Stephanie - Revision history

imported>Psych204B: /* Preprocessing */

2013-06-04T23:15:19Z

Preprocessing

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	==== Preprocessing ====		==== Preprocessing ====
	All data went through a standard preprocessing pipeline using Freesurfer and FSL, including motion (RapidART) and slice time correction, realignment (middle volume of each run), skull stripping, temporal filtering (high-pass cutoff 200 Hz, 128 Hz (as is standardly used in our lab), 30 Hz, 10 Hz), and surface-based coregistration (bbregister). Data were spatially smoothed (6 fwhm, SUSAN -- only averages a given voxel with local voxels that have a similar intensity), scaled grand median of timeseries to 10000, & normalized for group analyses (nonlinear warp to FSL’s MNI152 space). Data were modeled using a double gamma function, and the first 5 frames of each run were discarded.		All data went through a standard preprocessing pipeline using Freesurfer and FSL, including motion (RapidART) and slice time correction, realignment (middle volume of each run), skull stripping, temporal filtering (high-pass cutoff 200 s, 128 s (as is standardly used in our lab), 30 s, 10 s), and surface-based coregistration (bbregister). Data were spatially smoothed (6 fwhm, SUSAN -- only averages a given voxel with local voxels that have a similar intensity), scaled grand median of timeseries to 10000, & normalized for group analyses (nonlinear warp to FSL’s MNI152 space). Data were modeled using a double gamma function, and the first 5 frames of each run were discarded.

	[[File:coreg.png \| thumb\|400px\|center\|Example coregistration]]		[[File:coreg.png \| thumb\|400px\|center\|Example coregistration]]

imported>Psych204B: /* Background */

2013-06-04T21:00:13Z

Background

← Older revision		Revision as of 21:00, 4 June 2013
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	= Background =		= Background =
			== Recognition Memory ==
	Several models of recognition memory hypothesize that the probability of judging a test-probe as “old” is correlated with the similarity between the test-probe and studied items stored in memory. For instance, these models claim that as the congruence between test-probe and studied items increases, the activation level for that probe will increase, and consequently it becomes more likely that test-probe will be judged as familiar (Gillund and Shiffrin, 1984; Hintzman, 1988). In the present experiment (Olsen, 2009), we sought to examine the neural correlates of recognition memory using functional magnetic resonance imaging (fMRI). More specifically, we used a novel set of abstract shape stimuli to manipulate the perceptual similarity between test-probes and studied items, allowing us to directly test the assumptions of these recognition memory models.		Several models of recognition memory hypothesize that the probability of judging a test-probe as “old” is correlated with the similarity between the test-probe and studied items stored in memory. For instance, these models claim that as the congruence between test-probe and studied items increases, the activation level for that probe will increase, and consequently it becomes more likely that test-probe will be judged as familiar (Gillund and Shiffrin, 1984; Hintzman, 1988). In the present experiment (Olsen, 2009), we sought to examine the neural correlates of recognition memory using functional magnetic resonance imaging (fMRI). More specifically, we used a novel set of abstract shape stimuli to manipulate the perceptual similarity between test-probes and studied items, allowing us to directly test the assumptions of these recognition memory models.

imported>Psych204B: /* Group Results: Varying the Highpass Filter Cutoffs */

2013-06-04T20:48:29Z

Group Results: Varying the Highpass Filter Cutoffs

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	====Study > Test====		====Study > Test====
	[[File:group_study-vs-test_200.png \| thumb\|400px\|center\|Figure 11. Group Mixed Effects: Study > ~~Group~~, 200 s HPFC, left hemisphere]]		[[File:group_study-vs-test_200.png \| thumb\|400px\|center\|Figure 11. Group Mixed Effects: Study > Test, 200 s HPFC, left hemisphere]]

	Local maxima estimates (200 HPFC): [[File:max_study-vs-test_200.png]]		Local maxima estimates (200 HPFC): [[File:max_study-vs-test_200.png]]

	[[File:group_study-vs-test_128.png \| thumb\|400px\|center\|Figure 12. Group Mixed Effects: Study > ~~Group~~, 128 s HPFC, left hemisphere]]		[[File:group_study-vs-test_128.png \| thumb\|400px\|center\|Figure 12. Group Mixed Effects: Study > Test, 128 s HPFC, left hemisphere]]

