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A test of the embodied simulation theory of object perception: potentiation of responses to artifacts and animals

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Abstract

Theories of embodied object representation predict a tight association between sensorimotor processes and visual processing of manipulable objects. Previous research has shown that object handles can ‘potentiate’ a manual response (i.e., button press) to a congruent location. This potentiation effect is taken as evidence that objects automatically evoke sensorimotor simulations in response to the visual presentation of manipulable objects. In the present series of experiments, we investigated a critical prediction of the theory of embodied object representations that potentiation effects should be observed with manipulable artifacts but not non-manipulable animals. In four experiments we show that (a) potentiation effects are observed with animals and artifacts; (b) potentiation effects depend on the absolute size of the objects and (c) task context influences the presence/absence of potentiation effects. We conclude that potentiation effects do not provide evidence for embodied object representations, but are suggestive of a more general stimulus–response compatibility effect that may depend on the distribution of attention to different object features.

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Notes

  1. Currently, there is no unified theory of embodied cognition, and many accounts have been provided. For instance, see Wilson (2002) for a general overview of the issues associated with embodied cognition; see the perceptual symbols theory of Barsalou (2003); and see Lakoff and Johnson (1999) for a general theoretical and philosophical framework. A similar approach has been described in the literature on common-coding (e.g., Hommel, Müsseler, Aschersleben, & Prinz, 2001) and regarding semantic representation (Allport, 1985). Additionally, the idea that artifacts ‘afford’ actions was first developed by Gibson (1979). For our purposes, Shapiro (2011) has identified at least three distinct aspects of the embodied cognition hypothesis; in the present experiment, we are specifically referring to the ‘conceptualization’ hypothesis. This hypothesis suggests that simulations and reactivations in modality-specific systems form the basis of object identification. According to the conceptualization hypothesis, we must simulate (or reactivate) our visual, auditory and motor experiences with a hammer to know an object is hammer.

  2. This effect has also been referred to as the orientation effect (see Vainio, Ellis, & Tucker, 2007), the visuo-motor priming effect (Craighero, Fadiga, Umiltà & Rizzolatti, 1996) or simply the correspondence or compatibility effect (see Phillips & Ward, 2002; Cho & Proctor, 2010). However, we feel that ‘potentiation’ is a more descriptive and less ambiguous term.

  3. Consistent patterns of results have been reported using similar paradigms. For instance, Tucker and Ellis (1998, 2004) have shown that participants are faster at making precision grasps (e.g., pinching with the index finger and thumb) when shown an object that affords a precisions grasp (e.g., a clothes peg) than when shown an object that does not (e.g., a wine bottle); conversely, power grasps (e.g., grasping with the whole hand) were primed by objects that afford power grasps (e.g., a wine bottle) but not other objects (e.g., a clothes peg). While these results are consistent with theories of embodied cognition, it could be argued that they arise because of a general ‘dimensional overlap’ between stimuli and responses (Kornblum et al., 1990).

  4. Note, the effect size we report is the generalized eta squared. The generalized eta is designed to reduce the influence of the number of factors in an experiment. However, the standard interpretations of the small, medium, and large effect sizes still apply (see Olejnik & Algina, 2003).

  5. This conclusion is based on the interpretation of the pattern of data across Experiment 1 and 2. To ensure the reliability of this interpretation, we combined data from the two experiments and included Experiment as a between subject factor. The three-way interaction between experiment, compatibility, and category was marginally significant in the accuracy data, F (1, 48) = 2.9, p < 0.09, η 2G  = 0.009, and significant in the logRT data, F (1, 48) = 6.47, p = 0.001, η 2G  = 0.002. These interactions support our conclusions.

  6. We recognize that the direction of the potentiation effect depends on our definition of ‘compatibility’; therefore, in this way the effect’s direction is arbitrary. For simplicity, we retain our definition throughout this report and discuss the consequences in the “General Discussion”. Importantly, whether we consider the potentiation effect as ‘reversed’ or not has no consequences for the main conclusion we draw here.

