Complementary Category Learning Systems Identified Using Event-Related Functional MRI Howard J. Aizenstein, Angus W. MacDonald, V. Andrew Stenger, Robert D. Nebes, Jeris K. Larson, Stefan Ursu, and Cameron S. Carter University of Pittsburgh Medical School
Abstract & Event-related fMRI was used to dissociate the neural systems involved in category learning with and without awareness. Ten subjects performed a speeded response category learning task. Functional MR images were acquired during both explicit and implicit learning conditions. Behavioral data showed evidence of learning in both conditions. Functional imaging data showed different activation patterns in implicit and explicit trials. Decreased activation in extrastriate
INTRODUCTION In certain circumstances, individuals can learn implicitly, that is without overt awareness. This learning may occur even when they do not readily learn with awareness (Roediger, Guynn, & Jones, 1994; Seger, 1994; Schacter, Chiu, & Ochsner, 1993). The distinction between implicit and explicit learning was first described in amnesics (Claparede, 1911). These individuals, while severely impaired on explicit learning tasks, perform normally on a variety of implicit tasks (Graf & Schacter, 1985). The neural basis for the distinction between implicit and explicit learning is an open question. Some have argued that implicit learning relies on a neural system separate from explicit learning (Gluck & Myers, 1997; Knowlton, 1997; McClelland, McNaughton, & O'Reilly, 1995; Squire, 1992). This is a compelling hypothesis; it explains why brain lesions differentially affect implicit and explicit learning. This multiple system view, however, has been challenged by other researchers, who hold that a single neural system suffices to explain the observed behavior (Nosofsky & Zaki, 1998; Shanks & St. John, 1994). They argue that the behavioral dissociation of different learning tasks stems from the differing difficulties of these tasks. Within this framework, implicit learning is preserved in amnesics because it is easier and therefore places less of a demand on the same, compromised, neural learning system. It now may be possible, using functional neuroimaging, to adjudicate between these alternative cognitive theories. If implicit and explicit learning utilize the same D 2000 Massachusetts Institute of Technology
region V3 was found with implicit learning, and increased activation in V3, the medial temporal lobe, and frontal regions were found with explicit learning. These results support the theory that implicit and explicit learning utilize dissociable neural systems. Moreover, in both the implicit and explicit conditions a similar pattern of decreased activation was found in parietal regions. This commonality suggests that these dissociable systems also operate in parallel. &
system, then the activation patterns should completely overlap. In particular, if, as some have hypothesized, implicit learning phenomena are an artifact of low load on the explicit system, then the activation pattern during an implicit learning task should be fully subsumed by the analogous explicit learning task. Alternatively, dissociable activation patterns suggest the existence of multiple learning systems. Previous functional imaging studies have identified neural structures involved in a variety of implicit and explicit learning and memory paradigms (see Buckner & Koutstaal, 1998, for a review). The striatum is seen to play a role in sequence learning, procedure learning, and habit learning (Poldrack, Prabhakaran, Seger, & Gabrieli, 1999; Knowlton, Mangels, & Squire, 1996; Jackson et al., 1995). Frontal and medial temporal regions are activated during explicit declarative memory tasks (Schacter & Wagner, 1999; Gabrieli, Brewer, Desmond, & Glover, 1997). During implicit memory paradigms, relative decreases in neural activation have been identified; perceptual priming is associated with decreased activation in the extrastriate occipital cortex and conceptual priming has been associated with decreased activation in more anterior structures (Buckner & Koutstaal, 1998). One area of focus in these previous studies was the neural pathway for category learning. Both increases and decreases in neural activation have been found (Reber, Stark, & Squire, 1998b; Smith, Patalano, & Jonides, 1998). Category learning refers to the task of inducing a general category from individual examples of the Journal of Cognitive Neuroscience 12:6, pp. 977±987
category. For example learning the category ``dog'' from having seen specific pictures of individual dogs. Category learning can be done with awareness (Smith et al., 1998), and additionally, since amnesics are unimpaired at certain category learning tasks, it appears that category learning can also occur implicitly (Knowlton & Squire, 1993). In a PET study Smith et al. (1998) found increases in frontal activation during a task in which subjects were explicitly instructed to learn category rules. Using a random-dot category-learning task (the same task in which amnesics showed competence) Reber et al. (1998b) have found that with category learning there are relative fMRI decreases in posterior regions. Additionally, they found activation in the bilateral anterior frontal regions and the right inferior-frontal region. In a different fMRI study they compared implicit learning to recognition of similar stimuli and found a dissociation: The cortical regions that decreased during implicit learning increased during an explicit recognition task (Reber, Stark, & Squire, 1998a). These studies suggest that implicit category learning is associated with decreased activation in posterior regions while explicit category learning is associated with increased activation in frontal regions. However,
Figure 1. Experimental protocol: (a) sample stimuli and (b) breakdown of 153 trials for the implicit learning task. Condition A: Reds are distortions of prototype, yellows and greens are random. Condition B: Reds and yellow are random, greens are distortions of prototype. Colors were counterbalanced across subjects (i.e., for half of the subjects condition B preceded condition A). Note that the trial ordering was the same for implicit and explicit learning.
