Cerebral Cortex Advance Access originally published online on May 11, 2005
Cerebral Cortex 2006 16(2):280-290; doi:10.1093/cercor/bhi107
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Oscillatory EEG Correlates of Episodic Trace Decay
1 Department of Physiological Psychology, Institute of Psychology, University of Salzburg, Hellbrunnerstr. 34, A-5020 Salzburg, Austria and 2 University of California, Department of Psychology, One Shields Avenue, Davis, CA 95616-8686, USA
Address correspondence to Univ. Prof. Dr Wolfgang Klimesch, Department of Physiological Psychology, Institute of Psychology, University of Salzburg, Hellbrunnerstr. 34, A-5020 Salzburg, Austria. Email: wolfgang.klimesch{at}sbg.ac.at.
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Abstract |
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Recent studies suggest that human theta oscillations appear to be functionally associated with memory processes. It is less clear, however, to what type of memory sub-processes theta is related. Using a continuous word recognition task with different repetition lags, we investigate whether theta reflects the strength of an episodic memory trace or general processing demands, such as task difficulty. The results favor the episodic trace decay hypothesis and show that during the access of an episodic trace in a time window of
200–400 ms, theta power decreases with increasing lag (between the first and second presentation of an item). LORETA source localization of this early theta lag effect indicates that parietal regions are involved in episodic trace processing, whereas right frontal regions may guide the process of retrieval. We conclude that episodic encoding can be characterized by two different stages: traces are first processed at parietal sites at 300 ms, then further processing takes place in regions of the medial temporal lobe at 500 ms. Only the first stage is related to theta, whereas the second is reflected by a slow wave with a frequency of 2.5 Hz.
Key Words: episodic memory • ERD • LORETA • oscillation • theta
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Introduction |
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Inspired from animal research, human theta has been studied extensively in memory tasks during the last two decades and, compared with other EEG frequencies, is the best investigated example of a memory-related brain oscillation (for reviews, see Klimesch, 1999
; Kahana et al., 2001). Theta appears functionally related primarily to working memory (WM), but it is less clear to which of the different WM processes or other aspects of memory theta is associated. We argue in the following that different types of theta responses can be distinguished that reflect (i) the maintenance of information in WM; (ii) sustained attention; and (iii) episodic encoding/retrieval. In the present study, we test whether theta — or, more precisely, which aspect of theta — reflects the strength of an episodic trace.
Studies focusing on the maintenance of information in WM have shown that with increasing load, theta power increases (Mecklinger et al., 1992; Gevins et al., 1997, 1998; Grunwald et al., 1999; Klimesch et al., 1999; Gevins and Smith, 2000; Raghavachari et al., 2001; Fingelkurts et al., 2002; Jensen and Tesche, 2002). This load-dependent increase in theta power was found most consistently during the retention interval of different types of WM tasks, including the Sternberg and n-back tasks. In addition, studies using subdural electrodes or specific source analyzing methods have shown that the length of theta episodes is related to the duration of WM demands in a memory scanning (Tesche and Karhu, 2000; Raghavachari et al., 2001) or spatial WM task (Kahana et al., 1999; Araujo et al., 2002). With respect to the involvement of different brain areas, Sarnthein et al. (1998) have shown that during the maintenance of information in WM, theta coherence is significantly increased between frontal and posterior recording sites (for similar findings, see von Stein et al., 1999; Weiss and Rappelsberger, 2000; Sauseng et al., 2002, 2004, 2005). These results suggest that the active maintenance of information in WM (e.g. by rehearsal or other executive functions) is reflected by a sustained increase in theta power, particularly at frontal sites, and an increase in coherence between frontal and posterior sites.
Another type of theta response can be observed in tasks requiring sustained attention. Several studies indicate that rhythmic theta activity can be observed in the ongoing EEG over frontal midline sites (‘frontal midline theta’ or ‘Fm
’; Ishihara and Yoshii, 1972; Asada et al., 1999; Ishii et al., 1999; Aftanas and Golocheikine, 2001; Kubota et al., 2001) if subjects perform continuously a demanding task for some time period.