	Local maxima estimates (128 HPFC): [[File:max_study-vs-test_128.png]]		Local maxima estimates (128 HPFC): [[File:max_study-vs-test_128.png]]

	[[File:group_study-vs-test_30.png \| thumb\|400px\|center\|Figure 13. Group Mixed Effects: Study > ~~Group~~, 30 s HPFC, left hemisphere]]		[[File:group_study-vs-test_30.png \| thumb\|400px\|center\|Figure 13. Group Mixed Effects: Study > Test, 30 s HPFC, left hemisphere]]

	Local maxima estimates (30 HPFC): [[File:max_study-vs-test_30.png]]		Local maxima estimates (30 HPFC): [[File:max_study-vs-test_30.png]]

	[[File:group1_study-vs-test_10h.png \| thumb\|400px\|center\|Figure 14. Group Mixed Effects: Study > ~~Group~~, 10 s HPFC, left hemisphere]]		[[File:group1_study-vs-test_10h.png \| thumb\|400px\|center\|Figure 14. Group Mixed Effects: Study > Test, 10 s HPFC, left hemisphere]]

	Local maxima estimates (10 HPFC): [[File:max_study-vs-test_10.png]]		Local maxima estimates (10 HPFC): [[File:max_study-vs-test_10.png]]

imported>Psych204B: /* Recognition Memory */

2013-06-04T20:39:56Z

Recognition Memory

← Older revision		Revision as of 20:39, 4 June 2013
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	<br>		<br>
	Further, based on recent findings from Duncan & Davachi (2012), we were interested in whether a subject's recognition confidence on a given trial might be modulated by their response confidence on the ''preceding'' trial. In other words, if a subject rates a morph as "confident old," might they then be more likely to rate the next morph as old? In addition, we were curious whether this effect might be modulated by the current trial morph, and it's similarity with the encoded shape (i.e., identical, near, far). To investigate this, we binned each trial based on the current morph type (i.e., identical, near, far), and the response confidence on the previous trial (i.e., OLD: 4,5 or NEW: 1,2). Here, we used a linear mixed effects model (LMM) predicting recognition confidence on the current trial, with the current morph type and previous trial confidence as fixed effects, random intercepts for subjects, and random slopes by morph type and previous trial confidence. As hypothesized, we found a significant effect of previous trial confidence (beta = 0.10, t=3.10), and no interaction (p>0.05), such that response confidence on the previous trial influences response confidence on the current trial regardless of the current trial morph type (see ~~Fig. 6~~).		Further, based on recent findings from Duncan & Davachi (2012), we were interested in whether a subject's recognition confidence on a given trial might be modulated by their response confidence on the ''preceding'' trial. In other words, if a subject rates a morph as "confident old," might they then be more likely to rate the next morph as old? In addition, we were curious whether this effect might be modulated by the current trial morph, and it's similarity with the encoded shape (i.e., identical, near, far). To investigate this, we binned each trial based on the current morph type (i.e., identical, near, far), and the response confidence on the previous trial (i.e., OLD: 4,5 or NEW: 1,2). Here, we used a linear mixed effects model (LMM) predicting recognition confidence on the current trial, with the current morph type and previous trial confidence as fixed effects, random intercepts for subjects, and random slopes by morph type and previous trial confidence. As hypothesized, we found a significant effect of previous trial confidence (beta = 0.10, t=3.10), and no interaction (p>0.05), such that response confidence on the previous trial influences response confidence on the current trial regardless of the current trial morph type (see Figure below).

	[[File:conf_byPrevTrialxMorph.png \| thumb\|400px\|center\|~~Figure 7.~~ Recognition Confidence by Morph Type & Previous Trial Confidence]]		[[File:conf_byPrevTrialxMorph.png \| thumb\|400px\|center\|Recognition Confidence by Morph Type & Previous Trial Confidence]]