to our knowledge, there have not yet been functional imaging studies directly comparing category learning with and without awareness, using similar category learning tasks. We hypothesize that consistent with a multiple system theory contrasting fMRI signals can be detected in implicit and explicit category learning conditions. In the present study black and white dot patterns were displayed on a screen. Subjects were asked to press a button corresponding to a color (red, yellow, or green) as quickly as possible once the dot patterns turned a color. All subjects first performed this as an implicit learning task. Unbeknownst to the subjects a category rule determined the color based on the pattern of dots. For instance, whenever the dots were a distortion of a particular prototype, then the dots turned red. After 108 trials (12 sec inter-trial interval), the hidden rule changed. A category recognition task was done to assess whether implicit learning had occurred. The explicit learning phase was the same, except that subjects were instructed to look for a pattern. Figure 1a shows sample dot patterns and Figure 1b shows an outline of the experimental protocol. Functional MRI scanning was synchronized with the behavioral task. Event-related fMRI acquires images for
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individual trials rather than for blocks. Thus, one can resolve differences between individual learning trials and visualize the neural processes associated with learning. Moreover, by acquiring multiple scans per trial one can resolve the time course of neural activity over the course of a trial. Our primary imaging analyses involved the trials after the subject had already learned the pattern. Thus, we identified the areas that show differences during performance on learned versus random stimuli.
RESULTS Behavioral Performance We recorded reaction time (RT) and accuracy data while individuals performed a category learning task implicitly and then explicitly. After the implicit learning task, we tested learning by having the subjects classify 30 new patterns as to whether or not they belonged to the previously seen category. Ten pictures were new distortions from the previous set. Ten were distortions of a different prototype never before seen by the subjects. The other 10 were random patterns. Subjects were significantly more accurate in categorizing new distortions of the learned prototypes compared to distortions of a different prototype or random dot patterns (Figure 2). To test whether the individuals used an explicit learning strategy during the implicit block, we asked each subject at the end of the implicit block a series of questions (e.g., ``Did you use any strategy to
0.8
Functional MRI Results Analyses of variance (ANOVAs) were used to identify neuroanatomic regions that had different activations during category and noncategory stimuli. We looked for a scan by condition interaction. The results were thresholded at p = .01 with an 8-voxel contiguity threshold to correct for multiple comparisons. This corresponds to an image-wise false-positive rate of 0.01 (Forman et al., 1995). Planned analyses were performed to test for learning-related changes in neural activity during both the implicit learning condition and the explicit condition. The primary analyses were done using the second scanning block of both the implicit and explicit tasks. Thus, in both cases we focused on performance after most of the learning has occurred. For each of these analyses a scan by condition (category vs. noncategory dot patterns) interaction was tested. This reflects activity associated with responding to learned versus unlearned patterns. The neuroanatomic regions found to be significant in these analyses are identified in Table 1. Below we discuss in more detail the activations in the occipital cortex, the frontal lobe, and the medial-temporal lobe. In the Discussion section we discuss the other activations listed in Table 1. Implicit Learning
0.6 Fraction Chosen as Category
respond quickly?'' and ``Did you notice any pattern?''). None of the 10 subjects identified any pattern during the implicit learning phase of the experiment. For the explicit condition we analyzed the RT data to confirm that learning had occurred. Mean of median RTs were significantly different before and after the rule change (matched sample t(9) = 2.13, p < .05).
