Action Enhances Predicted Touch

Abstract

It is widely believed that predicted tactile action outcomes are perceptually attenuated. The present experiments determined whether predictive mechanisms necessarily generate attenuation or, instead, can enhance perception—as typically observed in sensory cognition domains outside of action. We manipulated probabilistic expectations in a paradigm often used to demonstrate tactile attenuation. Adult participants produced actions and subsequently rated the intensity of forces on a static finger. Experiment 1 confirmed previous findings that action outcomes are perceived less intensely than passive stimulation but demonstrated more intense perception when active finger stimulation was removed. Experiments 2 and 3 manipulated prediction explicitly and found that expected touch during action is perceived more intensely than unexpected touch. Computational modeling suggested that expectations increase the gain afforded to expected tactile signals. These findings challenge a central tenet of prominent motor control theories and demonstrate that sensorimotor predictions do not exhibit a qualitatively distinct influence on tactile perception.

Keywords

prediction perception attenuation motor processes

When we produce actions, we predict their sensory consequences. Prominent motor theories (Blakemore et al., 1998; Dogge et al., 2019; Kilteni & Ehrsson, 2017) propose that we attenuate—or downweight—perception of expected action outcomes. Such downweighting mechanisms are thought to finesse the limited capacity of our sensory systems, prioritizing perception of more informative unexpected events that signal the need to perform new actions or update our models of the world (Press et al., 2020b; Wolpert & Flanagan, 2001). For example, if we lift a cup of coffee that is lighter than expected, attenuated processing of expected signals (e.g., touch on our fingertips) will prioritize perception of unexpected events (e.g., accelerating motion of the cup), allowing swift updating of our beliefs about the environment (e.g., the weight of the cup) and supporting corrective action to avoid spillage. These downweighting mechanisms are invoked to explain why self-produced tactile sensations generate lower secondary somatosensory cortex activity (Blakemore et al., 1998; Kilteni & Ehrsson, 2020) and are perceived less intensely (Bays et al., 2005, 2006; Kilteni et al., 2019) than externally produced forces. This theory also provides an explanation for why it is difficult to tickle oneself (Blakemore et al., 1998).

However, outside of action, it is thought that prediction mechanisms generate a qualitatively opposite influence on perception. In these theories—typically couched in Bayesian frameworks—it is proposed that we combine our expectations (prior) with the input (likelihood) to determine what we perceive (posterior; Kersten et al., 2004). Such a process would upweight, rather than downweight, perception of expected events, enhancing the detectability and apparent intensity of events (Brown et al., 2013) and thereby enabling rapid generation of largely veridical experiences in the face of sensory noise (de Lange et al., 2018). For example, some theories propose that we use predictions to increase the gain on sensory units tuned to expected events and, via competitive local interactions, inhibit sensory populations tuned to unpredicted events (de Lange et al., 2018; Kok et al., 2012; Press & Yon, 2019; Yon et al., 2018). However, it is perhaps unclear why the adaptive arguments presented for downweighting (informativeness) and upweighting (veridicality) predicted perceptual experiences should apply differentially in the domain of action (note that we use the terms attenuation and downweighting, as well as enhancement and upweighting, interchangeably in the present article). Specifically, it appears just as crucial to optimize informativeness and veridicality regardless of how predictions are formed (Press et al., 2020b), and some evidence from the visual domain suggests that predictive influences on perception do not exhibit the qualitative differences assumed in the literature (Yon et al., 2018, 2021; Yon & Press, 2017).

Given the comparability of this recent visual evidence, a stark difference between studies that are purported to demonstrate upweighting and downweighting is that the former study visual perception whereas the latter study tactile perception. It is therefore widely believed that action predictions shape tactile perception in a qualitatively distinct way—including proposals that differences arise because tactile events are body related (Dogge et al., 2019) and tightly coupled with the motor system (Kusnir et al., 2020) in a way that many predicted visual or auditory events are not. Similarly, differences may also relate to assumptions that tactile attenuation during action is dependent on connectivity between the somatosensory cortex and cerebellum (Blakemore et al., 1998; Kilteni & Ehrsson, 2020), in contrast with hippocampal mediation of prediction in visual processing (Kok & Turk-Browne, 2018).