A third type of theta response is characterized by a brief event-related increase in theta power during episodic encoding and retrieval. Particularly research using the event-related desynchronization/synchronization (ERD/ERS) approach (a method measuring the percentage of power change during task performance with respect to a reference interval; for reviews, see Pfurtscheller and Lopes da Silva, 1999) indicates that during encoding and retrieval a pronounced but transient increase in theta within a time window of 100–400 ms after stimulus presentation can be observed (Klimesch et al., 1994, 1996, 1997a,b, 2000, 2001a,b; Krause et al., 1996, 2001; Burgess and Gruzelier, 1997, 2000; for a review, see Klimesch, 1999). In all of these studies the list of items presented during encoding was much too long to be kept in WM. Thus, in these studies, theta appears to reflect a different function, most likely episodic encoding and recognition. This conclusion is supported by (i) studies focusing on a direct comparison between semantic and episodic processes (Klimesch et al., 1994); (ii) findings that the extent of theta ERS during encoding predicts later memory performance (= theta subsequent memory effect; see Klimesch et al., 1996, 1997b); (iii) findings that during recognition ERS for old words is larger than for new words (= theta old–new effect; Klimesch et al., 1997b, 2000); and (iv) the topography of theta during encoding and retrieval, which is clearly related to parietal sites (Klimesch, 1999). The theta old–new and subsequent memory effect — characterized by a brief increase in ERS (100–400 ms) — indicate that theta ERS reflects successful encoding and retrieval of episodic information. The similar parietal topography during encoding and recognition, together with the brief and early increase in theta ERS, suggest that parietal areas play an important role for the early processing of an episodic memory trace. The parietal topography with its brief increase in theta distinguishes episodic processing from more general WM demands that are associated with a sustained increase in theta power, particularly at frontal sites.
In the present study, we focus on the episodic trace strength hypothesis, i.e. we investigate whether the short and transient increase in theta (within a time window of 100–400 ms) reflects (at least in part) the strength of a memory trace. Our assumption is that high trace strength is related to large theta ERS whereas low trace strength is related to small theta ERS. Although the above reviewed findings favor the episodic trace hypothesis, the following results question its general validity. During recognition, ERS is generally larger than during encoding and, most importantly, a significant increase in theta ERS can be observed not only for old items but also for new items (Klimesch et al., 1996, 1997b, 2001a). In addition, theta ERS for new items during recognition is also significantly larger than ERS for items during successful encoding (Klimesch et al., 2001a). Thus, theta ERS for new items during recognition cannot be interpreted to reflect retrieval of an existing memory trace nor can the entire extent of ERS reflect encoding of a new trace. It appears plausible to assume that at least part of the increase in theta during recognition is process specific (similar to Tulving's description of a ‘retrieval mode’; Tulving, 1983).
In a previous study, we used the remember–know (R–K) design, which allows the distinction between recollection and familiarity during episodic retrieval in order to study the influence of trace strength on theta ERS, but in a rather indirect way. Subjects were asked to indicate whether they consciously recollected the event in which a word was earlier presented (remembering) or whether they recognized it on the basis that it was familiar in the absence of recollection (knowing). R-judgements are associated with higher memory confidence than K-judgements. Trace strength, however, is not the only factor differentiating between these two types of judgements. According to Tulving (Tulving, 1985), R- and K-judgements reflect two different states of conscious awareness. We have tested the following hypotheses: (i) whether the time course and extent of theta ERS is different for both types of retrieval processes; and (ii) whether theta ERS is larger during recollection (Klimesch et al., 2001b). The first hypothesis was supported, the second not. We have found that an early and short lasting increase in theta predicted knowing, whereas a longer lasting increase (with a similar early onset latency) predicted remembering. This is in agreement with reaction time experiments which have shown that familiarity information is accessible earlier than recollected information (e.g. Hintzman et al., 1998). However, the absence of a difference between theta on recollection and familiarity trials suggests that theta ERS was not directly related to trace strength. Although recollection and familiarity are intended to gauge different states of conscious awareness, trials given familiarity responses are typically recognized with a lower level of confidence than recollect trials (for a review, see Yonelinas, 2002). If trace strength were the only relevant factor, a strong increase in theta would be expected for recollection only, which was not the case. But in the R–K design, the effects of trace strength and type of retrieval process (underlying a familiarity and remember judgement) might very well be confounded. For recollection the processing of retrieval cues is important, which is a time consuming process. Thus, retrieval will be comparatively slow, and this will be reflected be a prolonged increase in theta. In contrast, a familiarity judgement may simply be based on trace strength, information that is available very early during retrieval. Consequently, theta shows a short lasting and early increase in ERS. Thus, comparing theta ERS for familiarity and remember judgements may primarily reflect differences in retrieval strategies rather than differences in trace strength.
