Inhibitory Learning with Bidirectional Outcomes: Prevention Learning or Causal Learning in the Opposite Direction?

Abstract

Influential models of causal learning assume that learning about generative and preventive relationships are symmetrical to each other. That is, a preventive cue directly prevents an outcome from occurring (i.e., "direct" prevention) in the same way a generative cue directly causes an outcome to occur. However, previous studies from our lab have shown that many participants do not infer a direct prevention causal structure after feature-negative discrimination (A+/AB-) with a unidirectional outcome (Lee & Lovibond, 2021). Melchers et al. (2006) suggested that the use of a bidirectional outcome that can either increase or decrease from baseline, encourages direct prevention learning. Here we test an alternative possibility that a bidirectional outcome encourages encoding of a generative relationship in the opposite direction, where B directly causes a decrease in the outcome. Thus, previous evidence of direct prevention learning using bidirectional outcomes may instead be explained by some participants inferring an "Opposite Causal" structure. In two experiments, participants did indeed report an opposite causal structure. In Experiment 1, these participants showed the lowest outcome predictions when B was combined with a novel cause in a summation test, and lowest outcome predictions when B was presented alone. In Experiment 2, B successfully blocked learning to a novel cue that was directly paired with a reduction in the outcome, and this effect was strongest among participants who endorsed an Opposite Causal structure. We conclude that previous evidence of direct prevention learning using bidirectional outcomes may be a product of excitatory rather than inhibitory learning.

Full text

RESEARCH ARTICLE
CORRESPONDING AUTHOR:
Julie Y. L. Chow
School of Psychology,
University of New South Wales,
Sydney NSW 2052, Australia
julie.chow@unsw.edu.au
KEYWORDS:
causal learning; prevention;
causal structure; feature
negative; bidirectional
outcomes
TO CITE THIS ARTICLE:
Chow, J. Y. L., Lee, J. C.,
& Lovibond, P. F. (2023).
Inhibitory Learning with
Bidirectional Outcomes:
Prevention Learning or Causal
Learning in the Opposite
Direction? Journal of Cognition,
6(1): 19, pp. 1–24. DOI: https://
doi.org/10.5334/joc.266
ABSTRACT
Influential models of causal learning assume that learning about generative and
preventive relationships are symmetrical to each other. That is, a preventive cue
directly prevents an outcome from occurring (i.e., “direct” prevention) in the same
way a generative cue directly causes an outcome to occur. However, previous studies
from our lab have shown that many participants do not infer a direct prevention
causal structure after feature-negative discrimination (A+/AB–) with a unidirectional
outcome (Lee & Lovibond, 2021). Melchers et al. (2006) suggested that the use of a
bidirectional outcome that can either increase or decrease from baseline, encourages
direct prevention learning. Here we test an alternative possibility that a bidirectional
outcome encourages encoding of a generative relationship in the opposite direction,
where B directly causes a decrease in the outcome. Thus, previous evidence of direct
prevention learning using bidirectional outcomes may instead be explained by some
participants inferring an “Opposite Causal” structure. In two experiments, participants
did indeed report an opposite causal structure. In Experiment 1, these participants
showed the lowest outcome predictions when B was combined with a novel cause in
a summation test, and lowest outcome predictions when B was presented alone. In
Experiment 2, B successfully blocked learning to a novel cue that was directly paired
with a reduction in the outcome, and this effect was strongest among participants
who endorsed an Opposite Causal structure. We conclude that previous evidence of
direct prevention learning using bidirectional outcomes may be a product of excitatory
rather than inhibitory learning.
JULIE Y. L. CHOW
JESSICA C. LEE
PETER F. LOVIBOND
*Author affiliations can be found in the back matter of this article
Inhibitory Learning with
Bidirectional Outcomes:
Prevention Learning or
Causal Learning in the
Opposite Direction?

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INTRODUCTION
The ability to learn about causal or predictive relationships between events in the environment
is important for adaptation. It allows the person or animal to anticipate significant outcomes
like the presence of threat or rewards. In a generative causal relationship, the presence of
the candidate cause (sometimes referred to as a cue) directly predicts the presence of the
target outcome. For example, the presence of rain clouds signals incoming rain. Also important,
however, is the ability for people to predict the absence of significant outcomes. Preventive
relationships involve the absence of the outcome when the cue is present.
Although there are many ways in which people might acquire causal beliefs, for example
through verbal instruction, we are specifically interested in how people learn from covariation
between events in the environment — that is, evidence collected from their own experience
with the cue and the outcome. Learning from covariation has a longstanding history in
associative learning and cognitive psychology. The basic assumption here is that causation can
be inferred from statistical patterns such as the co-occurrence or non-occurrence of events
(e.g., Cheng, 1997; Shanks, 2004). When human participants are exposed to contingencies
where the probability of the outcome occurring increases or decreases in the presence of the
cue, they reliably rate the cue as generating or preventing the outcome respectively (Shanks &
Dickinson, 1988). Accordingly, most theories of associative learning or causal inference assume
that learning about a preventive relationship is largely the same as learning about a generative
causal relationship, that is, causation and prevention are equivalent but opposite in sign (e.g.,
Rescorla-Wagner (RW) model, Rescorla & Wagner, 1972; Power PC Theory, Cheng, 1997, Novick
& Cheng, 2004; Causal Support, Griffiths & Tenenbaum, 2005).
However, the idea that causation and prevention are equivalent but opposite in sign disguises
an important asymmetry in the conditions required to both learn and express these two types
of relationship. Generative causal learning can be achieved by pairing a cue with an outcome,
but preventive learning cannot be achieved by simply pairing a cue with the absence of an
outcome. This is because the latter situation is indeterminate – the cue could be associated
with the absence of an indefinite number of different outcomes. Instead, preventive learning
requires some expectation that a particular outcome should occur, either through another
causal agent or the context (i.e. base rate). In the laboratory, prevention learning is often
studied using a feature negative (FN) design, in which a causal cue A is followed by the outcome
except on trials where a preventive feature B is also present (A+/AB– discrimination).
Although associative and cognitive models differ in the assumed psychological processes
underlying learning, implicit in both associative and cognitive models of learning is that the
conditions or experimental procedure for acquiring preventive relationships are not symmetrical
to causal relationships. For example, in the Rescorla-Wagner model, inhibitory learning is driven
by expectation of an outcome that does not occur (negative prediction error). The asymmetry is
also reflected in the use of different equations for generative and preventive learning in Cheng’s
(1997) Power PC theory. Cheng addresses this asymmetry explicitly and asserts that “treating
a preventive cause as the mirror image of a generative cause…is problematic” (1997, p.388). To
cite an example provided by Cheng, if a researcher is interested in the efficacy of a treatment
in causing headaches, they need only administer the treatment to a population to determine
whether it is effective or not. In contrast, the efficacy of a treatment in preventing headaches
cannot be determined by presenting the treatment if the patient taking the treatment does not
already have a headache (i.e., causal agent is missing). Under these conditions, the preventive
power of the cue cannot be determined. Similarly, generative causal learning can be expressed
behaviourally (for example in anticipatory responding) simply by presenting the cue, but
preventive learning is typically behaviourally silent (Zimmer-Hart & Rescorla, 1974; Konorski,
1948), and additionally requires the presence of a causal cue in order to be detected – in the
associative literature this is known as a summation test (Pavlov, 1927).
Despite this asymmetry in how we learn about generative vs preventive relationships, both
cognitive and associative models appear to assume that once learnt, the representations
underlying generative and preventive learning are essentially the same, and differ only in the
direction of their action on the outcome. Cognitive models like Power PC (Cheng, 1997), and
Bayesian models that propose similar causal power principles (e.g., Griffiths & Tenenbaum,
2005), typically imply that generative and preventive causes are both encoded by the learner