However, studies examining touch perception during action have not manipulated predictability like those in wider sensory cognition; therefore, it would be premature to assume that qualitatively distinct mechanisms influence touch. The defining feature of prediction mechanisms is that they operate according to stimulus probabilities (de Lange et al., 2018). Thus, prediction mechanisms outside of action contexts are typically measured by presenting events with high and low conditional probabilities, allowing comparison of perception of expected events (e.g., 80% likely on the basis of a preceding cue) and unexpected events (20% likely; Cheadle et al., 2015; Kok et al., 2012; Richter & de Lange, 2019). In contrast, studies demonstrating tactile attenuation during action compare the perception of events in the presence or absence of action or when events are coincident as opposed to delayed with respect to action (Bays et al., 2005; Blakemore et al., 1998; Kilteni et al., 2019; Wolpe et al., 2018). In these experiments, it is assumed that the sensory events that coincide with action are more predictable, explaining why perception of them is attenuated. However, it is unclear whether these effects indeed reflect the operation of predictive mechanisms when stimulus probabilities have not been manipulated and various nonpredictive mechanisms influence perception during action (Press & Cook, 2015; Press et al., 2020a). For example, when we move, we suppress all tactile input to a moving effector (Williams & Chapman, 2000). Relatedly, “active-inference” predictive-processing accounts and even classic working memory models would predict reduced perception of all sensory events in the presence of action (Press et al., 2020a), regardless of the extent to which they were predicted on its basis. Thus, to test whether action predictions really influence touch perception via qualitatively distinct mechanisms, we combined (a) a force-judgment paradigm used widely in action domains to examine tactile attenuation with (b) a probabilistic predictive manipulation typically used in broader sensory cognition domains.

Statement of Relevance

The fact that we cannot tickle ourselves has fascinated scientists and the general public alike. This phenomenon is thought to result from effectively turning down the volume on our experience of expected action outcomes, which may be useful because it is better to devote our limited resources to processing unexpected elements of our environment. However, proposals have been made in action-unrelated areas of psychology that we should turn up—not turn down—the volume on what we expect, because sensory inputs are noisy. Such amplification processes would make our experiences more accurate in the face of this noise, allowing us to perceive more readily what we expect. Across three experiments, we found consistent evidence in line with these latter ideas. These findings suggest that we must reexamine existing theories concerning how action expectations shape our experiences and reassess a range of clinical theories based on their assumptions.

General Method

Thirty distinct participants were tested in Experiment 1 (16 female; age: M = 25.53 years, SD = 5.25), Experiment 2 (20 female; age: M = 22.80 years, SD = 3.18), and Experiment 3 (22 female; age: M = 24.3 years, SD = 4.34). Eight participants in Experiment 1, six participants in Experiment 2, and nine participants in Experiment 3 were replacements for those whose psychometric functions could not be acceptably modeled to their responses (flat functions), for those who were unable to follow instructions concerning movement performance (> 20% recorded movement errors), or because of technical malfunction. These criteria were established prior to participant testing, and replacements resulted in a total sample of 30 participants in each experiment. One participant’s point of subjective equality (PSE) score from Experiment 2 was Winsorized to meet the normality assumptions of parametric tests (z = 3.34–3; Tukey, 1962). Participants were recruited from Birkbeck, University of London, and were paid a small honorarium for their participation. No participant reported a current neurological or psychiatric illness, and all provided written informed consent prior to participation. The experiments were performed with local ethical committee approval (Birkbeck, University of London) and in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. The sample size was determined a priori on the basis of pilot testing to have at least 80% power to detect medium effect sizes (d = 0.5), and parametric assumptions were met. Data collection stopped after we attained the predetermined sample size of 30 participants, adhering to the above criteria in each experiment.

