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Action biases perceptual decisions toward expected outcomes.

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We predict how our actions will influence the world around us. Prevailing models of action control propose that we use these predictions to suppress or ‘cancel’ perception of expected action outcomes. However, contrasting normative Bayesian models in sensory cognition suggest that top-down predictions bias observers toward perceiving what they expect. Here we adjudicated between these models by investigating how expectations influence perceptual decisions about briefly presented action outcomes. Contrary to dominant cancellation models, we found that observers’ perceptual decisions are biased toward the presence of outcomes congruent with their actions. Computational modelling revealed this action-induced bias reflected a bias in how sensory evidence was accumulated, rather than a baseline shift in decision circuits. In combination, these results reveal a gain control mechanism that can explain how we generate largely veridical representations of our actions and their consequences in an inherently uncertain sensory world.
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Action biases perceptual decisions towards expected outcomes
Daniel Yon1,2*, Vanessa Zainzinger1, Floris P. de Lange3, Martin Eimer1 & Clare Press1
1. Department of Psychological Sciences, Birkbeck, University of London, UK
2. Department of Psychology, Goldsmiths, University of London, UK
3. Donders Institute for Brain, Cognition and Behaviour, Radboud University, NL
*Corresponding author: d.yon@gold.ac.uk / @danieljamesyon
Accepted as an Article at JEP:General on 17th April 2020
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Abstract
We predict how our actions will influence the world around us. Prevailing models in the
action control literature propose that we use these predictions to suppress or ‘cancel’
perception of expected action outcomes, to highlight more informative surprising events.
However, contrasting normative Bayesian models in sensory cognition suggest that we are
more, not less, likely to perceive what we expect given that what we expect is more likely
to occur. Here we adjudicated between these models by investigating how expectations
influence perceptual decisions about action outcomes in a signal detection paradigm. Across
three experiments, participants performed one of two manual actions that were sometimes
accompanied by brief presentation of expected or unexpected visual outcomes. Contrary to
dominant cancellation models but consistent with Bayesian accounts, we found that observers
were biased to report the presence of expected action outcomes. There were no effects of
expectation on sensitivity. Computational modelling revealed that the action-induced bias
reflected a sensory bias in how evidence was accumulated rather than a baseline shift in
decision circuits. Expectation effects remained in Experiments 2 and 3 when orthogonal cues
indicated which finger was more likely to be probed (i.e., task-relevant). These biases
towards perceiving expected action outcomes are suggestive of a mechanism that would
enable generation of largely veridical representations of our actions and their consequences in
an inherently uncertain sensory world.
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1. Introduction
Effectively acting on the world around us requires predicting the consequences of our actions
(James, 1890). We select actions based on their predicted outcomes and use these predictions
to generate rapid corrective movements when we experience deviant sensory input (Hommel,
Müsseler, Aschersleben & Prinz, 2001; Wolpert, Ghahramani & Jordan, 1995). Influential
‘Cancellation’ models in the action control literature propose that we also use these
predictions to suppress perception of expected sensory inputs, across sensory modalities
(Bays & Wolpert, 2007; Blakemore et al., 1998; Fiehler, Brenner & Spering, 2019; Kilteni &
Ehrsson, 2017; Kilteni, Houborg & Ehrsson, 2019; see also Müsseler & Hommel, 1997; Fig.
1a). Such a mechanism would allow us to ignore predictable sensations and therefore remain
maximally sensitive to more behaviourally-relevant unexpected events. Such cancellation
models provide an appealing explanation for why it is difficult to tickle oneself (Weiskrantz,
Elliott & Darlington, 1971). The idea has also drawn wide support from studies showing that
sensory events predictably resulting from action are perceived as less intense than similar
events presented in the absence of action (Bays, Wolpert & Flanagan, 2005; Sato, 2008).
Indeed, interest in cancellation mechanisms has been galvanised by studies suggesting an
intimate link between such effects and the feelings of control that accompany out movements
(the sense of agency’), with dysfunctions of cancellation associated with pathologies of
agency in a variety of psychiatric conditions (Frith, Blakemore & Wolpert, 2000).
However, the core principle guiding Cancellation models that perception of predicted
inputs is suppressed contrasts with prominent Bayesian models in the wider sensory
cognition literature (Fig. 1b). These models suggest that we are more, not less, likely to
perceive what we expect (de Lange, Heilbron & Kok, 2018; Press & Yon, 2019). They
emphasise how in an inherently ambiguous sensory world it is adaptive for organisms to
combine sampled sensory evidence with prior knowledge about what is likely to occur.
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Mechanistically this can be achieved by altering the weights on sensory channels, by
increasing the gainof expected relative to unexpected signals (de Lange et al., 2018;
Summerfield & de Lange, 2014). Increasing the gain afforded to expected sensory signals in
this fashion effectively ‘turning up the volume’ of events that conform to our prior
predictions would bias perceptual processing and predispose observers to perceive events
that they expect to occur (e.g., Wyart, Nobre & Summerfield, 2012; Hudson, Nicholson, Ellis
& Bach, 2016; Hudson, Nicholson, Simpson, Ellis & Bach, 2016). For example, a range of
curious illusory phenomena such as the tendency of observers to perceive concave faces as
convex (Gregory, 1997) could arise via such mechanisms. Importantly, while these kinds of
biases may lead to occasional misperceptions, they may nonetheless reflect an adaptive and
efficient way of generating veridical percepts, since expected events are by definition
more likely to occur. While these models have been developed outside of action contexts, a
mechanism that biases perception in line with expectations could be just as adaptive during
action. For example, if we are trying to flick the light switch in a darkened room, we will
generate more veridical estimates of our ongoing actions if we are biased to perceive
expected events (e.g. the sight of a moving hand).