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as acting directly on the outcome. In line with this interpretation, when both generative and
preventive links are included in the same causal diagram, they are both depicted by a direct link
between a cue and an outcome but with a positive or negative weight to indicate causation or
prevention (e.g., Carroll et al., 2013; Gong & Bramley, 2021).
Certain models are even more explicit in their assumption that preventive and generative
learning differ only in the direction of their action. In this paper, we will focus on the Rescorla-
Wagner model (Rescorla & Wagner, 1972). The RW model has been widely influential in human
and animal associative learning and makes clear, testable predictions. This model assumes
that a person or non-human animal tracks the covariation between events through mental
links connecting representations of these events in memory. The strength of the mental links
between cue and outcome change flexibly according to experience. A cue that reliably predicts
the presence of an outcome (i.e., an excitor) comes to directly activate the representation
of that outcome, acquiring positive associative strength. In contrast, a cue that predicts the
omission of an outcome (an inhibitor) is said to directly suppress the outcome representation,
acquiring negative associative strength (Konorski, 1948; Rescorla, 1969). The RW model is clear
in presenting these two types of learning as opposite to each other. Excitation and inhibition
are seen as lying on a single dimension of associative strength, with excitors having positive
associative strength and inhibitors having negative associative strength. Researchers have
previously noted the similarities between factors that influence Pavlovian conditioning in
animals and human contingency learning (e.g., Alloy & Abramson, 1979; Shanks & Dickinson,
1988), suggesting that these associative processes, i.e., ability for a cue to activate or suppress
mental representation of an outcome, may govern the extent to which we judge a putative
cause and effect to be causally related (Hume, 1888; Dickinson, 1980). Thus, although
originally developed to account for animal conditioning data, associative learning models like
the RW model have been suggested to provide a good account of causal learning in humans
(Dickinson, Shanks & Evenden, 1984).
We have previously argued that the RW model maps onto the idea of prevention learning
in so far as excitors and inhibitors act directly on the same outcome representation; that
is, causation and prevention are symmetric opposites of each other both in terms of the
continuum of associative strength and the structure underlying learning (Lee & Lovibond,
2021). Our interpretation of the RW model is consistent with other contemporary associative
accounts of inhibition, including those made by Rescorla and Wagner separately (e.g., Rescorla,
1969; Brandon, Vogel & Wagner, 2003). We refer to this causal structure as “direct prevention”.
Note that we will refrain from using the term “conditioned inhibition” since this term is often
used to describe a learning phenomenon (i.e., a negative feature passing a summation test)
that is consistent with multiple underlying causal structures (Lee & Lovibond, 2021; see Sosa,
2022 for a review). However, an alternative causal structure for inhibitory learning has also
been identified in the associative literature by Rescorla (1985) and Holland & Lamarre (1984).
These researchers showed that in an A+/AB– FN discrimination, a learner may encode B not as
directly inhibiting the outcome but as modulating or gating the relationship between A and
the outcome. That is, A alone causes the outcome to occur, but when B is also present, A no
longer causes the outcome to occur. This is known as negative occasion setting in associative
learning (Bonardi, Robinson & Jennings, 2017; Fraser & Holland, 2019). We will refer to this
causal structure as “modulatory”, since it explicitly describes the action of a preventive cue as
modulating the action of a causal cue rather than acting directly on the outcome.
Evidence for modulatory learning comes from studies showing that the ability of the feature
to modulate another cue’s relationship with the outcome is largely independent of its own
relationship with the outcome (Fraser & Holland, 2019). Thus, modulatory learning provides a
formal alternative causal structure to direct prevention learning that not only specifies the need
for a generative cause to be present during learning, but also assumes this cause is explicitly
incorporated into the learned causal structure.
To our knowledge, direct prevention and modulation are the two most clearly specified causal
structures that represent a negative relationship between a cue and an outcome. When cues
are presented in an A+/AB– FN arrangement, the causal scenario is ambiguous and it is unclear
which causal structure is “correct”. In the animal conditioning literature, a key determinant of
what is learned is whether the AB compound is presented serially or simultaneously. Animals
typically show modulatory learning (negative occasion setting) to the feature when the

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compound stimuli are presented serially (A is followed by the outcome unless preceded by cue
B, i.e., A+/B –> A–), and something like direct prevention learning (conditioned inhibition) when
the stimuli are presented simultaneously (A+/AB–; e.g., Holland & Gory, 1986). However, there
is relatively little evidence for direct prevention learning in humans. In our work on self-reported
causal structures after FN training, we have observed substantial individual differences in
the content of inhibitory learning (Lee & Lovibond, 2021). We have previously shown that in
addition to a direct prevention causal structure, many participants report learning a modulatory
causal structure regardless of whether a serial or simultaneous design is used (Lee & Lovibond,
2021; Lovibond & Lee, 2021). A third group of participants reported learning only about which
outcome will occur with each combination of stimuli; we classify this group of participants
as Configural learners, in the sense that they learn about all cues presented together (e.g.,
in a compound) rather than trying to infer their individual roles (see Figure 1). Importantly,
suppression of outcome predictions in a summation test, where B is presented with a different
causal cue, is usually incomplete in human causal learning (Karazinov & Boakes, 2007; Lee &
Livesey, 2012), and much smaller in magnitude compared to non-human animals. According
to the RW model, if B has acquired negative associative strength after FN discrimination, one
would expect the summation of opposing associative strengths (positive associative strength
of the causal cue and negative associative strength of B) to produce much stronger suppression
of outcome predictions than is typically observed. Further evidence against the idea of
arithmetic summation of opposing causal strengths comes from recent work demonstrating
that summation is heavily influenced by the similarity between trained and transfer stimuli,
suggesting that the summation test is better characterised as a test of generalisation (Chow
et al., 2022). Finally, in both humans and animals, repeated presentations of a preventive cue
alone do not lead to a loss of its preventive properties, in contradiction to the predictions of the
RW model (Lovibond, Chow, Tobler & Lee, 2022; Zimmer-Hart & Rescorla, 1974). Thus, evidence
of direct prevention learning is limited, especially in human causal learning (and maybe also
in animals).
Melchers, Wolff and Lachnit (2006) proposed that the difficulty in demonstrating direct
prevention learning of the type proposed in the RW model in humans could be a product of
the type of outcome used in causal learning studies. These researchers pointed out that most
studies employ outcomes that can only vary unidirectionally (e.g. an allergic reaction that
either occurs or does not occur, but there is no anti-allergic reaction). They suggested that
unidimensional outcomes do not accurately reflect the symmetrical continuum of associative
strengths described in the RW model. Instead, they proposed employing bidirectional outcomes
in causal learning tasks, where the outcome could increase or decrease relative to some
baseline. According to Melchers et al. (2006), this procedure would allow the feature B to elicit
an expectation that the outcome would decrease below baseline, an outcome they described
as being in line with the assumptions of the RW model (i.e., negative associative strength).
Thus, they argued that if a bidirectional outcome were employed during a FN discrimination,
participants would be more likely to infer that B directly prevents the outcome from occurring.
They provided some evidence for this claim by showing that the inhibitory properties of the
feature B could be extinguished by presenting the feature alone (B–) when the outcome was
bidirectional but not when it was unidirectional. The idea here is that when a bidirectional
outcome is used, negative associative links could increase from a negative value to zero,
allowing the feature to lose its inhibitory power.
However, there is an alternative way in which people could interpret the role of the feature when
FN training (A+/AB–) is carried out with a bidirectional outcome. If we consider the outcome
below baseline as a distinct outcome in itself, it is plausible that instead of acquiring prevention
learning, where the feature B prevents the outcome signalled by A, participants might instead
infer that B directly causes the opposite outcome to that elicited by A. For example, if cue A
predicted an increase in outcome level and AB predicted no change, participants might infer
that B directly caused a decrease in the outcome. This “opposite causal” structure is distinct
from both of the inhibitory structures, prevention and modulation, described earlier. Opposite
causal learning is different from direct prevention learning since it involves a generative causal
link between the cue and the opposite outcome (in associative terms, the feature activates a
representation of the opposite outcome, rather than suppressing a representation of the actual
outcome presented). An opposite causal structure is also distinct from a modulatory causal
structure since it involves a direct link between the feature and the outcome, and therefore the

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feature is able to be active on its own without an existing source of excitation (e.g., another
causal agent present) for its effect to be seen. In summary, the opposite causal structure is
distinct from genuine inhibitory structures like direct prevention and modulation, as it involves
a direct excitatory connection between the feature and an (opposite) outcome. In contrast,
both direct prevention and modulation involve the inhibition or suppression of an outcome,
either directly (direct prevention) or by gating the relationship between a causal cue and an
outcome (modulation). Diagrammatic representations of all four causal structures potentially
inferred in a FN discrimination task (direct prevention, modulation, configural and opposite
causal) are shown in Figure 1.
The goal of the present study was to test this alternative explanation that when presented
with bidirectional outcomes in a FN discrimination, some participants acquire opposite
excitatory learning to the feature. Across two experiments, we tested this hypothesis by
incorporating the new opposite causal structure into our previous method for assessing
individual differences in causal structure inference (see Experiment 1 Procedure). We used
a modified allergist task with the same causal scenario used by Melchers et al. (2006) and
Lotz and Lachnit (2009), with hormone levels as outcomes that either increased, decreased
or were unchanged. Our primary hypothesis was that a FN discrimination with a bidirectional
outcome could be solved by assuming that the feature directly causes a reduction in hormone
level (opposite causal) rather than preventing the hormone level from increasing. Thus, we
expected a proportion of participants to not only report such an opposite causal structure to the
feature alone (i.e., that it causes a decrease in hormone level), but also that these individuals
would show the greatest transfer effect in a summation test compared to participants who
reported an inhibitory (direct prevention or modulation) causal structure, where as noted
earlier transfer is often incomplete. These findings would suggest that we have identified a
novel causal structure when a bidirectional outcome is used, and demonstrate that opposite
causal is distinct from other known inhibitory structures.
Figure 1 Diagrammatic
Representation of All Four
Causal Structures.
Note: Pointed arrowheads
represent an excitatory
connection, and flat
arrowheads represent an
inhibitory connection.

