Schematic memory components converge within angular gyrus during retrieval
Associative knowledge structures in the form of so-called "mental schemas" ( Bartlett , 1932 ) are built on the basis of several encounters with similar material . They may be applicable to a wide range of instances in which new information is integrated into established or newly established knowledge ( Ghosh and Gilboa , 2014 ) , and thereby promote encoding and subsequent consolidation ( Tse et al . , 2007 , 2011; van Kesteren et al . , 2010b ) . This beneficial "schema effect" has been associated with hippocampal and medial prefrontal processing ( Tse et al . , 2007 , 2011; Kumaran et al . , 2009; van Kesteren et al . , 2010a , 2010b , 2013; Dragoi and Tonegawa , 2013; McKenzie et al . , 2014 ) , which is shifted towards a more neocortically centered system after consolidation ( Frankland and Bontempi , 2005; Takashima et al . , 2006; Takehara-Nishiuchi and McNaughton , 2008 ) . Despite the importance of schemas for learning , memory , and education , the current field is lacking a consistent definition . So far , attempts to operationalize schemas spanned an entire spectrum , ranging widely from simple , rule-like associations ( if A-B , and B-C , then A-C; Preston and Eichenbaum , 2013 ) , and more complex visuo-spatial layouts ( Tse et al . , 2007 , 2011; van Buuren et al . , 2014 ) , to pre-existing real-world knowledge ( students remember new study material related to their own field better than material from other disciplines; van Kesteren et al . , 2014 ) . Considering this spectrum of complexity , it remains an empirical question whether there is a clear border between simple sets of rules and schemas and if so , where this border should be drawn ( Kroes and Fernandez , 2012 ) . Regardless of these various definitions , schema memories are thought to be facilitated by a distributed system that stores components as separate "units" ( Bartlett , 1932; Schacter et al . , 1998 ) , or "features" ( van Kesteren et al . , 2010a ) , and that relies on inter-connected networks of neocortical representations ( Wang and Morris , 2010 ) . By the same token , this argues for the need to converge information in order to recombine associative schema components upon retrieval . Exactly where in the brain such recombination takes place is , however , still unclear . The medial prefrontal cortex ( MPFC ) and hippocampus ( HC ) , together with the parahippocampal cortex ( PHC ) , posterior cingulate cortex ( PCC ) , and angular gyrus ( AG ) have been identified as regions forming a network that is important for successful ( episodic ) memory retrieval ( Rugg and Vilberg , 2013; Watrous et al . , 2013; King et al . , 2015 ) . Especially the MPFC is involved in the retrieval of schemas ( Tse et al . , 2011; Kroes and Fernandez , 2012; Ghosh et al . , 2014; Richards et al . , 2014; Warren et al . , 2014 ) and , during this process , establishes functional connections to posterior representation regions ( Marr , 1970; Frankland and Bontempi , 2005; van Kesteren et al . , 2010a ) . Furthermore , the AG seems well suited to support integrative retrieval ( Wagner et al . , 2005; Gilmore et al . , 2015 ) , since it has been discussed to guide the "binding" , or recombination , of information ( Binder et al . , 2009; Shimamura , 2011; Price et al . , 2015 ) . In the present study , we asked where different schema components are neuronally represented and where such representations converge into a comprehensive signature during retrieval . We followed several steps to test this question: First , as mental schemas are dependent on memory consolidation ( Tse et al . , 2007 ) , we identified regions associated with this process . Second , we probed the functional coupling of these regions as they form a memory retrieval network . Third , and most importantly , we identified the distributed representations of schema components and tested where such representations would converge . We defined schemas as sets of conceptual , rule-based associations ( Kumaran et al . , 2009 ) , and reasoned that this approach would provide us with a