Cancellation and Bayesian accounts of how predictions shape perception have been difficult
to compare directly because experimental approaches differ between disciplines (Press, Kok
& Yon, 2020b). Support for Bayesian models within the normative sensory cognition
literature typically examines an organisms ability to detect a low intensity stimulus (Stein &
Peelen, 2015; Wyart, Nobre & Summerfield, 2012). In contrast, action studies reporting
cancellation have typically asked participants to judge the intensity of action outcomes (Bays
et al., 2005; Blakemore et al., 1998; Kilteni, Houborg & Ehrsson, 2019; Sato, 2008; Weiss,
Herwig & Schütz-Bosbach, 2011; see also Yon & Press, 2017, 2018). To render the
paradigms more comparable the present series of perceptual experiments used a signal
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detection paradigm within the domain of action (see also Cardoso-Leite et al., 2010; Schwarz
et al., 2018). They also presented visual action outcomes, given that both Cancellation and
Bayesian theories hypothesise comparable operation of mechanisms across sensory
modalities (Brown, Adams, Parees, Edwards & Friston, 2013; Wolpert, Doya & Kawato,
2003) and that visual events have been used more commonly in the normative sensory
cognition literature (see General Discussion).
Fig. 1: A schematic illustration of how predictive signals influence activation of sensory units under
Cancellation and Bayesian models, alongside their putative influences on perception. Cancellation models
developed in the action literature (a, left) hypothesise that when we move (e.g., depress our index finger) we
generate a predictive signal that suppresses activity in sensory units tuned to expected perceptual outcome s (e.g.
visual units tuned to the sight of a hand with a depressed index finger). Weakening activity in these units
reduces the signal-to-noise ratio of the sensory population leading to less intense percepts and biasing
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observers away from perceiving these outcomes (b, left). In contrast, Bayesian models developed in the wider
sensory cognition literature (a, right) suggest that predictive signals alter the weights on sensory channels such
that the volume is ‘turned up’ (e.g., the gain is increased) on expected relative to unexpected signals. Such
weighting leads to a higher signal-to-noise ratio when expectations are valid, leading to more intense percepts
and biases towards perceiving expected outcomes (b, right).
Participants produced manual actions (abducting their index or middle finger) and detected
visual action outcomes, which could be congruent or incongruent with their own movement.
This congruency manipulation exploits the fact that congruent action outcomes will be more
expected than incongruent ones, based either on inherited evolutionary expectations or our
extensive experience of controlling our actions (Hommel et al., 2001). Under Cancellation
models, suppressing activity in units tuned to expected stimuli should make it harder for
predictable action outcomes to reach detection threshold (see Fig. 1), making observers either
less sensitive to these events or biased to report that they did not occur. In contrast, under
normative Bayesian models selectively increasing the relative weight on expected sensory
channels should either bias observers to report that congruent events occurred or make them
more sensitive to congruent outcomes. However, given recent findings in the broader sensory
cognition literature, we anticipated that sensitising sensory channels tuned to expected events
would increase hits and also false alarms in signal detection terms, biasing observers rather
than increasing their sensitivity (Wyart et al., 2012). We subsequently used computational
modelling to pinpoint which aspect of perceptual decision making is influenced by
expectations.
2. Experiment 1: How do actions influence detection of expected outcomes?
2.1. Methods
2.1.1. Participants
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Twenty-four healthy participants (19 female, 5 male, mean age = 24.9 years, SD = 5.38) took
part in Experiment 1. All participants in all experiments reported normal or corrected-to-
normal vision and no history of psychiatric or neurological illness. A sample size of 24
participants per experiment was selected such that each would have at least 80% power to
detect a medium-sized effect of action-outcome congruency on perceptual decisions (Cohen's
dz = .6, N=24, alpha =0.05 provides 80.3% power - G*Power 3.1.9.2). All experiments were
approved by the local ethics committee at Birkbeck, University of London.
2.1.2. Procedure
The experiment took place in a dimly lit testing cubicle. Participants sat ~55 cm from the
monitor (153 x 32 cm, 60 Hz) used for stimulus presentation, with their hands placed above
two keypads. The participant’s right hand was rotated 90°, such that their knuckles were
aligned with the body midline. Each trial began with the presentation of a greyscale avatar
hand (Poser 10, Smith Micro Software). This image remained on screen until participants
executed either an index or little finger tapping action - depressing the relevant key.
Movements were freely-selected, but the experiment ran until participants executed at least
100 of each type. On 50% of trials, participant’s actions triggered a synchronous movement
of the onscreen hand (signal present) that was displayed for 17 ms. On the remaining 50% of
trials the hand remained still (signal absent). On signal present trials, half of observed
movements were congruent with the participant’s own action (e.g. execute index tap, observe
index tap), and half were incongruent (e.g. execute index tap, observe middle tap). Regardless
of signal presence, the index and middle region of the avatar hand was backwards-masked by
an oval texture comprised of avatar fingers (Cutting, Moore & Morrison, 1988) for 100 ms.
This image was in turn followed by a visual white noise mask presented for 300-600 ms.
Participants were subsequently asked about the movement of one of the two fingers (e.g. did
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the INDEX finger move?). They registered their decision with a button press with their left
thumb. On half of trials participants were probed about the congruent finger of the avatar
hand (i.e. the index finger if they moved their index finger), and on the remaining half the
incongruent finger was probed. Participants in Experiment 1 also made a confidence
judgement about their decision (‘high confidence’ or ‘low confidence’) to collect pilot data
for an additional experiment.
Participants completed at least 200 trials
1
. Trial types were randomised across the experiment
and breaks were taken every 40 trials. Before the main experiment participants completed a
short practice block which familiarised them with the main task (16 trials). Participants
subsequently completed a longer practice block without producing actions where they
completed a 1 up 1 down adaptive staircase, to adjust the difficulty of the perceptual
discrimination such that it was approximately matched for all participants. This staircase
targeted the amount of observed finger movement (minimum 1° rotation around the
metacarpophalangeal joint, maximum 16°) that was required for detection on ~50% of trials.
