In a large sample of well-educated healthy adults covering a wide age range, we examined both perceptual repetition priming and perceptual learning in the same task, fragmented picture identification, thereby controlling for task-and stimulus-specific confounds. Through hierarchical path analysis, we observed several dissociations among neural and cognitive mediators of age-related differences in perceptual repetition priming and perceptual skill learning. First, whereas variance in the magnitude of perceptual priming was explained by both direct and indirect effects of calendar age, age differences in perceptual learning were fully mediated by neural and cognitive variables. Aging per se and undeclared variables that are subsumed under calendar age did not affect the magnitude of the learning gains. Thus, in acquisition of a perceptual skill by older adults the integrity of specific brain regions and specific cognitive resources appear more important than calendar age. Second, the volume of the primary visual cortex, independent of age, predicted perceptual priming, but not perceptual learning gains (or baseline threshold) in the same task. Third, we observed dissociation between working memory type and picture identification: nonverbal working memory directly predicted perceptual priming (and baseline threshold) but not perceptual learning, whereas verbal working memory did not directly predict either learning or priming. In addition, both indices of working memory influenced performance via their effects on fluid intelligence. Lastly, variance in perceptual skill learning gains was explained not only by indirect influence of regional brain volume via mediating cognitive processes, but also by the variance in perceptual repetition priming and its total combined effects. Notably, the effect of priming on learning was significant and substantial. Participants who showed greater priming gains were also more likely to exhibit better generalized skill learning.
One of the main objectives of this study was to test the hypothesis that volumes of specific brain regions would predict performance on fragmented picture identification and explain the age-related differences in performance. This hypothesis was partially sustained. Larger volume of the visual cortex, a region that showed no age-related reduction, reliably predicted greater priming (but not learning) gains. Priming gains were also reliably predicted by greater volume of the lateral prefrontal cortex, an age-sensitive region that has been linked not only to effortful, strategy-driven cognition (Miller, 2000
) but also to repetition priming (e.g., Bunzeck et al., 2006
) and identification of filtered objects under rTMS disruption to this area (Viggiano et al., 2008
). By contrast, the volume of the prefrontal white matter, which also showed age-related declines, predicted reduction in cognitive resources (working memory, fluid intelligence), and via some of those factors, reduced perceptual priming and learning gains in this study.
It is possible that in addition to this direct involvement of the PFC, that finding also reflects the weakening in the white matter connections between the striatum and the prefrontal cortex, both age-sensitive regions (Raz et al., 2003
). Studies of patients with neurodegenerative disease of the neostriatum implicated cortico-striatal circuits in procedural performance deficits (Saint-Cyr & Taylor, 1992
). Although the lack of associations between caudate volume and performance in the current study does not provide support for that hypothesis, further research with direct indices of white mater integrity derived from diffusion tensor imaging may bring more clarity to the subject.
An unexpected finding was the link between larger
prefrontal white matter volume and poorer
priming and training identification thresholds. Although it may reflect a valid neurobiological phenomenon, we first discuss alternate explanations. First, the effect may reflect the nature of our sample, which, as in most similar studies, was a selected sample of convenience. In such a sample, the older participants with smaller prefrontal white matter, indicating worse state of brain health, could have been included because their relatively preserved functions were supported by cognitive reserve (Stern, 2002
; Staff et al., 2004
). Second, the finding may also reflect a statistical artifact known as a suppressor variable (Pedhazur, 1982
). When a suppressor variable (i.e., age) is at play, the regression (semi-partial) coefficient is larger than the corresponding zero-order correlation between the variable (Prefrontal white volume) and the criterion (Training and Priming thresholds). Indeed, the prefrontal white volume does not correlate with priming or training threshold (r
= −.11), whereas it correlates significantly with age (r
= −.33). The standardized path (regression) coefficient for the frontal white matter and priming is .26, indicating worse priming in persons with larger prefrontal white matter volumes.