Experiment 1

Method

We created a contact condition in Experiment 1 to determine whether we could replicate typical action-attenuation effects within our setup. Participants therefore moved their right index finger to make contact with a button, generating a simultaneous mechanical force to their left index finger below (see Procedure and Fig. 1a). We thereby examined whether left-hand stimulation is reported as less forceful during right-hand action (active trials) than when the right hand remains still (passive trials). We were also interested in the nature of effects in a no-contact condition, in which a similar downward motion of the right index finger triggered the same left-hand stimulation even though the right-hand finger did not make contact with a button. Stimulation to the left hand was instead triggered when a motion tracker detected movement of the right index finger. Given that intransitive actions frequently produce sensory effects, active finger contact should not be required to form sensorimotor predictions per se, and this generation of an active-finger sensory event simultaneous with target stimulation especially complicates interpretation. Specifically, studies examining predictive attenuation have focused on perception of events on passive effectors due to potential confounds of “generalized gating” when measuring perception on active effectors. Generalized gating is thought to reduce perception of any events delivered to moving effectors (Williams & Chapman, 2000) without exhibiting specificity to predicted consequences because it is hypothesized to occur at the earliest relay in the spinal cord (Seki & Fetz, 2012). One possible reason that active finger contact complicates interpretation of attenuation in contact setups is that concurrent gated sensory events on active effectors could bias responses about stimulation on passive effectors, for instance, because of response biases (Firestone & Scholl, 2016). If this is the case, we would expect to observe effects in the contact condition that are different from those observed in the no-contact condition, considering that this nonpredictive influence was removed in the latter.

Fig. 1.

Paradigm and results from Experiment 1. On each trial (a), participants made downward movements with their right index finger either over a motion tracker (no-contact condition) or to make a button press (contact condition). Each movement elicited a tactile punctate event to the left index finger positioned directly below. Points of subjective equality (PSEs; b) were calculated for each participant and indexed the point at which participants responded that the target stimulus intensity was the same as the subsequent reference stimulus intensity. Data from an example participant are shown for active trials (in which participants were cued to move their right index finger; dark blue slope) and passive trials (in which participants were cued to not move their finger; light blue slope) in the no-contact condition. Dashed lines indicate the PSEs for this participant. Mean PSEs in the contact and no-contact conditions (c) are shown for active trials (darker bars) and passive trials (lighter bars). Lower PSEs indicate more intense target percepts. Asterisks indicate significant differences between trial types (*p < .05, **p = .001). Error bars show standard errors of the mean. The PSE effect of movement (passive trials – active trials) for the contact and no-contact conditions (d) is plotted with rain-cloud plots. The shaded areas display probability density estimates. Box plots denote lower and upper quartiles (left and right sides, respectively), along with the middle quartile (median). Whiskers denote 1.5 times the interquartile range, and dots denote difference scores for each participant (N = 30). Positive effects of movement indicate more intensely perceived active target events relative to passive events, but negative values indicate the reverse—less intensely perceived active events.

Procedure

The experiment was conducted in MATLAB (The MathWorks, Natick, MA) using the Cogent 2000 toolbox (Version 1.32; University College London; http://www.vislab.ucl.ac.uk/cogent.php).Participants held their left-hand palm upward (see Fig. 1a) with their index finger positioned against a solenoid (diameter of metal rod = 4 mm, diameter of solenoid = 15 mm; TACT-CONTR-TR2 solenoid controller box; Heijo Research Electronics, London, UK) sitting on the apex of the fingertip. Their right hand rested on an armrest, positioned such that the distal phalange of the index finger was directly above the distal phalange of the left hand but rotated 90° counterclockwise relative to their left hand (see Fig. 1a). A small button box or infrared motion tracker (Leap Motion Controller using the Matleap MATLAB interface) was placed on a shelf supporting the solenoid (depending on block type—contact or no contact, respectively) and was therefore positioned directly above the solenoid.

At the start of each trial, participants were cued on screen to move their right index finger (“move”; active trials = 50%) or remain stationary (“do not move”; passive trials = 50%). In both contact and no-contact blocks, participants were required to hold their right-hand palm face down and their arm parallel with the computer monitor and the coronal body midline. In both conditions, participants’ left hand was positioned laterally from the body midline and in line with the shoulder. The experimenter demonstrated the appropriate actions for both conditions before the experiment began. These steps ensured that participants’ starting position remained the same regardless of trial type (active vs. passive) and that actions were executed in approximately the same way across contact and no-contact blocks (bar the contact with the button; see the Supplemental Material available online). Participants’ hands were visually occluded during the experiment, and white noise was played continually through headphones (53 dB; pilot testing confirmed that this level resulted in inaudible solenoid movement).