well-controlled vehicle to establish the nature of schema-related retrieval in humans . Subjects underwent fMRI during repeated , high-confident retrieval of two schemas ( day 2 ) that were trained on a previous day ( day 1; Figure 1A ) . These schemas were incorporated into a modified , deterministic weather prediction task ( Knowlton et al . , 1994; Kumaran et al . , 2009 ) in which subjects had learned that colored circle pairs predicted specific but fictive weather outcomes ( “sun” , “rain” ) , depending on the location ( spatial schema ) or color ( non-spatial schema ) of one of the circles ( Figure 1B; for a detailed description please see Materials and methods , Material and task ) . Crucially , our controlled design allowed us to independently capture the different schema components . During retrieval , visually presented circle pairs had to be combined with abstract rule-based information and could thus be used to predict specific trial outcomes . The combination of these different levels of information formed a simple schema . Therefore , the schema components consisted of ( 1 ) rule-based associations , and ( 2 ) low-level visual features of the task material ( Figure 1—figure supplement 1A ) . Considering whole-brain characteristics rather than zooming into the functional properties of isolated regions , we employed a combination of activation , connectivity , and multi-voxel pattern analyses . We hypothesized that schema retrieval would primarily engage neocortical midline structures , such as the MPFC . Further , the MPFC should act as a convergence zone that recombines the different schema components into a unique schema memory during retrieval . Additionally , if such recombination goes beyond MPFC-centered processing , we expected retrieval-related schema representations to be held by the AG . 10 . 7554/eLife . 09668 . 003Figure 1 . Study timeline and schema material . ( A ) Subjects underwent fMRI on two consecutive days , each containing 7 runs ( day 1: encoding and retrieval; day 2: retrieval ) . A transfer test was completed at the end of day 2 and consisted of two runs ( encoding and retrieval ) . ( B ) Stimulus material during encoding comprised four horizontal circle pairs . Spatial ( position ) or non-spatial ( color ) rule-based schemas were used to predict fictive "sun" or "rain" outcomes . Stimulus material during retrieval consisted of four vertical circle pairs ( for a detailed description of the experiment , please see Materials and methods , Material and task and Materials and methods , Procedure ) . We used retrieval trials on day 2 to dissociate the multi-voxel patterns of schema components that consisted of rule-based associations and low-level visual features ( Figure 1—figure supplement 1 ) . Figure 1—figure supplement 2 illustrates experimental trials during encoding , retrieval , and during the perceptual baseline condition . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 00310 . 7554/eLife . 09668 . 004Figure 1—figure supplement 1 . Multi-voxel pattern analysis ( MVPA ) . ( A ) We used MVPA to discriminate between schema components , which were defined as ( 1 ) rule-based associations ( spatial vs . non-spatial , while collapsing across circle pairs 1–4 ) , and ( 2 ) low-level visual features of the stimulus material ( circle pair 1 + 2 vs . 3 + 4 , while collapsing across spatial and non-spatial schema conditions ) . Numbers were not shown to subjects . ( B ) First , we trained a classifier on day 2 ( black ) and tested every independent run of the same day ( dashed line ) , using a seven-fold leave-one-run-out cross-validation procedure . ( C ) Next , we tested the generalization of multi-voxel patterns across days by training a classifier on day 2 ( black ) and testing it on day 1 ( dashed line ) . ( D ) Finally , a classifier was trained on day 2 ( black ) and tested on data from the transfer test ( dashed line ) . For a detailed description of the analysis please see Materials and methods , Multi-voxel pattern analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 00410 . 7554/eLife . 09668 . 