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EXPERIMENT 1
The aim of Experiment 1 was to explore whether participants endorse an “opposite causal”
structure to explain the role of the feature B in an A+/AB– feature negative discrimination
when the outcome is bidirectional in nature. In our design and the design of Melchers et al
(2006) and Lotz & Lachnit (2009), the term “bidirectional” is used to refer to outcomes that
can increase or decrease from baseline, while “unidirectional” refers to (binary) outcomes that
can only increase from baseline (outcome absent/present). In their design, Melchers et al.
(2006) included an individual cue F that was followed by no change (F0) in their unidirectional
outcome group but by a reduction in hormone level (F–) in their bidirectional outcome group.
Another aim of our study was to test whether giving such direct experience with a reduction
in hormone level was critical to establish the bidirectional nature of the outcome, or whether
simply instructing participants that it could change in either direction would be sufficient.
Therefore, we included a similar cue I in our training phase, which we labelled a “reference”
cue, and followed it by no change in hormone level in one group (the No Reference Group)
and by a reduction in hormone level in a second group (Reference group). In our experiment,
however, both groups were instructed that the outcome was bidirectional and could increase,
stay the same or decrease after a meal.
Apart from the reference cue I, participants were exposed to a range of food cues A–H that
followed the same design as in our previous experiments on prevention learning (e.g., Lee &
Lovibond, 2021) and implemented an A+/AB– feature negative discrimination in addition to
training a control cue D and filler cues (see Table 1). These cues were all followed by a hormone
increase or no change. After the training phase, participants were asked to make predictions
about novel compounds in which a separately trained excitor C was combined with the feature
B, as well as with a control cue D (summation test). This comparison assessed the transfer of
B’s properties from the training excitor A to the test excitor C, relative to the control cue D. We
hypothesised that participants in the Reference group would show a larger difference in CB vs
CD ratings, as well as more negative prediction ratings to B alone, compared to participants
who only ever saw an increase in hormone levels.
Our previous work on feature negative learning in causal judgment with unidirectional
outcomes suggested that humans infer different causal structures about the feature B in a
FN discrimination, regardless of whether AB was presented simultaneously or serially. Some
participants reported that B directly prevents the outcome (Direct Prevention), consistent with
the RW conception of prevention learning, whereas others reported the role of B as determining
whether the causal cue A will cause the outcome to occur (Modulation), much like the modulatory
role of a negative occasion setter (Lee & Lovibond, 2021). A third group of participants reported
learning only about which outcome will occur with each combination of stimuli (Configural).
Importantly, we found corresponding differences in transfer of B’s inhibitory properties to
the test excitor C, where participants in the Direct Prevention subgroup provided the greatest
transfer (lowest prediction ratings to the CB compound compared to control compound CD),
Configural participants showed the least amount of transfer, and Modulation participants were
in between the two (see also Glautier & Brudan, 2019). In the present experiment, where the
outcome was bidirectional, we predicted that some participants would infer a fourth causal
structure, namely an Opposite Causal structure. In order to differentiate between empirical
and theoretical aspects in this paper, we will use capital letters when referring to empirical data
involving subgroup categories (e.g., Opposite Causal) and lowercase letters for the abstract
theoretical causal structures.
In this study, we predicted that not only would there be more participants in the Reference
group who reported an Opposite Causal structure compared to the No Reference group,
but this subgroup of participants would also show strongest transfer compared to all other
subgroups, indexed by greater difference in ratings for CB relative to CD. Our rationale here
was that Opposite Causal participants would be more confident in predicting transfer since
they construed the feature as directly causing the opposite effect to the test excitor, where the
similarity of A and C should not matter.

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The Reference group experienced cue I paired with a decrease in hormone level (I–), whereas
the No Reference group experienced cue I paired with no change in hormone level (I0). Column
headings describe each phase of the experiment in sequence from left to right, beginning
with the Training phase and ending with the Forced-choice causal structure assessment.
All stimuli presented in each phase (and their associated outcomes) are denoted below the
relevant column heading.
METHOD
Participants
One-hundred and eighty nine participants recruited through Prolific (64 female, Mage = 30.3,
SD = 10.4) participated in this study in exchange for monetary payment (20min at £6GBP/hr).
Apparatus & Stimuli
The experiment was programmed using the jsPsych library (de Leeuw, 2015). Cue stimuli, which
included the image of the food and a text description, were presented on a blank background
that was 300 pixels wide by 300 pixels high. A total of 10 experimental cues were used in
this study (cues A–J); the assignment of cues was randomised across participants. Outcome
stimuli consisted of a verbal description of the change in hormone level (e.g. hormone level: no
change) in bold text. Outcome stimuli were presented on a blank background 609 pixels wide
and 300 pixels high.
Procedure
The task procedure reported here followed previous work from our lab (Lee & Lovibond, 2021;
Chow, Lee & Lovibond, 2022), apart from the bidirectional nature of the outcome and the
prediction scale.
Training phase
Participants were asked to imagine they were a doctor investigating which foods were causing
fluctuations in hormone level in “Mr X”. Participants were told that excessive levels of the
hormone are linked to a particular disease, and changes in hormone level are thought to be
related to diet. On each trial, participants were shown different meals that Mr X had eaten,
and asked to predict whether Mr X’s hormone level would increase, decrease, or experience no
change after eating that meal. Predictions were made on a scale from “Definitely DECREASE” to
“Definitely INCREASE”, with a midpoint of “No Change”. Ratings were recorded on a bidirectional
numerical scale from –100 to +100.
Once participants had made a prediction, the “Continue” button appeared at the bottom of
the scale and they were able to continue to the next screen. The prediction scale was then
replaced with outcome feedback in text (hormone level increase, decrease or no change),
which remained on screen for 2 seconds before a 2-second intertrial interval where all cues
disappeared from the screen.
Training trials consisted of 3 blocks with 2 presentations of each trial type in each block (see
Table 1). The order of presentation for compound cues was counterbalanced within-block (e.g.
AB and BA), such that participants saw both orders of presentation in a randomised order (AB
first or BA first). Trial types were also randomised in a way that participants never saw two
identical trials presented successively. In total, participants completed 42 training trials with 6
presentations of each trial type.
Table 1 Procedure of
Experiment 1.
Note: + = an increase in
hormone level, 0 = no change,
and – = a decrease.
TRAINING TEST PREDICTIONS CAUSAL
RATINGS
OPEN-ENDED
QUESTION
FORCED-CHOICE CAUSAL
STRUCTURE ASSESSMENT
A+ AB0A AB B A B
B B
C+ C CB CD CI C
DE0DE D E D E
F0 GH+ F F
I0/I– I J I J

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Outcome prediction at test
After completing the training phase, participants were asked to continue making predictions
about changes in Mr X’s hormone level, but were told they would no longer be presented with
feedback. The prediction scale used in this phase was identical to that presented during training.
Participants were shown summation compounds consisting of the separately trained excitor C
with the trained inhibitor B (CB), control cue D (CD), and reference cue I (CI), as well as familiar
cues from the previous phase (A–I) and a novel cue J. Each trial type was presented twice, and
the left-to-right order of presentation for compounds cues was again counterbalanced.
Causal ratings
Following the test predictions, participants were given instructions about the causal judgement
phase of the study. They were asked to rate the extent to which they thought different foods
caused or prevented an increase in hormone level in Mr X. Participants were informed that the
rating scale in this phase was different to the prediction scale they had seen in previous phases.
The causal rating scale ranged from “Strongly PREVENTED an increase” to “Strongly “CAUSED
an increase” with a midpoint of “No effect”. These ratings were recorded on a bidirectional
numerical scale from –100 to +100. The wording of the causal rating question was chosen
to match that used in our previous studies on feature negative learning with unidirectional
outcomes (e.g., Lee & Lovibond, 2021), and also so that it would be consistent with any of the
hypothesised causal structures.
Open ended question
After completing the causal judgement phase, participants were asked a single open-ended
question about the critical cue B. Participants were shown an image of cue B at the top of the
screen, followed by a text field box where they could explain what they had learned about the
role of cue B. Participants were encouraged not to leave the field box empty.
Causal structure assessment
In the final phase of the study, participants were asked to assess the role of cue B in a
4-alternative forced-choice (4AFC) question. An image of cue B was presented at the top of the
screen, and participants were asked to select the option that best described what they thought
about the role of cue B. Three of the options were similar to those used in Lee & Lovibond (2021),
Lovibond & Lee (2021) and Chow, Lee & Lovibond (2022). Specifically, the Modulation option
was “It prevented an increase in hormone level caused by specific foods”, the Direct Prevention
option was “It prevented an increase in hormone level in general”, and the Configural option
was “It is hard to know the exact role of individuals foods such as this one. I concentrated on
remembering which combinations of foods caused changes in hormone level and worked from
there”. In addition, we included a fourth option to capture an opposite causal structure, where
cue B is thought to produce the outcome in the opposite direction (i.e. hormone decrease).
The phrasing of the Opposite Causal option was “It caused a decrease in hormone level”. The
order of presentation of the four options was randomised between participants. After making
a selection, participants could proceed to the final screen where they were asked if they had
written anything down during the course of the experiment. Participants were encouraged to
provide an honest response and were told that their response would not affect their eligibility
to be paid.
Statistical analysis
The primary measure of interest was the outcome prediction ratings for summation compounds
CB and CD. To test these differences, and in particular whether they were systematic differences
as a function of group, we analysed the data with three orthogonal contrasts using the afex
(Singmann et al., 2018) and emmeans (Lenth, 2019) packages in R. We compared 1) average
ratings for compound CB compared to CD, 2) average ratings for participants in the Reference
group compared to the No Reference group for compounds CB and CD, and 3) the interaction
between these two contrasts. We also included simple effects to test the CB vs CD difference
for the Reference group and the No Reference group separately. We also directly compared
average ratings for the feature B for participants in the No Reference vs Reference group. For
analysis of causal ratings, we compared ratings for cues B vs D (averaged over group), ratings
for Reference vs No Reference group (averaged over cues B and D), and the interaction between
the two contrasts.

