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Lateralized Readiness Potentials Reveal Motor Slowing in the Aging Brain

¹ Department of Psychology, University of British Columbia, Canada.
² Départment de Psychologie, Université de Montréal, Canada.

Address correspondence to Alexa B. Roggeveen, 2136 West Mall, Department of Psychology, University of British Columbia, British Columbia, Canada. E-mail: alexar{at}interchange.ubc.ca

	Abstract

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Older adults consistently show slower reaction times (RTs) to the onset of motion. Both cognitive slowing and motor slowing have been suggested as causes of this effect. The lateralized readiness potential (LRP) of the electroencephalogram can be used to separate perceptual and decision processes from motor programming and execution as causes of RT differences. We used the LRP to discern the origin of slowing in RT to motion onset that occurs in elderly individuals. After the onset of motion in a visual display, we asked participants to identify the direction of that motion (up or down) by pressing a button. Older participants showed significantly slower RTs than did younger participants. The LRP showed that the bulk of slowed response arose from slowed motor processes, rather than perceptual processing. We discuss the differences found in amplitude and onset latency of the LRP in the context of theories of motion processing and inhibition in the aging brain.

WITH aging comes a slowing of many processes, both cognitive and motor, which can impair an older adult's ability to navigate in the world. One such type of slowing is evident in slowed motor responses to the onset of visual motion. Such slowing can have ramifications in important everyday tasks, such as driving a car, for which slowed response times to a motion stimulus can have tragic consequences.

Given the importance of understanding the origins of this type of slowing, it is unfortunate that previous research into the processing of moving visual stimuli and response times to the onset of visual motion does not paint a clear picture of the processing stages from which slowed response time originates. Are older adults slower to respond to the onset of motion because they cannot detect motion as quickly—a perceptual deficit—or because they are simply slower to initiate a response—a motor deficit? Could a combination of both effects underlie the slowed response to motion onset, or does the difference in reaction times between age groups lie mostly in one or the other, revealing differential effects of aging?

Evidence is mixed on how, or even if, the ability to detect motion changes as we age. Many studies have shown that motion detection thresholds increase with age (e.g., Ball & Sekuler, 1986; Trick & Silverman, 1991). Using an embedded figures test, however, Gilmore, Wenk, Naylor, and Stuve (1992) found that once field dependence was controlled for, age differences in the ability to detect motion all but disappeared. In contrast, recent work by Betts, Taylor, Sekuler, and Bennett (2005) has shown that older adults appear to have lower thresholds for the length of time needed to detect motion in large, high-contrast displays, meaning that older adults were actually better at detecting this type of motion than younger adults were. Although there were differences between the type of motion display utilized in these experiments, the overarching message is that there is no clear consensus on how well older adults perceive motion compared with younger adults.

It is also unclear why response to stimuli in general in speeded response tasks is slower for older adults. Specific to motion onset response, Porciatti, Fiorentini, Morrone, and Burr (1999) concluded that the slower response originates both in visual processing of the stimulus and in motor programming because, although older participants were always slower to respond than their younger counterparts, there was systematic variation in the amount of slowing that was dependent on the type of motion stimulus displayed, even when individual thresholds had been established for each subject. However, there is reason to believe that simply assessing contributions to motor slowing based on reaction time is incomplete. Using Ratcliff's (1978) diffusion model of response time, Thapar, Ratcliff, and McKoon (2003) found that, in a letter discrimination task, the overall slower responses of the older participants were due to slowing in perception, decision, and response execution. In contrast, electrophysiological work using the lateralized readiness potential (LRP; subsequently described) has indicated that older adults are slower to make responses to letter stimuli only because of slowing in response-related processing, rather than in visual processing of the stimulus (Yordanova, Kolev, Hohnsbein, & Falkenstein, 2004). Given these differences, it is probably necessary to carefully study each particular situation with several approaches to identify the processes responsible for response slowing, and it may be impossible to make a blanket statement about the origins of this slowing in aging humans.