	== fMRI Results ==		== fMRI Results ==

imported>Psych204B: /* fMRI Results */

2013-06-04T20:38:35Z

fMRI Results

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	== fMRI Results ==		== fMRI Results ==
	For these fMRI analyses, we will report results from four iterations through the data. In each case, data preprocessing and the overall models were kept the same, varying only the highpass filter cutoffs. Here, we selected four unique cutoffs: 200, 128, 30, and 10 seconds. Based on calculations set to optimize highpass filtering (i.e., minimizing the period for the highpass filter that still preserves a specified amount of variance in all the design matrix regressors; FSL's cutoffcalc function), the optimal HPFC for this study ranged from approximately 114 to 222 seconds, with sigma bounds around 20-30. From these calculations, we should expect that highpass filter cutoffs lower than ~100 seconds would remove some task-related variation in signal, rather than just low-frequency noise.		For these fMRI analyses, we will report results from four iterations through the data. In each case, data preprocessing and the overall models were kept the same, varying only the highpass filter cutoffs. Here, we selected four unique cutoffs: 200, 128, 30, and 10 seconds. Based on calculations set to optimize highpass filtering (i.e., minimizing the period for the highpass filter that still preserves a specified amount of variance in all the design matrix regressors; FSL's cutoffcalc function), the optimal HPFC for this study ranged from approximately 114 to 222 seconds, with sigma bounds around 20-30. From these calculations, we should expect that highpass filter cutoffs lower than ~100 seconds would remove some task-related variation in signal, rather than just low-frequency noise.

			[[File:hpfc_example.png \| thumb\|400px\|center\|Visualization of the 4 highpass frequency filter cutoffs relative to an example run]]

	Model parameters across all four iterations of highpass frequency cutoffs included study trials (regardless of the repetition number, or subsequent recognition confidence), and test trials were separated by whether the response confidence was old (rating of 4 or 5) or new (rating of 1 or 2), a guess (rating of 3), by morph type (identical, near, far).		Model parameters across all four iterations of highpass frequency cutoffs included study trials (regardless of the repetition number, or subsequent recognition confidence), and test trials were separated by whether the response confidence was old (rating of 4 or 5) or new (rating of 1 or 2), a guess (rating of 3), by morph type (identical, near, far).

imported>Psych204B: /* Conclusions */

2013-06-04T20:23:25Z

Conclusions

← Older revision		Revision as of 20:23, 4 June 2013
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	In summary, when the highpass frequency cutoff is lowered below 100 seconds (i.e., 30 or 10 seconds), the parameter estimates are decreased, and the t-statistics also decrease. Extending the temporal period of interest to 200 seconds did not seem to effect the results much; however, when data with temporal periods over 10 or 30 seconds was filtered out, the results changed significantly. Here, for the group analyses, the z-statistics decrease with lower highpass frequency cutoffs. For instance, for the study > test contrast, the highest probability precuneus cluster had a z-value of 6.48 for the 128s filter, but only 4.35 for the 10s filter. Similarly, for the identical > far test morph contrast, the frontal activation was below threshold for the 30s filter, and all activation was below threshold for the 10s filter.		In summary, when the highpass frequency cutoff is lowered below 100 seconds (i.e., 30 or 10 seconds), the parameter estimates are decreased, and the t-statistics also decrease. Extending the temporal period of interest to 200 seconds did not seem to effect the results much; however, when data with temporal periods over 10 or 30 seconds was filtered out, the results changed significantly. Here, for the group analyses, the z-statistics decrease with lower highpass frequency cutoffs. For instance, for the study > test contrast, the highest probability precuneus cluster had a z-value of 6.48 for the 128s filter, but only 4.35 for the 10s filter. Similarly, for the identical > far test morph contrast, the frontal activation was below threshold for the 30s filter, and all activation was below threshold for the 10s filter.

	[[File:calculated.png \| thumb\|400px\|center\|Figure 19. Calculated HPF cutoffs, based on design matrices (dashed lines = cutoffs tested in the present analysis)]]		[[File:calculated.png \| thumb\|400px\|center\|Figure 21. Calculated HPF cutoffs, based on design matrices (dashed lines = cutoffs tested in the present analysis)]]

	This pattern of results was predicted by using FSL's cutoffcalc function, which can be used to estimate an ideal highpass frequency cutoff (removing the low frequency trends elicited by non-task related drift, but also sufficiently long to avoid removing the signal of interest) based on the design matrices; for the present experiment, the mean estimated cutoff was 112.63 seconds, with a standard deviation of 25.57 (see Fig. 19). In summary, when choosing a highpass frequency cutoff one should carefully consider the temporal period of the design matrix to ensure that the filtering does not remove signal related to the task.		This pattern of results was predicted by using FSL's cutoffcalc function, which can be used to estimate an ideal highpass frequency cutoff (removing the low frequency trends elicited by non-task related drift, but also sufficiently long to avoid removing the signal of interest) based on the design matrices; for the present experiment, the mean estimated cutoff was 112.63 seconds, with a standard deviation of 25.57 (see Fig. 21). In summary, when choosing a highpass frequency cutoff one should carefully consider the temporal period of the design matrix to ensure that the filtering does not remove signal related to the task.