The staircase terminated after 12 reversals, and the average of the last six turning points was
taken as an estimate of the participant’s threshold. The main experiment began after
completing the adaptive staircase, and test stimuli (when present) were shown at this
threshold value on all subsequent trials.
2.2. Results and Discussion
All tests in all experiments used an alpha level of .05, and for non-significant results we
calculated Bayes Factors to quantify evidence for the absence of an effect (i.e. the null
hypothesis; Dienes, 2014). Separate signal detection theoretic measures of sensitivity (d’) and
1
Participants in Experiment 1 were successful in following the instruction to execute roughly equal numbers of
each action, and therefore completed on average 218 trials (SEM = 2.5), comprising on average 49.8 %
congruent trials (SEM=.0014).
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bias (c) were calculated using hit rates and false alarm rates on congruent and incongruent
trials. d’ reflects the extent to which participants are more likely to report the presence of a
stimulus when it is present than when it is absent (d’ = z[hit rate] z[false alarm rate]), while
c reflects the extent to which participants are more likely to respond ‘present’ or ‘absent’
regardless of objective stimulus presence ( c = -.05[z(hit rate) + z(false alarm rate)]. For some
participants in some conditions response counts were empty (e.g., no misses) which can
preclude calculation of d’ and c. In line with previous recommendations (Hautus, 1995) this
issue was overcome by adjusting counts of hits, misses, false alarms and correct rejections by
+.5 in all experiments, and this adjustment was applied to all participants to avoid introducing
biases into group-level analyses (Snodgrass & Corwin, 1988).
Sensitivity and bias on congruent and incongruent trials were compared using t-tests. These
analyses revealed that participants were more liberal in reporting the presence of congruent
action outcomes (lower c t23 = 2.35, p=.028, dz = .480; see Fig. 2) and were also more
sensitive when judgements probed the congruent finger (higher d’ t23 = 2.29, p=.031, dz =
.467; see Table 1). These findings are predicted by the Bayesian account and inconsistent
with the Cancellation account.
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Fig. 2: Action execution and detection task, with signal detection c results from Experiments 1-3. a. Participants
performed actions, which were paired with synchronous congruent or incongruent movements of an avatar hand
that they were required to detect. In Experiments 2 and 3, attentional arrow cues also informed participants
about which finger of the avatar hand was likely to be probed. b. We calculated the signal detection theoretic
measure c to index biases induced by action. These values were lower (i.e. responses were more liberal) on
congruent (saturated) relative to incongruent (desaturated) trials, irrespective of attentional focus. This effect
demonstrates that perceptual decisions were biased towards expected action outcomes. Error bars show 95%
within-participant confidence intervals of the mean difference between conditions.
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Table 1: Mean (SD) sensitivity (d’) values across all conditions in Experiments 1-3
3. Experiment 2: Dissociating effects of expectation and attention on detection
performance
Experiment 1 found that participants were more sensitive to (higher d’) and biased to report
the presence of (lower c) congruent action outcomes. However, one possibility is that these
results reflect effects of ‘attention’ rather than ‘expectation’ per se. That is, in both laboratory
tasks and natural settings, top-down expectations (i.e. what is likely to occur) are often
confounded with top-down attention (i.e. what is relevant for task performance; Summerfield
& Egner, 2016). While in our task movements of congruent fingers are just as probable as
incongruent ones making both types of event equally task-relevant actors may have
learned outside the laboratory to allocate top-down attention to congruent fingers as these are
typically more relevant for controlling our actions (note of course that our logic also assumes
that congruent movements are more expected due to learning outside of the experimental
setting; see Introduction). This interpretation is particularly likely, given that when
expectation and attention have been orthogonalised in the sensory cognition literature, the
former has been found to generate biasing effects and the latter sensitivity effects (Wyart et
al., 2012). Experiment 2 examined this possibility by orthogonally manipulating action-
outcome congruency and task relevance with a new sample. This was achieved by
introducing two separate cues to the task: a number cue indicating which action participants
should perform (thereby manipulating expectations about outcomes) and an orthogonal arrow
Congruent
Incongruent
1.42
(.567)
1.28
(.588)
Valid
Neutral
Invalid
Valid
Neutral
Invalid
0.73
(.532)
0.68
(.539)
0.63
(.455)
0.69
(.536)
0.59
(.537)
0.57
(.521)
1.14
(.743)
1.05
(.684)
0.90
(.590)
1.14
(.680)
0.993
(.591)
0.923
(.555)
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cue indicating which outcomes will be relevant for perceptual decisions (indicating what
participants should attend to).
3.1. Methods
3.1.1. Participants
A new sample of 24 participants took part in Experiment 2 (17 female, 7 male, mean age =
24.4 years, SD = 4.23). Data from one additional participant was lost due to a technical
malfunction.
3.1.2. Procedure
The procedure of Experiment 2 was identical to Experiment 1 with the following changes.
Trials began with the presentation of the neutral observed hand overlaid with an arrow cue.
On 50% of trials the arrow cue was valid, pointing to the finger that was subsequently
probed. On 25% of trials the cue was invalid, pointing to the finger that was not subsequently
probed. On the remaining 25% of trials the cue was neutral, pointing to both fingers and
indicating that they would be probed with equal probability. After 700 ms, an imperative cue
(‘1’ or ‘2’) was presented above the arrow, indicating which action participants were required
to perform (index or middle tap, respectively). After participants executed the correct action,
the same stimulus sequence was triggered as in Experiment 1. Participants made the same
detection judgements, but confidence judgements were not collected in this experiment. The
experiment comprised 320 trials. Trial types were randomised across the experiment, and
breaks were taken every 20 trials.