  7. This argument is based on findings that a participant’s own hand will bias attention in the visual field. We thank one reviewer for suggesting that it is unknown whether a hand image (that is not the participant’s own hand) will affect attention similarly. Future research should explore whether own vs. other hand images influence attention in similar ways.

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Conflict of interest

The authors report no conflicts of interest in the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Psychology, Dalhousie University, Life Sciences Center, Halifax, NC, B3H 4J1, Canada

    Heath E. Matheson & Patricia A. McMullen

  2. Department of Psychology, University of Toronto, Toronto, Canada

    Nicole C. White

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  1. Heath E. Matheson
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  2. Nicole C. White
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Correspondence to Heath E. Matheson.

Appendices

Appendix 1

In addition to the main analysis of variance (ANOVA), logRT measures were analyzed using linear mixed-effects (LME) modeling as it is implemented by the lmer() function of the lme4 library in R (Bates, Maechler, & Bolker, 2012). Although linear mixed-effects modeling is a relatively new advancement in statistical modeling, this approach has been used by a number of authors in the analysis of continuous data (e.g., see Baayen, 2008; see Newman, Tremblay, Nichols, Neville, & Ullman, 2012, and Lawrence & Klein, in press, for similar implementations as the one used here), and offers a number of advantages over the more traditional repeated-measures ANOVA. First, LME models permit both fixed-effects parameters (those corresponding to the controlled manipulated variables in the experiment, including the category of object and its orientation—compatible vs. incompatible) and random effects (the effects of a variable that are randomly chosen from a larger population such as the experimental stimuli used or the participants sampled). Modeling random effects allows the analysis to better meet the assumption of uncorrelated randomly distributed residuals, an assumption that is typically violated in analyses of repeated-measures RT data. Further, modeling random effects allows for greater generalization of the results beyond the participants and items used in the experiment (Baayen, 2008). This is an important advantage of the current study over previous research that has used single stimuli (e.g., Anderson, Yamagishi, & Karavia, 2002); in these cases, there is no confidence that the effects will extend to other stimulus sets. By accounting for variability due to items we are able to derive a more precise estimate of the effects within and between our two object categories (see Baayen, 2008 discussion comparing LME with quasi-F comparisons in item and subject analyses). Second, linear mixed-effects models account for non-sphericity and missing data points, which is often an issue when analyzing only a subset of RTs (i.e., from correct trials only), without the use of common corrections (e.g., using the Greenhouse-Geisser or Huynh–Feldt corrections). Third, mixed models do not aggregate the data, meaning that each data point is used in building the statistical model, increasing its power (see Baayen, 2008 for a thorough discussion of these advantages and more).

The statistical models were computed using the lme() and LMERConvenienceFunctions() packages (Tremblay & Ransijn, 2011). Models are fitted with the following method:

  1. 1.

    An initial model was computed containing all the theoretically relevant fixed effects (in this case object category and compatibility), but it only included a single random effect—one for subjects. Conceptually, modeling subjects as random is similar to performing a repeated-measures ANOVA, allowing for by-subject adjustments to the reaction time data, accounting for the fact that some subjects are faster than others overall.

  2. 2.

    The residuals of the initial model were inspected for outliers and any violations of normality. Though we analyzed logRTs, outliers more than 2.5 standard deviations were removed from the data. This is recommended prior to fitting optimal models (see Baayen, 2008), and facilitates comparing results between the LME reported here and the analysis in the main text. After trimming, the initial model was refit.

  3. 3.

    Next, random effects were ‘forward-fitted’. With this process, different random effects structures were added and compared to the initial model to investigate whether they were warranted for inclusion. In all our analyses we looked for the same collection of random effects structures, including a by-item intercept, and by-subject intercepts and slopes for both compatibility and category. By-item intercepts allow the model to account for the fact that some items are responded to more quickly than others. Thus, this random effect, if included, accounts for the fact that stimuli used in this experiment are only a random subset of all possible stimuli, and therefore, make the optimal model more generalizable. At this stage, only random effects that provide sufficient explanatory power over and above the by-subject random effect adjustments were retained.

  4. 4.