In the present study, we utilized the electrophysiological approach, in particular the LRP, to help disentangle the processes from which slowing in response to motion onset arises. The LRP is an electrophysiological measure uniquely poised to parse the time course of processing involved in visual stimulus identification and response choice, and subsequent motor programming and execution. Although the method cannot disentangle decisional from perceptual processes, because it provides a more direct (than model fitting) approach to differentiating these latter processes from motor preparation and execution, it provides a unique perspective on the question of the origins of response slowing in the elderly population.

The Lateralized Readiness Potential
The LRP is derived from scalp potentials that mainly originate in the primary motor cortex (i.e., Deecke, Grozinger, & Kornhuber, 1976). This measure, derived from electroencephalographic (EEG) recordings, represents the shifting of activation in the motor cortex from an even distribution of response readiness across both hemispheres to a greater degree of activation on the side contralateral to the response hand (Kutas & Donchin, 1977, 1980). The LRP can be calculated relative to the onset of the target (target-locked LRP) or to the onset of the response (response-locked LRP); both reveal distinct information about the process of selecting and initiating a motor response. Figure 1 displays representative LRP waveforms and related perceptual and motor processes (cf. Coles, 1989).

View larger version (15K): [in this window] [in a new window]   Figure 1. Illustration of target- and response-locked LRPs. The time from onset of the target to the onset of the target-locked LRP represents perception and response selection processing. The time from LRP onset to response in the response-locked LRP represents both response programming and execution

In contrast, the response-locked LRP is indicative of differences between the two cerebral hemispheres in the processes of motor programming and execution required to execute the given response rather than the withheld response (Masaki, Wild-Wall, Sangals, & Sommer, 2004). Consequently, the interval between the onset of the response-locked LRP and the moment of response is representative of the time needed to plan and execute the motor response.

If longer reaction times to the onset of visual motion arise both from a slowing in visual processing and response selection as well as from the time needed to program and execute the motor response, as predicted by the results of Porciatti and colleagues (1999), then we would expect to find significant differences between age groups in the onset latencies for both the target- and response-locked LRPs, with older adults showing longer latencies than younger adults in both measures. Longer onset latencies for the older group in only the target-locked LRP would support the idea that older adults show decrements only in the ability to perceive motion or to select a response (e.g., Ball & Sekuler, 1996). Alternatively, a difference between the groups in the onset latency for only the response-locked LRP would support the idea that the slower responses found in older adults were due to slower motor response programming or execution, not slower motion processing. This result would lend credence to the research that has shown that older adults do not show deficits in motion detection thresholds (Gilmore et al., 1992). In addition, this result would support previous LRP findings that have shown that older adults show decrements in response-related processes only in particular tasks (Yordanova et al., 2004).

Finally, if there were differences in the visual processing and detection of motion in favor of the older adults, as one might anticipate from the research of Betts and colleagues (2005), then the onset latencies actually could be shorter for older adults in the target-locked LRP, but still show (offsetting) longer onset latencies for the response-locked LRP that would generate the overall behavioral slowing. As our task was similar to that of Betts and colleagues in that it asked participants to resolve motion across an entire display, we consider this to be a plausible outcome of this study.

Event-Related Potentials
Other event-related potentials (ERPs) than the LRP can also operate as an indicator of a differing time course of early visual processing. These ERPs are the result of simple averaging of the EEG time locked to a specific event, such as target onset. The N1 component, or the first negative deflection of the ERP waveform, has been understood to be indicative of early visual stimulus processing at electrodes over visual cortical areas. Differences across condition in the temporal location of the peak of this component have been interpreted to index differences in the time needed to process a particular stimulus, whereas modulations of its amplitude are most typically seen when attentional processes are manipulated (e.g., Mangun & Hillyard, 1991). There is some evidence that the N1 peaks later in older adults in certain tasks (e.g., Diaz & Ame<--1-->nedo, 1998), reflecting slower processing of target stimuli. A longer N1 latency for older adults than for younger adults in the present study would be evidence that early stages of visual perception and discrimination of a motion stimulus take longer as we age. This evidence should converge with a difference in the peak latency of the target-locked LRP if such perceptual slowing is a source of the observed longer reaction times seen in older adults, although peak latency of an ERP component is not directly comparable with onset latency of an LRP (see the Methods section).