In the present study, we use a continuous word recognition task with different lag conditions to test the trace hypothesis. This task has two advantages. Subjects are not in a continuous retrieval mode (as in standard recognition tasks) because they also have to encode new items and, most importantly, trace strength can be directly manipulated by presentation lag (i.e. a word can by repeated after e.g. 2, 8 or 16 intervening words). On the basis of the trace hypothesis, we assume that theta ERS decreases with lag. If theta reflects general task demands (of WM), in the sense that increasing task difficulty increases theta ERS, we would have to expect that under the more difficult condition (lag-16) theta ERS should be larger than for the easier condition (lag-2). Thus, the trace hypothesis and the WM/cognitive load hypothesis make different predictions. According to the trace hypothesis, accessing a weak trace should be associated with a small theta ERS, but according to the WM/cognitive load hypothesis, processing of a weak trace should be related to a large theta ERS.
For the evaluation of these predictions, time poststimulus is of critical importance. Accessing a memory trace must occur before the recognition judgement can be made. In continuous word recognition tasks requiring old–new judgements, reaction times (RTs) vary between 650 and 750 ms depending on lag (e.g. Friedman, 1990b; Kim et al., 2001). Accordingly, accessing and processing a trace must occur before 500–600 ms. For the evaluation of the episodic trace hypothesis, we will thus focus on a time period of 200–600 ms in assuming that sensory semantic encoding already starts before 200 ms. The critical prediction is that theta ERS will be smaller for the long lag condition (lag-16) than for the short lag condition (lag-2).
Finally, it should be emphasized that we do not assume that the total extent of theta ERS reflects trace strength. As we know from the comparisons between ERS during encoding and retrieval and between ERS for old and new words during recognition, some extent of theta ERS must be process specific and not related to trace strength. Consequently, we have to expect that the effects of trace strength might be quite small and rather localized in topography. Based on earlier findings we expect that parietal and parieto-temporal sites will show the largest lag-related effects in theta (Klimesch et al. 2000, 2001a,b).
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Materials and Methods |
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Subjects
A sample of 28 right-handed students (nine males, mean age ± SD = 26.4 ± 4.5 years; 19 females, mean age = 22.8 ± 3.4 years) participated voluntarily and after informed consent.
Materials and Design
The present experiment was designed as a parallel study to a recent fMRI experiment (Brozinsky et al., 2005), and thus type of stimuli and task procedure were largely equivalent, with the following exceptions: Brozinsky et al. (2005) used an additional lag condition (lag-32), shorter trials [500 ms stimulus exposure time and 2000 ms inter-stimulus interval (ISI)] and English-speaking subjects.
Stimuli consisted of 600 nouns which were selected from the CELEX database and grouped into eight lists. Each word was presented for 1000 ms with an ISI of 3000 ms. Words that appeared on one list did not appear on other lists. Each list consisted of randomly interleaved trials comprising 75 new words (30 that never reappeared and 45 that were repeated). From these 45 words (termed first presentation words), 15 words were repeated after two intervening words (lag-2; SOA = 8 s), 15 words after eight intervening words (lag-8; SOA = 32 s) and 15 words after 16 intervening words (lag-16; SOA = 64 s). Thus, there were 30 + 45 + 45 presentations for each list, with a ratio of 75 new to 45 old items. Stimuli were presented at the center of a computer monitor, placed 1.2 m in front of the subjects. The words covered a visual angle of 2.2 x 3.3–9.5° and were presented in light gray on a dark gray background.
For the lag conditions, the range of possible list positions was identified, and broken into 15 equally spaced bins. Test items were then placed at a random location within each of these lag-specific bins. This procedure was used to prevent autocorrelation between adjacent trials, and also to reduce the confounding of lag and serial list position (i.e. lag-16 items cannot be presented until the seventeenth trial, whereas lag-2 items can begin after the third trial). Test lists were also balanced for word frequency (10–299 per million; Baayen et al., 1995), concreteness (204–593), imageability (275–638), number of letters (3–8) and number of syllables (1–3). Repeated and non-repeated words had the same linguistic properties within and across lists.