Alternately, if we assume that the observed association between larger prefrontal white matter volume and increased priming and training thresholds indeed reflects a real phenomenon, several speculations can be offered. For example, enlarged white matter volumes could be a sign of problematic developmental history resulting in incomplete pruning of irrelevant connections. Indeed, it has been found in adults 20–45 years old that white matter fiber length is negatively correlated with degree of connectivity (Lewis et al., 2008
), suggesting that smaller volume of regional white matter may indicate more efficient neural conduction via shorter networks. We provided weak evidence that despite larger frontal white matter volume, presence of white matter hyperintensities (WMH) compromises the quality of that white matter. This may be driving the association between larger frontal white matter volume and reduced priming, as frontal (and parietal) WMH volume correlates positively with priming. Further, it has been shown by many studies that across the lifespan white matter volume evidences a nonlinear trajectory that consists of ascent, plateau, and decline (Bartzokis et al., 2001
; Bartzokis et al., 2003
; Courchesne et al., 2000
; Fjell et al., 2005
; Jernigan et al., 2001
; Kennedy et al., 2008
; Raz et al., 2005
; see Raz & Rodrigue, 2006
for a review), whereas the gray matter regions are more likely to follow a linear age trajectory (see Raz & Rodrigue, 2006
for a review). Perhaps this inverted U-shaped trajectory could partially explain the current findings as our sample included adults from virtually the entire adult age span. A longitudinal design would be required to test this notion. The literature on the neuroanatomical foundations of cognitive aging has at least two other examples of negative association between regional volumes and cognitive performance. In one, the authors found negative correlations between local temporal lobe volumes and multiple memory indices in spite of replicating age-related reduction of volumes (Van Petten et al., 2004
). In the other, a negative association between working memory scores and orbitofrontal volume was observed (Salat et al., 2002
), although in that study an age-related increase
in regional volume was also reported.
As hypothesized, perceptual priming and learning were unrelated to the hippocampal volume, adding support for the dissociation between declarative and nondeclarative memory processes in the literature. It is noteworthy, however, that according to the extant literature the implicit/explicit memory distinction is not clear-cut, and any study of “implicit” processes brings the concern of explicit contamination. It is more likely the true case that some explicit processes need to be brought online when carrying out “implicit” activities such as priming and perceptual learning (cf Poldrack, 2002
). It also possible that the neural systems identified with nondeclarative (striatum) and declarative (medial temporal) processing interact or even compete during these procedural processes (Poldrack, 2002
We did not find a significant effect for the fusiform gyrus volume, despite its known role in perceptual and visual identification and processing (e.g., Blondin & Lepage, 2005
; Liu et al., 2006
; Poldrack, 2002
), nor did we find an effect of the caudate nucleus in this type of learning. It is unclear why we observed no such associations. It is possible that it was because the caudate (as well as the cerebellum) is involved in the multiple repetition aspect of priming, whereas prefrontal involvement is more stimulus-specific (Bunzeck et al., 2006
). A functional imaging study would be needed to demonstrate that temporal distinction. For example, throughout its time course, perceptual learning is differentially related to visual cortex activation (Yotsumoto, Watanabe, & Sasaki, 2008
In the fragmented picture identification task implemented in this study, we used the descending fragmentation method of stimuli presentation, in which presentation becomes incrementally more complete (i.e., less fragmented) until a correct identification is made. That approach is known to be more challenging to the observers than the ascending fragmentation order as it yields lower accuracy and longer latency of response for everybody, but it may be differentially more challenging for the elderly. In the extant literature, the differential effect of age has been explained by a difficulty to discard false hypotheses formed at the levels of stimulus degradation that precluded correct identification (Lindfield et al., 1994
), and interpreted as support for the inhibition-deficit hypothesis (Hasher & Zacks, 1988
). According to that hypothesis, efficient working memory relies upon successful inhibition of potential interference from task-irrelevant stimuli, an ability that is deficient in older adults (Salthouse, 1994
). Lindfield and Wingfield (1999)
suggested through a computational analysis that cognitive slowing might be the means by which inhibition deficits affect performance in identifying perceptually degraded objects. Our findings are in accord with this interpretation that relies on the working memory inhibition-deficit hypothesis (Hasher & Zacks, 1988
). However, the results of our study leave room for more complex and nuanced explanations of a deceptively simple phenomenon.
