in Cognitive Science 639 Biology, Society, or Choice Nettle et al. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / . / 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 (A) Mean rated effectiveness of different interventions (x axis) after explanatory framings Figure 5. belonging to different clusters (colours) have been offered, Study 3A. Error bars represent ± 1 stan- dard error of the mean. (B) Effect of providing a framing of a particular cluster (columns) on the rated effectiveness of a particular type of intervention (rows), relative to ‘no explanation’, Study 3A. Grey points and whiskers show standardized effect sizes and their 95% confidence intervals at the expla- nation cluster level. Superimposed black points are the standardized effect sizes when the individ- ual framing is used as the unit of analysis. explanation cluster affects rated effectiveness of interventions) in general, but P2 (framings enhance rated effectiveness of congruent interventions) and P3 (framings suppress rated effec- tiveness of incongruent framings) were only supported for ‘biological’ framings. Discussion Study 3A confirmed that the choice of explanation can matter for inferences about what kinds of interventions are potentially effective for changing the outcome. However, it was only bio- logical explanations that had clear implications for intervention effectiveness: providing a biologically-framed explanation both enhanced the perceived effectiveness of biological inter- ventions such as drugs; and, importantly, suppressed the perceived effectiveness of psycholog- ical and social interventions. These findings demonstrate the competitive nature of framings (Chater & Loewenstein, 2023): adopting a framing both facilitates cognition about interven- tions at the level of that framing, and discounts interventions at other levels. OPEN MIND: Discoveries in Cognitive Science 640 Biology, Society, or Choice Nettle et al. The lack of either enhancement or suppression effects when we provided psychological or sociocultural-type framings is perhaps puzzling. One possibility is that these are our partici- pants’ default-type explanations for the kinds of behaviours in our vignettes. Thus, providing explanations of these types did not change anything for our participants compared to provid- ing no explanation. Compatible with this possibility, rated effectiveness of social and psycho- logical interventions was higher overall than those of biological interventions, including in the ‘no explanation’ condition. Providing a ‘biological’ explanation may have caused the prag- matic inference that the default non-biological explanations did not apply here, making it quite natural to assume that their associated interventions did not work either. By contrast, providing an explanation with a ‘psychological’ or ‘sociocultural’ framing just confirmed assumptions people already held, including their prior assumptions about relevant ways of intervening. STUDY 3B Study 3A showed there can be competition between explanations with different framings, and that framings are linked to assumptions about what kinds of interventions will make a differ- ence: being given a biologically-framed explanation both increased the perceived effective- ness of a biological intervention, and reduced the perceived effectiveness of psychological and social interventions. Study 3B was an ancillary study, added to the questions of the Study 3A participants in the ‘no explanation’ condition, that tested a corollary of these findings. If ‘bio- logical’ and ‘non-biological’ explanations are perceived to stand in competitive exclusion, then the same might be true for interventions. That is, participants who learn that a biological intervention is effective in changing the behaviour might infer that other kinds of intervention will have no effect, whilst those who learn that a ‘biological’ intervention is ineffective at changing the behaviour might infer the opposite, that another interventional approach is required. In Study 3B, after participants had read the vignette and rated biological, psychological and social interventions for changing the behaviour, we gave them additional information that one of the intervention types had been tried and either had a big effect on the behaviour (effective condition) or had no effect on the behaviour (ineffective condition, between subjects). We then asked participants to rate whether they now thought the other two intervention types would be less effective than they previously estimated, more effective, or the same. We predicted that learning that a biological intervention is effective would reduce rated effectiveness of other kinds of interventions, and learning that a biological intervention is ineffective would increase rated effectiveness of other kinds of interventions. Methods Pre-registration. We pre-registered methods, materials, and planned analyses along with Study 3 at: https://osf.io/aqsz3. Participants. Participants from the ‘no explanation’ condition of Study 3 (n = 153). Design. Study 3B was a between-subjects three (intervention type) by two (effective or inef- fective) factorial design, with each participant reporting the dependent measure twice (once for each of the two intervention types other than the one mentioned). Stimulus. After completing the dependent measures of Study 3A, Study 3B participants were given the following information. OPEN MIND: Discoveries in Cognitive Science 641 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u o p m i / l a r t i c e - p d f / d o i / i / / . 1 0 1 1 6 2 o p m _ a _ 0 0 0 9 8 2 1 5 7 4 7 5 o p m _ a _ 0 0 0 9 8 p d . / i f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Biology, Society, or Choice Nettle et al. Scientists have discovered that [changing biology, for example through drugs/changing psy- chology, for example through thinking training/changing society, for example through better political representation] [makes no difference/substantially reduces] [the rate of homicide/the rate of teenage parenthood]. Dependent Measure. Participants were then asked: Does the information given above make you think that [changing psychology/changing biology/changing society] would be less effective in changing [the rate of homicide/the rate of teenage parenthood] than you previously thought, more effective, or no change? Two questions were displayed, for the two intervention types other than the one mentioned in the stimulus. Participants responded on a slider from ‘less effective’ to ‘more effective’, with ‘no change’ at the mid-point. Predictions and Analysis Plan. We pre-registered two predictions: P1. Learning that one class of intervention is ineffective will increase belief in the effective- ness of the other two classes of intervention. P2. Learning that one class of intervention is effective will decrease belief in the effective- ness of the other two classes of intervention.We specified that P1 and P2 might hold for some combinations of intervention classes but not others. We tested P1 and P2 with linear mixed models with intervention mentioned, intervention asked about, and effectiveness condition (effective or ineffective) as the predictors. We also used one-sample t-tests to examine whether average ratings deviated from ‘no change’ for particular combinations of intervention mentioned and intervention asked about. Results We fitted a linear mixed model with change in perceived effectiveness as the outcome vari- able, and predictors of intervention in question, intervention mentioned, and whether the mentioned intervention was described as effective or ineffective. There was a significant inter- action between intervention in question and intervention mentioned (F(1, 147) = 4.30, p = 0.04). All other interactions were non-significant (p values > 0.05). This suggests that learning
about the effectiveness or ineffectiveness of some interventions affects the perceived effective-
ness of some other interventions. To probe which ones affected which, we calculated the
méchant et 95% confidence interval of change in effectiveness for each combination of inter-
vention in question, intervention mentioned, and effectiveness or ineffectiveness (Chiffre 6).
Where the 95% confidence interval does not include zero, we can conclude that this class
of information significantly changes perceived effectiveness of that class of intervention. Comme
Chiffre 6 shows, learning that a psychological or social intervention is effective reduced the
perceived effectiveness of biological interventions. Curiously, learning that a psychological
or social intervention was ineffective did not increase the perceived effectiveness of ‘biolog-
ical’ interventions. Nor did learning that a biological intervention was effective reduced the
perceived effectiveness of psychological or social interventions. The only other significant shift
was that learning that a social intervention was effective increased the perceived effectiveness
of a psychological intervention. The converse did not hold.