The experimental session started and ended with a resting period. Subjects were asked to relax and the resting EEG was recorded first for 2 min with open eyes and then for 2 min with closed eyes. Prior to the recognition task, a training block was run to ensure that the subjects were able to respond fast and accurately. The recognition task consisted of eight blocks with 120 trials each. Subjects were asked to make a recognition judgement in each trial by rating the confidence of their judgement. They did so by pressing one of six buttons (1 = very certain old, 2 = certain old, 3 = uncertain old, 4 = uncertain new, 5 = certain new, 6 = very certain new).
Apparatus
EEG-signals were amplified by a 32-channel Neuroscan Synamps amplifier (frequency response: 0.15–45 Hz) and were then converted to a digital format. The sampling rate was 250 Hz.
Electrophysiological Recordings
A set of 30 silver electrodes were placed, according to the International Electrode (10–20) Placement system, at Fp1, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T3, C3, Cz, C4, T4, CP5, CP1, CP2, CP6, T5, P3, Pz, P4, T6, PO3, PO4, O1, Oz and O2. In addition, two earlobe electrodes were attached to the left and right ear. Data were recorded against a linked earlobe reference. For data analysis only the following ten electrodes were selected: F3, Fz, F4, T5, T6, Pz, PO3, PO4, O1 and O2. The electrooculogram (EOG) was recorded from two pairs of leads in order to register horizontal and vertical eye movements.
All epochs were carefully checked individually for artifacts and were categorized with respect to items judged correctly as new for first presentation words and as old at lag-2, lag-8 and lag-16. After rejecting artifacts and erroneous trials, an average of 76.5 epochs remained for first presentation words, 81.5 epochs for lag-2, 82.1 epochs for lag-8 and 83.6 epochs for lag-16.
Electrophysiological Variables and Data Analysis
Four different electrophysiological variables were calculated: (i) ERD/ERS; (ii) spectral estimates for whole power; (iii) latencies; and (iv) amplitudes of the N2, P2 and P3 of event-related potentials (ERPs). Spectral estimates were calculated in steps of 0.25 Hz for the resting period (of 3 min) with eyes closed. Statistical analyses are based on EEG data for correctly recognized old items. ERD/ERS analysis is based on a reference interval of 200 ms duration preceding (–600 to –400 ms) stimulus presentation.
Latencies and Amplitudes of ERP Components
The time windows for determining the N2, P2 and P3 component were 92–192, 192–352 and 352–660 ms, respectively. Components were determined with the aid of a computer (by searching for the maximal amplitude in the respective time windows) and then visually inspected for artificial results.
Source Analyzing Methods: LORETA
Low-resolution electromagnetic tomography (LORETA; Pascual-Marqui et al., 1994) was applied to individual ERPs to assess the underlying brain electric sources of the scalp potentials. LORETA is an inverse solution method that computes the three-dimensional distribution of neuronal generators in the brain as a current density value (A/m2) for a total of 2394 voxels, with the constraint that neighboring voxels show maximal similarity. All 30 electrodes were used for LORETA analyses.
Two time windows, one for the P2 (252–352 ms poststimulus) and another for the P3 component (400–600 ms), were analyzed. The same time windows were also used to assess the neural sources of evoked delta and theta. For this analysis the raw data were bandpass-filtered between 2 and 4 Hz for delta and between 4 and 6 Hz for theta. After averaging over all respective trials for each subject, LORETA was applied for the same time windows as for the LORETA analysis with unfiltered data.
Statistical Data Analyses
Analyses of variance (ANOVAs) were calculated to assess the influence of lag on behavioral data, ERP components and ERD/ERS. To test the influence of lag on the percentage of correct responses a two-way ANOVA with the factors lag (2,8, 16) and judgement (very certain old, certain old, uncertain old) was used. For ERP components, two-way ANOVAs with the factors lag (2,8, 16) and component (N2, P2 and P3) were calculated for each of the 10 recording sites. For ERD/ERS as dependent measure, two-factorial ANOVAs with the factors lag (2,8, 16) and time (t1 = 0–200, t2 = 200–400, t3 = 400–600, t4 = 600–800) were calculated for each recording site and frequency band. Greenhouse Geisser corrected results are reported where appropriate. The significance level for Scheffé tests was 5%. The use of electrode-specific analyses is motivated by theoretical considerations and experimental findings suggesting that the effects of trace strength are small and rather localized in topography. We expect that parieto-temporal sites will play a specifically important role.
