Learning via instructions about observations: Exploring similarities and differences with learning via actual observations

Abstract

Our behaviour toward stimuli can be influenced by observing how another person (a model) interacts with those stimuli. We investigated whether mere instructions about a model’s interactions with stimuli (i.e., instructions about observations) are sufficient to alter evaluative and fear responses and whether these changes are similar in magnitude to those resulting from actually observing the interactions. In Experiments 1 (n = 268) and 2 (n = 260), participants either observed or read about a model reacting positively or negatively to stimuli. Evaluations of those stimuli were then assessed via ratings and a personalized Implicit Association Test. In Experiments 3 (n = 60) and 4 (n = 190), we assessed participants’ fear toward stimuli after observing or reading about a model displaying distress in the presence of those stimuli. While the results consistently indicated that instructions about observations induced behavioural changes, they were mixed with regard to whether instructions were as powerful in changing behaviour as observations. We discuss whether learning via observations and via instructions may be mediated by similar or different processes, how they might differ in their suitability for conveying certain types of information, and how their relative effectiveness may depend on the information to be transmitted.

Full text

royalsocietypublishing.org/journal/rsos
Research
Cite this article: Kasran S, Hughes S, De
Houwer J. 2022 Learning via instructions about
observations: exploring similarities and differences
with learning via actual observations. R. Soc. Open Sci.
9: 220059.
https://doi.org/10.1098/rsos.220059
Received: 24 June 2021
Accepted: 28 February 2022
Subject Category:
Psychology and cognitive neuroscience
Subject Areas:
psychology
Keywords:
observational learning, learning via instructions,
evaluative learning, fear conditioning,
propositional models
Author for correspondence:
Sarah Kasran
e-mail: sarah.kasran@ugent.be
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.c.
5899168.
Learning via instructions about
observations: exploring
similarities and differences
with learning via actual
observations
Sarah Kasran, Sean Hughes and Jan De Houwer
Department of Experimental Clinical and Health Psychology, Ghent University, Gent, Belgium
SK, 0000-0002-3119-7630; SH, 0000-0001-7689-4272;
JDH, 0000-0003-0488-5224
Our behaviour toward stimuli can be influenced by observing how
another person (a model) interacts with those stimuli. We
investigated whether mere instructions about a model’s
interactions with stimuli (i.e. instructions about observations) are
sufficient to alter evaluative and fear responses and whether
these changes are similar in magnitude to those resulting from
actually observing the interactions. In Experiments 1 (n= 268)
and 2 (n= 260), participants either observed or read about a
model reacting positively or negatively to stimuli. Evaluations of
those stimuli were then assessed via ratings and a personalized
implicit association test. In Experiments 3 (n= 60) and 4 (n=
190), we assessed participants’fear toward stimuli after
observing or reading about a model displaying distress in the
presence of those stimuli. While the results consistently indicated
that instructions about observations induced behavioural
changes, they were mixed with regard to whether instructions
were as powerful in changing behaviour as observations. We
discuss whether learning via observations and via instructions
may be mediated by similar or different processes, how they
might differ in their suitability for conveying certain types of
information, and how their relative effectiveness may depend on
the information to be transmitted.
1. Introduction
For decades now, learning research has documented the conditions
under which regularities in the presence of events influence
behaviour (for reviews, see [1,2]). For instance, studies on fear
conditioning show that when a neutral conditioned stimulus (CS;
© 2022 The Authors. Published by the Royal Society under the terms of the Creative
Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits
unrestricted use, provided the original author and source are credited.

e.g. a light) is paired with an aversive unconditioned stimulus (US; e.g. an electric shock), the CS
subsequently elicits a fear response when presented on its own (conditioned response or CR). Similarly,
research on evaluative conditioning shows that pairing a neutral CS (e.g. an unknown brand) with a
positively or negatively valenced US (e.g. a picture of a cute kitten or a picture of a cockroach) changes
people’s evaluative responses to the CS (e.g. a brand paired with kittens is liked more than a brand paired
with cockroaches; see [3]).
Most learning research has been conducted in non-social contexts and with non-social stimuli.
However, research has also looked at how our behaviour changes when we observe a social agent (a
model) interact with the environment. Starting with seminal work on observational conditioning in
rhesus monkeys by Mineka et al. [4], a wealth of human and non-human studies now show that the
behaviour of a model can function as a ‘social’US that influences responses toward a CS that is
paired with it. For example, an observer might see that another person behaves fearfully (US) in the
presence of a novel animal (CS; e.g. [5]) or shows a painful expression (US) whenever a specific cue
(CS) is presented (e.g. [6]). As a result, the CS later evokes a fearful response in the observer (i.e.
observational fear conditioning; for reviews, see [7,8]). Threat-related behaviour in a model can also
serve to reinstate fearful responses that were previously extinguished via direct experience [9].
Likewise, evaluative responses can be influenced by observing a model’s behaviour as well (i.e.
observational evaluative conditioning). For instance, people can come to like or dislike a novel person
(CS) by simply observing how someone else behaves non-verbally (US) toward that person (e.g.
[10–13]; for related findings, see [14,15]). This phenomenon is by no means limited to humans: for
example, monkeys can quickly and flexibly come to prefer certain stimuli as a result of observing the
behavioural choices of another monkey (for a recent demonstration, see [16]). Put simply, both human
and non-human animals can change their behaviour as a result of regularities in the presence of
(social) events.
At the same time, humans do not only change their behaviour after observing others but also when
they receive from others verbal information or instructions about the presence of events in the environment.
Rather than experiencing that a stimulus is followed by an electric shock, people can simply be told about
the stimulus–shock relationship. More than 80 years’worth of research shows that such an instruction
can give rise to fear responding (even when no shock is ever administered; see [17] for a recent
review). Similarly, instructions about upcoming CS–US pairings can also change how much
participants like or dislike the CS (e.g. [18,19]). In addition to learning via direct experience and
learning via observation, humans thus have access to a third ‘learning pathway’[20], namely learning
via instructions.
Surprisingly, as far as we know, all studies on learning via instructions have focused on verbal
information about the presence of events in the environment (e.g. ‘The name of this cookie will be
paired with a positive word’). In principle, however, one could also give verbal information about
observations, that is, about how a model interacts with the environment (e.g. ‘This person ate
this cookie and showed a positive reaction’). Examining the effects of such instructions about
observations could not only lead to knowledge about a learning experience that may play an
important role in shaping behaviour (i.e. hearing about other people’s experiences) but would also
allow us to gather more information about how different learning pathways (in this case, learning via
observations and learning via instructions) relate to each other in terms of their moderating factors and
mediating processes. In developing this line of research, we took inspiration from prior research that
compared the effects of receiving verbal information about stimulus pairings with the effects of
actually experiencing pairings. In the next section, we therefore highlight some insights provided by that
prior research.
1.1. Comparing the effects of pairings and instructions about pairings
Although surprising parallels have been found between studies on the effects of actual pairings and
studies on the effects of receiving instructions about pairings (see [17] for a review), only a handful of
studies directly compared the two within a single study. Most of these studies compared the
magnitude of the behavioural changes resulting from these two learning pathways. Some of them
suggest that actually being exposed to pairings may have a stronger impact than merely receiving
instructions about them (e.g. [21]) and that experiencing pairings after they have already been
described has an additive effect on behaviour (e.g. [22,23]). However, other studies suggest that
instructions about pairings can be at least as effective, or even more so, than actual pairings (e.g.
[19,24,25]). For example, Kurdi & Banaji [19] conducted a series of studies wherein participants either
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experienced CS–US pairings, read a description of them, or first read the description and then actually
encountered the pairings. They found that instructions led to changes in evaluative responding that
were equal or even superior to those resulting from the actual pairings. They also found no benefits of
exposure to the pairings if participants had already been informed about them. A similar study in
children led to an even more striking result: instructions about pairings had the expected effects,
whereas no effects emerged when pairings were presented in the absence of such instructions [26].
These and related studies have attracted much attention because of their theoretical and practical
implications. On the practical side, it can be interesting to know whether certain learning pathways
are more ‘powerful’in shaping behaviour than others (e.g. lead to more intense behavioural responses
or responses that are more resistant to change), as this could help to optimize the effectiveness of
behavioural interventions. On the theoretical side, the findings can inform cognitive theories of
learning, because such theories often include assumptions in terms of the cognitive processes
mediating different pathways and in some cases make different predictions regarding the similarity of
the resulting effects.
With regard to the latter point, certain single-process associative theories argue that both directly
experienced events and instructions can lead to the formation of associations between memory
representations of stimuli that (are said to) occur together (e.g. [27–29]). Once a CS–US association has
been established, the presentation of the CS not only leads to the activation of its representation, but
this activation also spreads to the representation of the US, triggering a CR. It is not clear, however,
whether these theories predict differences in the extent to which pairings and instructions result in
CS–US associations and thus CRs. In contrast, a single-process propositional perspective assumes that
all learning pathways are mediated by the formation and truth evaluation of propositions [30,31].
Propositions differ from associations in a number of ways. First, they are defined in terms of their
informational content: they can specify the exact nature of the relation between events. For example,
relatively weak fear responding to a blue square could be the result of a participant having formed
the proposition that ‘the blue square sometimes predicts a mild electric shock’, which encodes
information about the events themselves (blue square, mild electric shock) as well as about the
specific relation between them (sometimes predicts). Second, the holder of a proposition can evaluate
the extent to which he or she considers this proposition to be true (i.e. evaluate its truth value). Third,
propositions can be used in inferential reasoning (i.e. combined with other propositions to generate
new propositions). Finally, the propositional perspective assumes that propositions can be based on a
wide range of experiences. Therefore, different pathways can result in the same behaviour change,
provided that the content of the information conveyed by those pathways is similar and similar
propositions can therefore be formed [32]. Hence, when applied to the comparison of the effects of
pairings and instructions about pairings, the propositional perspective predicts that if instructions
about pairings convey the same information as actually experienced pairings, the change in behaviour
should also be similar.
Other theories assume that multiple processes can drive learning effects. For example, Olsson &
Phelps [8] proposed that fear responses can be based on CS–US associations in the amygdala that do
not require conscious processing, as well as on associations represented in a distributed network of
cortical areas that do involve conscious processing. Whereas the theory assumes that fear conditioning
can be mediated by both, learning via instructions is considered to depend exclusively on the latter
process and to be subject to certain boundary conditions that do not apply to conditioning (such as
conscious awareness of the CS). Some dual-process theories of fear learning also make a strong
distinction with regard to the type of outcome of different processes, assuming that amygdala-based
associations produce automatic responses (such as physiological responses), whereas self-reports of
fear would be produced by different mechanisms (e.g. [33]). The theory of Olsson and Phelps does
not make such a strong distinction. Instead, it assumes that cortical associations created by verbal
instructions can also influence physiological responses, be it in a way that is more indirect and
therefore less powerful than the impact of amygdala-based associations that are created by repeated
pairings. Taken together, dual-process theories of fear responding would seem to predict that
instructions should have weaker effects on automatic fear responses than actual pairings.