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Study 3B results. Change in perceived effectiveness of an intervention (columns) par
Chiffre 6.
which other intervention had been mentioned, and whether it had been described as effective or
ineffective (rows). Points show the mean shift and whiskers the 95% Intervalle de confiance. Where the
95% confidence interval does not include zero, that category of information significantly shifts aver-
age perceived effectiveness.

Discussion

If different explanations suggest the effectiveness of different types of intervention, and different
types of explanation compete with one another, we reasoned that learning that one type of
intervention does (or does not) work might generate the inference that another type of inter-
vention will not (or will) do so. We found only partial evidence for such effects. Spécifiquement,
learning that a social or psychological intervention is effective led participants to reduce their
perceived effectiveness of a biological intervention. The converse did not hold: learning that a
biological intervention was effective did not reduce the perceived effectiveness of psycholog-
ical or social interventions. On the face of it, this is puzzling: a widespread concern in the
literature on psychological disorders, Par exemple, is the concern that publicity around phar-
maceutical treatments leads sufferers, clinicians and the general public to neglect the promise
of addressing their prevalence through psychological and particularly socioeconomic means
(Davies, 2021). Our results do not concur with this, though they do suggest the converse effect:
stressing the potential effectiveness of political and social approaches to psychological suffer-
ing would undermine the perception of effectiveness of drugs. A possible explanation is that,
for the particular behaviours described in our vignettes, our participants thought that psycho-
logical and social interventions were much more effective than biological ones at baseline (voir
Étude 3 résultats). Ainsi, learning that an intervention-class they regarded as weaker (biological)
was effective was not sufficient to diminish their belief in interventions they already regarded
as stronger; whereas learning that interventions of their favoured or default class were indeed
effective further inhibited the perceived effectiveness of the disfavoured class.