For LORETA, the lag effect was analyzed within the time interval for the P2 and P3 by a pairwise voxel-by-voxel comparison between lag 2, lag 8 and lag 16. Statistical significance was assessed using a nonparametric randomization test (Nichols and Holms, 2002). Results were corrected for multiple comparisons on the cluster level.
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Behavioral Data
Overall recognition performance (percentage of hits) was 91.5%. The false alarm rate was at 6.94%. Collapsed over the three ‘old’ judgement conditions, the percentage of correct responses decreased from 95% for lag-2 to 93% for lag-8 down to 85% for lag-16. The distribution of confidence judgements for the lag-conditions is plotted in Figure 1 and shows that with increasing lag, very certain old judgements decrease whereas certain old and uncertain old judgements increase. These findings are reflected by the ANOVA results with percent correct answers as dependent measure showing highly significant effects for the factors judgement [F(2,54) = 603.21; P < 0.001], lag [F(2,54) = 37.76; P < 0.001] and the interaction judgement x lag [F(4,108) = 51.54; P < 0.001]. Scheffé tests calculated for each of the levels of factor judgement indicate that for very certain old and certain old judgements, all comparisons between the three lag conditions are significant at an alpha level of 0.05. For uncertain old judgements, only the comparison between lag-2 and lag-16 reached significance.
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When using reaction time (RT) as the dependent measure, the one-way ANOVA with factor LAG showed highly significant effects [F(2,53) = 46.1; P < 0.01]. The respective means were 948.7, 1000.0 and 1083.2 ms for the lag-2, lag-8 and lag-16 conditions, respectively. Scheffé tests revealed that all lag conditions differed significantly from each other.
ERP Data
As illustrated by Figure 2, the lag effect (comparison of lag-8 and lag-16 with lag-2) is characterized by a more negative going ERP. Differences between the lag conditions emerge around the P2, but are most prominent during the positive going slope of the P3 at 500 ms. Inspection of Figure 2 indicates that at lag-16, ERPs become almost as negative going as during the first presentation.
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ANOVAs reflect this finding and show that for the interaction lag x component (which reached significance at all sites but F4), lag-related differences in ERP components were significant only for the P3 (tested with Scheffé's test; see Table 1). Note that the difference between lag-8 and lag-16 did not reach significance in any of the comparisons. The only site showing no lag-related differences at all was F4.
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For the latencies of the ERP components, no significant main effect for factor lag or the interaction lag x component were obtained.
Selection of Frequency Bands
The ERD/ERS results at T5 and PO3 (depicted in Fig. 3), averaged over the entire sample of subjects, were used in this study to select those frequency bands between 2 and 14 Hz that are most reactive. As Figure 3 illustrates, two particularly reactive frequency regions can be observed, one between 2 and 6 Hz, showing pronounced and prolonged ERS (from stimulus onset over the entire poststimulus interval, up to 1000 ms), and another between 8 and 12 Hz, exhibiting pronounced ERD over most of the post-stimulus interval. Delta and theta were defined as bands with a width of 2 Hz, from 2 to 4 Hz and from 4 to 6 Hz respectively. Alpha was determined as the broad band (around peak frequency of 10 Hz at PO3) from 8 to 12 Hz. Inspection of power (Fig. 3, lower row) demonstrates nicely the drop of alpha power (i.e. alpha suppression or desynchronization) in response to the presentation of a word in a frequency range of 8–12 Hz.
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ERD/ERS
Statistical findings for ERD/ERS are summarized in Table 2 and illustrated graphically in Figures 4 and 5. As already is evident from Figure 3, the overall pattern is that delta and theta show ERS but alpha shows ERD. This is reflected by factor TIME which is significant for all but two cases (T6 for delta and PO4 for theta; see Table 2). The time course of ERS is different for delta and theta. As exemplified in Figures 4 and 5, delta exhibits a continuous increase up to the latest time interval (t4), whereas theta shows maximal values for t2 (200–400 ms). Alpha ERD shows a pronounced increase up to t3 (400–600 ms), with only a marginal further increase up to t4 (600–800 ms).