3.2. Results and Discussion
Signal detection theoretic measures of sensitivity and bias were calculated for each
combination of action-outcome congruency (congruent, incongruent) and cue validity (valid,
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neutral, invalid), and effects were evaluated using ANOVAs with the same factorial structure.
Both analyses revealed an effect of cue validity, such that participants were more sensitive to
(higher d’ - F2,46 = 4.246, p=.020, ηp2=.156; Table 1) and more liberal in reporting (lower c -
F2,46 = 14.57, p<.001, ηp2=.388; Fig. 2) validly cued events validating that participants used
these cues to guide their attention (Hawkins et al., 1990). T-tests decomposing these cuing
effect found that judgements were more sensitive when cues were valid than invalid (t23 =
2.85, p=.009, dz = .581), though differences between sensitivity on valid and neutral cuing
trials (t23 = 1.98, p=.059, dz = .404, BF01 = 1.36) or neutral and invalid trials (t23 = .937,
p=.359, dz = .191, BF01 = 3.01) were non-significant. Comparable tests found observers were
more liberal on validly cued trials than invalid ones (t23 = 4.11, p<.001 , dz = .838) and more
liberal on neutrally cued than invalidly cued trials (t23 = 4.81, p<.001 , dz = .981), but
differences between valid and neutral cuing trials were not significant (t23 = 1.28, p=.214, dz
= .261, BF01 = 2.21). Moreover, effects of cue validity on judgement sensitivity did not
interact with action-outcome congruency (F2,46 = .184, p=.833, ηp2=.008, BF01 = 7.19).
Importantly, these analyses also found that participants were biased to report the presence of
congruent action outcomes (F1,23 = 8.47, p=.008, ηp2=.269)
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, and the magnitude of this bias
did not interact with the focus of attention (F2,46 = 1,29, p=.286, ηp2=.053, BF01 = 5.95),
suggesting that observers are biased to perceive predictable action outcomes irrespective of
task relevance. However, there was no significant effect of congruency on sensitivity (F1,23 =
1.70, p=.205, ηp2=.069, BF01 = 1.52). Therefore, when attention is orthogonally manipulated
participants are still biased (lower c) to report the presence of congruent action outcomes.
The effect of congruency on sensitivity was not detectable in Experiment 2, consistent with
2
In Experiment 2 one participant was an outlier, showing an especially large action-induced bias (z score =
3.76). Re-running the same analyses without data from this participant revealed the same effect of action
congruency on c values (p=.003), the same correlation between congruency effects and modelled drift biases
(r=-.924, p<.001) and did not change any other statistical patterns observed.
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the possibility that this effect in Experiment 1 was determined by attentional processes
(Wyart et al., 2012).
4. Experiment 3: Controlling imperative-outcome mapping
Experiment 3 investigated whether the congruency biasing effect was driven by expectations
engendered by action or a mapping between imperative cues and stimulus types (1 =
index/left; 2 = middle or right a common mapping used in musical training). To rule out
possible mapping between cues and outcomes, we compared one group of participants on an
identical procedure as Experiment 2 to another group who received arbitrary shapes as
imperatives rather than numbers (NB: no cue-outcome mapping can be learnt within the
experiment as there is no within-experiment contingency).
4.1. Methods
4.1.1. Participants
A new sample of 48 participants took part in Experiment 2 (33 female, 15 male, mean age =
24.7 years, SD = 4.08), with 24 in each of the two groups. This provides the same power to
detect the effect individually within each of the two groups as in Experiments 1 and 2, as well
as enabling examination of the interaction according to cue type.
4.1.2. Procedure
One group of 24 participants completed a procedure identical to Experiment 2, where
numbers (‘1’ or ‘2’) cued participants to perform index or middle finger actions. A separate
group of 24 participants completed a near identical procedure, except circles and squares
indicated to participants that they should execute index and middle finger movements. Half of
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these participants received a circle-index / square-middle mapping and half received a circle-
middle / square-index mapping.
4.2. Results and Discussion
Measures of sensitivity and bias were calculated separately for each combination of
experimental conditions and analysed with separate ANOVAs. Therefore, the only change
with respect to Experiment 2 was the addition of a between-participants factor of cue type
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.
Greenhouse Geisser corrections were employed where appropriate. Participants were again
more liberal in reporting the presence of congruent than incongruent action outcomes (F1,46 =
14.59, p<.001, ηp2=.241; Fig. 2), and more liberal in reporting the presence of validly cued
events (F2,92 = 5.062, p=.008, ηp2=.009). T-tests decomposing the attentional cuing effect on
c values found that observers were more liberal on validly cued trials than invalid ones (t47 =
2.73, p=.009 , dz = .394) and more liberal on neutrally cued than invalidly cued trials (t47 =
2.47, p=.017, dz = .356), but differences between valid and neutral cuing trials were not
significant (t47 = 1.19, p=.242, dz = .171, BF01 = 3.30).
The effect of action-outcome congruency did not interact with the focus of attention (p=.080,
ηp2=.025, BF01 = 3.81). Crucially, cue type (shapes vs numbers) also did not interact with the
factor action-outcome congruency (F1,46 = 1.192, p = .281, ηp2=.025, BF01 = 2.61) or generate
a three-way interaction with congruency and attentional focus (F2,92 = .234, p=.792, ηp2=.005,
BF01 = 7.69. Indeed, separate analyses of the number (F1,23 = 7.50, p=.012, ηp2=.246) and
shape cuing conditions (F1,23 = 8.02, p=.009, ηp2=.259) revealed a significant effect of
3
In Experiment 3 one participant was an outlier, showing an especially large action-induced bias (z score =
4.12). Re-running the same analyses without data from this participant revealed the same effect of action
congruency on c values (p<.001), the same correlation between congruency effects and modelled drift biases
(r=-.807, p<.001) and did not change any other statistical patterns observed.
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congruency in both groups underscoring that participants were more likely to report the
presence of congruent action outcomes irrespective of cue type.