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Figure 2. Category recognition test of implicit learning. The fraction chosen as category is shown for each of the three conditions. Difference between pattern and other or random is significant (matched sample t(9) = 2.06, p < .05). Error bars represent 1 SEM.
As predicted, activity in the extrastriate visual cortex (BA 19) was significantly decreased in the category as compared to the noncategory trials during implicit learning. Single subject analysis also showed this effect in the visual cortex for 7 of the 10 subjects. The groupaveraged fMRI signal increased over the course of both category and noncategory trials. However, responding to category patterns was associated with a significantly smaller increase in activity (Figure 3). This result is consistent with previous studies of implicit learning in that activation for the implicitly learned stimuli is relatively decreased compared to the nonlearned stimuli. This extends previous work by delineating the time course of activation (i.e., a smaller increase, rather than a decrease from baseline). This same analysis was also undertaken early in implicit learning, for the images acquired during the first scanning block. The particle in BA 19 showed the same pattern of activation during that block. However, the relative decrease beAizenstein et al.
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Table 1. Talairach Coordinates for all Significant Particles Brodmann's Areas
Region of Interest
BOLD Change with Category (C) vs. Noncategory (NC)
Talairach Coordinates (x, y, z)
Implicit learning B/L V3
19
(9,
71, 41)
NC > C
Right inferotemporal
38, 41, insula
(42, 7,
B/L superior and inferior parietal
7, 40
( 8,
B/L SMA
6
(33, 1, 61), ( 28,
Right V3
19
(22,
87, 33)
Right medial temporal
Hippocampal region
(19,
45, 4)
NC > C
L inferior frontal
44
( 43, 9, 22)
NC > C
B/L medial frontal
24, 25, 47
( 1, 36, 4), (4, 8,
B/L SMA
6
(30, 2, 61), ( 24, 2, 58), ( 19,
L frontal eye field
8
( 30, 39, 41)
B/L superior and inferior parietal
7, 40
(42,
34, 53), ( 33,
Right thalamus
Thalamus
(10,
10, 14)
16)
NC > C
52, 67), (46,
36, 55)
NC > C
9, 60)
NC > C
Explicit learning C > NC
B/L frontal 15), ( 28, 19,
15)
C > NC
60, 40)
NC > C C > NC
34, 53), ( 19,
60, 40)
NC > C NC > C
The table lists all particles that reached significance at threshold of p = .01 (8-voxel contiguity threshold). Talairach coordinates (Talairach & Tournoux, 1988) are given for the center of activation for each particle.
tween category and noncategory learning was smaller. That is, the decrement in the extrastriate cortex in-
creased in magnitude over the course of implicit learning.
Fraction Change from Baseline
Figure 3. fMRI results for implicit condition: category versus a) b) Time Course of Activation noncategory. (a) Extrastriate cortex region that is significant 0.75 at p = .01, with 8-voxel contiguity threshold. (b) Time Non-Category course of activation for particu0.5 lar region of interest (BA 19). This is displayed as mean change of signal intensity from baseline as a function of scan 0.25 number. Scans occur at the Category beginning of each trial, at 4, 8, and 12 sec. Since it is known that the BOLD (blood oxygena0 tion level dependent) signal peaks between 4 and 6 sec, we Left Right are most interested in the difference in activation seen in -0.25 scans 2 and 3. Error bars 2 1 3 represent 1 SEM. The cateScan # gory stimuli have a decreased activation as compared to the noncategory stimuli. This is consistent with theories that implicit learning is associated with processing efficiency and thus less activation occurs on the implicitly learned stimuli.