Similarly, to explain evaluative responses, dual-process theories often distinguish between associative
and propositional mechanisms, assuming that these vary in their sensitivity to specific experiences
(pairings versus instructions) and their influence on evaluative responses measured under conditions
suboptimal for cognitive processing (often referred to as automatic evaluations). Again, some theories
make a strong distinction. For example, Strack & Deutsch [34] proposed that instructions can influence
evaluations only via a propositional system that requires sufficient cognitive capacity, thus predicting
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that instructions should not influence automatic evaluations. Other theories merely assume that
automatic evaluations are less sensitive but not impervious to verbal instructions [35] or that
automatic evaluations are only indirectly influenced by instructions via the influence of the
propositional process on the associative process [36]. Given these assumptions, dual-process theories
of evaluative learning would predict instructions to generally have weaker effects on automatic
evaluations than pairings.
In sum, although not all available theories can be used to derive straightforward predictions,
empirical knowledge about similarities and differences between the effects of actual pairings and
instructions about pairings can inform and constrain theorizing about the cognitive processes that
mediate these two learning pathways.
1.2. Comparing the effects of observations and instructions about observations
Against this backdrop of research comparing the effects of actual pairings and instructions about
pairings, we examined for the first time what happens when instructions about observed events are
provided. That is, we informed participants about regularities between stimuli and a model’s
behaviour as they would be encountered during an observational conditioning procedure. If such
instructions about observations induce clear behavioural changes, this would be an interesting finding
as such because it would demonstrate the impact of a highly indirect learning experience. That is,
instructions about observations can be seen as one indirect learning pathway (i.e. instructions)
providing information about another indirect learning pathway (i.e. observations). Demonstrating
their effects would therefore further highlight the remarkable capacity of humans to learn in an
indirect manner (i.e. in the absence of any direct personal experiences).
Additional important information can be gained from directly comparing these effects to the effects of
actual observations. Similar to how studies which compared learning via pairings and learning via
instructions about pairings can inform theories that include assumptions about the processes
mediating these two types of learning (see above), comparing learning via observations and learning
via instructions about observations may inform theories that include assumptions about how the
mechanisms driving observational learning relate to those driving instructed learning. Although
not all of the theoretical perspectives discussed earlier include explicit assumptions about the
observational learning pathway, some do. Single-process perspectives assume that all pathways,
including learning via observations, are mediated by the same process (although only the
propositional perspective clearly predicts that their effects should therefore be similar as long as
the information is the same). In contrast, the theory of Olsson & Phelps [8] assumes that the processes
mediating observational fear conditioning partially diverge from those mediating instructed fear
learning and are instead highly similar to the processes involved in direct fear conditioning (with the
exception that social cognition mechanisms are likely to be involved in observational learning).
Finally, a related distinction that has recently been highlighted in this context is the computational
distinction between model-based and model-free learning. The former involves an internal model of
the environment
1
that can be updated instantly (similar to how the propositional perspective assumes
that propositions about relations between events can be changed based on a single instruction); the
latter does not involve such a model and thus requires (additional) learning trials in order for
behaviour to change. Whereas learning via observation has been assumed to depend mostly on
model-free learning (especially in simple cases) and only in some instances on model-based learning,
learning via instructions would always seem to require model-based learning [37].
In sum, once again the available theories seem to be divided into single-process and dual-process
perspectives. Comparing the effects of instructions and observations may inform these theories. For
example, although most dual-process theories would likely still be able to explain these two pathways
having highly similar effects, such a finding would be more in line with a single-process perspective.
While there have been a few studies that included a comparison between observations and
instructions [6,25], the informational content always differed between the two pathways (i.e. the
instructions were about future direct experiences rather than about a model’s reactions to stimuli),
meaning that any discrepancies in their effects could have been due to the difference in content rather
than due to different processes being at play.In contrast, we conducted a more direct comparison
because the instructions described the same information as one would encounter during an
observation phase. This also more closely parallels the earlier comparison between directly
1
Not to be confused with the ‘model’in the sense of the social agent observed during observational learning.
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experienced pairings and instructions about those pairings, where the instructions are generally designed
to convey the information encountered in the direct experience condition. In sum, by documenting the
similarities and differences between learning via observations and learning via instructions about
observations and exploring potential moderators of those differences, the outcomes of the current
direct comparison may be able to inform theorizing about the similarity of the underlying processes.
1.3. The current research
Across four experiments, we tested whether instructions about observations would lead to behavioural
changes in terms of evaluative (Experiments 1 and 2) and fear responses (Experiments 3 and 4). As the
introduction illustrated, theoretical perspectives mostly differ in their predictions regarding the effect of
instructions on responses that are more automatic in nature. Therefore, we included not only self-reports
but also a measure of automatic evaluations in Experiments 1 and 2 (a personalized implicit association
test ( pIAT); [38]) and a physiological index of fear in Experiment 3 (skin conductance responses (SCRs)).
In addition, we compared the effects of instructions about observations with the effects of actually
observing the events. This allowed us to look at similarities and differences between the two
pathways as well as potential moderators of those differences, which could have both theoretical and
practical implications. To guide our research, we explored predictions of a propositional perspective.
Specifically, to the extent that actually observed events and instructions about those observed events
can be expected to result in the same or a similar proposition being formed and considered valid, this
perspective predicts that both pathways would have a similar impact, regardless of whether self-
reports or more automatic responses are measured.
2
For all experiments, we registered our hypotheses, planned sample size, procedural details and
planned analyses on the Open Science Framework (Experiment 1: https://osf.io/hgcfy/; Experiment
2: https://osf.io/9v3cm/; Experiment 3: https://osf.io/7yt9z/; Experiment 4: https://osf.io/uw3q2/).
These registrations were followed unless otherwise specified. Materials, raw data, processed data and
all R code used for data processing and analysis are available on the OSF page (https://osf.io/ay25z).
2. Experiment 1
Experiment 1 focused on evaluative responses and was designed to examine three questions. First, do
instructions about observations lead to changes in evaluative responding? Second, are these changes
in evaluative responding as large as those that result from actual observations? Third, given that CS–
US pairings have previously been found to have no additive effect when participants had already
been informed about these pairings [19], is there an added value of actually observing the regularities
after receiving instructions about them?
Participants were divided into three groups. In the ‘observations’condition, participants watched
videos of a model who tasted two cookies (referred to as ‘Empeya’and ‘Plogo’) and reacted positively
to one cookie (the positive CS; CS
pos
) and negatively to the other (the negative CS; CS
neg
). We then
assessed their evaluations of both cookies by asking them (i) to provide ratings of how much they
expected to like each cookie and (ii) to complete a pIAT [38], a task that assesses automatic
evaluations based on the speed (reaction time) with which participants can categorize target stimuli
(in this case, the names of the two cookies) using the same keys as liked or disliked words.
The observations group was compared with two other groups. In the ‘instructions’condition,
participants were told that they would later watch videos in which a person tasted cookies and were
informed which reaction the person would show to each cookie. Critically, however, they never
watched those videos. Finally, in the ‘combined’condition, participants first received the
aforementioned instructions and then watched the videos. Based on a propositional perspective, we
2
As may have become clear from ourearlier discussion, the propositional perspective is quite broad and not formalized (hence the term
‘perspective’). As a result, it is difficult (if not impossible) to derive predictions that would allow one to unequivocally falsify this
perspective (see also [32,39]). We do not consider this to be a problem for the current research because our goal was not to falsify
any given theoretical perspective (in fact, as one reviewer pointed out, there are strong similarities between the information
assumed to be encoded in propositions and the information assumed to be contained in the internal model of the environment in
model-based reinforcement learning (e.g. [37]). Rather, we set out to document an interesting phenomenon and to generate
empirical knowledge about this phenomenon, which could have practical implications as well as inform future theoretical thinking
(in the sense that relevant theories would need to take the generated findings into account). The propositional perspective simply
served as a guide to give the current research direction.
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predicted that all three conditions would have a similar impact on evaluations of the cookies (with the
CS
pos
being evaluated more positively than the CS
neg
).
2.1. Method
2.1.1. Participants and design
Based on a power analysis indicating that a total sample size of n= 258 was required in order to have 80%
power to detect medium-sized differences in follow-up comparisons (with α= 0.016 to correct for
multiple comparisons), and that we estimated around 10% exclusions based on prespecified pIAT
performance criteria, we planned to recruit a sample of n= 288 participants via Prolific Academic
(https://www.prolific.co/). Slightly more participants completed the experiment due to a server lag,
resulting in complete data for n= 292 participants (177 men, 113 women, 2 non-binary people; M
age
=
28.70, s.d.
age
= 7.96).
We used a between-subjects design with three levels for acquisition type: observations, instructions
and combined. Stimulus assignment (whether Empeya or Plogo served as the CS
pos
), task order
(whether participants first completed the ratings or the pIAT) and pIAT block order (whether
participants first completed the learning-consistent or the learning-inconsistent block of the pIAT)
were counterbalanced across participants.
2.1.2. Materials
2.1.2.1. Videos
Two source videos were used, one showing a positive reaction and one showing a negative reaction. In
both videos, the model (a 23-year-old man) took a cookie from a plate, took a bite and displayed a
positive or negative reaction for approximately 5 s. A label placed next to the plate clearly showed the
name of the cookie (i.e. Empeya or Plogo). These two source videos were selected from a larger set of
videos based on pre-ratings in terms of valence and believability that were obtained in preparation for
another study (pre-rating materials and data are available at https://osf.io/4vbxz/). The two source
videos were edited to vary the name on the label in order to counterbalance stimulus assignment.
2.1.2.2. Personalized implicit association test
The target stimuli used in the pIAT consisted of six versions of each CS name (in lower- or upper-case and
regular, bold or italic font) presented in Arial. The two CS names (Empeya and Plogo) served as labels for
classifying these stimuli into the two categories. The attribute stimuli were six positive (Pleasure, Holidays,
Rainbows, Gifts, Peace and Friends) and six negative (Sickness, Accidents, Abuse, Death, Fear and Pain) words
in Arial Black. The words ‘I like’and ‘I dislike’served as labels for classifying these attribute stimuli.
2.1.3. Procedure
The experiment was programmed in Inquisit 4.0 and hosted via Inquisit Web (Millisecond Software,
Seattle, WA). After providing demographic information, all participants were told that we were
working with a start-up company that produced two new cookies (referred to as Empeya and Plogo)
and that we had recorded videos of a person who was asked to eat these cookies and to clearly
display whether he liked or disliked them. Participants then proceeded to the acquisition phase,
completed the evaluative measures and answered a number of exploratory questions.
2.1.3.1. Acquisition phase
The acquisition phase depended on the between-subjects manipulation (figure 1 shows an overview of
the different acquisition types). In the observations condition, participants watched one video in which
the model reacted positively to the CS
pos
by showing a facial expression of enjoyment and taking a
second bite, and a second video in which the model reacted negatively to the CS
neg
by displaying
disgust via his facial expression and body language. Both videos were presented three times in a
random order with an inter-trial-interval (ITI) of 3 s.
In the instructions condition, participants were told that later on in the experiment, they would watch
two videos several times. They were then given a description of the regularities, depending on the
counterbalanced stimulus assignment. For example, participants for whom Empeya served as the CS
pos
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read the following instructions: ‘In one video, the person eats the EMPEYA cookie and shows a POSITIVE
reaction. In the other video, the person eats the PLOGO cookie and shows a NEGATIVE reaction’.They
were then told that they would complete a number of tasks before they would watch these videos.