Another ERP component of interest is the P300, a late positive component that tends to peak around 300 ms or longer after target onset. The P300 has received quite a bit of attention in the aging literature (see Linden, 2005 and Polich, 1996 for reviews), as older adults have frequently shown longer latency of the P300 component than have younger adults in a variety of tasks. Because of the late nature of the component, however, it is not a very useful indicator that would serve to separate stimulus processing and response selection processes from response programming and execution processes. Therefore, we investigate latency differences between age groups in the P300 simply to underline the differences between overall processing speeds for the two groups.

	METHODS

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The methods of the study were approved by the Behavioural Research Ethics Board of the University of British Columbia, and participants were informed of their rights and gave informed consent prior to beginning the study.

Participants
All participants reported normal or corrected-to-normal vision and no neurological defect. All participants were naïve to the purposes of the study.

Older participants
The older participants group was composed of 14 adults (age, Mdn = 72, range = 65–81), 7 male and 7 female, whom we recruited by use of an advertisement in a local community newspaper. All were right handed. All participants were compensated for their time ($10 per hour) and for their travel expenses. We discarded 1 participant's data from the analysis because it had unusually high error rates (greater than 30%).

Younger participants
The younger participants group consisted of 16 adults (age, Mdn = 20, range = 18–28), 2 male and 14 female, whom we recruited by use of a posted sign in the Psychology Building of the University of British Columbia. All but 1 participant was right handed. All participants were compensated for their time ($10 per hour).

Materials: Behavioral
Each participant sat in a comfortable chair in a dimly lit room with her or his head stabilized by a chin rest that was 42 cm in front of a computer screen. The display, shown on a 17-in. (43-cm) monitor with a 1024 x 768 pixel resolution and a refresh rate of 60 Hz, spanned a visual angle of 32.5° x 24.5°. The experimenter first gave the participants a demonstration of the experiment: 1,000 white dots (115 cd/m²), each 1 pixel in size, appeared on a gray background (7.0 cd/m²). After a random interval between 450 and 800 ms, the dots would appear to move with 100% coherence either up or down at a rate of 12.3 mm/s. The experimenter asked participants to respond as quickly and as accurately as they could to identify the direction of motion, using their right index finger on the letter M on the keyboard to indicate that the dots moved up, and their left index finger on the letter C to indicate that the dots moved down. The experimenter presented the motion stimulus until a response was made, or for 1,600 ms, but gave no feedback, as this was a very simple task; the speed and luminance were both well above any thresholds for detecting and identifying the direction of motion. The experimenter asked participants to fixate in the center of the screen. A fixation cross was not presented in the center of the screen, however, because we discovered in a pilot study that, confusingly, it tended to induce the perception that the fixation cross was moving rather than the dots.

The experimenter gave the participants as much practice as they felt they needed to be proficient at the task, after which the experimenter started the experiment. Participants responded to 576 trials, half up motion and half down motion presented in a random order, and the experimenter gave them a self-paced break after every 48 trials.

Materials: Event-Related Potentials
We had EEG data collected from each subject during the experiment. The experimenter recorded EEGs at a sampling rate of 250 Hz from PO7, PO8, Pz, C1, C2, C3, and C4, per the standard 10-10 electrode montage. PO7 and PO8 are located over occipital cortex and Pz is located over parietal cortex; all three are typically used to measure the processing of visual stimuli. C1, C2, C3, and C4 are located centrally, over the motor cortex, in order to measure activity in the primary motor cortex, in our case the LRP. We had these electrodes referenced to the right mastoid and subsequently rereferenced to averaged mastoids. We had blinks and eye movements monitored by both the horizontal and the vertical electro-oculograms (EOGs), which were also sampled at 250 Hz. We kept impedances for all electrodes (EEG and EOG) below 10 k.