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a)
b)
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Figure 4. fMRI results for explicit condition: category versus noncategory. (a) Extrastriate cortex region that is significant at p = .01, with 8-voxel contiguity threshold. (b) Time course of activation for particular region of interest (BA 19). This is displayed as mean change of signal intensity from baseline as a function of scan number. Error bars represent 1 SEM. Unlike in the implicit learning case, here the category stimuli have an increased activation as compared to the noncategory stimuli. This is consistent with theories of attentional modulation of posterior visual systems.
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Figure 5. fMRI results for explicit condition: category versus noncategory. (a) Medial-temporal lobe region that is significant at p = .01, with 8-voxel contiguity threshold in the explicit condition. (b) During explicit learning, activation significantly increases for noncategories versus categories. (c) During implicit learning, however, this effect is not seen. Error bars represent 1 SEM.
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Explicit Learning
DISCUSSION
As in the implicit condition we were interested in the differences during categorization of learned dot patterns (i.e., those belonging to the category) as opposed to the novel, noncategory member, dot patterns. A right extrastriate, BA 19, particle was found to be significant in this contrast. The temporal pattern of activation in this extrastriate region was very different from that found in the implicit contrast described above. The time course of the fMRI signal (Figure 4) showed a significantly greater increase in activation for the category relative to the noncategory dot patterns. Single subject analysis showed this effect in the visual cortex for eight of the subjects. An ROI-based contrast confirmed that the visual cortex particles had different temporal patterns during implicit and explicit learning. The BOLD signal during explicit learning was examined in the particle that was identified in the implicit condition, and the BOLD signal during implicit learning was examined in the particle that was identified in the explicit condition. In both of these cases we found that there was no significant interaction of condition and scan. In the implicit condition (for the particle defined in the explicit condition) we found F(2,18) = .549, p = .587, and in the explicit conditions (for the particle defined in the implicit case) we found F(2,18) = .115, p = .892. A right medial temporal lobe particle, in the hippocampal region, also reached significance in our primary explicit learning contrast. Figure 5 shows this region and shows the time course for activation during explicit learning. For comparison, this figure also shows the time course for this particle during implicit learning. The particle peaks higher for noncategory than for category stimuli and similar low activation peaks are seen in this particle during the implicit task. This pattern of activity is consistent with the process of explicit encoding. We suspect that the peak is higher during noncategory trials as a result of the greater encoding effort at this point in the learning curve for the novel (unlearned) stimuli. This region has the opposite pattern of activity early in learning (during the first scanning block). That is, when the category stimuli are unfamiliar, they have a greater peak in activation. Bilateral particles in the frontal cortex (BA 44, 24, 25, 47, 6, and 8) also reached significance in this contrast. A right BA 44 particle was reported by Reber et al. (1998b). The BA 44 particle found in this study is more superior than their inferior frontal particle and is found on the left. The time course of activation for this particle is similar to the time course for the hippocampal particle shown in Figure 5. As with the hippocampal particle, the category peak is higher early in learning.
This study contrasted regional brain activity during implicit and explicit category learning. Our results support a multiple, as opposed to a single, system view of category learning. The different patterns of activation in implicit and explicit learning were not mutually exclusive, but rather they overlapped, with common areas of decreased activation in the superior and inferior parietal cortex. This common signal suggests that elements of these multiple systems can also operate in parallel. Previous behavioral experiments have suggested that different neural systems are involved in implicit category learning and explicit recognition. Knowlton and Squire (1993) demonstrated this using a random category learning task. In their study, individuals with amnesia were impaired at recognition of individual dot patterns, but performed as well as controls on a prototype categorization learning task. The behavioral evidence alone, however, does not confirm the multiple system theory. As argued by Nosofsky and Zaki (1998), it is possible that the differences are due to different loads on the same system; they show how a single exemplar learning system can account for behavioral dissociations similar to those shown by Knowlton and Squire (1993). Nosofsky and Zaki's model is first trained with a series exemplars, and later tested using either a recognition test or a categorization test. Recognition and categorization use different decision rules that rely on the same learned exemplar representations. The categorization decision rule is more sensitive than the recognition rule to the exemplar representation; therefore, categorization is preserved when the stored representations are degraded. Thus, a lesion can lead to behavioral impairment of implicit learning with preservation of explicit learning even though they may use a single system. Prior functional imaging studies have examined category learning. Reber et al. (1998a) looked at implicit category learning versus explicit recognition and found different patterns, thus supporting the multiple system view. However, as they describe, the learning and recognition tasks are sufficiently different to limit the interpretability of the fMRI differences. In their category learning studies subjects were asked to categorize mostly random dot pattern stimuli contrasted with blocks that were mostly distorted prototype images. Categorizing the implicitly learned prototype distortions led to a mixed pattern of fMRI changes. They found decreased signal in V1 and V2 (BA 17 and 18), which is consistent with expected decrements found according to implicit learning theories. However, they also found increased frontal activity, in the bilateral anterior frontal region (BA 10) and in the right inferior frontal region (BA 44/47). They suggested that explicit processes might account for some of the frontal activation during the blocks of category trials.