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Data from the training phase were analysed with planned contrasts that tested 1) ratings for
cues followed by hormone increase to those followed by no change common to both groups
(common predictive vs non-predictive cues), 2) linear trend over the 6 presentations, 3) average
ratings for Reference vs No Reference group, and 4) all interactions between these contrasts.
Contrasts 2–4 were repeated for the comparison between reference cue I and common non-
predictive cues.
For all the contrasts described above, we also included a between-subject factor of inferred
causal structure determined by participants response on the 4-AFC question. We tested two
planned contrasts: 1) Opposite Causal compared to the average of all other subgroups, and
2) Configural subgroup compared to the average of the two inhibitory subgroups (Direct
Prevention and Modulation). All contrasts were tested as main effects (averaged over all other
factors), as well as in interaction with all contrasts on other factors. These contrasts were
selected to compare the subgroup of participants who endorsed an Opposite Causal structure
to all other subgroups, as this structure is novel to the present study. We have also previously
found the Configural subgroup to produce qualitatively different pattern of results compared
to Modulation and Prevention subgroups (Lee & Lovibond, 2021); this is tested in the second
contrast. These participants tend to report only remembering the outcomes of the stimulus
combinations and remain agnostic about the effects of B alone. The phrasing of the Configural
option in the 4-AFC question also differed from the other options in that it did not involve
making any inferences about the causal status of B, and was therefore the more conservative
of the options. Thus, of the four causal structures assessed in this study, we predicted the
strongest transfer in the Opposite Causal subgroup, and the weakest transfer in the Configural
subgroup, with Modulation and Prevention subgroups in between. We did not include contrasts
comparing the two inhibitory subgroups in the present study as we have previously argued
that differences between participants who report a Direct Prevention and Modulation causal
structure does not reflect qualitative differences in what is inferred, but rather quantitative
differences in their willingness to generalise properties of the feature B to a novel test excitor
(Chow, Lee & Lovibond, 2022).
Finally, we also included a chi-square test of independence to compare participants’ causal
structure selection as a function of group to determine whether direct experience with a
negative outcome influenced the proportion of participants who inferred an opposite excitatory
causal structure to B.
The full dataset for the two experiments reported here is publicly available at the Open
Science Framework, and can be accessed at https://osf.io/9at4b/.
RESULTS
To recap our specific hypotheses for this experiment, we predicted that more participants
would report an Opposite Causal structure in the Reference group, where they were explicitly
presented with a cue that led to hormone level decrease, compared to the No Reference group.
We additionally predicted that Opposite Causal participants would show greatest transfer in
a summation test, indexed by greater difference in prediction ratings for CB compared to CD,
as well as more negative prediction ratings to B alone, compared to all other subgroups. For
brevity, figures presented on all test measures only include the primary cues of interest. For
participants’ ratings on all stimuli presented at test, see online Supplemental Materials.
Exclusion criteria
As in our previous published studies (Lee & Lovibond, 2021; Lovibond & Lee, 2021; Chow, Lee &
Lovibond, 2022), participants’ data were excluded from analysis if they reported writing down
information during the task or if they failed to meet the training criterion. To pass the training
criterion, all participants were required to provide 1) average rating > 75 for positive predictive
cues and, 2) average rating between –25 and 25 for non-predictive cues. Participants in the
Reference group were additionally required to provide an average rating < –75 for the reference
cue I (cue I was a non-predictive cue in the No Reference group). In addition to the two criteria
described above, participants were also presented with an instruction check prior to starting the
task (see Supplemental Materials for the exact wording). Failure to provide a correct response on

10Chow et al.
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all three questions resulted in the program restarting at the first instruction screen. Participants
who failed this instruction check more than twice (instructions were repeated at least three
times) were excluded from analysis. Of the 189 participants recruited, 17 participants admitted
to writing something down during the study, an additional 11 further participants failed the
instruction check, and an additional 20 failed to meet the training criterion. After applying
all three exclusions, 141 eligible datasets remained; 71 datasets from participants in the No
Reference group, and 70 datasets from participants in the Reference group.
Causal structure assessment
For consistency with previous published studies from our lab, we defined subgroups based on
participants’ forced choice causal structure selection and not their open-ended responses.
A breakdown of subgroup categorisation for participants in the No Reference and Reference
groups is shown in Table 2. Although there were some differences between the groups in the
pattern of causal structure choices, a chi-squared test of independence showed no statistical
difference in reported causal structure as a function of group, c2 (3) = 6.37, p = .095. Thus,
contrary to our hypothesis, we found no significant difference in the proportion of participants
reporting an Opposite Causal structure as a function of group.
Training
Figure 2 shows participants’ average predictions across the six presentations of each trial
type, separated by group. Analysis of training predictions showed a significant overall effect of
cue type comparing common predictive to non-predictive cues, F(1,133) = 1487, p < .001, ηp
2
= .918, that also interacted with linear trend of presentation, F(1,133) = 423.3, p < .001, ηp
2 =
.761. These findings suggest that participants successfully learned over trials which cues were
predictive of an increase in hormone level, and which cues produced no change. Importantly
there was no significant interaction with group, F(1,133) = 1.46, p = .228, ηp
2 = .011; learning
for the common predictive and non-predictive cues progressed similarly for the two groups.
Comparison of ratings for the reference cue I compared to the non-predictive cues showed a
main effect of cue type, F(1,133) = 405.7, p < .001, ηp
2 = .753, which interacted significantly
with linear trend of presentation, F(1,133) = 77.8, p < .001, ηp
2 = .369. Importantly we also
found a three-way interaction between cue type, linear trend and group, F(1,133) = 65.3, p <
.001, ηp
2 = .329, confirming that learning about the reference cue progressed differently for the
two groups, with participants in the Reference group successfully learning across successive
trials that cue I predicted a decrease in hormone level.
Causal structure comparisons in the Training phase revealed only a marginally significant
interaction that reflected slightly stronger discrimination between the common predictive
and non-predictive cues in the Opposite Causal subgroup compared to the other three
subgroups, F(1,133) = 5.03, p = .027, ηp
2 = .036. No other main effects or interactions were
significant, Fs < 1. Training data broken down by causal structure subgroup can be found in
Supplemental Materials.
Table 2 Number of
participants in each group
as a function of their 4-AFC
selection.
GROUP CAUSAL STRUCTURE NUMBER OF PARTICIPANTS
No Reference Configural 20
Modulation 23
Opposite Causal 19
Prevention 9
Reference Configural 28
Modulation 19
Opposite Causal 9
Prevention 14

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Outcome prediction at test
Each participant’s test predictions were averaged across the two presentations for each trial
type. Figure 3 shows the mean outcome predictions for primary stimuli of interest, B, CB, CD,
CI, and I, separated by group (3a) and broken down by causal structure subgroups (3b). We
were primarily interested in participants’ ratings for summation compounds CB and CD, as well
as test predictions for B alone. Comparison of participants’ ratings to cue I alone were also
included as a manipulation check.
Contrasts comparing CB and CD ratings showed a main effect of cue, F(1,133) = 43.2, p < .001,
ηp
2 = .245, which also interacted significantly with group, F(1,133) = 7.17, p = .008, ηp
2 = .051.
These results indicate that overall, participants showed successful transfer of B’s properties
from A to C, and the CB–CD difference was greater for participants who saw a reference cue
leading to hormone decrease than for those who never saw hormone decrease as an outcome.
There was no main effect of group, F < 1.
Importantly, we found the CB–CD difference to interact with causal structure, in particular
when comparing the Opposite Causal subgroup to all other subgroups, F(1,133) = 8.68,
p = .004, ηp
2 = .061. As hypothesised, the Opposite Causal subgroup showed more suppressed
Figure 2 Mean Outcome
Prediction (±SE) During
Training For Participants In
No Reference And Reference
Group In Experiment 1.
Note: Filled symbols denote
stimuli that predicted
hormone level increase, and
unfilled symbols denote
stimuli that predicted no
change (or hormone level
decrease in the case of cue I
in the Reference condition).
Figure 3 Average Outcome
Prediction at Test (±SE)
for Critical Summation
Compounds (a) as a Group
Average, and (b) Separated
by Causal Structure Subgroup,
for the No Reference and
Reference Group Respectively
in Experiment 1.