Equivalent analyses of d’ found that judgement sensitivity again improved with valid cues
(F2,92 = 9.87, p <001, ηp2=.257; Table 1). T-tests decomposing this cuing effect found
judgements were more sensitive when cues were valid compared to invalid (t47 = 4.49,
p<.001, dz = .648), valid compared to neutral (t47 = 2.42, p=.019, dz = .349) and neutral
compared to invalid (t47 = 2.07, p=.044, dz = .298). However, d’ was uaffected by action-
outcome congruency (F1,46 = .092, p=.763, ηp2=.002, BF01 = 7.63), nor did effects of
relevance interact with congruency (F2,92 = .638, p=.531, ηp2=.014, BF01 = 9.80). Neither the
main effect of validity (F2,92 = .194, p=.824, ηp2=.004, BF01 = 11.7) nor any interaction with
congruency (F2,92 = .542, p=.583 ηp2=.012, BF01 = 6.21) was affected by cue type.
Experiment 3 therefore replicated the findings from Experiment 2 while controlling for the
particular imperative mapping, suggesting that it was the action-based expectations that
generated the biasing effects.
5. Combined analysis
No significant congruency effects on d’ were found in Experiments 2 and 3, in contrast to
Experiment 1 where observers were more sensitive to the presence of congruent action
outcomes. As noted, one plausible explanation for this discrepancy is that effects on
sensitivity are driven by attentional mechanisms (see Wyart, Nobre & Summerfield, 2012)
and sensitivity effects were correspondingly abolished when attention was orthogonally cued
in Experiments 2 and 3. However, Bayes Factors suggested evidence for a null result was
convincing in Experiment 3 (BF01 > 3) but indecisive in Experiment 2 (BF01 < 3). Combining
data from Experiments 2 and 3 for greater precision, we found convincing evidence for the
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absence of a congruency effect on d’ - t71 = 1.02, p = .312, dz = .120, BF01 = 4.69
suggesting that sensitivity effects were absent across the two experiments.
6. Computational modelling: Which aspect of perceptual decision making is biased by
action?
We used computational modelling of participant choices and reaction-time distributions to
pinpoint which aspect of perceptual decision making is biased by action. Drift diffusion
models (DDMs) of perceptual decision making have enjoyed growing prominence in the
cognitive sciences (Ratcliff, Smith, Brown & McKoon, 2016; see Fig. 3). These models
assume that when making perceptual choices (e.g., was a stimulus present or not?), observers
have an internal representation of sensory evidence which is sampled by decision circuits.
Decision circuits continuously sample from the representations of sensory evidence, and
when the accumulated decision variable meets a response boundary (e.g. ‘respond present’),
the appropriate response is triggered. The representation of sensory evidence is therefore
separable from the representation of decisions about that evidence.
There are two ways that the DDM could accommodate the action-induced bias found in
Experiments 1-3. First, expectations during action could shift the starting point of the
evidence accumulation process toward the ‘respond present’ decision bound (varying
parameter z of the DDM; see Fig. 3a). Such effects are often thought to reflect biases in the
decision circuits. For example, predictive cues can induce preparatory motor activity before a
stimulus is presented (de Lange, Rahnev, Donner & Lau, 2013) and these starting point
effects could operate even if participants were insensitive to the perceptual information (e.g.,
they closed their eyes). However, a second alternative is that action directly biases sensory
representations, as though agents have selectively increased the gain (or ‘precision’) afforded
to expected sensory signals (Friston, 2018). This kind of bias would manifest as an
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asymmetric bias in the rate of evidence accumulation (Mulder, Wagenmakers, Ratcliff,
Boekel & Forstmann, 2012; ‘drift biasing’, affecting parameter db of the DDM; see Fig. 3b),
because as the sensory evidence is sampled more it provides progressively more evidence in
favour of the expected event.
6.1. Methods
To investigate whether either of these possibilities could account for the action-induced bias
we observed in our experiments, we fit hierarchical DDMs to participant choice and reaction
time data using the hDDM package implemented in Python (Wiecki, Sofer & Frank, 2013).
In the hierarchical DDM, model parameters for each participant are treated as random effects
drawn from group-level distributions, and Bayesian Markov Chain Monte Carlo (MCMC)
sampling is used to estimate group and participant level parameters simultaneously.
We specified four different models for data in each experiment. 1) a null model where no
parameters were permitted to vary between congruent and incongruent trials. 2) a start bias
model where the start point of evidence accumulation (z) could vary on congruent and
incongruent trials. 3) a drift bias model where a constant bias in evidence accumulation (db)
could vary between congruent and incongruent trials. 4) a start + drift bias model where both
parameters could vary. To improve model fits, all models in Experiments 2 and 3 also
allowed global drift rate (but not drift bias i.e., biases to drift asymmetrically towards
present or absent decisions) to vary as a function of cue validity to account for the effect of
attentional cues on d’ (Ratcliff et al., 2016). Varying drift rate (rather than drift bias) captures
effects that reflect more reliable evidence accumulation to the appropriate response boundary
(i.e., ‘respond present’ when stimuli are present and ‘respond absent’ when absent) and
therefore accounts for sensitivity effects seen as a function of cue validity, rather than a
biased accumulation toward one response boundary over another. In no model did we allow
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drift rate to vary between congruent and incongruent trials, as this would amount to assuming
that observers are generally more sensitive to all kinds of sensory evidence on congruent
trials (e.g. higher d’) , rather than sensitising particular sensory channels that encode expected
outcomes.
All models were estimated with MCMC sampling with 30,000 samples (‘burn-in’=7500).
Model convergence was assessed by inspecting chain posteriors and simulating reaction time
distributions for each participant. Models were compared using deviance information criteria
(DIC) as an approximation of Bayesian model evidence. Estimated parameters in each model
were compared using the Bayesian significance test implemented in hDDM, which computes
the posterior probability that group-level parameters differ across conditions.