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For delta, a significant main effect for LAG was found at Fz, Pz and PO4. Inspection of respective means reveals an almost linear decrease in ERS from lag-2 to lag-16 over all time intervals. The interaction time x lag at T5 reveals a decrease in delta ERS with lag that is significant only at t3 between lag-2, lag-8 and lag-16.
For theta, significant interactions between TIME and LAG were found at T5, Pz, PO3, PO4, O1 and O2. Inspection of the respective means indicate that during the early time intervals (t1–t3) there is a tendency that theta ERS decreases with increasing lag. The results of Scheffé comparisons indicate that the lag-related decrease in ERS is significant already at t2 between lag-2 and lag-16 at T5. It should be noted that in contrast to lag-2 and lag-8, theta fails to drop with time for lag-16 during later time intervals. This difference is significant between lag-8 and lag-16 at T5, Pz, PO4 and O2, as Scheffé comparisons reveal.
For alpha, a significant lag effect was found for Pz. The respective means indicate that ERD is slightly larger for lag-8 and lag-16 as compared with lag-2. Inspection of the respective means for the significant interaction time x lag, obtained at T5, also reveals a slight increase in ERD with increasing lag but only for early time intervals, whereas at the late interval t4 this effect reverses. Scheffé comparisons show that for none of the time intervals, the differences in lag reach significance.
Lag-related Decrease in Delta ERS and P3
The fact that the lag-related decrease in delta ERS and P3 occur in the same time interval (t3: 400–600 ms) and overlap at site T5 has led us to calculate correlations between the two measures. Significant correlations were obtained between the decrease in delta (measured as difference between lag 2 and lag 16) and P3 (amplitude difference between lag 2 and lag 16) at electrodes T5 (r = 0.53; P < 0.01), T6 (r = 0.44; P < 0.05), PO3 (r = 0.40; P < 0.05) and O2 (r = 0.49; P < 0.49).
LORETA Source Analysis
For the unfiltered P2 component, LORETA detected only one small source in the left inferior parietal lobe. The strength of this source showed a slight increase in strength for lag-16 as compared with lag-2 (t > 4.13, P < 0.05 corrected). Differences between lag-2 and lag-8 as well as lag-8 and lag-16 were not significant.
However, when evoked theta in the P2 time window was compared between lag-2 and lag-16, significantly higher current density was found for lag-2 in bilateral superior parietal lobes, left inferior parietal lobe, and right superior and middle frontal gyrus (t > 2.12; P < 0.05 corrected). The right frontal and asymmetric parietal activation (favoring the left hemisphere) are depicted in Figure 6. Stronger activation for lag-2 in right superior parietal and right superior frontal gyrus was also found when compared with lag-8. No significant lag effects regarding the neural sources were obtained for delta.
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The P3 component elicited stronger activity for lag-2 than lag-16 in left superior temporal gyrus and middle temporal gyrus, posterior cingulate gyrus, bilateral lingual gyrus and, most interestingly, in right hippocampus and parahippocampal gyrus (t > 3.77, P < 0.05 corrected). The respective findings are summarized in Figure 7. There were no significant differences between lag-2 and lag-8, or between lag-8 and lag-16.
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In the time window of the P3, there was only slightly more evoked theta activity for lag-2 than lag-16 in the right middle frontal gyrus (t > 2.59, P < 0.05 corrected). No significant differences were obtained for delta.
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Discussion |
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ERPs show the expected typical pattern of results known from other studies (e.g. Friedman, 1990a
; Nielsen-Bohlman and Knight, 1994; Chao et al., 1995; Guillem et al., 1999; Kim et al., 2001). The general finding is that the P2 component is sensitive to old–new differences and the P3 component is sensitive to lag effects. Specifically, lag-related differences in ERP amplitudes reached significance only for the comparatively late P3 component with a latency of 500 ms.