12Chow et al.
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ratings to CB compared to CD relative to the other subgroups. Importantly, there were no
further interaction with group as a factor, suggesting that individual differences in transfer
as a function of inferred causal structure were insensitive to the reference cue manipulation.
No other contrasts were significant, largest F(1,133) = 2.03, p = .157, ηp
2 = .015 (see
Supplemental Materials for additional results involving subgroup contrasts).
A similar comparison of ratings to B alone at test showed a main effect of group, F(1,133)
= 24.1, p < .001, ηp
2 = .154, with lower predictive ratings in the Reference group than No
Reference group. There was also a causal structure difference when we compared the
Opposite Causal subgroup to all other subgroups, F(1,133) = 19.4, p < .001, ηp
2 = .127. No
other contrasts or interaction involving causal structure subgroup was significant, Fs < 1.
Together, these results show that participants who saw a separate cue that caused a decrease
in hormone level were more likely to predict that B alone would result in hormone decrease
than participants who never experienced hormone decrease as an outcome. Furthermore, as
we hypothesised, participants who inferred an Opposite Causal structure were also more likely
to predict hormone level decrease in the presence of B alone, and showed greater transfer of
B’s properties in a summation test. Critically, however, the effect of direct experience with a
negative outcome and causal structure were additive, as we found no interaction between
causal structure and group.
Unsurprisingly, analysis of ratings to cue I alone showed a main effect of group, F(1,133) =
1144.7, p < .001, ηp
2 = .898, with lower ratings in the Reference group than the No Reference
group. No other contrasts tested were significant, largest F(1,133) = 2.27, p = .135, ηp
2 = .017.
Causal ratings
Figure 4 illustrates causal ratings for cues B, D and I, for each of the groups separately (4a) and
separated by causal structure subgroup (4b). This test differed from the outcome prediction
test in that participants were asked to rate the ability for each individual cue to cause or prevent
the outcome from occurring.
Comparison of mean ratings for cues B and D revealed a main effect of cue, F(1,133) = 51.7,
p < .001, ηp
2 = .280, with more negative (preventive) ratings for B relative to D averaged across
groups. There was no main effect of group, and no interaction between group and cue, Fs < 1.
Contrasts comparing the different causal structure subgroups revealed a small but significant
difference in mean ratings for the Opposite Causal subgroup relative to all other subgroups
(averaged over cues B and D), F(1,133) = 4.72, p = .032, ηp
2 = .034, and an interaction between
Figure 4 Mean Causal Ratings
at Test (±SE) for Critical Stimuli
Only for Participants in the
No Reference and Reference
Group as a (a) Group Mean,
and (b) Separated By Causal
Structure in Experiment 1.
Note: Causal ratings were
made on cause-prevent scale,
from –100 (Strongly prevented
an increase) to +100 (Strongly
caused an increase) with a
midpoint of 0 (No effect).

13Chow et al.
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this comparison and cue type (B vs D), F(1,133) = 5.27, p = .023, ηp
2 = .038. No other causal
structure interactions were significant, largest F(1,133) = .923, p = .657, ηp
2 = .001. Together
these results suggest some differences as a function of inferred causal structure on ratings of
B’s ability to prevent the outcome—the difference in causal ratings to B compared to D was
significantly greater for Opposite Causal compared to all others subgroups. These findings did
not appear to differ statistically as a function of group.
Analysis of participants’ ratings for cue I revealed lower (more preventive) ratings in the
Reference group compared to the No reference group, F(1,133) = 102.9, p < .001, ηp
2 = .436,
and higher ratings for the configural subgroup compared to the average of the two inhibitory
subgroups, F(1,133) = 13.2, p < .001, ηp
2 = .090. No other comparisons were statistically
significant, Fs < 1.
DISCUSSION
Experiment 1 showed that when the feature B in an A+/AB– FN discrimination was presented in
compound with the predictive cue C at test, ratings were lower compared to when C was paired
with a control cue, D. This difference in summation test ratings was greater when participants
had direct experience with a separate cue that led to a decrease in hormone level (Reference
group), compared to those who had never seen a negative outcome (No Reference group).
Participants in the Reference group were also more likely to predict a decrease in hormone level
when cue B was presented alone compared to participants in the No Reference group. However,
we did not find the same difference between groups on causal ratings for cue B relative to
cue D. We also did not find a difference in the proportion of participants reporting each causal
structure as a function of group. Thus, although experience with hormone level decrease
produced stronger transfer effects in the summation test, direct experience with a negative
outcome was not necessary for participants to infer an opposite excitatory causal structure
for B. A possible reason for this result is that simply presenting a bidirectional prediction scale
was sufficient to encourage some participants to consider that certain foods might lead to a
decrease in hormone level, even if they had no direct evidence of this in the experiment. This
suggests that the inclination for some participants to infer an opposite causal structure in a FN
discrimination might be greater than previously expected.
Contrasts comparing the different causal structure subgroups also showed differences in
summation test ratings as a function of self-reported causal structure for cue B. As predicted,
we found greater transfer for participants who endorsed an Opposite Causal structure
compared to all other subgroups in the summation test. Participants who reported an Opposite
Causal structure also provided more negative prediction ratings to cue B compared to all other
subgroups, suggesting that participants who inferred an opposite generative relationship
between B and the outcome were more likely to predict a decrease in hormone levels in the
presence of B alone, and to show greater transfer of B’s properties to a novel causal cue. Together
these results show that when the outcome has the potential to be negative, some participants
infer an opposite causal relationship between the negative feature and the outcome, and this
type of learning is distinct from prevention and modulatory learning.
In summary, in addition to the causal structures we have investigated in our previous work,
Experiment 1 provided initial evidence for a possible fourth causal structure inferred by
participants in a FN discrimination when the outcome is bidirectional. Importantly, these results
are the first to show that strong transfer in a summation test and negative prediction ratings to
the feature alone when a bidirectional outcome is used are largely driven by participants who
inferred an opposite excitatory causal structure to B (B causes a decrease in hormone levels)
rather than direct prevention learning as previously proposed (B prevents hormone levels
from increasing). The possibility of a negative outcome seems to allow participants to infer an
opposite excitatory causal structure for B whereby it is seen as directly causing a decrease in
outcome level.
EXPERIMENT 2
If our conception of an opposite causal structure is correct, then participants who endorse this
structure should treat the feature as equivalent to a cue that has been explicitly trained to
predict a reduction in the outcome. In Experiment 2, we tested this proposition by examining

14Chow et al.
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the degree to which the feature would block learning about a novel cue that was directly paired
with a decrease in hormone level. The blocking procedure is commonly used in the associative
learning literature to study the effects of cue competition (Kamin, 1969). The idea here is
that humans and animals judge the causal relationship between two events by considering
other potential causal cues in the environment, and all cues present concurrently compete for
predictive value. When an outcome is already well predicted by a cue, for example if participants
already expect hormone levels to decrease in the presence of B, learning about the predictive
significance of another cue present at the same time is impaired.
In this study, the feature was first presented in a FN discrimination like in Experiment 1.
Then, in a Blocking phase, the feature B was presented in compound with a novel cue (X) and
the compound was followed by a decrease in hormone level (BX–). We hypothesised that
participants who had inferred an opposite causal structure for B would show greater blocking
of X compared to a control cue Y that was also novel in phase 2 and followed by the same
decrease in hormone level but whose partner cue was never part of a FN discrimination
(DY–). This is because these participants should learn that B alone will cause a hormone
level decrease, which should effectively compete with the novel cue X for association with
the observed decrease in hormone level. In other words, when BX is presented with hormone
level decrease, the outcome is not surprising and learning about X is blocked. In contrast,
the genuinely inhibitory structures (direct prevention, modulation) would not be expected to
support strong blocking. In the case of modulation, participants are thought to learn that the
feature B modulates the relationship between the training excitor A and an increase in hormone
level, which is not directly relevant to the Blocking manipulation. Indeed, a modulator should
make no assumption about the ability for B to be active by itself without an excitor present. In
the case of direct prevention, participants are thought to learn that the feature prevents the
outcome produced by the training excitor A (hormone level increase), therefore a preventer
might learn that B alone would lead to no change in hormone levels, since B prevents hormone
levels from increasing. When presented with the BX compound for the first time, the outcome
(hormone level decrease) should be surprising since it is not predicted by the presence of B or
X, and the cues compete equally for association. Thus, we expected to find greater evidence of
blocking to X in the Opposite Causal subgroup relative to all other subgroups.
The design of Experiment 2 is shown in Table 3. Participants were presented with a similar
predictive learning task to Experiment 1, with foods as cues and changes in hormone level as
the outcome. The hormone level could again increase, decrease or stay the same on each trial.
However, unlike in Experiment 1, hormone level changes were presented as numeric values.
A feature negative contingency was set up such that cue A reliably predicted an increase in
hormone level by 20 units (A+20), and simultaneous presentation of cues A and B resulted
in no change in hormone level (AB0). To enhance transfer of inhibition, all participants were
presented with a reference cue that predicted a decrease in hormone level by 20 units
(C–20). This reference cue was also used in a comparison to B at test in order to determine
if participants treated B in the same way as a cue that was directly paired with a reduction in
hormone level, which we predict might be the case for participants who endorse an Opposite
Causal structure. DE was included to provide a control stimulus for feature B, where cue D
was similarly presented in compound with another cue and that the compound was always
followed by no change in hormone level (DE0). Cue F was included as a filler cue (F0) to prevent
participants from learning that all single cues led to some change in hormone level. A critical
difference in the training phase of Experiment 2 compared to Experiment 1 was the inclusion
of an additivity design, where cues G and H individually predicted an increase in hormone level
by 20 units (G+20, H+20), and GH in compound predicted an even larger increase in hormone
level of 40 units (GH+40). The inclusion of a magnitude additivity manipulation has previously
been shown to enhance blocking (Lovibond, Been, Mitchell, Bouton & Frohardt, 2003; see
also De Houwer, Beckers & Glautier, 2002). In the Blocking phase of the experiment, two new
compounds BX and DY, both predicting –20 hormone level change, were introduced. We also
included three familiar cues from the training phase (F0, G+20, GH+40) to maintain continuity
between the two phases. Throughout both the Training and Blocking phases, cues B and D
received identical training histories, and differed only as a function of whether they were part
of a FN discrimination. Similarly, cues X and Y were both novel in the blocking phase of the study
and were presented in compound with another familiar cue; both compounds were paired with
a decrease in hormone level of the same magnitude.