The dissociation between the effects of nonverbal but not verbal working memory on perceptual priming and learning is informative. The role of compromised spatial working memory in age-related reduction of priming gains may reflect reduced availability of the resources for item-specific imagery (Kosslyn, 1994
) in acquiring a skill. Such an explanation is in line with Anderson’s theory of skill acquisition (e.g., Anderson, 1982
), as older adults may experience difficulty in automatizing the different portions of the skill. The findings from visual search experiments by Rogers and colleagues (Fisk & Rogers, 2000
; Rogers, 1997
) support the latter interpretation.
Finally, less specific factors than those discussed above may be summoned to explain the observed effects. For example, as Strayer and Kramer (1994)
suggest, older adults might simply learn at a slower rate with the same (or better) accuracy as younger adults. Moreover, their apparent deficit might have been a result of adopting a conservative response bias. In a similar vein, some suggest that older adults might have handled the speed/accuracy tradeoff differently than younger adults and emphasize accuracy even in the absence of instructions to do so (Salthouse, 1979
). Although we see no direct way to evaluate those propositions on the basis of the data available in this study, the results of a recent meta-analysis engenders skepticism about the inherent conservatisms of the older participants (Marquié & Baracat, 2000
). Across multiple studies, there was no evidence of such a general trend, and we have to assume that it was unlikely to emerge in this study. The most general explanation of the observed working memory effects would be a reference to the fact that working memory tasks require commitment of attentional resources and that the association between working memory scores and identification threshold gains simply reflect that nonspecific cost of doing cognitive business (Maki et al., 1999
). However, the observed dissociation between verbal and nonverbal working memory makes such a generalization difficult to sustain. Nonetheless, in light of the reported greater aging effects on visuo-spatial than on verbal tasks (Botwinick, 1977
), more detailed direct comparisons of verbal and nonverbal skill acquisition and priming merit future research effort.
We observed that men were better than women in identifying fragmented pictures, with the effect being stronger for priming than for learning. The literature suggests that women perform better than men on verbal but worse than men on visuo-spatial tasks (Kimura, 1999
), and the results reported here are certainly consistent with that trend. However, we cannot rule out the role of mediators that were not specified in the model. For example, identification of fragmented pictures by premenopausal women may be mediated by fluctuating levels of estrogen with women in the low estrogen phase identifying objects at a higher level of fragmentation (Hampson et al., 2005
). In the current study, however, priming and learning performance were not affected by hormone replacement therapy1
. Another possible explanation for the sex differences could be that because women are more proficient with living categories, and men are better with nonliving objects (Barbarotto et al., 2008
; Gainotti, 2005
), the inclusion of 2:1 more nonliving than living objects as stimuli in the task could have given men an advantage.
Our behavioral results are generally in accord with the extant priming and learning literature (Caggiano et al., 2006
; Daaselaar et al., 2005; Fleischman et al. 2004
; Koutstaal, 2003
; Madden et al., 2005
; Marczinski et al., 2003
; Prull, 2004
; Wiggs et al., 2006
). A recent event-related potentials study using similar stimuli found that older adults were differentially affected by the repetition task at early and late components as compared to younger adults (Lawson et al., 2007
). Thus far, functional studies of aging and (word-stem) priming contradict each other as they show both age-related differences in word priming (Daselaar, et al., 2005
), and lack thereof (Lustig & Buckner, 2004
). Thus, that matter remains unclear until additional data are available.
The results of this study should be interpreted in the context of its limitations. First, because in path analysis only the models that are specified can be tested, only one conceptualization of the true state-of-affairs is examined. Similarly, only those variables measured and included in the path analysis can be considered; there are certainly more variables at play than those that can be examined in one study.
