Seventeen younger adults (all female) aged 21-28 years (mean age = 24.6 years; SD = 1.9 years) and 19 community-dwelling older adults, recruited through an active choral society (18 female) took part in this study. Participation was voluntary and individuals did not receive monetary compensation for their time. Data from two older participants were excluded from the analysis reported below: data from one male participant was removed to maintain consistently with the all-female sample in the younger group and data from one female participant were removed because the participant did not understand the task. The remaining 17 older adults were aged 68-84 years (mean age = 74 years; SD = 4.4 years). All younger and older participants reported to be right hand dominant and all reported normal or corrected to normal vision. All participants wore their usual corrective lenses, if needed, at the time of testing. All participants were not suffering from psychiatric or neurological illness by self-report and all provided written informed consent. Our younger and older samples were not strictly matched for years of education, however, older adults had secondary level education or higher and younger adults were college students. The experiments reported here were approved by the St. James Hospital Ethics Committee and conformed to the Declaration of Helsinki.

For the computer-based experiment, participants were seated at a distance of approximately 60 cm from the screen. They were instructed that they would view a number of videos of a person lifting either a small or a large box and that following each video presentation they would be asked to estimate the weight of the box the actor lifted by choosing one of four weight options shown onscreen (50, 300, 600, 900 g. for small boxes and 3, 6, 12, 18 kg. for large boxes). Participants were told that one option was always correct. Participants were offered the choice to view weight options in ounces and pounds and a number of the older adult sample opted for this option. On each trial, the video was presented for 4 s, which was then followed immediately by the response screen. Older participants responded verbally and the experimenter entered their responses by pressing the corresponding button on the keyboard. Younger adults responded by pressing the appropriate button themselves. In all cases, the button press immediately initiated the beginning of the next video. The experiment was presented in four blocks: two blocks contained only non-deceptive videos and two blocks contained both non-deceptive and deceptive videos. The blocks containing only non-deceptive videos were always shown first, however, the order of the small and large box blocks was counterbalanced across participants. In the non-deceptive blocks, each of the four weights was repeated 3 times in random order. In the deceptive blocks, each weight was repeated once in the deceptive and once in the non-deceptive form. Each block was preceded by two practice trials to familiarize the participants with the task. Excluding practice trials, the computer task comprised of 40 trials in total, 24 trials in the non-deceptive blocks and 16 trials in the deceptive block and was approximately 10 min in duration. Following the computer based task, older adult participants completed the questionnaire comprised of the 19 selected items from the Perceived Motor Efficacy Scale for Older Adults (Potter et al., 2009). The experimenter read aloud each item and asked the participant how strongly they agreed with the statement on a scale ranging from 0 (strongly disagree) to 10 (strongly agree). The participant's response was recorded by the experimenter on the sheet. The questionnaire took approximately 5-10 min to administer. Younger adults did not complete the questionnaire, as it is specifically designed to assess perceived motor ability in older adults and as such is not informative for a younger population. All younger adults were active and were not suffering from any mobility impairments.

Data for non-deceptive videos were analyzed using the mean weight estimates, as well as signal-detection measures of sensitivity (d′) and response bias (c) (Macmillian and Creelman, 2005). Mean weight estimates for each non-deceptive video were calculated by averaging the weights reported in three trials in the non-deceptive block and one trial in the deceptive block. Linear regression was used to obtain the slope and intercept of the best-fit line for each individual's estimated weights as a function of the physical weight of the box, for the small and large boxes separately. In this analysis, accurate perception of the weights would yield a slope of 1 and an intercept of 0, while a slope of 0 would indicate no relationship between perceived and actual weight.

d′ scores for discriminating between each pair of adjacent weights were calculated for each participant according to the standard procedure for one-dimensional classification experiments (Macmillian and Creelman, 2005). Cumulative d′ scores were then obtained by summing the d′ scores for discriminating weights (W) W1 from W2, W2 from W3, and W3 from W4, yielding an overall measure of sensitivity for the small and the large box conditions. The loglinear adjustment method was used to adjust for extreme values of hits and false alarms (Stanislaw and Todorov, 1999). Similar methods were used to obtain the cumulative response bias (c) scores for each participant in the small and large box conditions.

Due to the limited number of deceptive trials, it was impossible to calculate d′ and c measures for this condition, therefore, data were analyzed by obtaining the slope and intercept of the best-fit line to the weight estimates for the small and large box condition separately.

Whereas the mean weight estimates, and the fitted regression lines, are contaminated with participants' response bias, the d′ measure represents an unbiased estimate of the participant's sensitivity for discriminating the weights (Macmillian and Creelman, 2005). The measure of response bias (c) was used to determine whether participants showed a preference to use either the higher or the lower end of the weight scale.

Slope and intercept values of the linear regression fits, and d′ scores were analyzed using separate 2 × 2 mixed-design analyses of variance (ANOVA) with Age (older and younger) as the between-subjects factor and Box Type (small or large) as the within-subjects factor. c scores across Age and Box Type were tested against zero using one sample t-tests.