	= References =		= References =

imported>Psych204B: /* Group Results: Varying the Highpass Filter Cutoffs */

2013-06-04T20:22:43Z

Group Results: Varying the Highpass Filter Cutoffs

← Older revision		Revision as of 20:22, 4 June 2013
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	=== Group Results: Varying the Highpass Filter Cutoffs ===		=== Group Results: Varying the Highpass Filter Cutoffs ===
	Group results were analyzed with a mixed effects analysis using FSL and Lyman, taking into account the within-subject fixed effects variances across runs, and the between-subject random effect variance. Data were normalized to MNI space. All inflated surfaces are displayed with a z-threshold of 2.3.		Group results were analyzed with a mixed effects analysis using FSL and Lyman, taking into account the within-subject fixed effects variances across runs, and the between-subject random effect variance. Data were normalized to MNI space. All inflated surfaces are displayed with a z-threshold of 2.3 to 4.265.

	====Study > Test====		====Study > Test====

imported>Psych204B: /* Group Results: Varying the Highpass Filter Cutoffs */

2013-06-04T20:21:18Z

Group Results: Varying the Highpass Filter Cutoffs

← Older revision		Revision as of 20:21, 4 June 2013
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	=== Group Results: Varying the Highpass Filter Cutoffs ===		=== Group Results: Varying the Highpass Filter Cutoffs ===
	Group results were analyzed with a mixed effects analysis using FSL and Lyman, taking into account the within-subject fixed effects variances across runs, and the between-subject random effect variance. Data were normalized to MNI space.		Group results were analyzed with a mixed effects analysis using FSL and Lyman, taking into account the within-subject fixed effects variances across runs, and the between-subject random effect variance. Data were normalized to MNI space. All inflated surfaces are displayed with a z-threshold of 2.3.

	====Study > Test====		====Study > Test====
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	[[File:max_1vs3_10.png \| thumb\|400px\|center\|Figure 18. Group Mixed Effects: Identical > Far, 10 s HPFC, left hemisphere]]		[[File:max_1vs3_10.png \| thumb\|400px\|center\|Figure 18. Group Mixed Effects: Identical > Far, 10 s HPFC, left hemisphere]]


	=== Group Results: Additional Analyses ===		=== Group Results: Additional Analyses ===

imported>Psych204B: /* T-Statistics */

2013-06-04T20:19:13Z

T-Statistics

← Older revision		Revision as of 20:19, 4 June 2013
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	<br>		<br>
	[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]
	[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space. Error bars are within-subject standard error]]

	Interestingly, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10. A repeated measures ANOVA examining the effect of highpass frequency cutoff (10, 30, 128, 200 seconds) and voxel location (anterior, posterior) on the mean t-statistic indicated significant main effects of HPFC (''F''(1,18) = 20.86, ''p'' < 0.001] and voxel location [''F''(1,18) = 77.23, ''p'' < 0.001], but importantly, there was a significant interaction between highpass frequency cutoff and voxel location for this dataset [''F''(1,94) = 72.54, ''p'' < 0.001], such that the main effect of highpass frequency cutoff was only marginal for the anterior voxel (''p''=0.06), but was significant for the posterior voxel [''F''(1,18) = 43.06, ''p'' < 0.001]. It might be interesting to investigate this more in depth to see whether the effect of highpass frequency filter cutoffs is differential for other anterior/posterior voxels, and whether this might generalize across tasks.		Interestingly, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10. A repeated measures ANOVA examining the effect of highpass frequency cutoff (10, 30, 128, 200 seconds) and voxel location (anterior, posterior) on the mean t-statistic indicated significant main effects of HPFC (''F''(1,18) = 20.86, ''p'' < 0.001] and voxel location [''F''(1,18) = 77.23, ''p'' < 0.001], but importantly, there was a significant interaction between highpass frequency cutoff and voxel location for this dataset [''F''(1,94) = 72.54, ''p'' < 0.001], such that the main effect of highpass frequency cutoff was only marginal for the anterior voxel (''p''=0.06), but was significant for the posterior voxel [''F''(1,18) = 43.06, ''p'' < 0.001]. It might be interesting to investigate this more in depth to see whether the effect of highpass frequency filter cutoffs is differential for other anterior/posterior voxels, and whether this might generalize across tasks.