Learning that psychological and social interventions were ineffective did not increase the
perceived effectiveness of biological interventions. If anything, it tended towards decreasing it.
A possible explanation is the ineffectiveness of psychological and social interventions is a cue
that the behaviour is completely non-malleable. This is particularly true given that our partic-
ipants thought the psychological and social interventions to be the strongest available at base-
line. Ainsi, if even those do not work, it seems unlikely that a class of intervention perceived to
be weaker in the first place (biological) would do so. Ainsi, learning about effectiveness (le
behaviour is malleable, and responds to our best interventions) would not necessarily have the
mirror image of the consequences of learning about ineffectiveness (the behaviour is not

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malleable even with our best interventions). We also found that learning about the effective-
ness of a social intervention increased the perceived effectiveness of a psychological one, pre-
sumably because it provided evidence of malleability in general.

En résumé, Study 3B found some evidence that knowing that one class of intervention is
effective can undermine perceived potential of other kinds of intervention. Spécifiquement, ce
phenomenon only occurred across the biological/non-biological boundary: the effectiveness
of psychological and social measures undermined the perceived effectiveness of biological
measures like drugs, although the converse, did not hold.

GENERAL DISCUSSION

These studies have charted the mental maps of different framings held by our participants, et
begun to probe the inferences they make from the choice of one framing over another.
Although all three studies had pre-registered predictions and designs, we describe this work
as primarily exploratory rather than testing a strong theory or single causal model. We remind
the reader that our participants were not professional academics and mostly not students.
Although not a representative sample of the UK population, their responses are likely to be
a reasonable guide to how different types of explanation are understood in the non-expert
UK population at large.

Key Findings and Their Relation to Existing Literature

Our participants perceived family resemblance between certain kinds of explanations. Specif-
ically, they produced, unguided by us, ‘biological’, ‘psychological’ and ‘sociocultural’ clusters
of explanations (Étude 1). They rated the constituent explanations of each of these groupings to
be relatively similar to one another, and more different from the members of other groupings.
These clusters do map onto groupings of explanations that have been proposed in the literature
before. Par exemple, our biological and psychological clusters map respectively onto the ‘bio-
genetic’ and ‘psychological’ of Haslam and Kvaale (2015), and our sociocultural cluster
appears to correspond to social-structural explanation (in the sense explored by Vasilyeva
et coll., 2018; Vasilyeva & Lombrozo, 2020). The difference is that our methodology in Study
1 allowed these groupings to emerge from participant responses, rather than us assuming their
existence a priori.

Our participants were certainly not complete ‘incompatibilists’. C'est, in Study 1, ils
perceived many cases where, given the truth of an explanation with one framing, it was pos-
sible at least to some extent for an explanation with a different framing also to be true. Comment-
jamais, we were struck by the substantial degree of incompatibility that they perceived amongst
certain combinations of explanations. En particulier, the more dissimilar they perceived two
explanations to be, the more incompatible they felt them to be. As biological explanations
were perceived as the most dissimilar from others, that meant they were also rated as the most
incompatible with others. Ainsi, it seems that the non-expert conception of whether two expla-
nations are compatible is simply whether they seem similar. We return later in the discussion to
how this departs from the scientific conception of compatibility. We note here that it gives rise
to the possibility of ‘framing discounting’, similar to the explanation discounting that has been
studied elsewhere (Heussen, 2010; Kelley, 1973; Sloman, 1994). C'est, the acceptance or
availability of an explanation with one framing has the potential to make explanations with
different framings less plausible or compelling.

Provided with an explanation, our participants made corresponding inferences about the
locus of causation of the behaviour, and about malleability (Étude 2). Both ‘biological’ and

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‘psychological’ explanations led participants to infer that the causation is internal to the indi-
vidual rather than external. Cependant, ‘biological’ explanations were taken to imply that the
behaviour is not malleable, whereas ‘psychological’ explanations implied a high degree of
malleability. This finding echoes findings in the literatures on gender differences, and psycho-
logical disorders (among other topics, see Dar-Nimrod & Heine, 2011). Framing gender differ-
ences biologically produces the inference that they are relatively immutable, compared to
framing them socially (Brescoll & LaFrance, 2004). A ‘biogenetic’ conceptualization of psy-
chological difficulties encourages the view that those disorders are essential features of their
sufferers, and not easy to change (Berent & Platt, 2021un; Haslam & Kvaale, 2015; Lebowitz
et coll., 2013). On the other hand, ‘psychological’ framings are sometimes ‘cruelly optimistic’:
they seem to imply that change is easier than it actually is, especially when persistent socio-
structural factors are integral to the actual network of causes (Brickman et al., 1982; Chater &
Löwenstein, 2023).