Evaluation of the episodic trace decay hypothesis is based on the idea that during the access of an episodic trace in a time window of 200–600 ms, theta ERS decreases with lag. In good agreement with the proposed hypothesis, theta ERS shows a consistent decrease in ERS with lag during an early time interval (t2 and/or t3) over most recording sites (cf. Fig. 5a), which is statistically most reliable at T5 for interval t2 (200–400 ms; cf. Fig. 4b and Table 2). During the late interval t4 (600–800 ms), however, an increase in ERS can be observed for lag-16 (cf. Fig. 5). Because mean RT is 1000 ms and includes not only the old–new but also the confidence judgement and motor response, it appears reasonable to assume that trace processing is completed (at the latest) at 500 ms. This conclusion is based on the fact that typical reaction times vary between 650 and 750 ms, depending on lag (e.g. Friedman, 1990b; Kim et al., 2001), and on the assumption that execution of a motor response requires 200 ms. Consequently, the late increase in theta ERS (during 600–800 ms) for lag-16 items might reflect a decision related process. The interesting point here is that theta exhibits a dissociation between task difficulty and time. Whereas in the early time window t2 theta ERS for lag 16 is significantly smaller than for lag 2, in the latest time interval t4 ERS is larger for lag-16 than for lag-8 (cf. Fig. 5). The fact that the difference between lag-16 and lag-2 did not reach significance at t4 may be due to the generally higher level of ERS for lag-2.
This dissociation suggests that theta reflects different stages during the processing of an episodic trace. In an early time window, theta reflects encoding (of new items) or access of an existing trace (for old items), depending on the type of task. We know from previous studies that successful (as compared with not successful) encoding is reflected by a large theta ERS (e.g. Klimesch et al., 1996, 1997a,b). Successful encoding is associated with a strong trace, and thus a large ERS reflects a strong trace. The findings of the present study allow us to arrive at a similar conclusion. With increasing lag, subjects show a decrease in recognition confidence (cf. Fig. 1), increased RTs and a decrease in ERS. During a late interval (beyond 500 ms), the accessed trace will be evaluated by the subject. Evaluating a weak trace is more difficult (e.g. in terms of spreading of attentional resources) than a strong trace. Obviously, the late theta ERS reflects the difficulty of this evaluation process. Thus, theta reflects two different aspects of episodic retrieval, episodic trace strength during an early time window and evaluation of an episodic trace during a late time window.
We consider recognition confidence a behavioral variable reflecting trace strength. In general, we assume that high confidence is related to recollection and low confidence to familiarity. Even for lag-16, certain and very certain judgements account for 80% of correct old responses (cf. Fig. 1). Thus, the assumption that in the present experiment confidence reflects episodic trace strength appears plausible. Variations in confidence (within a lag condition), however, may also be due to other variables that influence in trace strength, such as specific encoding strategies or item difficulty. These variables are less likely to play an important role in the present study because items were presented at a fast rate (with an ISI of 3 s), subjects could not know in advance whether an item would be repeated, and items were controlled for word frequency, concreteness, imageability, number of letters and number of syllables.
Most remarkably, the predictions of the trace decay hypothesis were also confirmed by findings about delta. A lag-related decrease in ERS was found at Fz, Pz, PO4 and T5, but at the latter site for t3 only. The lack of a significant lag x time interaction at Fz, Pz and PO3 seems to imply that already at the earliest time, lag-related differences are significant. Inspection of Figure 4, however, reveals that delta shows a rather continuous increase from a pre-stimulus interval (–200 to 0 ms) up to t4. A time smear (in the range of half a theta cycle of 150 ms) and between-subject variability may have prevented the lag x time interaction from reaching significance. Although not significant, lag-related differences are more pronounced in late as compared with early time windows (cf. Fig. 4).
In contrast to delta and theta, alpha shows a weak increase in ERD with lag (at Pz and T5). The magnitude of this effect is small — in the range of a few percentage points.