In the combined condition, participants were told that in a minute, they would watch two videos
several times. They then read the same description of the regularities as the instructions group. Unlike
the instructions group, however, they watched the videos immediately after (i.e. prior to completing
the evaluative measures).
This is what the person’s
negative reaction looks like:
In one video, the person eats
the EMPEYA cookie and shows
a POSITIVE reaction.
In the other video, the person
eats the PLOGO cookie and
shows a NEGATIVE reaction.
This is what the person’s
positive reaction looks like:
positive
reaction
negative
reaction
In one video, the person eats
the EMPEYA cookie and shows
a POSITIVE reaction.
In the other video, the person
eats the PLOGO cookie and
shows a NEGATIVE reaction.
3× 3×
positive
reaction
negative
reaction
3× 3×
In one video, the person eats
the EMPEYA cookie and shows
a POSITIVE reaction.
In the other video, the person
eats the PLOGO cookie and
shows a NEGATIVE reaction.
positive
reaction
negative
reaction
(b)
(a)
(c)
(d)
Figure 1. Overview of the acquisition types in Experiments 1 and 2. (a) Observations condition (Experiments 1 and 2),
(b) instructions condition (Experiments 1 and 2), (c) combined condition (Experiment 1) and (d) enhanced-instructions
condition (Experiment 2). Because we do not have consent from the actor to publish images from the videos in their original
form, the actor’s face has been masked with labels in this figure. Naturally, this was not the case in the videos shown to
participants. In the examples provided in this figure, Empeya served as the CS
pos
while Plogo served as the CS
neg
. As stimulus
assignment was counterbalanced, half of the participants encountered this combination while the other half encountered the
opposite combination (i.e. Plogo as the CS
pos
and Empeya as the CS
neg
).
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2.1.3.2. Self-reports
Eight questions (four per CS) were presented in a random order. Participants were asked to indicate on
scales from −10 to +10 what they thought their opinion of the CS would be (from very bad to very good,
and from very negative to very positive), how much they thought they would like the CS (from Iwould
dislikeitverymuchto I would like it very much), and how pleasant or unpleasant they thought they would
consider the CS to be (from very unpleasant to very pleasant). Zero was indicated as a neutral midpoint.
2.1.3.3. Personalized implicit association test
The pIAT consisted of 180 trials. On each trial, a stimulus was presented in the middle of the screen and
participants had to use the D and K keys on their keyboard to classify this stimulus as quickly as possible
according to labels at the top left (D) and top right (K) of their screen. On target trials, participants had to
classify the names ‘Empeya’and ‘Plogo’into their respective categories; on attribute trials, participants
had to classify positive and negative words in terms of whether they liked or disliked them. On target
trials, incorrect responses were followed by error feedback (a red ‘X’presented for 200 ms) before the
trial ended (ITI: 400 ms).
The pIAT was divided into seven blocks. Before each block, participants were informed of the response
mappings for that block and reminded to respond as quickly and accurately as possible. Block 1 consisted
of 20 target trials: participants had to sort Empeya and Plogo into their respective categories. Block 2
consisted of 20 attribute trials: participants had to sort valenced words in terms of whether they liked
or disliked them. These initial blocks allowed participants to practise the response mappings for both
trial types. Block 3 (20 trials) combined the two trial types: on some trials, participants had to sort the
CSs into the CS categories and on other trials, they had to sort words in terms of whether they liked or
disliked them. Block 4 consisted of 40 trials but otherwise had the same structure as Block 3. In Block 5,
participants again practised sorting the CS names; however, the response mapping for the CS categories
was now reversed relative to the previous blocks. Block 6 (20 trials) again combined the two trial types
but with the ‘new’response mapping for target trials. Finally, Block 7 consisted of 40 trials but
otherwise had the same structure as Block 6. Trial order within each block was random and the
relevant labels remained on top of the screen throughout each block.
Because pIAT block order was counterbalanced, for half of the participants, the initial response
mappings were consistent with the acquisition phase (i.e. sorting the CS
pos
with the same key as liked
words and sorting the CS
neg
with the same key as disliked words) whereas for the other half, the
initial response mappings were inconsistent with the acquisition phase (i.e. sorting the CS
pos
with
the same key as disliked words and sorting the CS
neg
with the same key as liked words).
2.1.3.4. Exploratory questions
Before this final phase, participants in the instructions group were informed that they would not actually
watch the videos. Depending on the condition they were in, participants were then asked whether they
had read the instructions (instructions and combined conditions), as well as which reaction the model
had shown to each CS or had been said to show in the instructions. They were also asked to indicate
on scales from 0 to 10 how believable they considered the videos to be (observations and combined
conditions), to what extent they had believed they would watch the videos (instructions condition),
and how much they thought their ratings and their pIAT performance had been influenced by the
videos and/or by the instructions. Finally, they were asked to type in what they believed our
hypothesis to be and to indicate whether their behaviour on the self-reports and on the pIAT had
been driven by demand compliance or reactance (the response options being ‘Yes’,‘No’and ‘I don’t
know’). Participants were then fully debriefed and thanked for their participation.
2.2. Results
2.2.1. Data preparation
We first excluded the data of participants who provided incomplete data or who reported technical
issues during their participation (n= 11), as well as one participant who was not fluent in English.
This resulted in complete data for 292 participants (see Participants and design). We then excluded
participants who reported not reading the instructions (n= 3), who made more than 30% errors across
the entire pIAT (n= 4), who made more than 40% errors on any of the combined blocks (n= 16) or
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8

who completed more than 10% of pIAT trials faster than 300 ms (n= 1). The final sample consisted of 268
participants (160 men, 106 women, 2 non-binary people; M
age
= 28.72, s.d.
age
= 7.82).
The evaluative ratings were averaged to create two mean scores, one for the CS
pos
and one for the
CS
neg
. We then subtracted the CS
neg
score from the CS
pos
score to create a mean difference score (i.e. a
larger difference score indicated a stronger preference for the CS
pos
over the CS
neg
). Reaction times on
the pIAT were used to calculate participant-level scores according to the D1-algorithm [40], such that
positive pIAT scores reflected a more positive evaluation of the CS
pos
relative to the CS
neg
whereas
negative scores reflected the opposite.
2.2.2. Data analysis
2.2.2.1. Analytic strategy
To assess the impact of each manipulation on evaluative responding, we conducted one-sided, one-sample
t-tests to examine whether the rating difference scores and pIAT scores were larger than zero in each
condition (i.e. if the CS
pos
was evaluated more positively than the CS
neg
). To compare the different
conditions, we ran an analysis of variance (ANOVA) on each dependent variable to test for a main
effect of acquisition type. If the ANOVA indicated that scores indeed differed as a function of
acquisition type, we then conducted pairwise comparisons to test which groups differed from one
another (using Holm–Bonferroni correction). Finally, we used the Akaike information criterion (AIC) to
assess if any of the counterbalanced factors improved model fit, and if so, we tested whether the effect
of acquisition type remained significant in an ANOVA that included these factors (see the electronic
supplementary material for full models and results). All hypothesis tests were conducted at the α=0.05
significance level. Ninety-five per cent confidence intervals are reported for Cohen’sdand 90%
confidence intervals are reported for
h
2
p. Finally, we also report Bayes factors (BF
10
) which represent the
probability of the alternative hypothesis compared with the null hypothesis given the observed data [41].
2.2.2.2. Main analyses
Participants in all three conditions rated the CS
pos
more positively than the CS
neg
: the difference score
was significantly larger than zero in the observations group (M= 9.89, s.d. = 7.80), t
95
= 12.43, p< 0.001,
d= 1.27, [1.00, 1.54], BF
10
> 10 000, the instructions group (M= 6.13, s.d. = 7.99), t
85
= 7.11, p< 0.001,
d= 0.77, [0.52, 1.01], BF
10
> 10 000 and the combined group (M= 9.62,s.d. = 5.94), t
85
= 15.01, p< 0.001,
d= 1.62, [1.29, 1.94], BF
10
> 10 000. Participants’pIAT performance also indicated a more positive
evaluation of the CS
pos
relative to the CS
neg
: pIAT scores were larger than zero in the observations
group (M= 0.34, s.d. = 0.42), t
95
= 7.98, p< 0.001, d= 0.81, [0.58, 1.04], BF
10
> 10 000, the instructions
group (M= 0.17, s.d. = 0.42), t
85
= 3.76, p< 0.001, d= 0.41, [0.18, 0.62], BF
10
= 135.6 and the combined
group (M= 0.35,s.d. = 0.43), t
85
= 7.54, p< 0.001, d= 0.81, [0.57, 1.06], BF
10
> 10 000. In sum, all three
conditions led to the expected changes in self-reported and automatic evaluations.
The size of the difference between the ratings of the CS
pos
and the CS
neg
varied as a function of
acquisition type, F
2,265
= 7.23, p< 0.001,
h
2
p¼0:05, [0.01, 0.10], BF
10
= 25.11 (figure 2a). Specifically, the
difference was significantly smaller in the instructions condition than in the observations condition,
(corrected) p= 0.002, BF
10
= 17.93, and the combined condition, p= 0.004, BF
10
= 20.26. Scores in the
observations and combined conditions did not differ from each other, p= 0.81, BF
10
= 0.17. The main
effect of acquisition type remained significant when the model was updated based on AIC values,
F
2,260
= 7.19, p< 0.001, BF
10
= 26.87.
3
pIAT scores also differed as a function of acquisition type, F
2,265
= 5.06, p= 0.007,
h
2
p¼0:037, [0.006,
0.076], BF
10
= 3.69 ( figure 2b). Similar to the pattern found for self-reports, the instructions group showed
a smaller effect than both the observations group, p= 0.017, BF
10
= 5.33, and the combined group, p=
0.017, BF
10
= 5.74, while the latter two groups did not differ from each other, p= 0.89, BF
10
= 0.16. The
main effect of acquisition type remained significant when the model was updated based on AIC
values, F
2,262
= 5.34, p= 0.005, BF
10
= 4.57.
4
3
For completeness, please note that this main effect was qualified by an interaction with stimulus assignment, F
2,260
= 3.51, p= 0.03,
BF
10
= 1.34, such that the effect of acquisition type was significant only if Plogo served as the CS
pos
. However, this interaction was
likely spurious given the small BF.
4
For completeness, please note that this main effect was qualified by an interaction with block order, F
2,262
= 3.31, p= 0.038, BF
10
= 1.23,
such that it was significant only if participants completed the learning-consistent bloc k first. However, this is again probably a spurious
finding given the small BF.
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2.2.2.3. Exploratory analyses
Some points regarding participants’answers to the exploratory questions are worth mentioning (see the
electronic supplementary material for all exploratory analyses). First, most participants correctly
remembered the (observed or instructed) pairings, and the results of our main analyses were
unchanged when we included only participants with perfect memory for the pairings (n= 246).
Second, most participants in the instructions group indicated that they had believed they would
watch the videos (M= 8.26). Therefore, the smaller effects in the instructions group are unlikely to be
due to this group not remembering the instructions or not believing that they would actually watch
the videos. Finally, the rating results were unchanged when we included only participants who
reported that their ratings had not been influenced by demand compliance (n= 186) or only
participants who reported that their ratings had not been influenced by reactance (n= 174). Therefore,
it is unlikely that the positive rating of the CS
pos
relative to the CS
neg
was simply an artefact of trying
to comply with or resist the perceived experimenter demand.