005Figure 1—figure supplement 2 . Experimental trials . ( A ) During encoding trials , subjects received a cue ( 2 s ) to apply either the spatial or non-spatial schema . Circle pairs were presented ( 3 s ) and a response indicating the outcome was necessary ( 2 s ) . The correct feedback was provided ( 2 s ) . ( B ) During retrieval , subjects received a cue ( 2 s ) to apply either the spatial or non-spatial schema . Circle pairs were presented ( 3 s ) and a response indicating the outcome ( 2 s ) , and a confidence rating ( 2 s ) were necessary . ( C ) Perceptual baseline trials matched the structure and timing of encoding and retrieval trials . They were independent of the rule-based schemas and a response option was marked ( Materials and methods , Material and task and Material and task , Procedure ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 005
Subjects acquired schemas across seven runs throughout day 1 . These runs were structured in blocks of interleaved encoding and retrieval ( Materials and methods , Procedure and Materials and methods , Schema encoding ) . Retrieval trials did not provide feedback and thus allowed us to estimate rule-based schema proficiency at steady time-points . Further , these trials required the application of schema knowledge to related information ( vertical as opposed to horizontal arrangement of circle pairs , see Figure 1B ) . Investigating performance during schema retrieval , we found a significant three-way interaction of day ( day 1 , day 2 ) × run ( 1 to 7 ) × schema ( spatial , non-spatial ) ( F ( 3 . 8 , 63 . 7 ) = 3 . 3 , P = 0 . 017; interaction day × run: F ( 6 , 102 ) = 4 . 4 , P = 0 . 001; interaction day × schema: F ( 1 , 17 ) = 14 . 3 , P = 0 . 002; interaction run × schema: F ( 3 . 7 , 62 . 6 ) = 2 . 7 , P = 0 . 043; no main effect of day: P = 0 . 062; no main effect of run: P = 0 . 154; no main effect of schema: P = 0 . 057 ) . This interaction was followed-up by separate repeated measures ANOVAs for each day , with run and schema as within-subject factors . Only on day 1 we observed a significant interaction between runs and schemas ( F ( 3 , 50 . 1 ) = 3 . 9 , P = 0 . 014; main effect of run: F ( 3 . 4 , 56 . 9 ) = 3 . 8 , P = 0 . 011; main effect of schema: F ( 1 , 17 ) = 7 . 7 , P = 0 . 013 ) , which was caused by lower performance in the spatial as compared to non-spatial condition during the first run ( t ( 21 ) = -3 . 2 , P = 0 . 005; Figure 2A , left ) . Throughout day 2 , retrieval performance did not differ significantly between runs or conditions ( no main effect of run: P = 0 . 334; no main effect of schema: P = 0 . 666; no run × schema interaction: P = 0 . 761; Figure 2A , right ) . 10 . 7554/eLife . 09668 . 006Figure 2 . Behavioral performance during schema retrieval . ( A ) Data represents the % of correct responses , ( B ) the average reaction time ( s ) , and ( C ) the % of high-confident ratings ( i . e . “sure”-responses ) . Shaded error bars denote ± standard error of the mean ( s . e . m . ) . * marks a significant ( P < 0 . 05 ) difference between the schema conditions within the first run of day 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 006 Similarly , reaction times ( RTs ) and retrieval confidence between schema conditions only differed during the first run of day 1 ( Figure 2B , C , left ) . Here , we found longer RTs ( t ( 21 ) = 2 . 47 , P = 0 . 022 ) and lower retrieval confidence ( t ( 21 ) = -4 . 2 , P < 0 . 0005 ) for the spatial as compared to the non-spatial schema . On day 2 , we did not find any significant differences between runs or schema conditions in terms of RTs ( no main effect of run: P = 0 . 718; no main effect of schema: P = 0 . 749; no run × schema interaction: P = 0 . 849; Figure 2B , right ) , or retrieval confidence ( no main effect of run: P = 0 . 187; no main effect of schema: P = 0 . 397; no run × schema interaction: P = 0 . 