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The results of the current study are consistent with the above interpretations. Using an event-related design, we compared similar implicit and explicit learning tasks. The patterns of activation in extrastriate visual cortex area V3 coincided with our predictions: There was a decrease in activation with implicit learning and an increase with explicit learning. The prefrontal cortex was significantly activated by explicit, but not implicit, learning, which is consistent with other studies of explicit learning (Brewer, Zuo, Glover, & Gabrieli, 1998; Buckner & Koutstaal, 1998). Implicit learning has been described as primarily neocortical, relying on the circuitry utilized when processing the individual training examples (i.e., regions involved in the decision process are primed by the repeated exposure). This cognitive phenomena has been referred to as transfer-appropriate processing (Roediger et al., 1994), and has been visualized with event-related fMRI (Buckner, 1998). The decrease in activation that we found associated with implicit learning in BA 19 is consistent with these theories. Moreover, this decrease appears to be specific for the implicit learning condition (i.e., it was not found with explicit learning). Thus, it is unlikely that the decrease was due solely to the demand characteristics of category versus random trials, which are equivalent in the implicit and explicit conditions. Moreover, the presence of the decrease in the implicit, but not the explicit, condition implies that the decrease was not driven solely by RT changes between category and noncategory stimuli. In both the implicit and the explicit case the trend was for quicker RT on category versus noncategory stimuli (though not statistically significant in either case). If the RT change was driving the BOLD signal, then one would expect that it would have had the same effect in both the implicit and the explicit cases. However, the BOLD signal pattern in the visual cortex was opposite in these two cases: There was a decrease with category in the implicit case and an increase with category in the explicit condition. The event-related design of the current study allowed us to resolve the time-course of activation and provided a context for understanding the decreases. Thus, in this case, we see that the decrease in activation with category stimuli was not a decrease from baseline, but rather it was the result of a smaller increase relative to the noncategory stimuli. The distinctly different patterns of activation (posterior decreases with implicit learning, and frontal and posterior activation with explicit learning) suggest that implicit and explicit category learning utilize different neural systems. Additionally, our results suggest that these different systems may operate in parallel: Decreases suggestive of implicit learning were also observed during explicit learning. These were found in the superior and inferior parietal cortex (Table 1).