2.3. Discussion
In Experiment 1, we examined if instructions about observations would have an impact on likes and
dislikes, whether the magnitude of the impact would be comparable to that of actual observations,
and if there would be any added benefit of combining observations with instructions. We found that
all three manipulations induced changes in liking as reflected by evaluative ratings and pIAT scores.
Although instructions were enough to change what people liked or disliked, these effects were
significantly smaller than the effects of observations or observations combined with instructions. Thus,
it appears that actually observing regularities between stimuli and a model’s reactions influences
evaluations to a greater extent than simply being told about those regularities.
One potential reason for this weaker effect of instructions relative to observations is that our
instructions might have been somewhat vague. We presented two sentences that simply stated that
the model would show a ‘positive’reaction to the CS
pos
and a ‘negative’reaction to the CS
neg
.
10
5
0
observations instructions combined
observations instructions combined
acquisition t
y
pe
difference between CS ratings
0.5
0.4
0.3
0.2
0.1
0
pIAT score
(b)
(a)
Figure 2. (a) Difference between CS
pos
and CS
neg
ratings as a function of acquisition type (Experiment 1). (b) pIAT scores as a
function of acquisition type (Experiment 1).
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Without any further information, participants have no way of knowing how positive or negative the
model’s reactions are (especially in the case of cookies they might assume the negative reaction to be
a rather subtle one, while the reaction in the actual videos was fairly strong). Therefore, because the
instructions and the observations conditions differed not only in terms of the format in which
information was presented, but also in terms of the quality of that information, the obtained
difference between the conditions may not reflect the impact of the pathways themselves but rather of
the information that was conveyed.
Therefore, in Experiment 2, we examined if the difference between observations and instructions
would remain when participants in the instructions condition were given the opportunity to see what
a‘positive’or ‘negative’reaction actually referred to, before they were informed about the regularities
between those reactions and the two cookies. This also more closely resembles research that compared
evaluative conditioning and instructed evaluative conditioning, in which participants are often shown
all of the CSs and USs before receiving instructions about how they will be paired (e.g. [19]).
3. Experiment 2
We again exposed participants to the same observations and instructions conditions as in Experiment
1. However, we now added an ‘enhanced-instructions’condition. In this condition, participants first
saw cropped videos of the model’s positive or negative facial expressions, allowing them to gain a
sense of the nature and intensity of the model’s reactions. Afterwards they received the same
instructions as the instructions group, indicating that the CS
pos
would be followed by the positive
reaction and the CS
neg
would be followed by the negative reaction. Based on a propositional
perspective, we would predict that the effects in this enhanced-instructions condition would be (i)
larger than the effects in the instructions condition and (ii) similar in magnitude to the effects in the
observations condition (given that the information they conveyed was now closer in nature).
3.1. Method
3.1.1. Participants and design
We recruited participants on Prolific until we had complete data for 288 participants (157 men, 130
women, 1 non-binary person; M
age
= 28.33, s.d.
age
= 7.59). The design was similar to Experiment 1,
except that participants were assigned to an observations condition, an instructions condition or an
‘enhanced-instructions’condition (i.e. the combined condition of Experiment 1 was replaced by this
new condition).
3.1.2. Materials
3.1.2.1. Videos
The videos shown to the observations group were identical to those in Experiment 1. In addition, edited
versions of these videos were created for the enhanced-instructions condition by (i) cutting the videos so
that they started only after the model had taken his first bite of the cookie and (ii) cropping them so that
they only showed the model’s face rather than the entire setting. Consequently, the edited videos showed
only the positive or negative reaction, not the name of the corresponding CS.
3.1.2.2. Personalized implicit association test
The pIAT was identical to the task used in Experiment 1.
3.1.3. Procedure
Overall, the procedure was similar to that of Experiment 1, with one key difference. Participants in the
observations conditions and instructions conditions completed the same manipulations as their
counterparts in Experiment 1. However, the combined condition was replaced by the enhanced-
instructions condition (figure 1). Participants in this condition were informed that they would soon
see videos in which the model reacted positively to one cookie and negatively to another (similar to
the instructions group). However, unlike the instructions group, they were first shown cropped videos
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11

of the model emitting the positive and the negative reactions and only then read the instructions about
the regularities between the CSs and the model’s reactions.
3.2. Results
3.2.1. Data preparation
We first excluded the data of participants who provided incomplete data or who reported technical
issues (n= 13). This resulted in complete data for 288 participants (see Participants and design). We
then excluded participants who had completed some parts of the experiment twice (n= 2), who
reported not having read the instructions (n= 2), who made more than 30% errors across the entire
pIAT (n= 5), who made more than 40% errors on any of the combined blocks (n= 18) or who
completed more than 10% of pIAT trials faster than 300 ms (n= 1). The final sample consisted of 260
participants (140 men, 119 women, 1 non-binary person; M
age
= 28.45, s.d.
age
= 7.70). Difference scores
and pIAT scores were calculated in the same way as in Experiment 1.
3.2.2. Data analysis
The analytic strategy was identical to that of Experiment 1.
3.2.2.1. Main analyses
The CS
pos
was rated more positively than the CS
neg
in all three conditions: the difference between the CS
ratings was larger than zero in the observations group (M= 10.59, s.d. = 6.63), t
82
= 14.56, p< 0.001, d=
1.60, [1.27, 1.92], BF
10
> 10 000, the instructions group (M= 5.34, s.d. = 6.78), t
88
= 7.43, p< 0.001, d=
0.79, [0.55, 1.02], BF
10
> 10 000 and the enhanced-instructions group (M= 7.91,s.d. = 7.26), t
87
= 10.23,
p< 0.001, d= 1.09, [0.82, 1.35], BF
10
> 10 000. The same was true for pIAT scores (observations group:
M= 0.31, s.d. = 0.40, t
82
= 7.09, p< 0.001, d= 0.78, [0.53, 1.02], BF
10
> 10 000; instructions group: M=
0.28, s.d. = 0.43, t
88
= 6.11, p< 0.001, d= 0.65, [0.42, 0.87], BF
10
> 10 000; enhanced-instructions group:
M= 0.29,s.d. = 0.43, t
87
= 6.31, p< 0.001, d= 0.67, [0.44, 0.90], BF
10
> 10 000). In sum, all manipulations
had the expected impact on self-reported and automatic evaluations.
The ratings varied as a function of acquisition type, F
2,257
= 12.44, p< 0.001,
h
2
p¼0:09, [0.04, 0.14],
BF
10
= 2329.66 ( figure 3a). Specifically, all three conditions differed significantly from each other.
Replicating the pattern of Experiment 1, the effect in the observations condition was larger than the
effect in the instructions condition, p< 0.001, BF
10
> 10 000. Although the effect in the enhanced-
instructions group was slightly larger than that in the instructions group, p= 0.024, BF
10
= 2.51, it was
still not as large as that in the observations group, p= 0.024, BF
10
= 2.99. In sum, the self-reported
effect was largest in the observations condition, slightly smaller in the enhanced-instructions
condition, and still smaller in the instructions condition. The main effect of acquisition type remained
significant when the model was updated based on AIC values, F
2,256
= 13.91, p< 0.001, BF
10
= 8200.
In contrast, pIAT scores did not differ as a function of acquisition type, F
2,257
= 0.16, p= 0.86,
h
2
p¼0:001, [0.00, 0.01], BF
10
= 0.05 (figure 3b). All Bayes factors for the follow-up comparisons
favoured the null hypothesis (observations versus instructions: BF
10
= 0.19; observations versus
enhanced-instructions: BF
10
= 0.18; instructions versus enhanced-instructions: BF
10
= 0.16). In other
words, there was evidence for the absence of differences between groups, suggesting that all three
conditions led to similar pIAT effects. In addition, the main effect of acquisition type was no longer
included when the model was updated based on AIC values.
3.2.2.2. Exploratory analyses
Two points are noteworthy (see the electronic supplementary material for all exploratory analyses). First,
our main results were again unchanged when we included only participants who had perfect memory
for the regularities (n= 221). Second, the pairwise comparison between the instructions and the
enhanced-instructions conditions became non-significant when we only included participants who
reported no demand compliance (n= 163). When we only included participants who reported no
reactance (n= 178), only the difference between the observations and the instructions conditions
remained significant.
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3.3. Discussion
We replicated our prior findings insofar as both observations and instructions gave rise to self-reported
changes in liking, and that observations were relatively more effective in doing so than instructions.
Although enhancing the instructions (by clearly depicting what a positive or negative reaction meant)
did slightly boost the effects relative to regular instructions, actual observations were still more
effective. Somewhat surprisingly, this pattern was not evident on the pIAT. While all three conditions
gave rise to automatic evaluations of the cookies in the expected direction, the size of pIAT scores did
not differ across conditions, even though the observations and instructions conditions were identical
to those used in Experiment 1. In the light of this failure to replicate the pIAT score pattern of
Experiment 1, we will avoid drawing conclusions about differences between conditions from the pIAT
scores in Experiments 1 and 2.
Thus for self-reported evaluations, we can conclude from Experiments 1 and 2 that (i) instructions
about observations are effective in establishing likes and dislikes, (ii) actual observations seem to be
more effective in doing so, and (iii) augmenting instructions through the addition of relevant
information seems to slightly increase the magnitude of the effects, but still not to the same level as
actually observing events for oneself. In Experiments 3 and 4, we examined if a similar pattern
emerges when we look at fear rather than evaluations.
4. Experiment 3
We exposed participants in Experiment 3 to either an observations or an enhanced-instructions condition.
Those in the observations condition encountered an observational fear conditioning procedure [42].
During the acquisition phase, they watched a sequence of videos in which a model was exposed to an
unpleasant sound (delivered via his headphones) following four out of six presentations of one
coloured square (CS+) but never following the presentation of another coloured square (CS−). The
sound elicited clearly visible distress in the model (social US). Moreover, after his first exposure to the
(b)
(a)
observations instructions enhanced instructions
observations instructions enhanced instructions
acquisition t
y
pe
10
5
0
difference between CS ratings
0.5
0.4
0.3
0.2
0.1
0
pIAT score
Figure 3. (a) Difference between CS
pos
and CS
neg
ratings as a function of acquisition type (Experiment 2). (b) pIAT scores as a
function of acquisition type (Experiment 2).
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sound, the model also showed anxious reactions whenever the CS+ was presented (indicating that he
expected the sound to follow). The enhanced-instructions condition again showed participants
examples of these reactions, followed by a description of the regularities between the CSs and the
model’s reactions. Note that unlike in Experiments 1 and 2, the contingency in Experiment 3 was
probabilistic (i.e. the CS+ was followed by the presentation of the sound to the model on only four
out of six trials, in line with recommendations for observational fear conditioning [42]). This
constitutes an important difference between Experiments 1 and 2 and Experiments 3 and 4, which we
will return to in the General discussion.
After the acquisition phase, all participants proceeded to a test phase. They were informed that the
two CSs would now be presented on their own screen and that they might encounter an unpleasant
sound through their own headphones. SCRs and self-reports of fear and sound expectancy were
assessed during this phase in order to test if participants showed a stronger fear response to the CS+
than to the CS−. Importantly, no sounds were actually presented, meaning that any differences in
responses to the CSs would be due to the model reactions that they were (said to be) paired with.