On active trials, participants bent their right index finger downward at the metacarpophalangeal joint. The target stimulus was delivered to the left index finger for 30 ms immediately after the right finger pressed the button (contact blocks) or as soon as the right finger moved an approximately equivalent distance over the motion tracker (i.e., at least 20 mm from the stationary starting position at trial onset; no-contact blocks; see the Supplemental Material). Pilot testing confirmed that stimulation was in apparent synchrony with movement termination in both contact and no-contact blocks. After 1,000 ms, a reference stimulus was presented for 30 ms. The target stimulus was chosen from one of seven logarithmically spaced suprathreshold forces, and the reference stimulus was always the fourth (middle) force (for more details about the generation of tactile events, see the Supplemental Material). After a 300-ms to 500-ms delay, participants were asked which tap was more forceful, responding with a left foot pedal for the first stimulus and a right foot pedal for the second stimulus. The next trial started after 1,000 ms. In passive trials, the target stimulus was delivered 500 ms after the cue to remain still. Because our manipulation (contact vs. no contact) affected only active trials, passive trials were identical in both contact and no-contact blocks and served as comparisons within blocks.

There were 560 trials in total: 140 for each of the active and passive conditions in both the contact and no-contact blocks. The order of blocks was counterbalanced across participants, and the order of trial type (active vs. passive) was randomized across blocks. Participants completed eight practice trials before the main test blocks.

Modeling psychometric functions

Using the Palamedes toolbox (Version 1.8.2; Prins & Kingdom, 2018) in MATLAB, we modeled participant responses using cumulative Gaussian functions to estimate psychometric functions. This procedure was performed separately for active and passive trials during the test phase in Experiment 1. The mean of the modeled Gaussian function was taken as the PSE, describing the point at which participants judged the target and reference events to have equal force. Lower values are indicative of more intense target percepts.

Results

PSE values were analyzed in a 2 (contact: contact vs. no contact) × 2 (movement: active vs. passive) within-participants analysis of variance, which revealed no main effect of contact, F(1, 29) = 3.11, p = .089, η_p² = .10, or movement, F(1, 29) = 1.24, p = .274, η_p² = .04. However, there was a significant interaction between contact and movement, F(1, 29) = 15.39, p < .001, η_p² = .35. This effect was driven by lower force judgments (higher PSEs) in active (M = 4.73, SD = 1.22) compared with passive (M = 4.35, SD = 0.80) trials in the contact condition, t(29) = 2.07, p = .047, d = 0.38, but higher force judgments (lower PSEs) in active (M = 3.82, SD = 1.11) compared with passive (M = 4.45, SD = 0.97) trials in the no-contact condition, t(29) = −3.80, p = .001, d = 0.69 (see Fig. 1c).

Experiment 2

Method

Experiment 1 replicated previous findings (Bays et al., 2005; Kilteni et al., 2019) that tactile events on a stationary left finger are perceived less intensely during active right-hand movement, but only when the active finger makes contact with a button, as in the typical version of the paradigm. When there is no contact, events are perceived more intensely during movement. One possible explanation for the difference between conditions is that perception of gated stimulation on the active effector contributed to the active–passive difference in the contact condition.

Having established that we can observe typical attenuation during action—but that attenuation becomes enhancement in a no-contact condition—in Experiment 2, we isolated the particular functional influence of prediction mechanisms by manipulating conditional probabilities between actions and outcomes. This is particularly important for establishing the role of action predictions in determining perception because—as outlined in the introduction—an active–passive comparison does not isolate predictive influences of action even in a no-contact condition, for example, because it confounds the number of tasks. Therefore, we compared perception of tactile events when they were expected or unexpected on the basis of learned action–outcome probabilities established in a preceding training session but always in the presence of action. Although downweighting accounts predict that expected tactile events will be rated less intensely than unexpected events, upweighting theories predict that expected events will be rated more intensely.