We excluded trials on which participants made an error and trials with reaction times slower than 200 ms or quicker than 800 ms from the analysis. This time window appropriately excluded all responses greater than 2 SD from the mean response of each group, thereby excluding outliers that could skew the group means. We removed blocking and blinking artifacts prior to averaging by applying automated artifact detection routines. We did not exclude trials in which vertical eye movements were made to follow the apparent motion from the analysis. Because there was no fixation cross in the display, and although participants in both groups were instructed to fixate centrally, a large number of trials for most of the participants would have been rejected because of eye movements resulting from a natural tendency to follow the movement of the stimulus. We therefore included trials in which eye movements were uniformly vertical, and thus that did not result in any deflection in the horizontal EOG, in the analysis, as such eye movements could not distort the LRP because it is computed from differences between lateralized ERP components. The impact of this on the resulting ERP waveforms was negligible because of the more posterior positioning of the recording electrodes.

We calculated LRPs from the records of the C3–C4 electrode pair by using the averaging method (Coles, 1989). After we performed the averaging, we low-pass filtered the LRPs by using a 4.5-Hz Gaussian filter to eliminate high-frequency artifacts in the waveforms. In order to reduce between-subject variability, we used a jackknifing technique that has been successfully used previously in the analysis of LRPs to increase power (R.G. Miller, 1974; J. Miller, Patterson, & Ulrich, 1998). We measured the onsets of both the target- and response-locked LRPs for the jackknifed data by using the 1DF regression technique (Mordkoff & Gianaros, 2000; Schwarzenau, Faulkenstein, Hoormann, & Hohnsbein, 1998). We then submitted the LRP onsets so calculated to analyses of variance (ANOVAs) computed by the unpublished MrF program supplied to us by Jeff Miller, which implements the recommendations of Ulrich and Miller (2001). This program uses appropriately modified sums of squared errors to adjust for the jackknifing procedure.

To calculate the ERPs, we averaged the activity recorded at each of PO7, PO8, and Pz, and we low-pass filtered the resulting ERPs by using a 13.25-Hz Gaussian filter. We then submitted the filtered ERPs to the same jackknifing procedure as we used for the LRPs. Also, to determine the onset of the N1 component—arguably a better measure than its peak to compare with the onset of the target-locked LRP—we applied the 1DF regression technique described earlier for the LRPs to the jackknifed ERPs.

We statistically assessed the differences between the older and younger groups for the N1 onset as measured herein, as well as the peak latency and amplitudes for both the N1 and P300 data sets, for the jackknifed data by using ANOVAs with appropriately modified sums of squared errors as for the LRPs already described.

	RESULTS

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Behavioral Results
Reaction time results are presented in Table 1. Older adults responded significantly slower to the onset of motion than did younger adults, while not committing a greater number of errors. We performed a 2 (Age Group: older vs younger) x 2 (Direction: up vs down) repeated measures ANOVA on the reaction time data. We included the direction of the motion stimulus in this first analysis to determine whether direction of motion or hand of response had an effect on the reaction time results. The 2 x 2 ANOVA did not reveal a significant Age x Direction interaction. There was a main effect of age on the reaction time data, F(1, 27) = 12.41, p <.002, ² = 0.32. Younger participants were on average 85 ms faster than older participants were to respond to the onset of visual motion.