Based on a propositional perspective, we predicted that both conditions would lead to similar
changes in fear responding, with fear responses to the CS+ predicted to be larger than those to the CS−.
4.1. Method
4.1.1. Participants and design
Because it was more labour intensive to test participants in the laboratory and it was difficult to
determine an effect size of interest, we opted to use a sequential Bayes factor design [43] rather than
carry out a conventional power analysis. In our pre-registration, we specified that we would initially
collect data for 60 participants and then calculate BFs for the differences between the two conditions.
If the BFs were smaller than 1/6 (clearly favouring the null hypothesis) or larger than 6 (clearly
favouring the alternative hypothesis), we would terminate data collection; otherwise, we would collect
additional data from 20 participants at a time until the BFs did fall below or above these values or
until we reached our specified maximum sample size (n= 120).
After the first 60 participants had completed the experiment, the BF for the expectancy ratings was
larger than 6 and the BF for the SCRs was smaller than 1/6, while the BF for the fear ratings was still
inconclusive. Unfortunately, however, the participant pool at our faculty was unexpectedly small and
by the time we were able to recruit these 60 participants, we had already reached the end of the
semester. Therefore, we decided to deviate from our pre-registration, terminate our data collection for
Experiment 3, and instead try to replicate the initial self-report findings of Experiment 3 with a larger
Prolific sample (i.e. Experiment 4).
In sum, we collected complete data for 60 participants (11 men, 49 women; M
age
= 22.30, s.d.
age
=
4.87). We employed a between-subjects design with two levels for acquisition type: observations
versus enhanced-instructions. Stimulus assignment (whether the blue square or the yellow square
served as the CS+) was counterbalanced across participants.
4.1.2. Materials
4.1.2.1. Conditioned stimulus
A blue and a yellow square served as CSs. Both for the model in the videos as for participants during the
test phase, they were presented in the middle of the screen on a black background. The videos were
edited to ensure that the colours of the CSs shown in the videos exactly matched the colours of the
CSs presented during the test phase.
4.1.2.2. Videos
To create the impression that the model was exposed to a fear conditioning procedure over the course of
several trials, we used four 17 s source videos, which were taken from a larger database shared with us
by the Emotion Lab at Karolinska Institute (https://www.emotionlab.se/). The videos showed a male
model who was seated in front of a computer screen and wore headphones. Each video started with a
fixation cross presented on the model’s screen for 2 s, after which a coloured square (CS) was
presented for 6200 ms, followed by a 9 s black screen.
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The behaviour of the model differed between the four source videos. In the ‘unexpected sound’
video, the model calmly watched the screen during the CS presentation, but showed clear discomfort
as soon as the CS disappeared from screen (indicating that the unpleasant sound was presented via
his headphones at CS offset). In the ‘expected sound’video, the model already showed an anxious
reaction as soon as the CS appeared on screen (indicating that he expected the unpleasant sound
to be presented soon) and then again displayed clear discomfort when the CS disappeared from
screen (indicating that the unpleasant sound was indeed presented at CS offset). In the ‘omitted
sound’video, the model also showed an anxious reaction when the CS appeared (indicating that he
expected the unpleasant sound to be presented soon) but then visibly relaxed when the CS
disappeared (indicating that no sound was actually presented at CS offset). Finally, in the ‘safe’video,
the model calmly watched the screen throughout the video without showing any anxiety or
discomfort upon (dis)appearance of the coloured CS (indicating that he did not expect nor hear
the sound).
The unexpected, expected and omitted sound videos were selected from a larger set of videos
from three different models, based on pre-ratings by a separate sample of participants who were
asked how they interpreted the modelled reactions at CS onset and offset (i.e. as negative and/or
relieved reactions) and how believable they considered the video to be (pre-rating materials and
data are available at https://osf.io/hpj9g/). All four source videos were edited to vary the colour of
the CS (blue versus yellow) in order to counterbalance stimulus assignment, leading to eight videos
in total. In addition, cropped versions of the unexpected and expected sound videos that only
showed the model’s face were created for the enhanced-instructions condition. Consequently, these
two cropped videos showed only the negative reactions, not the CS that was presented on the
model’s screen.
4.1.2.3. Skin conductance measurement
SCRs were recorded using the Biosemi ActiveTwo system. Before the start of the experiment, the
participant was asked to wash their hands in preparation of the placement of the skin conductance
electrodes. After gently scrubbing the participant’s forehead, two ground electrodes were placed
approximately 3 cm apart just below the hairline. Two Ag/AgCl electrodes were attached to the
thenar and hypothenar eminences of the participant’s left hand. If the measured signal did not show
a noticeable SCR when the participant was asked to breathe in deeply, the skin on their hand was
cleaned and the electrodes were attached a second time. If the signal again showed no noticeable
SCR, the experiment still proceeded but a note was made of the issue.
4.1.3. Procedure
4.1.3.1. Preparation
After the participant received information about the experiment (which mentioned the possibility that an
unpleasant sound would be presented) and provided their informed consent, the experimenter attached
the SCR electrodes and placed the headphones over the participant’s ears. Note that a calibration of the
unpleasant sound was not included because the sound would never actually be presented during the
study (similar to standard observational fear conditioning procedures, wherein electric shocks are not
calibrated for the observer [42]). Finally, the experimenter explained that all necessary instructions
would be provided on the screen, that the participant should move as little as possible, and that they
should not speak unless they wanted to terminate the experiment (note that all instructions were
provided in Dutch throughout the experiment).
4.1.3.2. Acquisition phase
Figure 4 depicts the acquisition phase in both conditions. In the observations condition, participants were
informed that they would watch videos of another participant (i.e. the model) wearing headphones and
that a highly unpleasant sound could be presented through the model’s headphones. Participants then
watched a series of 12 videos in which the model sometimes heard the unpleasant sound after the
presentation of one coloured square (the CS+) but never after the presentation of another coloured
square (the CS−).
To create the impression that the CS+ was followed by the sound on four out of six trials and that the
model began reacting anxiously to the CS+ after its first occurrence, the series of videos consisted of a
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six CS+ videos (CS followed by sound in four out of six videos):
six CS-videos (CS never followed by sound):
anxious
behaviour
(except
first trial)
distressed
behaviour
(4 out of 6)
relieved
behaviour
(2 out of 6)
neutral
behaviour
neutral
behaviour
Sometimes the participant reacts
negatively immediately after the
square DISAPPEARS from screen:
After the first trial, the participant
sometimes already reacts
negatively as soon as a square
APPEARS on screen:
In six of these videos, the participant sees a BLUE square. In four of
these videos, the participant reacts in a negative way immediately
after the blue square disappears from screen.
In all videos with a blue square, except for the very first video, the
participant also already reacts negatively when the blue square
appears on screen.
In the other six videos, the participant sees a YELLOW square. In
none of these videos does the participant react in a negative way.
neutral
behaviour
distressed
behaviour
anxious
behaviour distressed
behaviour
(b)
(a)
Figure 4. Overview of the acquisition types in Experiments 3 and 4. (a) Observations condition (Experiments 3 and 4) and (b)
enhanced-instructions condition (Experiments 3 and 4). Because we do not have consent from the actor to publish images
from the videos in their original form, the actor’s face has been masked with labels in this figure. Naturally, this was not the
case in the videos shown to participants. In the examples provided in this figure, the blue square served as the CS+ while
the yellow square served as the CS−. As stimulus assignment was counterbalanced, half of the participants encountered this
combination while the other half encountered the opposite combination (i.e. yellow square as the CS+ and blue square as the
CS−). Please note that one condition (the information-matched observations condition of Experiment 4) is not depicted here as
it only involved adding certain pieces of verbal information (see text). Figure included with permission from the Emotion Lab
at Karolinska Institute (https://www.emotionlab.se/), who provided the videos depicted in the screenshots.
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16

fixed combination of the unexpected sound, expected sound, omitted sound and safe videos (see
Materials). The first two videos always showed the model reacting negatively upon the disappearance
of the CS+ (i.e. unexpected sound video), while displaying no emotional reactions during a CS−trial
(i.e. safe video). The remainder of the observation phase showed five more CS- trials (i.e. safe video),
three trials in which the CS+ was presented and the model both expected and then actually heard the
sound (i.e. expected sound video), and two trials in which the CS+ was presented and the model
expected but did not actually hear the sound (i.e. omitted sound video). With the exception of the
first two videos, video presentation order was random with an inter-video interval of 2 s.
In the enhanced-instructions condition, participants were told that later on in the experiment they
would watch videos of a participant who could be exposed to an unpleasant sound via his
headphones. The videos were then described to them. First, participants were told that during each
video, a blue or yellow square would be presented on the model’s screen and that the model might
hear an unpleasant sound when the square disappeared from screen. Participants were then informed
that the model would sometimes simply watch the screen, sometimes react negatively when a square
disappeared from the screen (at this point, the cropped version of the unexpected sound video was
played), and after the first trial sometimes also react negatively as soon as a square appeared on
screen (at this point, the cropped version of the expected sound video was played).
The regularities between the CSs and the model’s reactions were then described. Participants were
told that during six of the 12 videos, the model would see the CS+ and would react negatively after
its disappearance in four of those videos. With the exception of the very first video, the model was
also said to react negatively as soon as the CS+ appeared on screen. Participants were also told that
during the other six videos, the model would see the CS−and never react in a negative way. A small
version of each CS was shown next to the corresponding paragraph of instructions. Participants were
told to read these instructions and remember what they had been told.
4.1.3.3. Test phase
Participants were first told that the two CSs would now be presented on their own screen and that a
sound could also be presented through their own headphones. The test phase (consisting of three
blocks of trials) then began. Each trial started with the presentation of a fixation cross on a black
background. After 4 s, a CS was presented, which remained on screen for 8 s, followed by an ITI of
10, 12 or 16 s (i.e. a black screen during which the sound could presumably be presented). Every
block consisted of three CS+ trials and three CS−trials in a random order. After each block,
participants were asked to think back to the last time they saw each CS and to indicate on scales from
1 to 9 how anxious they had felt (from not at all to very anxious) and to what extent they had thought
the sound would be presented (from not at all to very certain). Skin conductance was measured
continuously throughout the test phase. Note that no sound was actually presented.
4.1.3.4. Exploratory questions
After the test phase, theelectrodes were detached from participants’left hand and they were asked to indicate
what they believed our hypothesis was, followed bya debriefing about the absence of sounds during the test
phase (and the absence of the full videos for the enhanced-instructions group). They were then asked which
reaction the model had shown after each CS (or had been said to show), whether they had paid attention to
the videos (or read the instructions), and whether they had paid attention throughout the test phase. Finally,
they were askedhow believable they considered the (cropped) videosto be, to what extent they had believed
that they would see the full videos (enhanced-instructions group), to what extent they had believed a sound
would be presented during the test phase, and whether their ratings had been driven by demand compliance
or reactance. Participants were then thanked and debriefed.
4.2. Results
4.2.1. Data preparation
All self-report data were complete and were therefore included in the analyses. We excluded the skin
conductance data of participants whose responses were not in range during (part of) the test phase (n=3)
or whose considerable movement during the test phase interfered with the measured signal (n=4).