At test, we assessed predictive ratings for cues X and Y individually. If participants inferred an
Opposite Causal structure for B, they might determine from training that B alone produced a
–20 change in hormone level. A consequence of this inference is that when participants were
subsequently presented with BX leading to –20 hormone level change, they would be able
to infer that X had no additional impact on hormone level. In contrast, we expected greater
learning about Y since D had not already been established as a predictor of hormone decrease.
As a result, we would expect greater blocking, indexed by less negative ratings to X compared
to Y, in the Opposite Causal subgroup compared to participants in all other subgroups. We
additionally predicted that participants in the Opposite Causal subgroup would provide similar
ratings to B and to C, a cue that had been presented alone and predicted a –20 change in
hormone levels.
METHOD
Participants
One-hundred and fifty participants recruited through Prolific (91 female, Mage = 24.8, SD = 6.23)
participated in this study in exchange for monetary payment (20 min at £6GBP/hr).
Apparatus & Stimuli
A total of 11 cues were presented in this study, labelled as A–I, X and Y. X and Y were novel cues
presented in the second phase of training as part of the blocking manipulation. Presentation of
stimuli in this study was identical to that in Experiment 1, with the exception of the outcomes.
Outcomes presented in this study consisted of numeric values from –20 to +40 in 20-point
increments. Positive and negative signs were included to denote increases and decreases in
hormone levels respectively (e.g. hormone level: +20). When there was no change in hormone
level, a verbal description of the outcome was also presented in text below the numeric value
(see Figure 5).
Table 3 Design of Experiment 2.
Note: + = an increase in
hormone level, 0 = no change,
and – = a decrease. Numeric
values indicate the magnitude
of change. Column headings
describe each phase of the
experiment in sequence
from left to right, beginning
with the Training phase and
ending with the Forced-choice
causal structure assessment.
All stimuli presented in each
phase (and their associated
outcomes) are denoted below
the relevant column heading.
TRAINING BLOCKING PHASE TEST PREDICTIONS CAUSAL RATINGS FORCED-CHOICE
CAUSAL STRUCTURE
ASSESSMENT
A+20 AB0 BX-20 A B AB A B
B
C-20 DY-20 C C
DE0 DE D E D E
F0 F0
G+20 H+20 G+20 G GH G H
GH+40 GH+40 I X Y I X Y
Figure 5 Example Screenshots
from a Single Training Trial in
Experiment 2, Where the Cue
is Followed by No Change in
Hormone Level.

16Chow et al.
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Procedure
The method used in this experiment was largely similar to Experiment 1 with the exception
of an additional blocking phase. In the blocking phase, two novel compounds BX and DY were
presented for the first time, both of which were followed by an outcome of –20. X and Y were
also presented in test predictions and causal ratings at test. In the interest of saving time, we
removed the open-ended assessment for B that was presented in Experiment 1.
Another point of difference between this experiment and Experiment 1 was the outcome
prediction scale presented on each trial and in the test predictions. In this study, predictions
were made on a scale from DECREASE to INCREASE with a mid-point of NO CHANGE. Numeric
markers were also included on the scale from –40 to +40 with a mid-point of 0 aligning with the
text description NO CHANGE. However, the extremes of the scale did not have a numeric value;
this was to illustrate that the hormone level could decrease or increase by an indefinite value,
and thereby avoid floor and ceiling effects (see De Houwer et al., 2002). Numeric markers were
included to ensure that participants were learning the cue-outcome associations appropriately
and made predictions that were consistent with outcomes they had been presented with. In
both the Training and Blocking phase of the study, feedback on the change in hormone level
was provided on each trial after participants had made a prediction. No feedback was provided
at test, consistent with Experiment 1.
Statistical Analyses
In this experiment, we were primarily interested in the magnitude of the blocking effect
determined by participants’ ratings to cue X compared to cue Y on both outcome predictions
at test and causal ratings. Note that lower ratings to Y compared to X are indicative of greater
blocking to X. We also compared participants’ ratings for cue B alone relative to the control
cue D, as well as to the directly trained cue C, as a function of their reported causal structure
subgroup in both outcome test predictions and causal ratings. Predictive ratings from the
training phase were analysed using planned within-subject contrasts that tested average
ratings for 1) stimuli that predicted a +20 outcome increase (G, H and A) compared to no
change (AB and DE; +20 vs 0), 2) the stimulus predicting a +40 outcome increase (GH) vs the
average of standard predictive cues (40 vs +20), 3) stimuli followed by no change compared
to stimulus C which was followed by hormone level decrease (0 vs –20), 4) linear trend over six
presentations, and 5) the interactions between contrasts 1–3 and linear trend of presentation.
A similar set of contrasts was tested in the blocking phase, in addition to a comparison of
participants’ ratings to BX and DY on the first trial of the blocking phase (prior to any feedback).
For all contrasts described above, we also tested the interaction with causal structure subgroup
using the same between-subjects contrasts as Experiment 1.
RESULTS
Exclusion criteria
The same exclusion criteria were used in this study as in Experiment 1. Of the 150 participants
who completed the study, 11 were excluded for writing down information during the
experiment, 5 were excluded for failing the instruction check more than twice, and an additional
16 participant was excluded for failing to meet the training criterion. After applying all the
exclusions, 118 participants remained.
Causal structure assessment
Based on participants’ responses on the 4AFC question, we had 37 participants reporting a
Configural structure, 22 reporting a Modulation structure, 23 reporting a Direct Prevention
causal structure, and 36 reporting an Opposite Causal structure.
Training
Figure 6a illustrates mean prediction ratings across each presentation of the different trial types
presented in the training phase average across all participants. No main effect or interaction
contrasts involving causal structure were significant on this measure, all Fs < 1. Analysis of
stimuli predicting hormone level +20 compared to stimuli predicting no change showed a main
effect of cue type, F(1,114) = 1849, p < .001, ηp
2 = .942, which interacted significantly with linear

17Chow et al.
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trend of presentation, F(1,114) = 259.3, p < .001, ηp
2 = .695. Similarly, there was a significant
main effect of cue type when comparing standard predictive (+20) to the additivity predictive
cues (+40), F(1,114) = 1417, p < .001, ηp
2 = .926; this also interacted significantly with linear
trend of presentation, F(1,114) = 7.46, p = .007, ηp
2 = .061. Finally we found a main effect of
cue type comparing stimuli leading to no change versus the reference cue C that predicted a
decrease in hormone level (0 vs –20), F(1,114) = 948.5, p < .001, ηp
2 = .893, which also interacted
with linear trend of presentation, F(1,114) = 59.6, p < .001, ηp
2 = .343. There was no main effect
of linear trend, F(1,114) = 1.53, p = .219, ηp
2 = .013. Overall, these results confirm that across the
six presentations, participants learned which outcomes were associated with each trial type,
and there were no differences in acquisition as a function of causal structure subgroup.
Blocking phase
Figure 6b illustrates participants’ mean outcome predictions across six presentations of each
trial type collapsed across causal structure subgroups. Contrasts comparing standard predictive
cue, G+20, to the cue that was followed by no change (F0), revealed a main effect of cue type,
F(1,114) = 4146, p < .001, ηp
2 = .973. There was also a main effect of cue type when we compared
standard vs additivity predictive cues (G+20 vs GH+40), F(1,114) = 19909, p < .001, ηp
2 = .994,
and when we compared the non-predictive cue to the blocked cues (0 vs –20), F(1,114) = 1420,
p < .001, ηp
2 = .926. The effect of 0 vs –20 interacted significantly with linear trend across trials,
F(1,114) = 148.9, p < .001, ηp
2 = .566. There was also an overall linear trend, F(1,114) = 117.2, p <
.001, ηp
2 = .507, driven by the sharp decrease in ratings for cues BX and DY across trials. No other
interaction contrasts, including main effect and interaction contrasts involving causal structure
subgroups, reached statistical significance, largest F(1,114) = 1.71, p = .194, ηp
2 = .015.
Analysis of participants’ first rating on BX and DY trials revealed an overall main effect of cue
type, F(1,114) = 15.6, p < .001, ηp
2 = .120, driven by higher ratings to DY than BX. However, this
difference did not interact with any of the causal structure comparisons. Ratings in the Blocking
phase broken down by causal structure subgroup can be found in Supplemental Materials. No
other main effects or interactions were significant, Fs < 1.
Outcome prediction at test
Participants’ test predictions were averaged across the two presentations of each trial type.
Figure 7 illustrates the mean outcome predictions for the cues of interest and their relevant
controls, B, C, D, X and Y, averaged across all participants (7a) and separated by causal
structure subgroup (7b). We were primarily interested in whether Opposite Causal participants
show greater blocking of X relative to Y, indexed by less negative ratings to X, compared to all
other subgroups. We additionally predicted that this subgroup of participants would provide
equivalent (negative) prediction ratings to B alone and to a cue that directly predicted hormone
level –20.
Figure 6 Mean Outcome
Prediction (±SE) During (a)
Training and (b) Blocking
Phase in Experiment 2.
Note: Filled symbols denote
trials that were paired with a
hormone level increase (e.g.,
G+20 and GH+40), and unfilled
symbols denote trials that
were followed by no change
(F0) or by a hormone level
decrease (e.g., BX-20 and
DY-20).