An increase in ERD or ERS may be interpreted in terms of increased cortical activation (see e.g. Klimesch, 1999). Thus, the expected decrease in cortical activation with lag — possibly reflecting trace decay — can be observed only for theta and delta. Given that the lag-related decrease in theta ERS is significant for t2 (200–400 ms), we assume that the episodic memory trace is accessed at 300 ms. In agreement with this assumption, the overall time course of theta ERS exhibits a maximum at 300 ms. Because delta ERS shows a gradual increase over time with a maximum at t4 (600–800 ms), we assume that delta reflects processes that are different from those reflected by theta. It is possible that delta reflects the evaluation of a memory trace in the sense of a ‘target detection/evaluation’ process. Consistent with this interpretation is the finding that the lag-related decrease in delta ERS (in the time window of the P3) is significantly correlated with the lag-related decrease in P3. This is also in agreement with the finding that the ERP old–new effect (a P3 like component is larger for correctly recognized old than new items) is generated by frequencies in the delta range (Klimesch et al., 2000).
Most interestingly, LORETA revealed bilateral parietal and right frontal sources for evoked theta — but not delta — at 300 ms, a time window related to the appearance of the P2 component. These sources were weaker for lag-16 as compared with lag-2. This is further evidence that theta plays an important role in a time period of 300 ms post-stimulus. For the unfiltered P2 component, LORETA detected a small parietal source that increased with lag. In the time window of the P3, LORETA reveals only marginally significant sources for evoked theta and delta but large sources for the unfiltered P3. The P3 component showed a lag-related decrease in temporal areas (including the right hippocampal and parahippocampal regions), in the posterior cingulate gyrus and the right and left bilateral lingual gyrus.
The parallel fMRI study by Brozinsky et al. (2005) showed that, compared with lag-2 items, lag-16 items are associated with an increase in the BOLD signal in different regions of the temporal lobe, including the left rhinal cortex, and the left posterior and right posterior hippocampus. Consistent with single-unit studies demonstrating repetition suppression (Brown and Xiang, 1998), lag-2 and lag-8 items exhibited a decrease in the BOLD signal when compared with new items. No such repetition suppression was found for lag-16 and lag-32 items. With respect to topography, there is some overlap with the sources of the P3 in the present study within regions of the temporal lobe. However, the direction of lag-related changes in signal strength are different. The BOLD signal increases with lag, but the P3 amplitude decreases with lag. Inspection of the ERPs depicted in Figure 2 reveals that the P3 is characterized by a slow component between 400 and 600 ms (that may be interpreted as a half period of a slow wave with a frequency close to 2.5 Hz). In assuming that a positive going wave reflects inhibitory processes whereas a negative going wave indicates excitatory processes (Elbert et al., 1981), the significant decrease in P3 amplitude with lag (cf. Fig. 2) suggests that excitation increases with lag. Thus, in theory, both the lag-related increase in the BOLD signal and the lag-related increase in (relative) negativity (decrease in the P3 amplitude) may reflect similar processes that are related to the evaluation and/or further processing of the accessed trace which become more difficult when the trace decays.
It should also be noted that the comparatively fast and transient decrease in theta during the access of an episodic trace most likely cannot be detected with fMRI. We assume that a parietal network found for evoked theta (cf. Fig. 6) is related to the access of an episodic trace. The functional meaning of the early right frontal source is difficult to interpret, but might reflect (consistent with the HERA model; Tulving et al., 1994) a control process related to episodic retrieval. Sources in medial temporal areas, found in a later time window, may mediate the further processing of the retrieved trace (cf. Fig. 7).
We hypothesize that the sequence of encoding can be described as follows. Up to the time window of the N1 (160 ms) a sensory code is established, then at 300 ms (in the time window of the P2) the episodic trace is processed. The parietal–frontal network detected by LORETA for evoked theta most likely reflects two different processes. Whereas the right frontal source may be related to the process of retrieval (this is consistent with Tulving's HERA model; Tulving et al., 1994), early episodic trace processing takes place at parietal areas. Then, during the time window of the P3, the episodic trace is processed further in areas of the temporal lobe (including the hippocampal formation). The proposed hypothesis is different from the traditional view with respect to the early time that the episodic trace can be accessed. ERP studies have demonstrated that episodic processes are related to the comparatively late window of a P3-like component with a latency between 400 and 800 ms (e.g. Allan et al., 1998; Fernandez et al., 1999; Mecklinger, 2000). Our conclusion, however, is based on findings about theta which are consistent with data from other laboratories. As an example of a recent study, Düzel et al. (2003) found an early parietal theta old–new effect in a time window around 300 ms.
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This research was supported by the Austrian Science Fund (FWF), P-16849.
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