Étude 3 provided some evidence for an intuitive principle that ‘the medicine must fit the
cause’. C'est, the interventions that seem most likely to be effective are congruent to the
framing of the causes of the behaviour. Spécifiquement, providing a ‘biological’ explanation for
a phenomenon both boosted the perceived effectiveness of intervening biologically, tel que
through drugs, and reduced the perceived effectiveness of psychological and societal change
(Study 3A). De plus, providing evidence that psychological or societal interventions were
effective reduced the perceived likelihood of a biological intervention working (Study 3B).
These effects are readily interpreted in the light of the literature on the cognitive function of
causal explanation. Since causal explanations guide the search for ways of acting to make the
world different (Quillien, 2020; Woodward, 2003), framing the causes in a certain way will
naturally activate the search for interventions at the level of that framing.

Origins of Observed Patterns

Having studied just one population, we cannot say how widespread or robust these cognitive
patterns are. They could represent no more than the influence of a particular set of discursive
pratiques, cultural and religious histories, or educational practices in the UK. Cependant, it is
worth investigating the hypothesis that they represent more recurrent and reliably developing
features of human minds in general. En particulier, we suggest that the key findings might be
explained by rooting them in two bodies of existing work: core cognition (par exemple., Jeux &
Kinzler, 2007) and relevance theory (Sperber & Wilson, 1995).

Human core knowledge embodies domain-specific principles and priors. These are differ-
ent for the domains of biology, psychologie, and social relations, giving rise to distinct intuitive
theories in each of these areas (Jeux & Kinzler, 2007). Activation of one these theories will
feel different from activation of the others, and possibly incommensurate, giving rise to the
perception of incompatibility. Different intuitive theories have different operating principles.
Par exemple, positing fixed and typical essences is central to intuitive biology (Atran, 1998;
Linquist et al., 2011; Machery et al., 2019), whereas tracking and changing transient mental
states is central to intuitive psychology (Ho et al., 2022). The different inferences about mal-
leability when a biological versus a psychological explanation is offered may follow directly
from these different operating principles.

Domain-specific intuitive theories interact with pragmatic principles when explanations are
framed. When a speaker offers an explanation, they are aiming not just to utter a true causal
statement, but to draw the hearer’s attention to the kinds of actions that could have most easily
made a difference to the outcome (Hilton, 1990; Quillien, 2020). The default intuitive theory

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to bring to bear on human behaviour is probably intuitive psychology, and the default assump-
tion for human competences appears to be that they are malleable ( Wang & Feigenson, 2019).
If a speaker chooses a different framing (Par exemple, a genetic or neuroscientific one), le
hearer can use pragmatic principles (Sperber & Wilson, 1995) to infer that this is because that
speaker believes that the default framings do not work well in this case, and is guiding the
hearer to look elsewhere. Ainsi, the speaker’s mere adoption of a framing can be taken as
some evidence that they believe the most obvious assumptions (par exemple., of malleability) do not
apply, and the most obvious interventions (par exemple., social or psychological ones) would not be
good. This could explain the effects in Study 3A, where the adoption of a ‘biological’ expla-
nation both enhanced the perceived efficacy of interventions like drugs, and suppressed the
perceived efficacy or social or psychological interventions.

The claims of this section—that the patterns we observe arise from the structure of core cog-
nition, combined with pragmatic communicative principles—are speculative. To test them
would require additional kinds of data. There are already bodies of research on the emergence
of distinctively biological, psychologique, and sociological modes of thought in childhood
(Thomas et al., 2022; Wellman & Gelman, 1992), and these could be extended to thought
about compatibility, malleability, and interventions. De même, the cross-cultural generality of
these patterns should be investigated. A possible model is the recent work on the distinction
between psychological experience (‘mind’) and bodily sensation (‘body’) in different cultures
( Weisman et al., 2021). The authors found a robust distinction between properties of minds and
properties of bodies across adults and children in five cultures, but also subtle differences
between cultures in where ambiguous properties like emotion were placed.