18Chow et al.
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The comparison of ratings for X and Y showed a main effect of cue type, F(1,114) =
6.45, p = .012, ηp
2 = .054, with more negative ratings to Y relative to X. This is evidence of
an overall blocking effect. There was also an interaction between cue type (X vs Y) and the
contrast comparing Opposite Causal to all other subgroups, F(1,114) = 5.27, p = .023, ηp
2 =
.044, indicative of a greater blocking effect in the Opposite Causal subgroup. No other main
effects or interactions involving causal structure subgroup were significant, largest F(1,114)
= 1.31, p = .256, ηp
2 = .011. Simple effects comparing the difference in ratings to X and Y for
the Opposite Causal subgroup only showed a significant effect of cue type, F(1,35) = 19.1,
p < .001, ηp
2 = .353.
Comparison of participants’ ratings to B relative to D also showed an overall effect of cue type,
F(1,114) = 40.3, p < .001, ηp
2 = .261, which interacted significantly with causal structure contrasts
comparing Opposite Causal to all other subgroups, F(1,114) = 26.7, p < .001, ηp
2 = .189. These
results are similar to the finding from Experiment 1 that the Opposite Causal subgroup showed
a stronger prediction that B would decrease hormone level compared to the other subgroups.
Similarly, comparison of prediction ratings to B vs C, a cue which was presented alone followed
by –20 hormone level, revealed a significant main effect of cue type, F(1,114) = 59.5, p < .001,
ηp
2 = .343, and a significant interaction with causal structure contrasts comparing Opposite
Causal to all other subgroups, F(1,114) = 14.9, p < .001, ηp
2 = .116. These results suggest that
ratings to B were more similar to C for the Opposite Causal participants compared to all other
subgroups. We additionally found significant main effect contrasts where Opposite Causal
participants gave significantly lower ratings compared to all other subgroups collapsed across
cues B and C, F(1,114) = 22.9, p < .001, ηp
2 = .167. Simple effects comparing ratings to B vs C for
the Opposite Causal subgroup alone also showed no significant difference between ratings for
the two cues, F(1,114) = .780, p = .379, ηp
2 = .007. Together, these results suggest that ratings
to B were much more negative, and were in fact equivalent to ratings to C, for participants in
the Opposite Causal subgroup compared to all other subgroups; these results are consistent
with our predictions. Finally, participants in the Configural subgroup also gave significantly less
negative ratings compared to all other subgroups averaged across both cues, F(1,114) = 10.4,
p = .002, ηp
2 = .084. No other analysis involving causal structure subgroup was significant, F < 1.
Causal ratings
Mean causal ratings for cues B, C, D, X and Y are shown in Figure 8 as a group average (8a) and
separated by causal structure subgroups (8b). Consistent with the outcome prediction ratings,
analysis of causal ratings for X and Y showed a main effect of cue, F(1,114) = 6.07, p = .015,
ηp
2 = .050, which interacted significantly with the contrast comparing Opposite Causal to all
other subgroups, F(1,114) = 4.53, p = .035, ηp
2 = .038. This finding confirms stronger blocking
of learning that X predicted a decrease in hormone level. Simple effects comparing X and Y for
the Opposite Causal subgroup only showed a significant difference in ratings, F(1,35) = 14.0,
p < .001, ηp
2 = .286. No other main effects of interactions involving causal structure subgroups
were significant, Fs < 1.
Figure 7 Average Outcome
Prediction at Test (±SE) For
Critical Stimuli (a) as a Group
Average, and (b) Separated By
Causal Structure Subgroup in
Experiment 2.

19Chow et al.
Journal of Cognition
DOI: 10.5334/joc.266
Comparison of causal ratings to B vs D showed a similar pattern of results to the test predictions;
there was a main effect of cue type, F(1,114) = 20.0, p < .001, ηp
2 = .149, which interacted
significantly with contrasts comparing Opposite Causal to all other subgroups, F(1,114) = 8.72,
p = .004, ηp
2 = .072. As in Experiment 1, the Opposite Causal subgroup gave stronger preventive
ratings for B than the other subgroups. No other main effect or interactions involving causal
structure contrasts were significant, largest F(1,114) = 2.41, p = .124, ηp
2 = .021.
Similar comparison of causal ratings to B vs C produced the same pattern of results, with a
main effect of cue type, F(1,114) = 40.8, p < .001, ηp
2 = .264, which interacted significantly
with contrasts comparing Opposite Causal to all other subgroups, F(1,114) = 9.99, p = .002,
ηp
2 = .008. No other main effects or interactions were significant, largest F(1,114) = 3.34, p =
.070, ηp
2 = .028. Simple effects comparing ratings to B vs C for the Opposite Causal subgroup
only revealed no significant difference in causal ratings to the two cues, F(1,114) = .586, p =
.445, ηp
2 = .005. These findings present confirmatory evidence that participants who reported
an Opposite Causal structure treated the feature B as equivalent to a cue that independently
produced a decrease in hormone level. Importantly, this pattern of results was significantly
different to all other subgroups, including the two inhibitory subgroups (see Figure 8b).
DISCUSSION
The results from Experiment 2 extended the findings from Experiment 1 to a blocking
manipulation, where the feature in a FN discrimination blocked learning of a novel cue paired
with hormone level decrease in a subsequent phase. Outcome test predictions for X compared to
Y revealed a significant overall blocking effect, with more negative ratings (indicating hormone
level decrease) to the control cue than to the blocked cue. Importantly, the magnitude of the
blocking effect was greater for participants who endorsed an Opposite Causal structure than
for the other subgroups. These subgroup differences were also found when we compared
predictions to B relative to D, with greater negative predictions to B in the Opposite Causal
subgroup. This subgroup difference for B compared to D was also obtained in the causal ratings.
We additionally showed that participants who reported an Opposite Causal structure gave
ratings to B similar to those of a cue that directly led to a hormone level decrease (C–20). This
is further evidence that when presented with a FN discrimination, some participants infer that
B alone directly led to the opposite outcome, and treated B as equivalent to a cue that was
explicitly paired with hormone level decrease.
One potential criticism of the design of Experiment 2 is in the introduction of the additivity
compound GH+40, where each element G and H were separately paired with an outcome
of +20. The inclusion of this additivity compound might encourage participants to solve the
FN discrimination by calculating the arithmetic difference between the outcome associated
with A alone (+20) and the no change outcome on AB trials, which might then lead them
to infer that B directly causes a decrease in hormone level. However, it should be noted that
this arithmetic summation process is consistent with Melchers et al.’s (2006) argument that
bidirectional outcomes mirror the symmetrical continuum of associative strengths assumed
Figure 8 Mean Causal Ratings
at Test (±SE) for Critical Cues
(a) as a Group Average, and
(b) Separated by Causal
Structure Subgroup in
Experiment 2.