SCRs were calculated in the following way: after extracting the signal from a window of 2 s before CS
onset until 8 s after CS onset, this signal was baseline-corrected by subtracting the mean value of the
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window before CS onset. One SCR per trial was extracted by taking the maximal value of the window
between 1 and 7 s after CS onset. These SCRs were then rescaled to microsiemens (μS), filtered by setting
all values below 0.02 µS to zero, range-corrected by dividing each SCR by the largest SCR measured for
that specific participant (to account for overall individual differences), and normalized by calculating the
square root of each SCR.
4.2.2. Data analysis
4.2.2.1. Analytic strategy
Both the fear and expectancy ratings were subjected to mixed ANOVAs to test whether the ratings indeed
differed between the two CSs (main effect of CS) and whether the size of this effect differed between the
two groups (CS × acquisition type). We conducted another mixed ANOVA on participants’average SCRs
per block to test these main and interaction effects. We also ran a linear mixed effects model on the non-
aggregated SCRs (using Satterthwaite approximation for the inference tests), which included fixed effects
for CS, block, condition and their interactions, to check whether the results converged with the results of
the mixed ANOVA. In our pre-registration, we stated that the random effects structure of the linear mixed
model would consist of only a by-participant random intercept, but following a reviewer recommendation
we deviated from this plan and included all random effects supported by the design [44], meaning a by-
participant intercept as well as by-participant slopes for CS, block and CS× block. This led to a singular fit,
so we simplified the random effects structure until this was no longer the case (by first removing the
random correlations, then removing the random slopes for the interaction and for block and then
including the remaining correlation again). As the interpretation of the fixed effects did not diverge
between the full and the reduced model, we report the results for the (theoretically more justifiable) full
model (see [45]). For all ANOVAs, degrees of freedom and the resulting p-values were subjected to
Greenhouse–Geisser correction if the sphericity assumption was violated. Finally, all BFs reported in this
section are ‘inclusion BFs’, which indicate the evidence in favour of including a specific term in the
model across ‘matched’models (i.e. all models that did not include any interactions with the term of
interest but did include the underlying main effects if the term of interest was itself an interaction term).
4.2.2.2. Main analyses
Fear ratings. Figure 5ashows the mean fear ratings as a function of CS, block and acquisition type. There
was a significant main effect of CS, F
1,58
= 95.22, p< 0.001,
h
2
p¼0:62, [0.48, 0.70], BF
10
> 10 000, such that
participants reported feeling more anxious on CS+ trials than on CS−trials. The two-way interaction
between CS and acquisition type was not significant, F
1,58
= 1.89, p= 0.17,
h
2
p¼0:03, [0.00, 0.13],
BF
10
= 1.09, suggesting that the size of this effect did not differ between the two groups. However,
there was a significant CS × block × acquisition type interaction, F
1.7,97.6
= 4.02, p= 0.027,
h
2
p¼0:06,
[0.01, 0.14] (although the BF did not support this, BF
10
= 0.38). Specifically, the CS × acquisition type
interaction was not significant in the first and second blocks (respectively, p= 0.88 and p= 0.22),
whereas it was significant in the third block ( p= 0.005), where the difference between CSs was smaller
in the observations condition (M= 0.53) than in the enhanced-instructions condition (M= 1.47),
although it remained highly significant in both groups (both ps < 0.001). Finally, the effects of block
(p< 0.001), CS × block ( p< 0.001) and block × acquisition type ( p= 0.02) were also significant, while
the main effect of acquisition type was not ( p= 0.22).
Expectancy ratings. Figure 5bshows the mean expectancy ratings as a function of CS, block and
acquisition type. There was a significant main effect of CS, F
1,58
= 109.87, p< 0.001,
h
2
p¼0:65, [0.53,
0.73], BF
10
> 10 000, such that participants expected a sound more after the CS+ than after the CS−.
Importantly, there was a significant interaction between CS and acquisition type, F
1,58
= 7.54, p= 0.008,
h
2
p¼0:12, [0.02, 0.25], BF
10
= 264.33, such that the overall difference between CSs was smaller in the
observations condition (M= 1.53) than in the enhanced-instructions condition (M= 2.62). However, it
was highly significant in both groups (both ps < 0.001). Other than effects of block ( p< 0.001) and
CS × block ( p< 0.001), there were no other significant effects (acquisition type: p= 0.13; block ×
acquisition type: p= 0.24; CS × block × acquisition type: p= 0.31).
SCRs. Figure 5cshows the mean SCRs as a function of CS, block and acquisition type. There was a
significant main effect of CS, F
1,51
= 20.08, p< 0.001,
h
2
p¼0:28, [0.12, 0.43], BF
10
> 10 000, such that SCRs
were larger during CS+ presentations than during CS−presentations. It did not vary as a function of
acquisition type, F
1,51
= 0.43, p= 0.52,
h
2
p¼0:01, [0.00, 0.09], with the BF (BF
10
= 0.13) suggesting
similar fear learning in the two conditions. Other than a main effect of block ( p< 0.001), there were
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no other significant effects (CS × block: p= 0.39; acquisition type: p= 0.22; block × acquisition type: p=
0.85; CS × block × acquisition type: p= 0.41). Finally, the results of the linear mixed effects analysis on
the non-aggregated SCRs were highly similar, showing only significant effects of CS, F
1,56.8
= 19.71,
p< 0.001, and block, F
2,67.6
= 10.59, p< 0.001.
4.2.2.3. Exploratory analyses
A few points are worth mentioning (see the electronic supplementary material for all exploratory
analyses). First, far fewer participants in the observations condition correctly recalled that the CS+
was sometimes followed by the model’s negative reactions (17 participants as opposed to 29
participants in the enhanced-instructions condition). When only participants who had perfect memory
for the regularities were included (n= 44), the main results were largely unchanged, except that by the
third block, the observations group no longer showed an effect in terms of fear ratings. Second, our
main results were unchanged when we only included participants who reported no demand
compliance (n= 57). When we only included participants who reported no reactance (n= 43), the CS ×
block × acquisition type effect on the fear ratings was no longer significant.
6
4
2
6
4
2
0.5
0.4
0.3
0.2
0.1
observations enhanced instructions
observations enhanced instructions
observations enhanced instructions
123 123
block
12 3 123
12 3 123
fear ratingexpectancy ratingSCR
CS+ CS–
CS+ CS–
CS+ CS–
(b)
(a)
(c)
Figure 5. (a) Fear ratings as a function of CS, block and acquisition type (Experiment 3). (b) Expectancy ratings as a function of CS,
block and acquisition type (Experiment 3). (c) SCRs as a function of CS, block and acquisition type (Experiment 3).
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4.3. Discussion
Experiment 3 moved from evaluations to fear and examined if observations would once again give rise to
relatively larger effects than instructions about observations. Results indicated that both observations and
instructions about observations led to a change in behaviour on all measures (fear ratings, expectancy
ratings and SCRs). Unlike what we found for evaluations, the effects of actual observations were not
larger than those generated via enhanced instructions about observations. If anything, evidence
suggested that the latter had a larger impact on expectancy ratings and (to some extent) fear ratings.
Given the results of Experiments 1 and 2, it may seem somewhat surprising that the enhanced
instructions had slightly larger effects than the observations in Experiment 3. One possible
explanation may be that participants in the enhanced-instructions condition more accurately
remembered which CS was followed by negative reactions than those in the observation condition (i.e.
they had better contingency memory). The current pattern of findings could also have been due to the
fact that the enhanced-instructions group was explicitly told that a sound could be presented to the
model at CS offset, that only one of the CSs would be followed by negative reactions, and that they
needed to remember which. The observation group received no such instructions.
5. Experiment 4
Because of these differences between the two conditions in Experiment 3, and the smaller than expected
sample size in that experiment, we decided to conduct a follow-up experiment with a larger sample to
see if the stronger impact of enhanced instructions (relative to observations) on expectancy and fear
ratings would replicate. We additionally included an ‘information-matched observations’condition, that
is, an observations condition that did include the above pieces of information and thus was more directly
comparable to the enhanced-instructions condition. If these pieces of information were indeed responsible
for the pattern observed in Experiment 3, we would expect (i) a larger effect in the enhanced-instructions
condition than in the observations condition, replicating Experiment 3, (ii) a larger effect in the
information-matched observations condition than in the standard observations condition, and (iii) similar
effects in the enhanced-instructions and information-matched observations conditions.
Experiment 4 also contained a number of other changes. First, and most importantly, we collected the
data via Prolific Academic, which allowed us to recruit a much larger sample of participants. Although
this naturally reduced the experimental control that we could exert over participants’behaviour, we gave
clear instructions about the need to use their headphones and the volume they should set (see below),
and we asked at the end whether they had followed these instructions or not. Collecting the data
online also meant that we could no longer measure physiological responses. Second, we considerably
shortened the test phase and asked participants to rate their fear and expectancy after every trial
rather than in a blocked fashion. Finally, because participants in Experiment 3 often seemed to
interpret the videos in a different way than we had intended (e.g. assuming that the model was also
exposed to a sound whenever he showed an apprehensive reaction at CS onset), we asked a number
of exploratory questions about how they interpreted the videos.
5.1. Method
5.1.1. Participants and design
Based on a power analysis indicating that we required a total sample size of n= 189 in order to have 80%
power to detect an effect size of
h
2
p¼0:03 (the smaller of the two relevant effect sizes obtained in
Experiment 3) in a mixed ANOVA, we planned to recruit a sample of n= 192 participants. As only a
small percentage of participants reported not following all of the instructions with regard to the
headphones and sound settings, we collected data until we had complete data for 192 participants
who reported following all of these instructions (no other data analysis or exploration was performed
in the meantime). The total sample consisted of 211 participants (138 men, 71 women, 1 non-binary
person, 1 person whose gender was not recorded properly; M
age
= 27.05, s.d.
age
= 6.89).
We used a between-subjects design with three levels for acquisition type: observations, information-
matched observations and enhanced-instructions. Stimulus assignment was again counterbalanced
across participants.
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5.1.2. Materials
The CSs and videos were identical to those used in Experiment 3.
5.1.3. Procedure
5.1.3.1. Introduction
After providing their informed consent, participants were instructed to plug in their headphones, set the
volume on their computer to 20% and wear their headphones throughout the study. They were also
informed that during one of the tasks a highly unpleasant sound would be presented to them from
time to time.
5.1.3.2. Acquisition phase
The manipulations in the observations and enhanced-instructions conditions were largely identical to those
in Experiment 3, with the exception that on instructions pages participants could not proceed until a
certain amount of time had passed (in order to maximize the probability that they read all instructions).
In the information-matched observations condition, participants watched the same series of videos as the
observations group. However, in order to make this condition as comparable as possible to the enhanced-
instructions condition (i.e. ‘matched’in terms of the information that was communicated), they were
informed that (i) when a square disappeared from screen, a very unpleasant sound might be presented
to the model, (ii) the model might react in a negative way after one of the squares, and (iii) they had
to remember which coloured square went together with the negative reactions because they would
need this information later on.