In order to exclude the possibility of gender effects on our results, we performed a 2 (Gender) x 2 (Age) ANOVA on the reaction time data from the two groups. We found no significant Gender x Age interaction. To ensure that the lack of interaction was not due to unequal numbers of males and females in the two groups, we performed a one-way ANOVA on the reaction time data from the older subject group to compare reaction times between gender, because the number of male and female participants was equal in that group. We found no significant main effect of gender for the older adults.

LRP Results
LRP results are shown in Table 2 and in Figure 2. Older adults and younger adults differed by only 22 ms in the onset latency of the target-locked LRPs, but older adults had dramatically longer, by 114 ms, onset-to-response latencies than younger adults in the response-locked LRPs. Older adults also showed interesting amplitude differences from the younger adults for both sets of LRPs (Figure 2).

View larger version (11K): [in this window] [in a new window]   Figure 2. Target- and response-locked LRP results. Older subjects are represented by the solid line; younger subjects are represented by the dotted line

The response-locked analysis, in contrast, showed that the 114-ms difference between the onset latencies of the LRP was statistically significant, F(1, 27) = 17.7, p <.001, ² = 0.95. Though differing by 0.50 µV, however, the response-locked amplitudes were not significantly different between age groups, F(1, 27) = 1.46, p <.24, ² = 0.59.

ERP Results
ERP waveforms are presented in Figure 3; means are presented in Table 3. We performed separate latency and amplitude analyses for the parietal (Pz) and occipital (PO7, PO8) N1 components. For the analysis of activity at electrode Pz, we performed separate one-way ANOVAs on the averaged waveforms for the latency and amplitudes of the peak of the N1 component between age groups, as well as for the onset of the N1. There was a significant difference in the latencies of the peak of the N1 at Pz between age groups, F(1, 27) = 4.41, p <.05, ² = 0.81, with older participants showing N1 peak latencies approximately 20 ms longer than younger participants. There was no significant difference in the amplitude between the age groups. The onset of the N1 component also showed significant latency differences between groups, F(1, 27) = 10.98, p <.01, ² = 0.92, with a mean difference of 41 ms (younger, 44 ms; older, 85 ms).

View larger version (6K): [in this window] [in a new window]   Figure 3. ERP results. Older subjects are represented by the solid line; younger subjects are represented by the dotted line

To analyze the P300 component, we performed one-way ANOVAs on the peak latencies and mean amplitudes of the jackknifed averages for the two age groups for electrode Pz, PO7, and PO8. We found significant latency differences at all three electrodes: Pz, F(1, 27) = 13.78, p <.01, ² = 0.93; PO7, F(1, 27) = 28.38, p <.001, ² = 0.94; and PO8, F(1, 27) = 20.69, p <.001, ² = 0.95. We found no significant amplitude differences between the age groups at any of the three electrodes.

	DISCUSSION

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Our results show that the majority of the slowed motor response to the onset of visual motion originates in motor programming or execution processes, and that only a small proportion of the slowing can be credited to the processing of the visual stimulus or initial selection of a response. This is indicated by the large and statistically significant differences between the groups in the onset latencies of the response-locked LRPs. Although onset latencies of the target-locked LRPs are not significantly different between older and younger participants, however, the significant latency differences between the groups in both onset and peak of the N1 component at electrode Pz does indicate at least some contribution of perceptual or decisional slowing to the slower reaction times of the older adults. These results are similar to that of previous research that showed slowing in the latency of the onset of the response-locked LRP for older adults (Yordanova et al., 2003) in a letter discrimination task. Importantly, these results conflict with the earlier conclusion that credits slowing in the reaction time to motion onset to more equal amounts of slowing in both perceptual and motor processes (Porciatti et al., 1999).