6.2. Results and Discussion
Fitting the DDM to the behavioural data found in all experiments that the drift biasing
model provided a better fit than the start biasing model, and that in Experiments 2-3 a model
only implementing a drift bias outperformed a model implementing both biases (see Fig. 3c).
Analysing modelled parameters revealed higher drift biases on congruent relative to
incongruent trials (posterior probabilities that congruent db > incongruent db: Experiment 1 =
0.819, Experiment 2 = 0.959, Experiment 3 = 0.899 higher values indicate greater
differences between conditions; Wiecki, Sofer & Frank, 2013). To confirm that these
differences in drift bias explained the effect of expectations on perceptual decisions, we
calculated and correlated the difference between drift bias parameters (congruent db
incongruent db) and the magnitude of the behavioural bias (congruent c incongruent c) for
each participant. This analysis revealed strong relationships within all three samples
20
(Experiment 1: r24=-.877, p<.001; Experiment 2: r24=-.973, p<.001; Experiment 3: r48=-.855,
p<.001, see Fig. 3d).
This modelling thereby suggests that action-induced biases were best accounted for by a
sensory drift biasing mechanism where observers are biased to accumulate sensory
evidence in line with their expectations rather than a change in later decision circuits.
Fig 3. Illustration of how the DDM could explain action-induced biases, and results of computational modelling.
a. For an unbiased decision process (black lines) sensory evidence integrates toward the upper response
boundary when stimuli are present (solid lines) and toward the lower response boundary when stimuli are absent
21
(dotted lines). Baseline shifts in decision circuits could shift the start point of the accumulation process nearer to
the upper boundary for congruent events (influencing the parameter z; blue lines - Start bias model). b.
Alternatively, selectively altering the weights on sensory channels could bias evidence accumulation in line with
expectations (influence parameter db; red lines Drift bias model). c. Across all experiments the Drift bias
model provided a better fit than the Start bias model (lower Deviance Information Criteria [DIC] indicates
better model fit). d. Moreover, in each experiment there was a strong correlation between the drift bias values
modelled to each participant and the empirical action-induced bias on c values.
7. General Discussion
Cognitive scientists have proposed models of perceptual prediction that disagree about how
our expectations should shape what we perceive (Press, Kok & Yon, 2020b). Cancellation
models influential in action control have suggested that agents are less likely to perceive the
predictable consequences of their actions (Bays & Wolpert, 2007), in contrast to Bayesian
models from the wider sensory cognition literature which suggest that observers weight
perception towards prior knowledge (see de Lange, Kok & Heilbron, 2018; Press & Yon,
2019). The present experiments suggest that evidence accumulation is biased in line with
expectations during action, such that observers are more likely to perceive the outcomes they
expect. This pattern concords with Bayesian accounts of expectation developed in the wider
sensory cognition literature, which assume observers increase the weight they give to
expected inputs when making perceptual judgements. Biasing perceptual decisions in this
fashion is an effective way to rapidly generate more veridical percepts from sensory signals
corrupted by irreducible internal and external noise. While this process increases the
likelihood that expected signals are detected (i.e. more hits), it also makes observers prone to
hallucinate events when confronted with signal-like noise (i.e., more false alarms; Wyart et
al., 2012), and therefore is thought to generate biasing rather than sensitivity effects as
observed here. Indeed, we found these action-induced biases in perception were dissociable
from top-down attention, with orthogonal task-relevance cues altering judgement sensitivity.
22
This finding is in line with previous studies of expectation and attention outside of action
contexts (Wyart et al., 2012), and perhaps therefore indicative of domain-general
mechanisms.
These patterns are consistent with findings of how previous decisions about sensations
influence subsequent decisions (Talluri, Urai, Tsetsos, Usher & Donner, 2018), and with a
recent fMRI study which found that expected action outcomes are more readily decoded from
early and late visual brain areas (Yon, Gilbert, de Lange & Press, 2018). These neural effects
are predicted by Bayesian models, but it has alternatively been suggested that effects of
expectation in sensory brain areas could reflect feedback from decision-related areas in
higher-level cortex that have no causal effect on perception (Bang & Rahnev, 2017; Choe,
Blake & Lee, 2014). The current findings importantly indicate effects of action expectation
on perception, and a corresponding drift biasing mechanism that is consistent with an early
sensory biasing account.
The predictive relationship exploited in these experiments e.g. that observed index finger
movements are an expected consequence of moving one’s index finger – is strong and stable.
It reflects an expectation that is likely acquired through our extensive experience of
controlling our actions (Hommel et al., 2001). However, we would predict that in principle
the same underlying mechanisms operate when we acquire new predictions. In line with this
assumption, our effects are akin to those found outside of action settings when
participants learn within an experiment that colour cues predict the orientation of gratings
(Wyart et al., 2012). The hypothesis that similar mechanisms operate when we acquire new
predictions may appear inconsistent with previous conflicting findings when participants are
presented with novel action-outcome mappings within an experiment. In an elegant training
study, Cardoso-Leite, Mamassian, Schütz-Bosbach and Waszak (2010) found evidence that
participants are less sensitive to grating orientations ‘predicted’ by actions that had been
23
paired with the gratings during training, with no influence of actions on bias measures.
However, sensitivity was especially high in this study, such that a bias towards perceiving the
expected, accompanied by ceiling effects on the hit rate, would appear as a sensitivity
reduction. Perhaps more importantly, this single study used a small sample and the findings
may not replicate (Schwarz, Pfister, Kluge, Weller & Kunde, 2018). It may therefore be
suggested that there was insufficient opportunity in this paradigm to acquire the action-
outcome mappings reliably. However, in principle, given sufficient opportunity for prediction
acquisition, we would hypothesise prediction mechanisms to operate in a qualitatively similar
fashion when predictions are both ‘old’ and ‘new’ (Dogge, Custers, Gayet, Hoijtink & Aarts,
2019).