There were several procedure changes relative to Experiment 1. Each participant now performed one of two movements that predicted one of two tactile effects (see Fig. 2a). Participants were positioned with their left index and middle fingers making contact with independent solenoids (see Fig. 2a). At the beginning of each trial, an arbitrary cue (either a square or circle) instructed participants to move their right index finger either upward or downward from the metacarpophalangeal joint, tracked by an infrared motion sensor. This action triggered delivery of the target stimulus to either solenoid in the same way as in the no-contact condition of Experiment 1 (for more details, see the Supplemental Material). During training, participants’ right index-finger action (e.g., downward movement) was 100% predictive of the location of left-hand tactile events (e.g., index finger). In a test session 24 hr later, the action–outcome relationship was degraded to measure perception of expected and unexpected events—the expected finger was stimulated on 66.7% of trials, and the unexpected finger was stimulated on the remaining 33.3% of trials.

Fig. 2.

Paradigm and results from Experiments 2 and 3. On each trial in Experiment 2 (a), participants made a downward or an upward movement with their right index finger over a motion tracker, which elicited tactile punctate events to the left index or middle finger. In Experiment 3 (b), participants made only downward movements with either their right index or middle finger, which again elicited tactile punctate events to the left index or middle finger. An example training procedure (c) is shown for Experiment 2. Movements were 100% predictive of tactile events during the training session, and 66.7% predictive in the test session. This procedure was also adopted in Experiment 3 but with different action types. Flash symbols illustrate locations of stimulation. Mean points of subjective equality (PSEs) in Experiments 2 and 3 (d) are shown for expected trials (darker bars) and unexpected trials (lighter bars). Lower PSEs indicate a more intensely perceived target stimulus. Asterisks indicate significant differences between trial types (*p < .05). Error bars show standard errors of the mean. The PSE expectation effect (unexpected trials – expected trials) for each experiment (e) is plotted with rain-cloud plots. The shaded areas display probability density estimates. Box plots denote lower and upper quartiles (left and right sides, respectively), along with the middle quartile (median). Whiskers denote 1.5 times the interquartile range, and dots denote difference scores for each participant (N = 30 for each experiment). Positive expectation-effect values indicate more intensely perceived expected events relative to unexpected events.

Presenting two action types and two stimulation types also allowed us to compare perception of expected and unexpected events while controlling for repetition effects. It should be noted that any action predictions should determine where stimulation will be received, rather than its intensity. However, in Bayesian models, it is assumed that enhanced detection and intensity of expected events relates to the precision of the estimate (Brown et al., 2013; e.g., a force precisely estimated to have occurred on a certain region of tactile space should feel more intense because of the precise estimate of spatial information rather than an estimate of the force per se). Because these models assume that predictions enhance the precision of resultant estimates, they would also predict enhancements in perceived force (and indeed other sensory attributes, such as brightness or loudness).

There were 420 trials in each session. Trial order was randomized, and the action–stimulus mapping was counterbalanced across participants. The cue–action mapping was reversed halfway through each session to account for effects resulting from possible learning of cue–outcome associations instead of action–outcome associations. The training and test sessions were carried out at the same time on consecutive days. Participants completed 12 practice trials before the main session trials.

Results

Psychometric functions were modeled to participants’ responses similarly as in Experiment 1 but now separately for expected and unexpected events. PSE values were lower on expected trials (M = 3.72, SD = 0.96) than unexpected trials (M = 3.93, SD = 0.80), t(29) = −2.13, p = .041, d = 0.39 (see Fig. 2d), demonstrating more forceful perception of expected than unexpected action outcomes.

Experiment 3

Method

Experiment 3 was designed to provide a conceptual replication of Experiment 2 using a setup more closely aligned with typical action paradigms (e.g., Experiment 1)—whereby one always makes a movement toward another effector. We additionally controlled for possibilities that the expectation effect in Experiment 2 resulted from cue–outcome learning by removing the cue stimulus and requiring free selection of action. The explicit reference stimulus was also removed, and comparisons were made against an implicit reference, eliminating the possibility that effects would be determined by forming predictions about the reference stimulus.