On the surface, our results also may seem to conflict with the conclusions of the study of Betts and colleagues (2005), which would have predicted an advantage in the target-locked LRP for the older adults because of lowered inhibition of motion processing, while still showing a decrement in performance in the response-locked LRP. Or, taken in the most literal sense, given the results found by Betts and colleagues, we would have anticipated faster reaction times to the onset of motion in older adults than in younger adults. However, there are several reasons why our results may reasonably differ from those of Betts and colleagues. First, theirs was not a reaction time task; therefore, the fact that older adults may have needed less exposure to a motion stimulus in order to distinguish movement does not include the amount of time needed to execute the response. Second, our stimuli were of very high contrast, and therefore well above thresholds for both old and young observers, so that there would be no differences in the ability to detect motion between the two age groups. (ERPs are very difficult to extract from visual stimuli that are close to threshold; in the interest of also collecting interpretable ERPs as well as LRPs, our stimuli were well above threshold levels. While older adults do show an increasing threshold for the perception of random dot motion correlated with increasing age (Ward & Bullimore, 1995), we did not feel that this was at issue due to the above-threshold nature of the stimuli.) What was at issue in the current study was not whether participants could detect the onset of motion, but how quickly they could discriminate direction of visual motion. So, although sensory processes as represented by target-locked LRPs may have shown differences had the stimuli been harder to discriminate, at the suprathreshold levels we used there is no large sensory processing difference between the groups.

LRP Amplitude Differences
Although our results showed that the amplitudes were only marginally significantly different between age groups for the target-locked LRP, and nonsignificant for the response-locked LRP, the amplitudes of the LRPs for the older participants are visibly larger than those of the younger participants. This trend could offer some explanation for the difference between our results and those of Betts and colleagues (2005). LRP amplitude appears to be sensitive to variations in the amount of inhibition applied to the response. DeJong, Coles, Logan, and Gratton (1990) used the LRP to investigate inhibition of response to a stop signal, finding that when the inhibition of the response was very late or altogether absent, the amplitudes of the LRP were greater than those on trials in which the response had been successfully inhibited. Amplitudes of the LRP, in fact, appeared to vary with the degree of successful inhibition of the response; greater amplitude reflected a greater lack of inhibitory success. In relevant behavioral work, older adults take significantly longer to refrain from making a response given a stop signal (Kramer, Humphrey, Larish, Logan, & Strayer, 1994), indicating that the processes necessary to inhibit a particular response slow with age—and, possibly, elicit larger LRP amplitudes. Interestingly, the single subject in the present study who was excluded for error rates above 30% showed markedly larger amplitudes for both the target- and response-locked LRPs, exceeding 4 µV. As the remaining participants did not make many response errors, it was not possible for us to make a reasonable analysis of the LRPs on the basis of trials in which participants made an error.

This reading of the LRP amplitude differences we found is consistent with the interpretation of Betts and colleagues (2005), who stressed the impact of a loss of neural inhibition (mediated by GABAergic synapses) in visual areas of the aging brain. The increased amplitudes of LRPs in older adults could also have arisen from reduced inhibition within the primary motor cortex. To illustrate this, we find it important to clarify that the LRP is a quantification of the inequality of the ERPs contralateral and ipsilateral to the hand making the response. The two competing sides—left versus right—are in equal activation until one side gets the advantage, which subsequently shuts the other process down, allowing the selected hand to initiate a response. This is where the LRP onsets: when the disparity between the two sides becomes nonzero. When inhibition is weaker, the baseline activation for both of the competing sides when ready to make a response is higher, meaning that both ERPs ultimately have to build up to a higher level before the losing side is shut down. Older adults show less at-rest presynaptic inhibition than younger adults, causing older adults to rely more on general control mechanisms in order to execute a controlled movement (Earles, Vardaxis, & Koceja, 2001). This could result in larger amplitude differences when comparing the side that has won—which has already reached a large degree of activation after increasing from the higher readiness baseline—and the side that has been shut down. Greater activation as a marker for decreased inhibition is supported by findings that have shown that in an inhibition task, older adults not only show activation in the same areas as younger adults when asked to inhibit a response, but that activation is more extensive—and in those participants with poorer inhibitory performance, activation of presupplementary motor areas is increased (Neilson, Langenecker, & Garavan, 2002).