This pattern of results is difficult to reconcile with cancellation models, and their central
claim that observers are less likely to perceive the multisensory predictable consequences of
their movements (Bays & Wolpert, 2007; Blakemore et al., 1998). For example, key support
for cancellation models has come from studies that show predictable signals generated by
action are perceived to be less intense than similar (i.e., unpredicted) events presented in the
absence of action (Bays et al., 2005). These studies are in fact often difficult to interpret,
because there are several differences between the predicted and unpredicted conditions. Most
notably, many of the studies compare perception of ‘predicted’ self-generated events with
perception of ‘unpredicted’ sensory events generated by external sources while participants
themselves remain passive or where the sensory and motor events overlap less due to
temporal misalignment (Blakemore, Frith & Wolpert, 1999). This comparison is perhaps
confounding the operation of expectation mechanisms with that of other processes (see Press,
Kok & Yon, 2020a). For example, if conceptualising action as an additional task, classic
working memory models would hypothesise reduced sensory processing when events are
presented in combination with action (Baddeley, 1996). It is therefore difficult to isolate
24
effects of prediction mechanisms from those introduced by the dual- vs single-task design.
The measures employed typically also cannot isolate perceptual effects from decisional
biases (see Firestone & Scholl, 2016) or expectation from attention-based processes (see
Summerfield & Egner, 2016).
One explanation for the difference between the present and previous studies could be that we
have examined visual action effects whereas much evidence to support cancellation comes
from tactile paradigms (e.g., Juravle, McGlone & Spence, 2013; Juravle, Binsted & Spence,
2017; Kilteni & Ehrsson, 2017). The models of which we are aware hypothesise similar
operation of predictive Bayesian or Cancellation mechanisms across domains (Wolpert et al.,
2003; Brown et al., 2013), and there may be no reason to hypothesise distinct adaptive
arguments for the sense of touch. However, touch may be influenced differently because of
operation of additional generalised ‘suppression’ mechanisms. Such suppression mechanisms
are thought to attenuate tactile sensations during action regardless of whether they are
predicted effects of action or not and are thought to be mediated by spinal mechanisms (Seki
& Fetz, 2012), and comparable mechanisms may similarly attenuate sensory processing in a
non-predictive fashion across modalities in humans and other animals (Crapse & Sommer,
2008). Importantly, recent experiments in touch suggest that when confounds related to
sensory suppression are removed, action predictions may influence perception in a
qualitatively similar fashion irrespective of sensory modality (Thomas, Yon, de Lange &
Press, 2020).
Nevertheless, there remain studies that are less prone to these alternative explanations and
report ‘cancelled’ percepts for expected, relative to unexpected, action outcomes (e.g.,
Roussel, Hughes & Waszak, 2013; 2014). It is therefore important that future research
unpacks the features that have driven previous reports of ‘cancellation’ – including the
possibility that additional mechanisms are recruited as sensory processing unfolds (see Press,
25
Kok & Yon, 2020b). Specifically, some of us have recently proposed that the influence of
expectation on perception may not be as simple as suggested in either Bayesian or
Cancellation theories. Under this proposal, observers are initially biased towards perceiving
expected sensory events, but any especially surprising inputs likely relevant for model
updating are reactively upweighted (Press, Kok & Yon, 2020b). Such a reactive
upweighting process only for a subset of ‘unexpected’ events is consistent with evidence
from the learning and inference literature, and is in line with the proposed adaptive function
of a cancellation mechanism i.e., highlighting events that are informative to the organism.
This account may explain some discrepancies in the literature e.g., finding perceptual
upweighting of rapid, punctate consequences of movement, in contrast with evidence for
cancellation-like phenomena when action outcomes unfold dynamically (e.g., Lally, Frendo
& Diedrichsen, 2011). Future studies should therefore establish whether expectation effects
are modulated by the time at which perception is probed and the extent of the surprise elicited
by an ‘unexpected’ stimulus which is low with the barely detectable sensations used in the
present signal detection task (Press, Kok & Yon, 2020b).
The concept of cancellation has had a wide-ranging influence on research investigating the
sense of agency and its aberration in psychiatric disease. For example, experiences of
passivity in schizophrenia where patients feel moved by an external force have often been
attributed to a failure of predictive cancellation, which causes self-produced action outcomes
to appear unusually intense (Frith et al., 2000). However, our results suggest that
sensorimotor predictions can increase the weight we give to expected sensory signals, which
may suggest that these mechanisms contribute to the experience of agency in a different way.
Specifically, biases towards perceiving expected outcomes may help observers to overcome
sensory noise, giving us higher fidelity representations of our ongoing movements and their
consequences. An inability to incorporate this kind of top-down knowledge into perceptual
26
estimates could leave agents with higher levels of uncertainty about their actions, leaving
them vulnerable to developing the unusual beliefs that characterise psychosis (Fletcher &
Frith, 2009).
Important contributions to work on sensory prediction during action have also come from
models of predictive processing and active inference developed in computational
neuroscience (Friston, 2005). These models suggest that all aspects of perception, action and
cognition arise as agents minimise the mismatch between their models of the extracranial
world and incoming sensory evidence. Notably, this can be achieved either by using the
evidence to update the models (i.e. perception) or by using action to change the evidence (i.e.
to alter the world so it conforms to the model). In principle, the architecture of these models
can account for the tendency of agents to up- or down-weight perception of predictable action
outcomes through changes in the gain or ‘precision’ afforded to sensory evidence (Friston,
2008; Yon, de Lange & Press, 2019). For example, researchers using this framework have
suggested that sensory precision on certain channels is attenuated during action explaining
‘cancellation’ phenomena (Brown et al., 2013; Van Doorn et al., 2015) while also
suggesting that observers can increase the precision afforded to expected sensory signals
leading to ‘representational sharpening’ and biases toward perceiving what we expect
(Friston, 2018). However, while models of precision-weighting during action have important
advantages over forward-model based accounts (e.g., where it has been difficult to specify
computationally how predictions could ‘cancel’ sensory signals – see Brown et al., 2013), it
remains the case that the precision on a sensory channel, and perception of a single event,
cannot be up- and down-weighted simultaneously. This makes it difficult to use existing
models of active inference to make precise predictions about those conditions under which
perception of expected action outcomes should be enhanced and those where it should be
attenuated. Our present findings may provide some constraints on such models that aid the
27
generation of more precise behavioural predictions in the future, as these results suggest that
the precision afforded to visual channels encoding expected outcomes is augmented rather
than attenuated certainly when sensory events coincide temporally with action initiation.