A similar time-course of activation was observed in both implicit and explicit tasks for these regions: The activation peak was higher with noncategory stimuli. These neural activation decrements are expected with implicit learning and are similar to the time-course found in the extrastriate cortex during the implicit task, as shown in Figure 3. These decrements in the explicit condition suggests that a similar form of habituation occurs during explicit learning. That is, the neural circuit engaged during implicit learning, which is associated with activation decrements, is also engaged, in parallel with frontal activation, during explicit learning. This analysis is limited by the fact that slightly different locations show decrements in the implicit versus the explicit case. If the two learning systems were engaged in parallel, a similar decrement would occur in the same area during explicit learning as that which occurred during implicit learning. To test this we took the regions identified during the implicit condition and looked at the activation patterns for these regions during the explicit case. The extrastriate and inferotemporal regions showed little difference between category and noncategory stimuli. However, in the parietal area, identified in the implicit condition, the time course for explicit learning was similar to that seen during implicit learning: There was a greater activation during category as compared to noncategory stimuli. The mean MR signal for category stimuli had a significantly lower peak across the three scans as compared to the noncategory (p < .005). Thus, it does appear that a decrement in neural activity similar to the implicit learning pattern occurred in parallel with the explicit learning process. In the explicit learning condition the extrastriate cortex showed the opposite pattern of activation: Instead of decreases with the learned pattern, the BOLD signal associated with the learned category was greater than that associated with the random stimuli. Reber et al. (1998a) reported similar findings during their explicit recognition task. They theorized that the increase represented either the effort of retrieval or the effect of successful retrieval. The current study was not a retrieval task per se, but rather required explicit comparison to a learned category. Thus, it was not surprising that we found similar activations. Single cell recording studies in monkeys (Desimone, 1996; Treue & Maunsell, 1996) have identified posterior increases in activation associated with attention. The posterior increases noted in the current study may represent the same attentional processes. Perhaps posterior attentional effects resulted in explicit processes (of recognition or learning) that overrode the decrements associated with implicit learning. The other areas of significant change included the anterior cingulate cortex, the posterior parietal cortex, the supplementary motor area, and the thalamus (Table 1). These regions were not in our initial hypothesis; nevertheless, the activation patterns we Aizenstein et al.
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found fit with our understanding of the implicit and explicit category learning processes. For example, the changes in the parietal cortex are located near changes also reported in Reber, Stark and Squire's category learning studies. These parietal changes may be due to the different response behavior in the learned category versus noncategory trials, as hypothesized by Reber et al. (1998a). Additionally, Kraut, Hart, Soher, and Gordon (1997) have argued that the dorsal visual processing stream is involved in certain abstract object recognition tasks. In this way the human visual processing system is different from the monkey's in that object identification occurs in both the ventral and the dorsal visual stream. Thus, the observed parietal decrements may result from the efficiency gained with learned patterns. In comparing implicit and explicit processing of similar tasks in the same individual, the order of the tasks was constrained. The implicit condition occurred prior to the explicit instructions to decrease the chance that the subjects would use explicit strategies during the implicit task. Fixing the order of conditions in this way, however, does potentially bias the results. We minimized this possibility by performing contrasts within each condition. Thus, during implicit and explicit learning the category trials were compared to random. Moreover, our behavioral data confirmed that learning did occur in both conditions, and analysis of movement (as a proxy for restlessness) showed that there was no significant difference between the two conditions. While this study supports the multiple system view of implicit and explicit learning, the particular neural mechanisms that account for these differences are uncertain. The synaptic and neuromodulatory influences that account for the different learning effects are unclear. Also, at a neural system level there is uncertainty as to the network dynamics that lead to learning and that are reflected in the fMRI changes observed in this study. Neural network modeling provides some insight at the system level. Previous models have shown how very simple learning phenomena, such as habituation (Gotts & Plaut, 1998), may be modeled using an interactive dynamic network in such a way as to reflect observed neural activation decrements (Miller, Gochin, & Gross, 1991). A similar mechanism may underlie more complex implicit learning phenomenon. With multiple exposures to a stimulus, the network may become more efficient, and settle more readily, leading to a decrement in neural activity. It remains to be demonstrated, however, how these same computational mechanisms can lead to the observed stimulus generalization, decreased response latency, and decreased neural activity. Nevertheless, the intuition of a more readily settling parallel distributed network offers insight into the system properties. We are currently exploring detailed models of these phenomena. 984