There were several procedure changes relative to Experiment 2. Independent solenoids were now attached to the left index and middle fingers via adhesive tape (diameter of metal rod = 4 mm, diameter of solenoid = 18 mm; TactAmp 4.2 solenoid controller box; Dancer Design, St. Helens, UK). The foot pedals were positioned at either a 45° angle or 90° angle (for stronger and weaker responses, respectively) relative to participants’ right foot to record responses to account for any spatial biases resulting from positioning foot pedals as “left” and “right.” At the start of each trial, participants made a downward movement with either their right index or middle finger. These action types were selected to ensure that effects in Experiment 2 were not specific to those action types and to determine whether similar effects could be observed with actions that are always made toward another effector. Participants’ hands, and therefore index and middle fingers, were spatially aligned with each other (see Fig. 2b). Actions were freely selected, and the frequency of index- and middle-finger movements was monitored to ensure approximately equal numbers of both action types. Participants’ actions (e.g., downward movement of right index finger) were still perfectly predictive of the location of tactile events (e.g., left index finger) during training, and this contingency was again degraded to 66.7% in the following test session. Participants were asked whether they perceived the test force to be more or less forceful than the average force intensity. An example of the average force was presented to each finger once at the end of short breaks every 21 trials (note that the average force was identical to the intensity of the reference force).

The experiment consisted of two training blocks followed by a test block, all occurring in the same session of testing. In the first training block, participants responded “yes” or “no” to the question “Tap on index or middle finger?” and in the second training block, they were asked about the force, as in Experiment 2 and in subsequent test blocks. For half of the participants, moving the right index finger resulted in stimulation of the left index finger, and moving the right middle finger resulted in stimulation of the left middle finger. For the other half of the participants, moving the right index finger resulted in stimulation of the left middle finger, and moving the right middle finger resulted in stimulation of the left index finger. There were 210 trials in each session.

Results

As in Experiment 2, PSE values were lower in expected (M = 4.08, SD = 1.00) than unexpected (M = 4.28, SD = 0.99) trials, t(29) = −2.56, p = .016, d = 0.47 (see Fig. 2d), again demonstrating more forceful perception of expected than unexpected action outcomes. Additional post hoc analyses revealed that the specific kinematics of action were similar in expected and unexpected trials and that the PSE expectation effect was comparable at the start and end of 21-trial miniblocks (see the Supplemental Material).

Computational Modeling

Method

The present findings are consistent with predictive-upweighting theories of perception, which propose that observers combine sensory evidence with prior knowledge—biasing perception toward what we expect (Kersten et al., 2004). This may be achieved mechanistically by altering the weights on sensory channels, increasing the gain of expected relative to unexpected signals (de Lange et al., 2018). However, expectation effects may instead reflect biasing in response-generation circuits if action biases people to respond that expected events are more intense rather than altering perception itself (Firestone & Scholl, 2016).

Perceptual and response biases can be dissociated in computational models that conceptualize perceptual decisions as a process of evidence accumulation. Perceptual biases are modeled as growing across time—every time response units sample from perceptual units, they will be sampling from a biased representation, therefore increasing the magnitude of biasing effects across a larger number of samples (Yon et al., 2021). In contrast, response biases are modeled as operating regardless of current incoming evidence and to be present from the outset of a trial. According to this logic, we can model the decision process with drift-diffusion modeling (Ratcliff & McKoon, 2008) to identify the nature of the biasing process. We can thus establish whether action expectations shift the starting point of evidence accumulation toward a response boundary (“start biasing”; z parameter; see Fig. 3a) or instead bias the rate of evidence accumulation (“drift biasing”; db parameter; see Fig. 3b).

Fig. 3.