We hope that behavioural experiments like ours will play an important role in informing and
constraining such models elaborating a comprehensive account of how and why sensory
gain is augmented and attenuated as we act upon the world around us.
In conclusion, these results have shown that observers are biased towards perceiving the
expected outcomes of their movements. These findings are difficult to reconcile with
dominant cancellation accounts in action control, but concord well with normative models of
Bayesian perceptual inference. Namely, increasing the weight we give to expected sensory
signals may explain how we develop a largely veridical representation of our actions and
their consequences in an inherently uncertain sensory world.
Context
Previous work in the lab has investigated whether specific types of sensorimotor
representations i.e., visual-motor mirror representations of action are the product of
domain-general statistical learning processes. After finding broad support for this idea (e.g.,
Cook, Bird, Catmur, Press & Heyes, 2014; Press et al., 2012), we began to investigate
whether domain-general models can explain the functional influence of sensorimotor
predictions on perception as well as the origin of such representations. In a recent
neuroimaging study (Yon et al., 2018) we found representations in visual brain areas were
reshaped toward expected action outcomes, which is more in line with domain-general ideas
in the broader sensory cognition literature than the cancellation models in action. However,
while these neuroimaging data are consistent with these models, it has been suggested that
28
expectation-related activity in sensory brain areas plays no causal role in determining what
we perceive (Bang & Rahnev, 2017; Choe et al., 2014). Here we directly investigated how
expectations influence the perception of action outcomes finding evidence that we are
biased toward perceiving expected action outcomes.
29
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Acknowledgements
This work was funded by grants from the Leverhulme Trust (RPG-2016-105) and Wellcome
Trust (204770/Z/16/Z). We are grateful to Richard Cooper for advice concerning the
modelling.
Author contributions
All authors contributed to the design of the study. V.Z. and D.Y. collected the data, which
were analysed and modelled by D.Y. in conjunction with C.P. D.Y. wrote the manuscript and
all authors were involved in revisions. C.P. supervised this work.
Competing interests
The authors declare no competing interests.
Correspondence
Correspondence should be addressed to Daniel Yon (d.yon@gold.ac.uk)
... an increase in sensitivity (contrast gain) for stimulus-like external signals (Reynolds & Heeger, 2009). Decreases in criterion induced by expectation have recently been explained as involving changes in stimulus-specific gain (Wyart, Nobre, & Summerfield, 2012;Yon, Zainzinger, de Lange, Eimer, & Press, 2020), suggesting that a similar mechanism may be in play here. According to these accounts, expectation makes stimulus-specific sensory units more sensitive to incoming signals, in turn making the detection threshold more likely to be breached. ...
... Furthermore, in contrast to top-down signals generated by expectations, internally generated signals during imagery are generally strong enough to lead to a visual experience in the absence of external input (Kosslyn et al., 2001). Fully disentangling these two accounts of the effect of imagery on perceptual detection may be possible in future studies by using drift diffusion modelling (Yon et al., 2020) or by varying stimulus energy in a systemic way over trials (Wyart et al., 2012). ...
... Another important issue is whether the imagery effects observed here could be (partly) due to imagery increasing visual attention to the external stimulus. Indeed, spatial attention has also been shown to sometimes decrease criterion (Downing, 1988;Hawkins et al., 1990;Yon et al., 2020), leading to more presence responses. Furthermore, similar to imagery, attention increases sensory activation for the attended stimulus (Ling & Carrasco, 2006;Ling, Liu, & Carrasco, 2009;Liu, Larsson, & Carrasco, 2007;Liu, Pestilli, & Carrasco, 2005), even in the absence of external input (Chelazzi, Duncan, Miller, & Desimone, 1998;Chelazzi, Miller, Duncan, & Desimone, 1993;Peelen & Kastner, 2011;Tanaka, 1996). ...
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... However, in the animal kingdom corollary discharge has been found to influence sensory processing in myriad ways besides cancellation of reafference 43 . Contrary to cancellation theories, recent sharpening models propose that perception is biased towards the expected input 44,45 in line with evidence showing enhanced BOLD responses to self-generated stimuli 46,47 and increased discharges in some neurons during self-initiated vocalizations 48 . The discrepancy between cancellation and sharpening accounts is also reflected in human studies attempting to assess the behavioural correlates of the neurophysiological effects of self-generation on stimulus processing. ...
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... Here, sensory signals that are in line with prior expectations, such as those generated by own actions, are assumed to be perceptually enhanced rather than attenuated Kok et al., 2012;Press et al., 2020;Yon et al., 2018). Indeed, competing with cancellation models, several studies found enhanced perceptual sensitivity for sensory action feedback when compared to less predictable externally generated sensory signals (Myers et al., 2020;Reznik et al., 2014;Schmalenbach et al., 2017;Straube et al., 2020;van Kemenade et al., 2016;Yon et al., 2020). ...
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... The copyright holder for this preprint this version posted November 23, 2020. Reznik et al., 2014;Yon et al., 2020). Collectively, the discrepancy in the results reported 108 so far points to factors other than self-generation that may interactively modulate sensory 109 processing during motor actions. ...
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