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METHODS Research Participants Twelve healthy right-handed individuals between the ages of 18 and 25 were recruited by advertisement in a community newsletter. Two individuals were excluded from the analysis. One was excluded because he was not performing the task appropriately while in the scanner (he was responding to the stimuli before the color change). Another participant was excluded because a computer failure during MR data acquisition led to corruption of the MR images. Of the remaining 10 participants, 3 were men and 7 were women, with a mean age of 21.2 (SD = 3.2). Each individual was paid US$50. Stimuli Two different sequences of 153 different patterns were generated. Each pattern consisted of nine black dots on a white background. These stimuli were displayed in the center of a screen. After 1500 msec, the dots changed color to either red, yellow, or green. For each stimulus, the dots all changed to the same color. A different randomly generated dot pattern was used to define the category in these two sequences (see Figure 1). One hundred and two random patterns and 51 high distortion patterns were generated for each of the two prototype patterns. The distortions were as defined in Posner and Keele (1968) and were identical to those used by Knowlton and Squire (1993) and Reber et al. (1998a, 1998b). Stimulus presentation and response recording was controlled by a Power Macintosh computer, using the PSYSCOPE software package (Cohen, MacWhinney, Flatt, & Provost, 1993). The stimuli subtended approximately 308 of the visual field. Responses were measured using a fiber optic button box attachment. This equipment provided a 1-msec temporal resolution. It was strapped to the participant's right hand. Participants were instructed to press these buttons in response to the stimuli, as per the procedure described below. Procedure This experiment was divided into four different conditions: pre-practice, implicit learning, category recognition, and explicit learning. Pre-Practice Prior to performing the category learning tasks the subjects were pre-practiced on the mapping of color to button press. This pre-practice occurred just before entering the MRI machine. The subjects spent 7 min responding to patches of red, yellow, and green, by pressing the left button, under their index finger for red, Volume 12, Number 6
the middle button (third finger) for yellow, and the right button, under their ring finger, for green. Implicit Learning The task was a variant of the serial RT paradigm. Each stimulus appeared in the center of the screen, initially in black and white (black dots on a white background). After 1500 msec the black dots turned red, yellow, or green. The subject was instructed to look at each black and white picture and when the dots become colored to quickly and accurately press the button corresponding to the color. The colored stimulus stayed on the screen for 1500 msec. A fixation stimulus was then displayed on the screen for 9 sec, until the onset of the next trial. During the implicit learning condition, participants were instructed to respond as quickly as possible to the color. They were not told that there was an association between the pattern of dots and the color. The patterns that turned red were all distortions of the prototype, whereas those that turned green or yellow were random patterns. During pilot testing the subjects occasionally developed explicit awareness of the category rule when there were repeats of the red color (i.e., the pattern). Therefore, we disallowed repeats of the same color. Otherwise, the stimuli were presented in a random order. For the first 117 trials, the prototype distortions predicted red. Then, for the remaining trials in the implicit learning phase (trials 118±154) a different categorization rule was used. Every pattern that would have been categorized as red then turned green. Random patterns turned red or yellow. The effect of learning was measured during the recognition phase of the experiment (see below). Pilot testing confirmed that implicit learning effects were measurable after 117 trials. RT data was analyzed as mean of median times. For each subject medians were calculated for every window of 36 trials. Means were then calculated across subjects. Implicit learning of the rules was reflected as an RT change with the rule change. This was analyzed using a paired t test comparing the medians for the block of 36 trials prior to the switch to those immediately after the switch. The change in median RT during implicit learning demonstrated a trend of increased RT when the rule change occurs. The direction of this change was as predicted. The subjects took longer, as one would expect due perhaps to interference in the response. However, the size of this effect was not statistically significant (p = .2). Category Recognition After completing the implicit learning phase of the study, we assessed for learning using a category recog-
nition task. First, the subjects were asked a series of questions to determine whether they were explicitly aware of a rule during the implicit learning phase. The subjects were then told that in fact there was a pattern associating some of the patterns with a color, ``. . . such that initially all the patterns that turned red generally belonged to the same category, in the same way as if they were all dogs they would generally look like a dog.'' They were then shown a series of 30 black and white dot patterns. Subjects were instructed that some of these patterns belong to the same category as the category created with red in the previous trials. They were asked to look at each pattern and decide whether they thought it belonged to the category they previously saw and if so to press the red button (the button under their index finger). Otherwise, they were to press the green button (the button under their ring finger). Subjects were instructed to make their best guess for each trial. The 30 patterns that were shown belonged to three different groups. None of these patterns were seen previously by the subjects. Ten of the patterns belonged to the same category used in the implicit learning phase of the study; they were distortions created in the same way as those for the implicit learning block. Ten were created from a different random prototype, not otherwise used in the study. This was done to control for the possibility of the subjects learning the category during the recognition task. The other 10 patterns were random dot patterns. RT and response selection data were collected during this task. Explicit Learning The explicit learning phase was very similar to the implicit except for the instructions. Subjects were specifically told that some of the stimuli are from a category. ``I'd like you to look for a pattern and use this information to respond more quickly to the color change.'' A different prototype was used than was used in the implicit condition. Counterbalancing The prototype rule used in the implicit and explicit phases of this study are counterbalanced, such that half the subjects saw the prototype of Figure 1a during the implicit condition and a different prototype during the explicit condition, whereas the other subjects saw the Figure 1a prototype in the explicit condition and the other prototype during the implicit condition. Similarly, the color of the prototype was counterbalanced. The order of implicit and explicit phases of the study was fixed. To maintain the covert nature of the implicit learning task all subjects performed the implicit condition before the explicit condition. Aizenstein et al.