Illustration of how the drift-diffusion model could explain expectation biases (top row) and results of computational modeling (bottom row). For an unbiased decision process (black lines in a and b), sensory evidence integrates toward the upper response boundary when stimuli are stronger than average (solid lines) and toward the lower response boundary when weaker than average (dotted lines). In a start-bias model (blue lines in a), baseline shifts in decision circuits could shift the start point of the accumulation process nearer to the upper boundary for expected events (influencing the parameter z). Alternatively, in a drift-bias model (red lines in b), selectively altering the weights on sensory channels could bias evidence accumulation in line with expectations (influencing parameter db). The simulated start-plus-drift-bias (winning deviance information criterion [DIC] model) expectation effect is plotted against the empirical expectation effect in (c). The simulated drift-bias expectation effect is plotted against the empirical expectation effect after accounting for simulated start-bias effects (plotted as the residuals from a model in which the simulated start-bias effect predicts the empirical effect) in (d). All expectation effects were calculated by subtracting expected points of subjective equality (PSEs) from unexpected PSEs.

We fitted drift-diffusion models to participant choice and reaction time data from Experiment 3 using the hDDM package (Version 0.6.0; Wiecki et al., 2013) implemented in Python (note that reaction times were not collected in Experiments 1 and 2). In the hDDM, model parameters for each participant are treated as random effects drawn from group-level distributions, and Bayesian Markov chain Monte Carlo sampling is used to estimate group- and participant-level parameters simultaneously. We specified four different models: (a) a null model in which no parameters were permitted to vary between expected and unexpected trials, (b) a start-bias model in which the start point of evidence accumulation (z) could vary between expectation conditions, (c) a drift-bias model in which a constant added to evidence accumulation (db) could vary according to expectation, and (d) a start-plus-drift-bias model in which both parameters could vary according to expectation.

All models were estimated with Markov chain Monte Carlo sampling, and parameters were estimated with 30,000 samples (burn-in = 7,500). Model convergence was assessed by inspecting chain posteriors and simulating reaction time distributions for each participant. Models were compared using the deviance information criterion (DIC) as an approximation of Bayesian model evidence, a common method used to determine model fit. Lower DIC values relative to a baseline, or null, model are indicative of a better model fit.

A posterior predictive check was conducted using the hDDM package to establish how well each model was able to reproduce the patterns in our data. The posterior-model parameters for the start-bias, drift-bias, and start-plus-drift-bias models were used to simulate a distribution of 500 reaction times and choices for each trial for each participant. From these simulated data, we calculated the probability that a stronger than average response was given at each intensity level, separately for expected and unexpected trials. This allowed us to model simulated psychometric functions for expected and unexpected trials, exactly as we had done for empirical decisions. Performing this procedure for each model yielded separate simulated expectation effects (unexpected PSE – expected PSE) for each participant under the start-bias, drift-bias, and start-plus-drift-bias models.

Results

Fitting the drift-diffusion model to the behavioral data revealed that the model allowing both start and drift biases to vary according to expectation provided the best fit (DIC relative to null = −234.8) relative to both the start-bias (DIC relative to null = −191.06) and drift-bias (DIC relative to null = −8.62) models. This finding may suggest that observed biases are a product of both start and drift-rate biasing. However, although the DIC measure does include a penalty for model complexity, it is thought to be biased toward models with higher complexity (Wiecki et al., 2013), and it indeed favored the most complex model here.

We conducted a posterior predictive check to evaluate how well simulated data from each of the models could reproduce key patterns in our data. Correlations were calculated to quantify how well simulated expectation effects reproduced empirical expectation effects, which revealed significant relationships for all three models—start-bias model: r(30) = .39, p = .034; drift-bias model: r(30) = .43, p = .017; start-plus-drift-bias model: r(30) = .53, p = .003 (see Fig. 3c).

Given that we were interested in whether any of the PSE expectation effects were generated by sensory biasing—rather than possible additional contributions of response biasing—we examined whether drift biasing accounted for any further variance in expectation effects than start biasing alone by conducting a stepwise linear regression to predict the empirical expectation effect (unexpected PSE – expected PSE). In the first step, we included the simulated expectation effect from the start-bias model to predict the empirical expectation effect. The simulated start-bias data were able to predict the empirical expectation effect, R² = .15, F(1, 28) = 4.96, p = .034. In the second step, we included the simulated expectation effect from the drift-bias model as an additional predictor of the empirical expectation effect, importantly providing a significant improvement to the model fit, ΔF(1, 27) = 6.72, p = .015; R² = .32, F(2, 27) = 6.34, p = .006. This analysis revealed that a model implementing a drift-biasing mechanism better predicted empirical effects of expectation on perceptual decisions by explaining unique variance in participant decisions that cannot be explained by start biasing.