Second, although we believe that the findings we present are largely due to procedural/nondeclarative processes, there is the possibility that they represent an admixture of both implicit and explicit processes and not “pure” perceptual priming and perceptual learning. However, several aspects of the study suggest that the likelihood of this possibility is relatively low. For example, explicit contamination is known to be reduced by encouraging the participants to respond quickly, which limits the time available to try to deliberately recall the practice session (Mitchell & Bruss, 2003
). Another way to reduce explicit contamination is introduction of a demanding task during delay (here, fluid reasoning, CFIT) thus leaving little to no time for intentional encoding of the pictures (Mitchell & Bruss, 2003
). In addition, as the participants were not informed of the delayed test, the incentive for intentional rehearsal is minimized. Further, research has shown that perceptual processes and identification responses are far less susceptible to explicit contamination than conceptual and semantic processes and production responses (Mitchell & Bruss, 2003
). Indeed, some implicit tests are not susceptible to explicit contamination effects, particularly priming of pictures (Brown et al., 1991
; Mitchell & Brown, 1988
; Mitchell & Bruss, 2003
). Moreover, we did not find a significant role of the hippocampal volume, usually associated with explicit memory processes, in either the perceptual learning or priming conditions.
Third, this study is limited by the shortcomings of a cross-sectional design (i.e., cohort effects, sample contamination by preclinical dementia cases as well as confounding aging with individual differences from other sources). Future projects should follow the individuals over time to investigate the existence and extent of the long-term effects of priming and skill retention. Longitudinal follow-up of a small subsample of this sample in our laboratory (Kennedy et al., 2007
) is generally consistent with the current pattern of results. Specifically, in that study, poorer working memory was a strong predictor of a longitudinal age-related decline in both priming and skill learning. Additional tasks that measure both priming and skill acquisition are needed to compare patterns of performance in older adults across domains of priming and learning (see Perruchet & Baveux, 1989
). We have preliminary evidence from a mirror-reading task that suggest similar age-related patterns in priming and skill learning, suggesting generalization across tasks (Raz, unpublished data). In addition, skill learning should also be investigated over a period of several days to fully understand the time course of learning to identify fragmented pictures. Perhaps spaced, rather than massed practice would provide differing results (but see Perruchet, 1989
The implications of the current results are that age-related differences in perceptual priming and skill learning have dissociable cognitive and neural correlates, and the fragmented pictures identification task is more complex than it appears on the surface, resource-wise. Several design-features of this study allowed us to reach our conclusions. A particular strength of this study is the use of highly reliable manual tracings to measure regional brain volumes, which circumvents the problems inherent in automated and semi-automated methods (e.g., voxel-based morphometry, VBM). These problems range from tissue misclassification, errors in automated segmentation and registration, and drastic loss of resolution due to smoothing (Bookstein, 2001
; Crum et al., 2003
; Davatzikos, 2004
; Jernigan et al., 2001
). Despite our method’s strengths, it does have its limitations, namely that it is time-consuming and labor-intensive, and, in the hands of less than expertly competent operators, can suffer from low reliability. Most importantly, it requires hypothesized, a priori
selection of the regions of interest involved in perceptual priming and skill learning, and it is possible that age-related differences in additional regions beyond those we measured are associated with these processes. A follow-up study using VBM, which allows for association of the variable of interest at each voxel in the brain, may suggest other brain regions of interest to explore; however those findings should be supplemented with manual tracing of the additional regions (see Kennedy et al., 2008
; Tisserand et al., 2002
for a discussion of manual vs. VBM methodology in the study of aging). Second, a major strength of this study is the examination of regional brain volume and mediating cognitive processes in the same task, fragmented picture identification, thereby controlling for any unwanted task- or stimulus-specific variance between the perceptual priming and learning measures. Finally, in light of a relatively modest magnitude of the observed effects, the findings from the current study underscore the need for large sample sizes and the importance of multivariate approaches, especially when examining brain involvement. Using the general linear model approach alone would have missed these indirect effects of brain on priming and learning. Only through path analysis were we able to detect the mediating influence of the prefrontal white matter volume on fluid intelligence and working memory.
In sum, the results of this study indicate that although neither item-specific repetition priming nor more general skill learning of fragmented pictures identification are immune to aging, age effects on performance are largely mediated by multiple and dissociable neuroanatomical and cognitive factors. Moreover, availability of cognitive resources affect priming and learning in a differential fashion, and individuals who show stronger priming effects attained better learning gains. The fragmented pictures identification paradigm that allows examination of priming and learning effects within the same task framework merits further attention in cognitive aging research.