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Scanning Procedures MRI scanning was conducted on a 1.5-T General Electric Signa Scanner. Structural MRI was performed prior to the functional scans. These were used to align the functional MRI acquisition, and also for cross-registration of the functional scans for the planned group analysis. The structural scans were acquired as T1 weighted images. Thirty-six 3.8-mm slices were obtained in the oblique axial plane, aligned within the AC±PC line. Functional scanning was performed using a 2-shot spiral pulse sequence, with TE = 35 and TR = 2000. Twenty-five contiguous 3.8-mm slices were obtained in the oblique-axial plane. The sixth most inferior slice was aligned on the AC±PC line. The functional images had in plane resolution of 64 64, with 3.75 3.75 mm pixels. Images were acquired in three 36-block trials over the course of learning, in both the implicit and explicit conditions. One scanning block occurred at the very beginning of learning. One scanning block occurred at the end of learning, and one final scanning block occurred after the rule switch. Three images were acquired per trial. The presentation of stimuli was synchronized with the scanning such that three complete scans were acquired during the course of a trial, allowing us to examine the temporal dynamics of the BOLD response during the implicit and explicit learning trials. Image Analysis For each subject, movement correction was performed using a 6-parameter linear algorithm (6-parameter AIR; Woods, Grafton, Holmes, Cherry, & Mazziotta, 1998). The structural scans for all 10 subjects were registered to the first structural scan in this study. This was done using a 12-parameter linear algorithm (12-parameter AIR) that aligned the structural scans to a reference brain (the first subject of our study). 3-D Gaussian smoothing using 8-mm FWHM was then done for each image. The analysis for significant differences in activations was then performed voxel-wise. F values were generated for each voxel using a 2 (category or noncategory) by 3 (scan number) interaction ANOVA, with subject as a random variable. For each voxel, significance was set at F(2,18) > 5.59, which is equivalent to p = .01. We corrected for multiple comparisons by only accepting those regions that reached significance for 8 contiguous voxels. This contiguity threshold was chosen based on a study by Forman et al. (1995), which used Monte Carlo simulations to show that a contiguity threshold of eight controls for image-wise Type I error in a similar fMRI paradigm. The group analyses were validated using standard single subject analyses focusing on the visual cortex. For each of the 10 subjects F values were generated for each voxel using a 2 (category or noncategory) by 3 986
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(scan number) interaction ANOVA with significance set at F(2,22) > 5.33, which is equivalent to p = .01, and an 8-voxel contiguity threshold. Acknowledgments This research was supported by Alzheimer's Disease Research Center grant AG05133-16, National Institute of Mental Health grant MH01306, an National Alliance for Research in Schizophrenia and Depression young investigator award (to C.S.C.), and an NIMH postdoctoral fellowship (to H.J.A.). We thank Jonathan Cohen, Matthew Botvinick, Kate Fissell, Victor Ortega, Laura Ross, and Barbara Baumann for their assistance. We also thank two anonymous reviewers. Reprint requests should be sent to Howard J. Aizenstein, Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. E-mail:
[email protected].
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