20Chow et al.
Journal of Cognition
DOI: 10.5334/joc.266
by the Rescorla-Wagner model, and should therefore encourage direct prevention learning. In
contrast, our results suggest an alternative explanation that the discrepancy between A and
AB trials encourages excitatory learning of B leading to the opposite outcome, indexed by the
ability for B alone to generate predictions of hormone level decrease and block subsequent
learning to X.
GENERAL DISCUSSION
In this study, we were interested in testing the possibility that using bidirectional outcomes
in an A+/AB– feature negative task allows participants to acquire an opposite excitatory
causal structure to B that is distinct from known inhibitory structures like direct prevention
and modulation. Both experiments showed that some participants inferred an Opposite
Causal structure when presented with an outcome that could either increase or decrease. In
Experiment 1, we showed that direct experience with a negative outcome was not necessary
for participants to infer an Opposite Causal structure, nor did it lead to more participants
reporting this causal structure to B. It appears that using an outcome that has the potential to
vary in both directions is sufficient to lead some participants to the conclusion that the feature
B directly causes a decrease in hormone levels, even without having seen a negative outcome.
This finding poses a challenge for previous studies that have used the presence of a negative
outcome (hormone level decrease) to differentiate between bidirectional and unidirectional
outcomes, particularly when changes in hormone level were from an unspecified baseline (e.g.,
Melchers et al., 2006; Lotz & Lachnit, 2009; Baetu & Baker, 2010). It is plausible that in these
studies, the manipulation used to establish outcome directionality was not effective, and that
differences between groups at test were a product of extraneous factors, such as differences in
the scales presented during training vs test (e.g., Baetu & Baker, 2010) or differences in scales
presented for the unidirectional vs bidirectional outcome groups (e.g., Melchers et al., 2006;
Lotz & Lachnit, 2009).
We also found evidence of a subgroup difference in Experiment 1, where participants in the
Opposite Causal subgroup showed the greatest transfer of B’s properties to a novel cause
in a summation test (i.e., greater CB–CD difference) compared to all other subgroups. We
have previously proposed that inhibitory summation is best thought of as an instance of
generalisation (Chow et al., 2022; see also Bonardi, Robinson & Jennings, 2017). Specifically, we
suggested that in a FN discrimination task with unidirectional outcomes, individual differences
in inhibitory transfer between Modulators and Preventers may not reflect qualitative differences
in what is learned (i.e., occasion-setting vs direct prevention), but differences in participants’
willingness to generalise inhibitory properties of the feature from training to test. In the present
study, participants who endorsed an Opposite Causal structure were perhaps solving the FN
discrimination differently to the Direct Prevention and Modulation subgroups. That is, they
learned that B by itself directly produces a reduction in hormone level. This causal structure
is straightforward to apply to a summation test since it does not involve generalisation—B’s
relationship with the outcome is independent of A. Thus, prediction ratings to CB are low
since the expectation of hormone level increase in the presence of C is offset by the strong
expectation of hormone level decrease to B, with minimal generalisation decrement in the
summation test. The strong transfer seen among Opposite Causal participants together with
strong preventative ratings to B alone in causal ratings compared to all other subgroups,
provide further support that at least for some participants, causal learning about B with the
opposite outcome has occurred.
In Experiment 2, we found further evidence of opposite causal learning with bidirectional
outcomes, where the feature was again inferred to cause a decrease in hormone levels.
Presenting B together with a novel cue X successfully blocked learning that X predicted hormone
level decrease when the compound was directly paired with a hormone level decrease. These
findings are diagnostic of opposite causal learning to B since no additional information was
provided by directly pairing B with hormone decrease, hence leading to blocking of learning to
X. Causal and predictive ratings to B alone were also similar in magnitude to cue C, which had
been explicitly paired with hormone level decrease. These results suggest that for participants
in the Opposite Causal subgroup, the feature B was treated as equivalent to a cue that was
active in producing a reduction in hormone levels by itself. These findings are in contrast
to established theories of inhibitory learning which involve the suppression of the outcome

21Chow et al.
Journal of Cognition
DOI: 10.5334/joc.266
elicited by a causal/excitatory cue, either by modulating the effect of the excitor or by directly
preventing the outcome from occurring. Consistent with these accounts, inhibitors are typically
thought to be behaviourally silent (Rescorla, 1969), with few demonstrations where the inhibitor
directly elicits behaviour (but see Wasserman, Franklin & Hearst, 1974).
In summary, our findings propose an alternative explanation to the idea proposed by Melchers
et al (2006) that bidirectional outcomes in FN discrimination encourages direct prevention
learning. We have shown that under these conditions, some participants are learning a
generative causal relationship between the feature and the opposite outcome. This is distinct
from an opposite inhibitory mechanism like the negative associative strength governing
inhibition in the RW model, since in opposite causal learning the action of the feature is direct
(i.e., does not require the presence of a background cause), and generative (does not involve the
suppression of outcome activation). It is also important to note that the RW model in its original
conception was designed to explain inhibitory learning with unidirectional outcomes, such as
the presence or absence of a foot shock or food pellets in animal conditioning procedures. These
outcomes may vary in magnitude, but they are unable to take on negative properties (i.e., anti-
outcome). Thus, the model does not make any assumptions about the symmetry between the
dimension of associative strength and the properties of the outcome, nor does it specify this
as a requirement for the feature acquiring negative associative strength when an expected
outcome is omitted. Importantly, any model that seeks to explain how inhibitory relationships
are learned should not be limited to outcomes with a positive and negative dimension, but
should account also for how we learn about discrete events where negative values are not
possible. Indeed, it seems like there is nothing inherently special about a negative outcome
that encourages learning distinct from that of causal learning in the positive direction. Negative
outcomes are commonly used in human causal learning experiments, such as in a medical
scenario where a drug cue is thought to lead to recovery from illness (e.g., Matute, Yarritu &
Vadillo, 2011; Chow, Colagiuri & Livesey, 2019). These scenarios typically frame the relationship
between treatment use and recovery as causal (treatment causes recovery); however recovery
from illness may be more accurately construed as the reduction or termination of illness.
Returning to the issue we began this paper with, what exactly are people learning when they
experience a negative relationship? We have previously shown that there are substantial
individual differences in human inhibitory learning using a feature negative arrangement with a
unidirectional outcome (Lee & Lovibond, 2021; see also Glautier & Brudan, 2019). Additionally,
we found that self-reported causal structure was predictive of the degree of transfer in a
summation test. We have also suggested that differences in inhibitory transfer between Direct
Prevention and Modulation subgroups is a result of differences in willingness to generalise
inhibitory properties of the feature from training to test, rather than qualitative differences in
what is learned (Chow et al., 2022). The novel contribution of the current paper is to present
another possible causal structure in a FN procedure, which arises when a bidirectional outcome
is used. We propose that rather than encouraging direct prevention (inhibitory) learning, as
originally suggested by Melchers et al. (2006), bidirectional outcomes encourage at least some
participants to learn an opposite excitatory causal structure. In our experiments, this subgroup
of participants produced significantly different results to Configural, Direct Prevention and
Modulation participants, showing greatest transfer in a summation test, greatest prediction
of a decrease in hormone level when B was presented alone, and strongest blocking of a
novel cue paired with a reduction in hormone level. Importantly, these participants did not
report learning an inhibitory relationship between the feature and the outcome produced by
the causal cue. Instead, they reported having formed a generative association between the
feature and the opposite outcome. These findings suggest that evidence of a direct prevention
structure is less prevalent than previously assumed. Together, these findings support our
previous proposal that inhibitory learning in humans may perhaps be largely modulatory in
nature (Lee & Lovibond, 2021).
In conclusion, we propose an alternative to the claim that FN discrimination with a bidirectional
outcome encourages direct prevention learning. We have shown that FN discrimination with
a bidirectional outcome can be solved by assuming a causal relationship between the feature
and the opposite outcome to that signalled by the partner cue during FN training. Together with
previous findings that most participants infer a modulatory causal structure when solving a FN
discrimination with unidirectional outcomes, we recommend that more attention be given to
alternative mechanisms of prevention learning such as modulation.

22Chow et al.
Journal of Cognition
DOI: 10.5334/joc.266
DATA ACCESSIBILITY STATEMENT
All data is made available on the Open Science Framework and can be accessed at https://osf.
io/9at4b/. None of the experiments were pre-registered.
ADDITIONAL FILE
The additional file for this article can be found as follows:
• Supplemental Materials. Analyses and Figures. DOI: https://doi.org/10.5334/joc.266.s1
ETHICS AND CONSENT
All reported experiments were approved by the University of New South Wales Human Research
Ethics Advisory Panel C (approval number 3136). Participants provided online consent for their
participation.
FUNDING INFORMATION
This study was funded by an Australian Research Council Discovery Project Grant
[DP190103738] to Peter Lovibond. Jessica Lee was supported by a Discovery Early Career
Researcher Award from the Australian Research Council [DE210100292].
COMPETING INTERESTS
The authors have no competing interests to declare.
AUTHOR AFFILIATIONS
Julie Y. L. Chow orcid.org/0000-0002-7515-9601
UNSW, Sydney, Australia
Jessica C. Lee orcid.org/0000-0003-4253-2008
The University of Sydney, Australia; UNSW, Sydney, Australia
Peter F. Lovibond orcid.org/0000-0003-2146-9054
UNSW, Sydney, Australia
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TO CITE THIS ARTICLE:
Chow, J. Y. L., Lee, J. C.,
& Lovibond, P. F. (2023).
Inhibitory Learning with
Bidirectional Outcomes:
Prevention Learning or Causal
Learning in the Opposite
Direction?
Journal of Cognition,
6(1): 19, pp. 1–24. DOI: https://
doi.org/10.5334/joc.266
Submitted: 14 November 2022
Accepted: 24 February 2023
Published: 10 March 2023
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in any medium, provided the
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are credited. See http://
creativecommons.org/
licenses/by/4.0/.
Journal of Cognition is a peer-
reviewed open access journal
published by Ubiquity Press.
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