Figure 2 shows the group average mean weight estimates of younger and older participants for non-deceptive videos in the small and large box conditions, as well as individual subjects' regression line fits. The 2 (Age) × 2 (Box Type) ANOVA on slope values revealed a significant main effect of Age [F_{(1, 32)} =8.56, p = 0.006], as slopes were shallower in the older group (mean = 0.36) compared to the younger group (mean = 0.57). There was also a significant main effect of Box Type [F_{(1, 32)} = 6.43, p = 0.02], with shallower slopes in the small box (mean = 0.39) compared to the large box (mean = 0.53) conditions (see Figure 3). There was no significant Age x Box Type interaction [F_{(1, 32)} < 1]. The 2 (Age) × 2 (Box Type) ANOVA on intercept values revealed significant main effects of Age [F_{(1, 32)} = 5.32, p = 0.03], with higher intercepts in the older group compared to the younger group. The main effect of Box Type was also significant [F_{(1, 32)} = 71.33, p < 0.001], as intercepts in the large box were higher than in the small box. The Age × Box Type interaction was also significant [F_{(1, 32)} = 4.4, p = 0.04]. Tests of simple main effects revealed a significant effect of Age for the small box [F_{(1, 32)} = 4.47, p = 0.04; mean younger = 0.13, mean older = 0.25] and the large box [F_{(1, 32)} = 4.86, p = 0.03; mean younger = 3.78, mean older = 6.31] conditions (see Figure 3). Thus, older participants showed overall shallower slopes and higher intercepts for both small and large box conditions.

As noted earlier, perceptual weight judgments involve visual analysis of the observed scene and changes in the velocity of movements provide strong diagnostic criteria for accurately deducing the weight of a lifted object (Shim and Carlton, 1997; Hamilton et al., 2007). Overall older adults' performance was worse than that of younger adults, with shallower slopes in weight estimation performance especially in the small box condition, when the weights were light (<1 kg) and the differences between the weights were small (~300 g.). The velocity profiles of the lifting actions in this condition were relatively similar across weights and may have been more challenging for the ageing visual system to exploit. Indeed, motion perception studies have demonstrated that ageing is associated with marked decreases in speed discrimination (Scialfa et al., 1991; Snowden and Kavanagh, 2006). Interestingly, older adults showed similar weight estimation performance to younger adults (as measured by slope estimates) for deceptive trials in the small box condition. This may be due to differences in the available visual cues in the deceptive and non-deceptive videos. In deceptive videos, when the actor is given incorrect information regarding the box weight (e.g., “you are going to lift a light weight” when the weight is heavy), this deceptive information results in online adjustment of the weight lifting behavior. The resulting motion profile increases the ratio of lift phase vs. the reach/grasp phase durations in the deceptive condition relative to the non-deceptive condition (Bosbach et al., 2005). It is possible that older adults are better able to exploit the visual cues in this condition and hence support more efficient performance. However, it also is important to consider that the number of trials in this condition was limited in the current study and these results should be interpreted with caution. Overall, results in the small box conditions suggest that older adults rely heavily on visual cues to judge weight from the actions depicted in the videos.

Consistent with previous studies, perceptual weight sensitivity was greater in the large box compared to the small box condition (Bosbach et al., 2005). For example, Bosbach et al. (2005) demonstrated that participants were more accurate in detecting whether an actor was surprised by the weight of a lifted box in the large relative to the small box condition. They concluded that there are additional and more salient perceptual cues available when full body motion to heavy weights is employed, possibly leading to a better performance in heavy box condition in their study. These more salient visual cues may relate to the velocity information pertaining to the weight lifted in the action sequences. In this condition, the weights were heavy and differences between the weights was also substantial (changes of 3 kg. between weights). Consequently, the differences in the motion profiles of the lifting sequences may have been more salient. Interestingly, we found no differences in weight discrimination of older and younger participants in the large box condition, suggesting that the visual cues available in this condition were sufficiently salient for older adults to exploit. In addition, previous studies have suggested that older and younger adults may rely more on global form information when processing biological motion (Pilz et al., 2010). Unlike the small box condition, the large box condition contained full body motion, which also may have increased the relative importance of global form information in this condition.

In light of a decline in motor ability, it is possible that older adults may become more dependent on visual analysis of the observed action sequence. Indeed, previous findings suggest that individuals with proprioceptive (Bosbach et al., 2005; Toussaint and Meugnot, 2013) and motor disorders (Poliakoff et al., 2010) may engage a visual strategy for the purpose of action understanding. For example, individuals with short term limb immobilization may rely more on visual analysis for tasks which naturally induce internal motor simulation in a normal population (Toussaint and Meugnot, 2013). Our results similarly suggest that a more visual strategy may be adopted with advancing age. Specifically, we observed that sensitivity in detecting the weight of a lifted object increased as a function of the saliency of the visual cues.