Discussion

Models disagree about how predictions should shape perception of action outcomes. We examined whether sensorimotor prediction influences touch perception via qualitatively distinct mechanisms from other types of prediction by adapting the force-judgment paradigm typically used in the action literature (Bays et al., 2005, 2006; Kilteni et al., 2019) and applying predictive manipulations from broader sensory cognition (de Lange et al., 2018; Richter & de Lange, 2019). Experiment 1 replicated typical findings that self-produced forces are rated as less intense than externally generated ones, but this effect reversed when there was no active finger contact with a button. Experiments 2 and 3 manipulated the predictability of tactile action outcomes and found that expected events were perceived more, not less, intensely than unexpected events. Computational modeling suggested that expectations alter the way sensory evidence is integrated—increasing the gain afforded to expected tactile signals.

These findings are consistent with predictive-upweighting accounts from outside of action domains, which propose that prior expectations are combined with sensory evidence to generate veridical perceptual interpretations of our noisy environment—thereby rendering expected events more intense. The present findings indicate that these upweighting mechanisms operate similarly in touch. It is therefore essential to consider how the present findings can be resolved with data cited in support of downweighting theories. As well as the data already outlined in humans, there is a range of related findings in other species—for example, attenuating internally generated electric fields in mormyrid fish is thought to improve detection of prey-like stimuli (Enikolopov et al., 2018), and virtual-reality-trained mice show suppressed auditory responses to self-produced tones generated by treadmill running (Schneider et al., 2018). However, studies have not demonstrated whether underlying mechanisms operate according to stimulus probabilities. There are a number of nonpredictive mechanisms that could explain attenuation, and on the basis of the current findings, we propose that many effects are instead generated by identity-general gating mechanisms, and others are possibly generated by mechanisms shaping perception according to event repetition—given that repetition is frequently confounded with expectation (e.g., Kilteni et al., 2019; see Feuerriegel et al., 2021). The assumption that action predictions specifically attenuate perception is central to a number of clinical models, including accounts of sensory differences in healthy aging (Wolpe et al., 2016), motor severity in Parkinson’s disease (Wolpe et al., 2018), and hallucinations in schizophrenia (Corlett et al., 2019). However, if predictions shape perception similarly regardless of domain, then these theories may need revisiting.

Importantly, however, the present data should not be taken to reflect that predictive attenuation cannot occur, especially given the importance of generating perceptual experiences that are informative across domains (Press et al., 2020b). Nevertheless, they suggest that predictive mechanisms during action operate differently from current assumptions. It has been widely claimed that attenuation likely results from subtracting the prediction from the input (Wolpert & Flanagan, 2001), and such a mechanism would be hard to reconcile with the present findings demonstrating upweighted sensory gain of predicted action outcomes. These data more likely suggest that purported predictive mechanisms must have the capability of generating both up- and downweighting, but under different circumstances. One possible resolution to the current debate has been recently outlined by some of us assuming opposing processes with differing roles (Press et al., 2020b), and resolving it must be a focus of future work.

Supplemental Material

sj-docx-1-pss-10.1177_09567976211017505 – Supplemental material for Action Enhances Predicted Touch

Supplemental material, sj-docx-1-pss-10.1177_09567976211017505 for Action Enhances Predicted Touch by Emily R. Thomas, Daniel Yon, Floris P. de Lange and Clare Press in Psychological Science

Footnotes

Transparency

Action Editor: Sachiko Kinoshita

Editor: Patricia J. Bauer

Author Contributions

All the authors developed the study concept and design. E. R. Thomas collected and analyzed the data under the supervision of C. Press. E. R. Thomas and C. Press were the primary writers of the manuscript, and the other authors provided revisions. All the authors approved the final manuscript for submission.

ORCID iDs

Emily R. Thomas

Daniel Yon

Clare Press

Supplemental Material

Additional supporting information can be found at

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Supplementary Material

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