5.1.3.3. Test phase
The test phase consisted of eight trials (four CS+ and four CS−trials). Each trial started with a black
screen presented for 1.5, 2 or 2.5 s, followed by a fixation cross presented for 2 s. Next, the CS was
presented and stayed on screen for 6 s, followed by a black screen presented for 5, 6 or 7 s (during
which the sound could presumably be presented). After each trial, participants were asked to rate on
scales from 1 to 9 how anxious they felt when they saw the coloured square (from not at all to very
anxious) and to what extent they had thought the unpleasant sound would be presented (from not at
all to very certain). Once again, no sounds were actually presented.
5.1.3.4. Exploratory questions
Participants received an initial debriefing about the absence of sounds during the test phase (as well as
the absence of full videos for the enhanced-instructions group). To assess whether they had interpreted
the videos (or our description thereof) as intended, they were then asked when (i.e. during, after or both
during and after the presentation of a CS) the model could hear the unpleasant sound, after which CS the
model had shown (or been said to show) negative reactions, and how often that CS had been followed by
the sound to the model (i.e. never, sometimes or always). Importantly, given the online setting, they were
also asked whether they had followed all of our instructions (i.e. plugged in and wore their headphones,
as well as set their sound to 20%), and if not, what they had not done and why. In order to encourage
participants to respond honestly to these questions, it was explicitly stated that their answers would not
affect the payment they would receive for their participation and that it was very important that they
answered truthfully. They also answered the same questions as in Experiment 3, after which they
were thanked and debriefed.
5.2. Results
5.2.1. Data preparation
We first excluded the data of participants who provided incomplete data or who reported technical
issues (n= 18), as well as one participant who was not fluent in English. This resulted in complete
data for 211 participants. We also excluded the data of one participant who had read the instructions
twice. Finally, we excluded 20 participants who reported not following all of our instructions with
regard to their headphones and sound settings (six reported not using headphones, eight reported not
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setting their volume to the specified value, four reported both not using headphones and not setting the
specified value, and two reported taking off their headphones during the test phase). Our final sample
consisted of 190 participants (129 men, 59 women, 1 non-binary person, 1 person whose gender was not
recorded properly; M
age
= 27.05, s.d.
age
= 6.82).
Differential ratings were calculated by subtracting the (fear or expectancy) rating on the first CS−trial
from the corresponding rating on the first CS+ trial, subtracting the rating on the second CS−trial from
the corresponding rating on the second CS+ trial, and so on.
5.2.2. Data analysis
5.2.2.1. Analytic strategy
Both the differential fear and expectancy ratings were subjected to mixed ANOVAs to test whether they
were significantly larger than zero overall (i.e. the intercept), whether they changed across the test phase
(i.e. a main effect of trial), and whether they varied as a function of acquisition type (i.e. a main effect of
acquisition type). We then conducted pairwise comparisons to test which of the three groups differed
from each other, using Holm–Bonferroni correction to account for multiple comparisons. Corrections
for sphericity violations were applied as in Experiment 3 and BFs again reflect the evidence for
including a specific term in the model.
5.2.2.2. Main analyses
Fear ratings. Figure 6adepicts the fear ratings as a function of CS, trial and acquisition type. The intercept
was significantly larger than zero, indicating that participants reported more fear after the CS+ than after
the CS−,F
1,187
= 206.47, p< 0.001,
h
2
p¼0:52, [0.44, 0.59]. Crucially, the differential fear ratings varied as a
function of acquisition type, F
2,187
= 4.84, p= 0.009,
h
2
p¼0:05, [0.01, 0.10], BF
10
= 4.53. Specifically, the
effect in the information-matched observations condition (M= 2.61) was larger than the effects in the
observations condition (M= 1.77), p= 0.03,BF
10
= 2.78, and the enhanced-instructions condition (M=
1.63), p= 0.01, BF
10
= 6.77. The latter two conditions did not differ, p= 0.68, BF
10
= 0.21. Finally, the
effect of trial was significant, p= 0.018, while the interaction between acquisition type and trial was
not, p= 0.19.
Expectancy ratings. Expectancy ratings were also higher for the CS+ relative to the CS−(figure 6b), as
reflected by the intercept, F
1,187
= 217.46, p< 0.001,
h
2
p¼0:54, [0.46, 0.60]. Similar to the fear ratings, the
size of this effect varied as a function of acquisition type, F
2,187
= 6.18, p= 0.003,
h
2
p¼0:06, [0.01, 0.12],
BF
10
= 12.7. Once again, the effect in the information-matched observations group (M= 3.02) was
larger than the effect in the observations group (M= 2.15), p= 0.047,BF
10
= 1.78, as well as larger than
the effect in the enhanced-instructions group (M= 1.71), p= 0.002, BF
10
= 29.84, while the observations
and enhanced-instructions groups did not differ, p= 0.25, BF
10
= 0.37. The main effect of trial was
significant, p< 0.001; the acquisition type × trial interaction was not, p=0.20.
5.2.2.3. Exploratory analyses
A number of points are worth mentioning (see the electronic supplementary material for all exploratory
analyses). First, the information-matched observations group showed the best memory in terms of which
CS was (not) followed by negative reactions. In line with the possibility that memory for the pairings
might have been partially responsible for differences between groups, the main effect of acquisition
type on the fear ratings became non-significant when participants with imperfect contingency
memory (n= 32) were excluded. Second, participants’answers suggested that a substantial number of
them did not interpret the videos as we had intended. Specifically, while the majority in the
enhanced-instructions group (correctly) indicated that the CS+ was only sometimes followed by the
sound to the model, the majority of the other two groups indicated that the CS+ was always followed
by the sound to the model. Similarly, the majority of the enhanced-instructions group also correctly
reported that the sound was presented to the model at CS offset, while relatively more participants in
the other two groups believed that the model could hear a sound at CS onset, or at both CS onset
and offset. Finally, most participants believed that a sound would be presented during the test phase,
suggesting that our instructions with regard to the sound were still believable in the online setting.
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5.3. Discussion
Once again, both observations and instructions about those observations had a clear impact on fear and
expectancy ratings. Despite using identical enhanced-instructions and observations manipulations as in
Experiment 3, we did not replicate the finding that enhanced instructions led to larger effects. Instead, the
two groups produced effects of comparable size. However, when the observations condition was
augmented with the additional information included in the enhanced-instructions condition (i.e. the
information-matched observations condition), a slight increase in effects emerged. That said, the
evidence for the differences between the two observations conditions was anecdotal at best (based on
the Bayes factors) and these differences became non-significant when participants who had incorrect
contingency memory or reported reactance were excluded (see electronic supplementary material).
The comparison between the enhanced-instructions condition and the information-matched
observations condition is perhaps the most relevant of all, as these two groups received the same
pieces of information (i.e. the content presented in the two conditions was equated) but in a different
format. The results of this comparison suggested that observations had a stronger effect than
instructions about those observations.
Taken together, we can conclude based on the results of Experiments 3 and 4 that instructions about
observations (i.e. about the relation between stimuli and a model’s behaviour) consistently resulted in
fear learning, both in terms of self-reports as well as for a physiological measure of fear (SCRs).
Whereas Experiment 3 suggested that instructions may have a larger effect on self-reports than
observations, this pattern was not replicated in Experiment 4, and a more direct comparison
suggested that observations may actually have a larger impact on behaviour.
observations matched observations enhanced instructions
observations matched observations enhanced instructions
6
4
2
fear rating
6
4
2
expectancy rating
CS+ CS–
CS+ CS–
1234 1 2 341234
1234 1 2 341234
trial
(b)
(a)
Figure 6. (a) Fear ratings as a function of CS, trial and acquisition type (Experiment 4). (b) Expectancy ratings as a function of CS,
trial and acquisition type (Experiment 4).
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6. General discussion
Past conditioning research provided extensive evidence for several learning pathways: the experience of
CS−US pairings, instructions about CS−US pairings, and the observation of a model’s behaviour in the
presence of a stimulus. Whereas previous research on instructions generally focused on the impact of
information about CS−US pairings that would be experienced directly, we examined for the first time
whether instructions about the behaviour of a model in the presence of a stimulus would lead to
changes in behaviour, more specifically evaluative (Experiments 1 and 2) and fear responding
(Experiments 3 and 4). We also compared the relative power of observations versus instructions about
observations in triggering behavioural changes. In doing so, we explored whether differences in the
impact of instructions and observations relate to differences in the information that those events
convey. Finally, in Experiment 1, we also explored whether there would be an additive effect of actual
observations on top of receiving instructions about observations.
The question of whether instructions about observations influence behaviour can be answered
unequivocally: we obtained clear effects of instructions about observations in all of our experiments
and for all measures of evaluative and fear responding (self-reported, automatic and physiological). In
other words, simply telling someone about a regularity between a stimulus and the behaviour of a
model can suffice to change their behaviour.
When it came to the relative effectiveness of instructions about observations versus actual
observations, no clear picture emerged. In some cases, instructions were as effective as observations.
Yet most of the time observations led to slightly larger effects than instructions. In addition, observing
the regularities after already receiving instructions still had an additive impact on evaluations in
Experiment 1.
Throughout our research, we also endeavoured to increase the ‘match’between the two pathways in
terms of the information they conveyed. In Experiment 2, we added examples of the model’s reactions to
the instructions, thereby matching more closely the information conveyed by actual observations.
Interestingly, adding information about the nature of the model’s reactions slightly increased the
impact of those instructions on evaluative ratings. In Experiment 4, we added information to the
observations that was given only to the enhanced-instructions group in Experiment 3, namely that
there would be a regularity and that participants had to remember it. This also slightly increased the
effects of the observations on fear responding, which highlights the potential impact of mentioning
the presence and importance of a regularity (for a related finding, see [46]). Although these findings
should be interpreted with caution (the Bayes factors indicated only anecdotal evidence), they suggest
that subtle differences in the provided information may influence the relative effectiveness of learning
pathways and underline the importance of closely matching this information if one wants to compare
different pathways.
6.1. Theoretical considerations
As noted in the introduction, cognitive models of learning differ in the way that they account for the
effects of observations and instructions. In this section, we discuss how our results may inform
the available theoretical perspectives. From a propositional perspective (e.g. [31]), one would predict
the effects to be similar if the information that is conveyed by actual events and instructions about
events is similar. In line with this idea, differences between groups in our studies seemed to be
partially due to ‘mismatches’in terms of the information they were given, such as information about
the precise nature of the model’s reactions or about the presence of an important regularity between
the reactions and the CSs. Both of these are aspects that would be predicted to influence the
effectiveness of the learning pathways from a propositional perspective.
On the other hand, even when the information was closely matched (Experiments 2 and 4),
observations still seemed to have stronger effects than instructions about observations. How might
one explain these findings? A first possibility is that learning via instructions and learning via
repeatedly experienced or observed regularities depend on partially distinct (neuro)cognitive processes
and therefore do not necessarily have similar effects on behaviour, as assumed by dual-process
perspectives (e.g. [8,34,35]). Note that while some specific dual-process theories cannot easily
accommodate the effects of instructions on automatic evaluations found in Experiments 1 and 2
without additional assumptions (e.g. [34]), other theories can (e.g. [36]). Generally speaking, however,
the mixed findings that we obtained seem to fit well with the idea that multiple processes are involved.