Accuracy and Implications for Scientific Communication

We have conceptualized the compatibility and malleability findings as arising from pre-scientific
intuitive theories. Does this mean that our participants’ judgements were wrong? The present case
is not comparable to the study of intuitive theories of motion, where there is, for each stimulus, un
actual motion path to which participants’ intuitions can be compared (McCloskey et al., 1980).
Our vignettes and explanations are fictional, so the true answers are not defined. We cannot
therefore show that our participants erred. En effet, for everyday purposes, it might work well
to divide the world into some phenomena that need to be thought of biologically, some psycho-
logically, and some structurally, and have different priors about each of these classes. Cependant,
there are many complex phenomena where scientists wish to pursue the causal processes at mul-
tiple levels. Our findings suggest characteristic ways that science may be misunderstood.

D'abord, participants treated all ‘biological’ explanations (genes, hormones, evolution) as largely
interchangeable and with similar implications for malleability and interventions. But not all ‘bio-
logical’ explanations are alike. Positing a neural or hormonal basis for a behaviour does not in
any way imply the belief in a genetic basis. Previous literature has also shown that hearers infer
genetic transmissibility from any mention of a proximal neural mechanism (Berent & Platt,
2021un). This is logically a non-sequitur: a researcher could coherently believe that depression
has a characteristic neural mechanism, and yet is still distally caused by, Par exemple, poverty,
and not genetically heritable. Our findings suggest that researchers who frame their explanations
in terms of one type of ‘biology’ (adaptive consequences, neural substrate) could often find their
claims mistakenly assumed to be about some of other type of ‘biology’ (par exemple., genetic heredity).

Deuxième, ‘hybrid’ or ‘multilevel’ accounts of causality will be hard for people to understand,
and may often be misunderstood. Par exemple, the evolutionary psychology paradigm char-
acterizes human behaviour as arising from the interaction between current social

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environments and flexible evolved cognitive mechanisms (Tooby & Cosmides, 1989). Comment-
jamais, it is often mischaracterized, even in the scholarly literature and by some adherents, comme
claiming that human behaviour is innate and inflexible (‘hard-wired’) and the current environ-
ment causally unimportant (Frankenhuis et al., 2013; Nettle & Scott-Phillips, 2023). De même,
there is a literature on the effects of socioeconomic disadvantage on the developing brain
(Hyde et al., 2022). This is integrative research that pursues causal chains from their social-
structural origins through to proximal neural mechanisms, via the psychological mediating
variables involved in parenting. Contributors to this literature encounter two hazards. Sur
the one hand, they have been accused of genetic determinism, even eugenicism, because they
are taken to be positing some inherent and immutable biological deficiency in people who live
in poverty (Tolwinski, 2019). On the other, they have been accused of blaming impoverished
parents for not taking good enough care of their children, when the real culprits are structural
(Hyde et al., 2022). C'est, the researchers are truly studying social-structural factors and their
consequences, but because they measure biological variables like brains, they are misunder-
stood as making claims about non-malleable, heritable essences; and because they measure
psychological variables such as caregiving, they are misunderstood as saying that low-income
parents just need to behave differently. The researchers counter these misunderstandings with
an active strategy, ‘plasticity talk’ (Tolwinski, 2019). This is the deliberate and repeated insis-
tence that the brain, although a biological organ, is malleable and is both directly and indi-
rectly influenced by what is going on in society.

The idea of ‘plasticity talk’ leads us to our final point about scientific communication. Notre
findings suggest that pluralism about causes (causes can be identified at multiple compatible
levels), and pluralism about interventions (being able to identify a substrate at a biological
level does not mean the only possible intervention is at that level) are not completely intui-
tively obvious. Cependant, people can presumably appreciate them if they are actively and
clearly pointed out. Ce, we contend, explains the continued market for papers and books
that set out ways of integrating multiple levels of causality, and avoiding fruitless dichotomies
such as immutable nature versus formless nurture (Mameli & Bateson, 2011; Marr, 1982;
Nettle, 2009; Tinbergen, 1963). C'est, active ‘plasticity talk’ and ‘plurality talk’ continue
to be required, in order to counteract the cognitive attractors formed by the separateness of
intuitive biological, psychological and sociocultural clusters. We would recommend any
researchers whose work spans causal levels, or seeks internal substrate of structural causes,
to actively anticipate likely misunderstandings and use appropriate pre-emptive talk.