Event-Related Potentials
Interestingly, though the N1 component is typically not analyzed for the time of component onset (rather, the peak latency and amplitude are typically of interest), the onset of the N1 could be an interesting analogue to the onset of the target-locked LRP. As the target-locked LRP onset is the point at which one hemisphere begins to show a preference for a particular response, rather than comparing it with the time point at which the N1 component reaches its peak, it is more reasonable to compare the target-locked LRP with the point at which processing begins in this component reflecting perceptual processing. The onset of the N1 indeed precedes the onset of the target-locked LRP, reflecting the stream of processing of the target stimulus from visual areas of the brain to areas responsible for motor response.

Although the P300 did not necessarily contribute to separating processes of perception–response selection and response preparation–execution, it is interesting to point out that the latency shift of the N1 did accompany a similar shift in the P300 latency, as has been found in previous research with older adults (e.g., Czigler, Csibra, & Ambró, 1994; Verleger, Neukäter, Kömpf, & Vieregge, 1991; for a review, see Onofrj, Thomas, Iacono, D'Andreamatteo, & Paci, 2001).

Conclusions
Achieving an understanding of how specific processes begin to slow with age is essential to a thorough understanding of the human brain, and to attempts to find ways to ameliorate the deleterious effects of aging. Slower reaction time to motion onset, particularly to motion that is well above threshold levels of detection, has clear implications for the health and safety of older adults. Our results show that the decrements in performance arise primarily in the programming or execution of a response and only in a small way from the ability to perceive and identify motion. Although slowing in perception may have a greater influence on slower motor response when a stimulus is close to threshold, the world is largely a suprathreshold place, indicating that when older adults respond more slowly to something in their environment, that slowing is primarily due to response preparation and execution processes.

	Acknowledgments

 
This research was supported by a Discovery Grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada to L. M. Ward and an NSERC Predoctoral Graduate Scholarship to D. J. Prime. There is no financial association that would constitute a conflict of interest for any of the authors. We thank Jeff Miller for the use of his MrF software to complete our statistical analysis of the LRP measures, and we thank Vivian Pan for research assistance.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication December 9, 2005. Accepted for publication August 14, 2006.

	References

TOP Abstract Methods Results Discussion References

 