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A second possibility is that the two pathways rely on the same processes but differ in other respects,
which could account for at least some of the discrepancies observed in our data. What might those
differences be? First, and most trivially, some of the differences observed in our studies may have
been driven by differences in attention. Especially in online studies, participants may read instructions
quickly and superficially, whereas the use of videos may increase attention and engagement with the
experiment. This may also explain why we observed larger effects of instructions only in Experiment
3, where participants completed the experiment in the laboratory and were more likely to take the
time to read every page of instructions.
Second, the instructions and observations might differ in terms of believability. For example, the
impact of instructions about observations depends on participants believing that the described events
actually occurred, whereas they can see the events ‘with their own eyes’when they watch the videos.
Unlike the previous point about attention, believability may constitute a more intrinsic difference
between pathways as it seems difficult to fully match the believability of the information.
Nevertheless, future research could test whether differences between pathways are related to
differences in reported believability of those pathways. Likewise, differences between pathways might
be reduced by reducing differences in believability (e.g. by adding a pre-training phase in which
instructions are proven to be accurate). Also note that believability could in principle influence the
relative effectiveness of pathways in either direction. For example, if the model’s behaviour seems
acted or exaggerated, believability may actually be lower when one watches a video relative to when
one is simply told that the model reacted in a certain way, leading to a smaller effect of observations
relative to instructions.
Third, in our studies (as well as in many other learning studies), learning might depend on the
successful transmission of several components of information. A first component is the (strength of
the) regularity between events (i.e. which stimulus is followed by which reaction and how often). A
second component is the nature of the events themselves (i.e. the precise nature and intensity of the
model’s reaction, which may in turn be used to infer the properties of the CS). There may be
differences in terms of how well pathways can convey either component.
On the one hand, instructions may be more suited for communicating the first component, because
they can clearly and directly describe the regularities as well as their strength, avoiding the need for
participants to ‘figure out’the regularities themselves. In line with this idea, instructions have been
found to be more effective in changing the evaluative responses of children [26]. When discussing this
component, we should also highlight that our experiments contained two different types of
regularities. In Experiments 1 and 2, the regularities were deterministic (i.e. the CS
pos
was followed by
a positive reaction and the CS
neg
was followed by a negative reaction). Therefore, after receiving the
instructions or after seeing one video of each reaction, participants no longer needed to update their
representation of the regularities (i.e. figure them out). In Experiments 3 and 4, however, the
regularity was probabilistic (i.e. the CS+ was followed by a distressed reaction four out of six times).
While participants who received instructions were informed directly about the strength of this
regularity, participants in the observations conditions needed to discover this strength over the course
of the entire observation phase (in computational terms, the learned value of the CS+ continuously
needed to be updated based on error prediction across several trials; for recent discussions of
(probabilistic) social learning from a computational perspective, see [37,47]). As a result, the
observations conditions in Experiments 3 and 4 involved considerably more ‘figuring out’than the
observations conditions in Experiments 1 and 2 or the instructions conditions in any of our
experiments.
5
This probably left more room for error. Some of the findings from Experiment 4 seem
to support the notion that accurately encoding the strength of the regularity was more challenging in
the observations conditions. Specifically, while participants in the information-matched observations
condition accurately remembered which CS was followed by which reaction, they actually
overestimated the strength of the regularity, reporting that the CS+ was always followed by the
unpleasant sound (whereas in reality the model showed distress only after some presentations of the
CS+). In other words, it seems that the videos were less suited to convey the imperfect contingency
than the instructions, which may have led to stronger, but less accurate, fear learning. Finally,
although the contingency was deterministic in Experiments 1 and 2, some participants in the
observations condition may have mistakenly interpreted the repeated videos as showing three
different instances of the model tasting each cookie, rather than showing the same instance three
times. If so, this would constitute an important difference with the instructions condition from a
5
We thank an anonymous reviewer for drawing our attention to this important point.
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25

propositional perspective (whereas only the number of repetitions and the strength of the resulting
association would seem to matter from a purely associative perspective). Specifically, if some
participants believed that the model consistently reacted a certain way to a CS, this may have
increased the extent to which they attributed the model’s reaction to an inherent property of the CS
(e.g. see [48]), which may in turn have increased the size of effects. Therefore, it would be informative
for future research to assess in a more fine-tuned way what exactly participants believe they have
observed (e.g. single or multiple reactions), as their perception of the strength of the relation may vary
accordingly.
On the other hand, observations may be more suited for conveying the second component (i.e. the
nature of the events themselves): a few seconds of video can show a richness of dynamic facial
expressions and body language that may be difficult to put into words. In line with this idea,
providing participants with an example of the model’s reaction prior to the instructions seemed to
increase the similarity of the effects to those found in the observations group.
The idea that certain types of information are better conveyed by certain pathways is interesting,
because it implies that which pathway has the most powerful impact on behaviour depends on the
specific information that is conveyed. Moreover, as we hinted at above, the fact that a pathway is
more suited for conveying a certain piece of information does not mean that the resulting change in
behaviour would necessarily be stronger. If a video successfully conveys to an observer that a model’s
reaction is very subtle and not at all intense in nature, observations may actually lead to smaller
effects than instructions describing that reaction with a few words. Conversely, instructions may have
smaller effects than observations if they successfully convey that a regularity is not very strong.
In sum, it is possible that learning via observations and learning via instructions are mediated by
different cognitive processes, and some of our findings seem in line with this idea. However, because
of inherent differences in the way these two pathways can convey information, their effects may differ
even if the underlying process is the same. From this latter perspective, it may not be very interesting
to try and determine which pathway is ‘more powerful’in some absolute sense, as this may heavily
depend on the type of information that is communicated and on the context. Instead, it may be more
useful to explore the conditions under which the different pathways have a larger impact on behaviour.
6.2. Limitations and future directions
Our research has a number of limitations which can inform future research on this topic. First, most of
our conclusions about differences between groups are based on self-reports. Although we did include a
measure of automatic evaluations ( pIAT), the conflicting findings from Experiments 1 and 2 prevent us
from drawing any conclusions regarding the comparison between the two pathways in terms of
automatic evaluative responding. We also included a physiological measure of fear in Experiment 3
(i.e. skin conductance). Although it was encouraging to see an effect of instructions about
observations on this measure as well, the sample size in this study was rather small and thus
statistical power to detect differences between the two groups was relatively low. Future research on
the comparison between observations and instructions could therefore include (other) measures of
automatic and physiological responding, especially if one aims to test cognitive theories that predict
discrepancies between these measures and self-reports, as is the case for some of the theories
discussed in the introduction.
Second, we have focused on differences between conditions in terms of the learning effects averaged
across participants. However, distribution plots (see the electronic supplementary material) suggest that
the means may not always reflect the full picture. For example, in both evaluative learning experiments,
there were some participants who showed no learning effect at all on self-reports, especially in the
instructions condition. For practical purposes, it seems relevant to keep this in mind: if a learning
procedure has a strong impact on a few participants but no impact at all on the majority, only looking
at the mean could suggest that the procedure changes behaviour, while it may have limited practical
value (see also [49]).
Third, based on participants’responses to the exploratory questions, the videos in our studies were
acceptable but rather limited in terms of believability. As we have discussed above, believability is likely
to play a role in how strongly participants are influenced by the learning procedure. Therefore, future
research could attempt to increase the believability of videos used in the observational learning task,
or even use live modelling instead of videos (although this may also increase ambiguity and thus
reduce learning [50]).
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26

Fourth, the instructions provided to participants in Experiments 1 and 2 were very minimal, merely
describing the model’s behaviour as ‘positive’or ‘negative’. This left much room for participants’own
assumptions in terms of how strong the reactions would be, which we tried to reduce by showing an
example in Experiments 2 and 4. Because such assumptions and the resulting mental imagery
regarding the precise nature of the model’s behaviour are likely to have an impact, future research
could (i) look at the role of imagery (e.g. by measuring participants’general tendency to imagine
events vividly or actively asking participants to imagine the reactions) and (ii) investigate if it is
possible to achieve equally large effects of a purely verbal manipulation if the model’s reactions are
described in a very detailed manner, similar to how someone’s behaviour may be described in a work
of fiction.
Fifth, Experiments 1 and 2 involved food-related preferences, which may be highly subjective. From a
propositional perspective, whether participants assume that they are likely to share the model’s
preferences probably plays an important role in determining their own preferences. Because this
consideration applies equally to the effects of both actual observations and the effects of instructions
about those observations, we consider it unlikely that the groups in our experiments differed with
regard to this assumption. Nevertheless, it would be interesting to include an exploratory question
about it in future studies, as this may provide more insight into the determinants of participants’
responses.
Finally, we attempted to make the information contained within the different pathways as similar as
possible within the boundaries inherent to the different formats. Although this certainly created
challenges, we believe that future research aimed at comparing pathways could benefit from trying to
match the information as closely as possible. Especially if one wishes to make claims about the
cognitive processes that mediate the different pathways, it is crucial that the compared manipulations
contain the same information (content) and differ only in terms of the format in which it is presented.
7. Conclusion
To our knowledge, our research constitutes the first attempt to examine the effects of instructions about
observations and directly compare the effects of observing regularities between stimuli and the
behaviour of a model (observational conditioning) to the effects of receiving a verbal description of
those same events. We consistently found that both pathways (observations and instructions) were
effective in changing what people fear as well as what they like and dislike. Yet observing regularities
for oneself led to larger effects in some (but not all) of the experiments. Although this could reflect
different cognitive processes mediating the two learning pathways, another possibility is that the
pathways differ in terms of believability and in how suitable they are for conveying certain pieces of
information (e.g. the nature and strength of a relation between events versus the exact nature of the
events). Therefore, it may be more interesting to investigate the conditions under which each pathway
has the strongest impact on behaviour rather than try to make judgements about which pathway is
more effective in some absolute sense.
Ethics. All experiments were conducted after obtaining advice and approval from the Ethical Committee of the Faculty
of Psychology and Educational Sciences (application no. 2018/53). All participants provided informed consent.
Data accessibility. All datasets and analysis code supporting this article are available on the Open Science Framework
(http://dx.doi.org/10.17605/OSF.IO/AY25Z) [51]. With the exception of the videos, which have been replaced by
anonymized versions because we did not have consent from the actors to publish them in their original form, all
research materials are also available.
Additional figures and results are provided in the electronic supplementary material [52].
Authors’contributions. S.K.: conceptualization, data curation, formal analysis, funding acquisition, investigation,
methodology, project administration, resources, software, visualization, writing—original draft and writing—review
and editing; S.H.: conceptualization, funding acquisition, methodology, software, supervision and writing—review
and editing; J.D.H.: conceptualization, funding acquisition, methodology, resources, supervision and writing—
review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
Competing interests. We have no competing interests.
Funding. S.K. is supported by PhD fellowship FWO18/ASP/119 from the Research Foundation Flanders (FWO) to S.K.
S.H. and J.D.H. are supported by grant no. BOF16/MET_V/002 from Ghent University to J.D.H.
Acknowledgements. We would like to thank the members of the Emotion Lab (led by Andreas Olsson) at the Karolinska
Institute for providing us access to their database of observational fear conditioning videos, and Sophie Wüstefeld for
royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 9: 220059
27

collecting part of the data for Experiment 3. We also thank three anonymous reviewers for their helpful comments on
an earlier draft of the paper.
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