In line with our study, previous research involving action perception in older adults has reported a decline in the ability to mentally represent or simulate actions. Older adults show a decline in the ability to accurately predict the timing of perceived actions, possibly due to a difficulty in building internal forward models, especially when visual cues are not always available (Diersch et al., 2012). Such behavioral changes in action perception are also reflected in the differential neural activity seen in the ageing brain during action observation. Functional brain imaging studies have shown that although a similar, yet less lateralized, action observation network is activated in younger and older adults (Diersch et al., 2013), older adults tend to engage additional cortical regions during action perception (Nedelko et al., 2010; Diersch et al., 2013). For example, in a task involving action prediction, Diersch et al. (2013) demonstrated that even when viewing familiar movements older adults tended to recruit additional visual regions of the brain to carry out the task, compared to younger adults. This suggests an overreliance on visual processing for action perception with increasing age. Similarly, differential neural activation patterns have been observed during motor execution (Seidler et al., 2011). Behaviorally older adults also exhibit an overreliance on visual input in movement tasks (Seidler-Dobrin and Stelmach, 1998; Romero et al., 2003; Barrett et al., 2013). This overreliance on visual feedback for motor execution may be modulated by functional and structural changes in motor and somatosensory areas of the brain (review see Seidler et al., 2011).

Although older adults' performance may be modulated to a greater extent than younger adults by the saliency of the visual cues, age-related changes in motor ability may also underlie task performance. Specifically, older adults' difficulty in discriminating between the weights of lifted objects in the small box condition parallels behavioral evidence of marked changes in simple motor behavior (e.g., Romero et al., 2003), possibly arising due to degradation in proprioceptive input with advancing age. Older adults also find it more difficult to detect small differences in the weight of physically lifted objects compared to younger adults (Norman et al., 2009). Weight ratio judgments (i.e., how much lighter is object A compared to object B) become significantly less accurate with ageing (Holmin and Norman, 2012) and the thresholds for accurately detecting such differences are over fifty per cent higher in older, compared to younger adults (Norman et al., 2009). If these behavioral changes are linked to impaired (or imprecise) motor simulation of the same actions, they may at least partly explain the age difference in our task.

We also observed a systematic bias in weight estimation, which may be reflective of the motor system of the observer. Specifically, older adults tended to report that all weights were toward the upper end of the weight scale in the large box condition, but not the small box condition, while younger adults showed a bias to report lighter weights in the small box condition and showed no bias in the large box condition. We can speculate that some form of motor simulation was recruited, as older participants would be expected to experience more difficulty lifting heavier weights, whereas younger participants should be more confident in their abilities with all weights. Interestingly we also observed that older adults' subjective judgment about their action-related skills was reflected in task performance. Specifically, accuracy performance (slope) in the deceptive small box condition correlated positively with older adults' perceived manual ability to use small tools and perform actions related to the use of the hands. Weight estimation in the large box condition was also related to older adults' perceived confidence in movement. Specifically, those who reported being more cautious in carrying out movements, i.e., perceived their own movements to be slower than usual and monitored them more, tended to have better performance in the large box condition than those with higher confidence indicator scores, a score that has been linked previously to physical motor performance (Potter et al., 2009). Potter et al. (2009) noted that higher confidence indicators may be associated with higher minor errors in simple motor execution with advancing age. This may relate to the fact that, while some older adults may experience evolving changes in motor ability, they have yet to revaluate and integrate such declines into perceived abilities (Potter et al., 2009). Therefore, those who were more aware of their motor abilities showed better performance in the current perceptual weight judgment task across the small and large box conditions. However, it must be acknowledged that such findings are based on the self reported motor abilities of the older adult participants. The inclusion of more objective measurements of neuropsychological and physical motor capacity would be of benefit to future studies.

The current findings advance our understanding of how action perception is affected by the ageing process. Our results strongly suggest that we become increasingly reliant on robust visual cues to interpret the actions of others with advancing age. One possible consequence of this change is that older adults may be compromised in detecting subtle differences between motion profiles in action sequences, which may carry information about the intention of the actor. For example, a recent study showed that older adults were less sensitive to differences in the timing of interactions between two human characters (Roudaia et al., 2013). The timing of events carries important information about causality (Michotte, 1963). When the events involve human movements, the timing of movements carries important social information, such as deception. Due to such changes, it is possible that the ageing brain may use compensatory strategies for action interpretation. For example, the context in which an action is embedded may become essential for older adults to interpret the action, as it has been shown for younger adults in terms of mapping (Iacoboni et al., 2005) and/or inferring the meaning of others' actions, particularly when the observed actions are not encountered on a regular basis (Liepelt et al., 2008). Recent evidence from an object categorization study shows that the effect of context is more pronounced in older than younger adults (Rémy et al., 2013). It may be the case that a similar effect can be found in action understanding with advancing age.

Although the role of visual cues appears to be a plausible account for the present findings, similar to younger adult studies (e.g., Hamilton et al., 2004), the bias found for heavier weights and the correlation between weight judgment and self-perceived action capabilities in older participants suggests that some level of motor engagement may have affected task performance. Future studies should aim to disentangle the relative contribution of declines in physical and perceptual function on action perception with ageing. Finally, examining action understanding at multiple levels of analysis, including why an action is performed, may provide further insight into which facets of action perception remain intact or are negatively affected by the ageing process.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.