• Ball, K., Sekuler, R. (1986). Improving visual perception in older observers. Journal of Gerontology, 41,176-182.
• Betts, L. R., Taylor, C. R., Sekuler, A. B., Bennett, P. J. (2005). Aging reduces center-surround antagonism in visual motion processing. Neuron, 45,361-366.[Medline]
• Coles, M. G. (1989). Modern mind-brain reading: Psychophysiology, physiology, and cognition. Psychophysiology, 26,251-269.[Medline]
• Czigler, I., Csibra, G., Ambró, Á. (1994). Event-related potentials and aging: Identification of deviant visual stimuli. Journal of Psychophysiology, 8,193-210.
• Deecke, L., Grozinger, B., Kornhuber, H. H. (1976). Voluntary finger movements in man: Cerebral potentials and theory. Biological Cybernetics, 23,99-119.[Medline]
• DeJong, R., Coles, M. G. H., Logan, G. D., Gratton, G. (1990). In search of the point of no return: The control of response processes. Journal of Experimental Psychology: Human Perception and Performance, 16,164-182.
• Diaz, F., Amenedo, E. (1998). Ageing effects on flash visual evoked potentials (FVEP) recorded from parietal and occipital electrodes. Clinical Neurophysiology, 28,399-412.
• Earles, D., Vardaxis, V., Koceja, D. (2001). Regulation of motor output between young and elderly subjects. Clinical Neurophysiology, 112,1273-1279.[Medline]
• Gilmore, G. C., Wenk, H. E., Naylor, L. A., Stuve, T. A. (1992). Motion perception and aging. Psychology of Aging, 7,654-660.
• Kramer, A., Humphrey, D. G., Larish, J. F., Logan, G. D., Strayer, D. L. (1994). Aging and inhibition: Beyond a unitary view of inhibitory processing in attention. Psychology of Aging, 9,491-512.
• Kutas, M., Donchin, E. (1977). The effects of handedness, of responding hand, and of response force on the contralateral dominance of the readiness potential. In J. Desmedt (Ed.), Attention, voluntary contraction, and event-related cerebral potentials (pp. 189–210). Basel, Switzerland: Karger.
• Kutas, M., Donchin, E. (1980). Preparation to respond as manifested by movement-related brain potential. Brain Research, 202,95-115.[Medline]
• Linden, D. E. J. (2005). The P300: Where in the brain is it produced and what does it tell us? The Neuroscientist, 11,563-576.
• Mangun, G. R., Hillyard, S. A. (1991). Modulations of sensory-evoked brain potentials indicate changes in perceptual processing during visual-spatial priming. Journal of Experimental Psychology: Human Perception and Performance, 17,1057-1074.
• Masaki, H., Wild-Wall, N., Sangals, J., Sommer, W. (2004). The functional locus of the lateralized readiness potential. Psychophysiology, 41,220-230.[Medline]
• McClelland, J. L. (1979). On the time relations of mental processes: An examination of systems of processes in cascade. Psychological Review, 86,287-330.
• Miller, R. G. (1974). The jackknife—A review. Biometrika, 61,1-15.[Abstract/Free Full Text]
• Miller, J., Patterson, T., Ulrich, R. (1998). Jackknife-based method for measuring LRP onset latency differences. Psychophysiology, 35,99-115.[Medline]
• Mordkoff, J. T., Gianaros, P. J. (2000). Detecting the onset of the lateralized readiness potential: A comparison of available methods and procedures. Psychophysiology, 37,347-360.[Medline]
• Neilson, K. A., Langenecker, S. A., Garavan, H. (2002). Differences in the functional neuroanatomy of inhibitory control across the adult life span. Psychology of Aging, 17,56-71.
• Onofrj, M., Thomas, A., Iacono, D., D'Andreamatteo, G., Paci, C. (2001). Age-related changes of evoked potentials. Clinical Neurophysiology, 31,83-103.
• Polich, J. (1996). Meta-analysis of P300 normative aging studies. Psychophysiology, 33,334-353.[Medline]
• Porciatti, V., Fiorentini, A., Morrone, M. C., Burr, D. C. (1999). The effects of ageing on reaction times to motion onset. Vision Research, 39,2157-2164.[Medline]
• Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 8,59-108.
• Schwarzenau, P., Faulkenstein, M., Hoormann, J., Hohnsbein, J. (1998). A new method for the estimation of the onset of the lateralized readiness potential (LRP). Behavior Research Methods, Instruments, & Computers, 30,100-117.
• Thapar, A., Ratcliff, R., McKoon, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology and Aging, 18,415-429.[Medline]
• Trick, G. L., Silverman, S. E. (1991). Visual sensitivity to motion: Age-related changes and deficits in senile dementia of the Alzheimer type. Neurology, 41,1437-1440.[Abstract/Free Full Text]
• Ulrich, R., Miller, J. (2001). Using the jackknife-based scoring method for measuring LRP onset effects in factorial designs. Psychophysiology, 38,816-827.[Medline]
• Verleger, R., Neukäter, W., Kömpf, D., Vieregge, P. (1991). On the reasons for the delay of P3 latency in healthy elderly subjects. Electroencephalography and Clinical Neurophysiology, 79,472-480.
• Yordanova, J., Kolev, V., Hohnsbein, J., Falkenstein, M. (2004). Sensorimotor slowing with ageing is mediated by a functional dysregulation of motor-generation processes: Evidence from high-resolution event-related potentials. Brain, 127,351-362.[Abstract/Free Full Text]

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