imported>Psych204B: /* T-Statistics */

2013-06-04T20:17:24Z

T-Statistics

← Older revision		Revision as of 20:17, 4 June 2013
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	[[File:tstat_study-vs-test.png \| thumb\|400px\|center\|Figure 9a. Fixed effects t-stats for Study > Test]]		[[File:tstat_study-vs-test.png \| thumb\|400px\|center\|Figure 9a. Fixed effects t-stats for Study > Test]]

	~~Further, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study~~ > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10.		<br>

	[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]
	[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]

			Interestingly, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10. A repeated measures ANOVA examining the effect of highpass frequency cutoff (10, 30, 128, 200 seconds) and voxel location (anterior, posterior) on the mean t-statistic indicated significant main effects of HPFC (''F''(1,18) = 20.86, ''p'' < 0.001] and voxel location [''F''(1,18) = 77.23, ''p'' < 0.001], but importantly, there was a significant interaction between highpass frequency cutoff and voxel location for this dataset [''F''(1,94) = 72.54, ''p'' < 0.001], such that the main effect of highpass frequency cutoff was only marginal for the anterior voxel (''p''=0.06), but was significant for the posterior voxel [''F''(1,18) = 43.06, ''p'' < 0.001]. It might be interesting to investigate this more in depth to see whether the effect of highpass frequency filter cutoffs is differential for other anterior/posterior voxels, and whether this might generalize across tasks.

	[[File:tstat_old-vs-new.png \| thumb\|400px\|center\|Figure 10. Fixed effects t-stats for Respond OLD > NEW]]		[[File:tstat_old-vs-new.png \| thumb\|400px\|center\|Figure 10. Fixed effects t-stats for Respond OLD > NEW]]

← Older revision		Revision as of 20:17, 4 June 2013
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	[[File:tstat_study-vs-test.png \| thumb\|400px\|center\|Figure 9a. Fixed effects t-stats for Study > Test]]		[[File:tstat_study-vs-test.png \| thumb\|400px\|center\|Figure 9a. Fixed effects t-stats for Study > Test]]

	~~Further, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study~~ > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10.		<br>

	[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean.png \| thumb\|400px\|center\|Figure 9b. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]
	[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]		[[File:tstats_mean2.png \| thumb\|400px\|center\|Figure 9c. Mean t-statistics for OLD > NEW at 2 voxels in MNI space]]

			Interestingly, looking at the t-statistics at a single voxel averaged across subjects for the 4 different highpass frequency cutoffs, voxels that had significant negative statistics for study > test (i.e., higher activation for test trials), such as the posterior voxel [23,23,35], showed an increase in t-statistics as the highpass frequency filter cutoffs were lowered; similarly, an anterior voxel [19,83,35] with more positive t-statistics for study > test demonstrated the opposite trend, such that the t-statistics tended to decrease as the HPFC was lowered (Fig. 9b and 9c). In both cases, the contrasts became less significant for HPFCs of 30 and 10. A repeated measures ANOVA examining the effect of highpass frequency cutoff (10, 30, 128, 200 seconds) and voxel location (anterior, posterior) on the mean t-statistic indicated significant main effects of HPFC (''F''(1,18) = 20.86, ''p'' < 0.001] and voxel location [''F''(1,18) = 77.23, ''p'' < 0.001], but importantly, there was a significant interaction between highpass frequency cutoff and voxel location for this dataset [''F''(1,94) = 72.54, ''p'' < 0.001], such that the main effect of highpass frequency cutoff was only marginal for the anterior voxel (''p''=0.06), but was significant for the posterior voxel [''F''(1,18) = 43.06, ''p'' < 0.001]. It might be interesting to investigate this more in depth to see whether the effect of highpass frequency filter cutoffs is differential for other anterior/posterior voxels, and whether this might generalize across tasks.

	[[File:tstat_old-vs-new.png \| thumb\|400px\|center\|Figure 10. Fixed effects t-stats for Respond OLD > NEW]]		[[File:tstat_old-vs-new.png \| thumb\|400px\|center\|Figure 10. Fixed effects t-stats for Respond OLD > NEW]]