    Next, the fixed effects were ‘back-fitted’. With the random effects structure in place, the importance of each fixed effect was re-evaluated (this is done to determine whether the inclusion of any random effect structures altered the predictive power of the fixed effects). To do so, a full model, which includes each fixed effect and their interactions (and the random effect structure), was fitted. Any non-significant terms were removed with each interaction until all of the highest-order interactions were significant. In this way, only the fixed effects that provide explanatory power are included. This allows us to consider the final model ‘optimal’.

  5. 5.

    Finally, though there is no agreed upon way of determining statistical significance of each component of the model individually (because there is no consensus on which degrees of freedom to use), we provide an ANOVA table using the upper bound and lower bound degrees of freedom. This allows us to readily compare the results to the more restricted ANOVA.

Results

Experiment 1

Our initial model included category and compatibility as fixed-effects factors and by-subjects random intercepts. The optimal model included only an effect of object category and no interaction terms, as well as by-subject random intercepts (i.e., a by-subject adjustment to the mean), and by-item random intercepts (i.e., a by-item adjustment to the mean). The results of the model are shown in Table 3. Overall, the mean RTs in response to animals were faster than to artifacts; difference score = 0.102 in natural log units).

Table 3 ANOVA table of significant factors in the LME model of RT data
Full size table

Experiment 2

We performed LME modeling on the natural logarithm of reaction times. Our initial model included category and compatibility as fixed-effects factors, as well as by-subjects random intercepts. The optimal model included the category and compatibility factors, and the interaction between category and compatibility. The model also included by-subject random intercepts (i.e., a by-subject adjustment to the mean) and by-item random intercepts. Additionally, there was a by-subject adjustment to the slope for the category effect, allowing us to model variance in the size of the category effect for each subject. The results of the model are shown in Table 4. Overall, the mean RTs in response to animals was faster than the response to artifacts (difference score = 0.084 in natural log units). The Category × Compatibility interaction was due to faster responses on compatible trials relative to incompatible trials for artifacts (difference score = 0.019), while the opposite pattern was observed for animals (difference = −0.037).

Table 4 ANOVA table on significant factors in LME model of RT data
Full size table

Experiment 3

We performed LME modeling on the natural logarithm of reaction times. Our initial model included category, compatibility, and hand prime as fixed effects, as well as by-subject random intercepts. The optimal model included the category, compatibility and hand prime factors, as well as a two-way interaction between category and compatibility. The model also included by-subject random intercepts (i.e., a by-subject adjustment to the mean), and a by-item random intercepts, in addition to a by-subject random slope for the compatibility factor. Consistent with the results of the ANOVA, the interaction was driven by different compatibility effects for the animals (different score = −0.018) and the artifacts (difference score = 0.007). Importantly, this pattern replicates the pattern of Experiment 2. The results of the model are shown in Table 5.

Table 5 ANOVA table on significant factors in LME model of RT data
Full size table

Experiment 4

We performed LME modeling on the natural logarithm of reaction times. Our initial model included category and compatibility as fixed-effects factors, as well as by-subjects random intercepts. The optimal model included the category and compatibility factors with no interaction term. This model also included by-subject random intercepts and a by-subject slopes and intercepts for category. The results of the model are shown in Table 6. The effect of compatibility was due to faster responding on incompatible than compatible trials (difference score = 0.016 in natural log units).

Table 6 ANOVA table on significant factors in LME model of RT data
Full size table

Discussion

The results of the optimal linear mixed-effects models are consistent with the analysis of variance (ANOVA) in each experiment reported in the main text. This provides confidence in the general patterns of data reported here and the interpretations of them. Providing converging statistical analyses in this way has a number of advantages in the present study (see Baayen, 2008). First, by using linear mixed-effects models we were able to account for variability due to the specific stimuli in our experiment and have shown effects of object category over and above the effects of stimulus. Specifically, of particular importance is the fact that the linear mixed models predicted much smaller category effects after accounting for stimulus differences. Thus, using this approach, we were able to provide a much more precise prediction of differences due to object category (the size of the main effects). Further, unlike the ANOVA, the linear mixed model allows us to be confident in our generalization of our effects to stimuli beyond those used in our experiments. Finally, using linear mixed models have allowed us to show interactions between category and compatibility even after accounting for by-subject differences in the effects of these variables (in the cases where there was large variance in by-subject effects). Thus, this powerful technique allows us to make more precise predictions of the effects of our factors and their generalization beyond our study.