Societal Implications: Framing Contests

If framings partially compete with one another, and guide the cognitive search for interven-
tion, it is in the interests of any party favouring a certain class of intervention to advocate the
framing that suits their interests. Such ‘framing contests’ have long been studied in communi-
cation studies (Dan & Raupp, 2018; Entman, 1993). Par exemple, more conservative newspa-
pers, in reporting research on gender differences, privilege biological explanations, because
these suggest non-malleability of traditional roles (Brescoll & LaFrance, 2004). The food
industry prefers to frame obesity in terms of psychological traits or lack of knowledge, lequel
suggests interventions to inform and educate consumers ( Jenkin et al., 2011). Adopting this
framing rather than a social-structural one pre-empts reflection on regulation and taxation
interventions that might be unwelcome to these companies. Pharmaceutical companies
strongly promote the framing of psychological problems as ‘diseases of the brain’, which nat-
urally privileges drug intervention (Davies, 2021; Moncrieff, 2007; Read & Cain, 2013). Chater
and Loewenstein (2023) argue that behavioural public policy, by focusing on individual-level

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‘nudges’, has become effectively complicit with interests that want to avoid social-structural
reform (though for a rebuttal of this argument, see Hallsworth, 2023).

The existence of the literatures discussed in the previous paragraph shows that controlling
the framing is often an important goal, since it subtly directs the discourse on what if anything
could be done. The resistance encountered by brain researchers who study the consequences
of socioeconomic disadvantage is presumably not due to activists literally disbelieving that
poverty could affect the brain. Instead it seems to be attributable to the fear that, the moment
we are foregrounding brains, we are no longer foregrounding structural inequity (Tolwinski,
2019). The ‘plasticity talk’ used by these researchers is employed to show, counterintuitively,
it is by talking about brains that we can demonstrate just how serious structural inequity is.

Malleability

We conclude with a final question: how easily can the cognitive patterns we have described
can be overcome. Formal training in the social and behavioural sciences involves teaching
ways of holding multiple explanations in mind simultaneously. Examples include Marr’s three
levels in cognitive science (Marr, 1982), Tinbergen’s four questions in behavioural biology
(Tinbergen, 1963), and Giddens’ structuration theory in sociology (Giddens, 1984). To our
connaissance, the extent to which that training succeeds has not been studied. Cependant, là
is some related research in the domain of psychological disorders. Sufferers from depression
become pessimistic about their prognosis when their problems are framed as ‘biological’
(because biological implies non-malleable). Lebowitz et al. (2013) employed a brief audio-
visual intervention explaining how brains respond dynamically to environmental inputs. Voir-
ing this intervention reduced prognostic pessimism amongst depression sufferers who
endorsed biogenetic explanations, presumably because it broke the intuitive link between
‘biology’ and ‘non-malleable’. It would be interesting to study whether such interventions have
a more general effect on people’s perceptions of compatibility, and implied malleability, quand
biological explanations are given. If successful, such work could suggest pathways to facilitat-
ing the acceptance of explanatory pluralism.

REMERCIEMENTS

The authors thank Emma Bridger and Eva Wittenberg for their feedback, and Coralie Chevallier
and the team Evolution et cognition sociale at the Institut Jean Nicod for their support.

CONTRIBUTIONS DES AUTEURS

Contributed to conception and design: DN, WEF, KP. Led the acquisition and analysis of data:
DN. Contributed to interpretation of data: DN, WEF, KP. Drafted and/or revised the article,
and approved the submitted version for publication: DN, WEF, KP.

INFORMATIONS SUR LE FINANCEMENT

DN’s research is supported by the EUR FrontCog grants ANR-17-EURE-0017 and ANR-10-
IDEX-0001-02 to Université PSL; and ANR grant ANR-21-CE28-0009. WEF’s contributions
have been supported by the Dutch Research Council ( V1.Vidi.195.130) and the James S.
McDonnell Foundation (https://doi.org/10.37717/220020502).

DATA AVAILABILITY STATEMENT

All data and code are available at: https://osf.io/wte2c/.

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