Appendix 2

Because our use of logRT in the main analysis may make it difficult to compare our results with previous studies that analyzed raw reaction times (RTs), we provide a complete analysis with raw RTs here. Note, the pattern of results both within and between experiments does not change, and the final values for each effect are comparable to those reported in the main text.

Experiment 1: raw RT

In line with the accuracy findings, the ANOVA revealed a significant effect of object category, F (1, 25) = 148.81, p < 0.001, η 2 G = 0.11, due to faster responses to the animals (raw RT = 558.34, SD = 78.44) than the artifacts (raw RT = 617.05, SD = 90.99). No other effects were significant, ps > 0.05.

Experiment 2: raw RT

Consistent with the accuracy results, ANOVA on the raw RTs revealed a significant effect of category, F (1, 23) = 60.24, p < 0.001, η 2G  = 0.08, due to faster responses to animals (raw RT = 534.53, SD = 84.67) than to artifacts (raw RT = 590.64, SD = 100.81). Additionally, there was a significant Category × Compatibility interaction, F (1, 23) = 17.68, p < 0.001, η 2G  = 0.009, indicating that the compatibility effect was different for the animals and the artifacts. For animals, responses were faster on incompatible trials (raw RT = 522.79, SD = 78.79) than compatible trials (raw RT = 546.73, SD = 94.05). For artifacts, the opposite pattern was shown, with faster responses on compatible trials (raw RT = 585.49, SD = 101.34) than incompatible trials (raw RT = 596.14, SD = 102.55). (Fisher’s least significant difference showed that the compatibility effect for the animals was significant though it was not in the artifacts. However, an a priori directional paired samples t test in artifacts was significant, p = 0.04).

Experiment 3: raw RT

ANOVA revealed a significant effect of category, F (1, 32) = 234.76, p < 0.001, η 2G  = 0.08. Participants responded faster to animals (raw RT = 517.92, SD = 75.37) than to artifacts (raw RT = 563.50, SD = 80.49). Additionally, there was a significant effect of hand prime size, F (1, 32) = 14.61, p < 0.001, η 2G  = 0.004; responses were faster to images presented after the large hand prime (raw RT = 535.87, SD = 77.06) than the small hand prime (raw RT = 545.55, SD = 78.61). Again, there was a significant Category × Compatibility interaction, F (1, 32) = 11.79, p = 0.002, η 2G  = 0.002, indicating that the compatibility effect was different for animals and artifacts, replicating the pattern of Experiment 2. For animals, responses were faster on incompatible trials (raw RT = 513.39, SD = 78.23) than compatible trials (raw RT = 522.44, SD = 73.62). In the artifacts, the opposite pattern was shown, with fastest responding on the compatible trials (raw RT = 561.11, SD = 78.48) than the incompatible trials (raw RT = 565.89, SD = 83.86). (Fisher’s least significant difference tests showed that, while the compatibility effect for the animals was significant, it did not quite reach significance in the artifacts despite the hypothesized direction). Importantly, there were no interactions with hand prime, ps > 0.05.

Experiment 4: raw RT

The ANOVA revealed a significant effect of category, F (1, 29) = 15.49, p < 0.001, η 2G  = 0.022. As in the previous experiments, participants responded faster to animals (raw RT = 444.77, SD = 46.86) than to artifacts (raw RT = 458.77, SD = 47.94). There was a significant main effect of compatibility, F (1, 29) = 9.49, p = 0.004, η 2G  = 0.004, due to faster responding on incompatible trials (raw RT = 448.91, SD = 47.91) than on compatible trials (raw RT = 454.63, SD = 45.39). The Compatibility × Category interaction was not significant, p > 0.05.

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Matheson, H.E., White, N.C. & McMullen, P.A. A test of the embodied simulation theory of object perception: potentiation of responses to artifacts and animals. Psychological Research 78, 465–482 (2014). https://doi.org/10.1007/s00426-013-0502-z

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