549; Figure 2C , right; for details see Materials and methods , Schema retrieval: reaction times and Materials and methods , Schema retrieval: confidence ) . In summary , subjects rapidly learned to apply both rule-based schemas . Retrieval performance was stable at the end of day 1 and throughout day 2 , and did not differ between the conditions . Across both days , schema retrieval was associated with increased blood oxygen-level dependent ( BOLD ) responses in bilateral lingual gyrus , superior occipital gyrus , cuneus , left supplemental motor area , and right parahippocampal cortex ( day 1 & day 2; Figure 3A; Table 1 , upper part ) . Since consolidation is considered a prerequisite for mental schemas ( Tse et al . , 2007 ) , we next performed a contrast between days . We asked whether retrieval would yield increased engagement of neocortical midline regions after a delay of 24 hours . Additionally , we controlled for differences in schema performance , confidence , and RTs by performing a specific contrast between runs that were similar with regard to these aspects ( i . e . the last three runs of day 1 with the first run of day 2; approx . 48 vs . 32 trials , respectively ) . Behaviorally , subjects were able to retrieve and confidently apply both schemas ( Figure 2 , left and right ) . Schema retrieval performance and confidence did not differ significantly between runs or schema conditions ( retrieval performance: no main effect of run: P = 0 . 103; no main effect of schema: P = 0 . 173; no run × schema interaction: P = 0 . 437; confidence: no main effect of run: P = 0 . 261; no main effect of schema: P = 0 . 16; no run × schema interaction: P = 0 . 427 ) . Further , there was no significant difference in RTs ( no main effect of run: P = 0 . 09; no main effect of schema: P = 0 . 355; no run × schema interaction: P = 0 . 547; Figure 2 , middle ) . This specific comparison yielded increased activation in bilateral lingual gyrus , superior occipital gyrus , cuneus , and left supplemental motor area on day 1 relative to day 2 ( day 1 > day 2; Figure 3B; Table 1 , middle part ) . After initial consolidation , activation was increased in PCC , precuneus , and MPFC , as well as in a set of right lateralized regions including the supramarginal gyrus , middle temporal gyrus , and inferior temporal gyrus ( day 2 > day 1; Figure 3C; Table 1 , lower part ) . Conclusively , we found stronger retrieval-related activation within MPFC , PCC , and higher-level sensory regions after a 24-hour-delay . 10 . 7554/eLife . 09668 . 007Figure 3 . Activation during schema retrieval . ( A ) Increased BOLD responses during rule-based schema retrieval across both days ( schema retrieval > perceptual baseline ) , ( B ) during rule-based schema retrieval on day 1 ( day 1 > day 2 ) , and ( C ) after an initial consolidation of 24 hours ( day 2 > day 1 ) . Contrasts B and C include runs 5 to 7 from day 1 , and the first run from day 2 . For display purposes , results were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 1 . Results are superimposed onto the average structural scan derived from all subjects . L–left . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 00710 . 7554/eLife . 09668 . 008Table 1 . Activation during schema retrieval . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 008MNIBrain regionxyzZ valueCluster sizeDay 1 & day 2 L superior frontal gyrus-5552766L superior parietal gyrus-22-60409440L middle frontal gyrus-28-545938R middle frontal gyrus32-248260L insular cortex-322085 . 53126Day 1 > day 2 L cuneus-2-95102814L superior frontal gyrus-55526 . 14165Cerebellum-35-50-324 . 37161Day 2 > day 1 L cingulate gyrus0-40425 . 38593R supramarginal gyrus55-22304 . 32156R superior frontal gyrus5-45-23 . 9395R middle temporal gyrus58-5003 . 85106Clusters that showed significant BOLD increases during retrieval of rule-based schema memories across days , before , and after a 24-hour-delay . Bold font indicates contrasts . Retrieval was compared to the perceptual baseline . MNI coordinates represent the location of peak voxels . We report the local maximum of each cluster . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster size: 86 ) . L – left , R – right . So far , we identified stronger retrieval-related activation within MPFC and PCC after a 24-hour-delay ( day 2 > day 1; Results , Schema consolidation and Figure 3C ) ; and here we used this contrast to derive seed regions for our following connectivity analyses . We applied Psychophysiological Interaction analysis ( PPI; Materials and methods , Connectivity analysis ) to identify the connectivity profiles of the two rule-based schemas during retrieval as compared to the perceptual baseline ( Figure 1—figure supplement 2C ) on day 2 , and placed seeds within MPFC ( x = -2 , y = 35 , z = -2 ) and PCC ( x = 2 , y = -45 , z = 22 ) . First , we investigated functional coupling of the MPFC ( Figure 4A; Table 2 , upper part ) : During retrieval of the spatial schema , the MPFC was more strongly coupled to surrounding medial prefrontal regions , HC and PHC , PCC , precuneus , and left AG . For non-spatial retrieval , the MPFC showed enhanced coupling with its locally surrounding regions . However , lowering the statistical threshold ( P < 0 . 005 , uncorrected ) revealed comparable results for both conditions . Further , there were no significant connectivity differences between spatial and non-spatial schema retrieval ( tested with a paired-sample t-test ) . 10 . 7554/eLife . 09668 . 009Figure 4 . Schema retrieval networks: MPFC and PCC . ( A ) MPFC seed ( x = -2 , y = 35 , z = -2; based on the contrast day 2 > day 1 , Figure 3C; here marked in white ) . ( B ) PCC seed ( x = 2 , y = -45 , z = 22; based on the same contrast; here marked in white ) . General retrieval effects are shown in purple ( schema retrieval > perceptual baseline ) . For display purposes , connectivity maps were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 2 . L – right . Additionally , connectivity results ( PCC seed ) are projected onto a surface-based flatmap . Relevant structures are labeled: AG , angular gyrus; dACC , dorsal anterior cingulate cortex; FG , fusiform gyrus; HC , hippocampus; IFG , inferior frontal gyrus; INS , insula; MPFC , medial prefrontal cortex; PCC , posterior cingulate cortex; PHC , parahippocampal cortex; PreC , precentral gyrus; STG , superior temporal gyrus . Regions of the retrieval network are highlighted in bold font . Dashed lines are inserted to aid orientation: a , border between medial and lateral prefrontal cortices; b , central sulcus; c , superior temporal gyrus; d , border between ventromedial and -lateral temporal cortices . LH – left hemisphere , RH – right hemisphere . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 00910 . 7554/eLife . 09668 . 010Table 2 . Schema retrieval networks: MPFC and PCC . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 010MNIBrain regionxyzZ valueCluster sizeSeed MPFC , spatial > perceptual baseline R superior frontal gyrus105855 . 11362L angular gyrus-45-72354 . 84206R parahippocampal gyrus28-35-104 . 69126L precuneus-10-60204 . 661008L precentral gyrus-52-12454 . 1196L parahippocampal gyrus-28-38-103 . 82141Seed MPFC , non-spatial > perceptual baseline L cingulate gyrus-53884 . 26314Seed PCC , spatial > perceptual baseline L cingulate gyrus-10-4585 . 071746R cingulate gyrus1230204 . 68685L precentral gyrus-55824 . 641002R insular cortex35-2284 . 62120L parahippocampal gyrus-32-35-154 . 49553R inferior frontal gyrus5218124 . 44279L angular gyrus-48 70404 . 42 106R superior temporal gyrus45-2-124 . 30663Cerebellum12-72-284 . 23142Cerebellum-15-58-354 . 00164Seed PCC , non-spatial > perceptual baseline L precuneus-2-65305 . 493597L precentral gyrus-55-8454 . 89680R middle temporal gyrus65-18-84 . 58716R superior frontal gyrus1225284 . 27587L angular gyrus-48-70384 . 19184Cerebellum-20-68-284 . 03282R middle frontal gyrus1865123 . 81156Clusters that showed a significant increase in connectivity during schema retrieval: MPFC ( x = -2 , y = 35 , z = -2 ) and PCC ( x = 2 , y = -45 , z = 22 ) . Bold font indicates contrasts . Retrieval was compared to the perceptual baseline . MNI coordinates represent the location of peak voxels . We report the local maximum of each cluster . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster sizes; MPFC seed: spatial , 89 voxels; non-spatial , 95 voxels; PCC seed: 89 voxels for both conditions ) . L – left , R – right . Second , we turned to the seed region within the PCC ( Figure 4B; Table 2 , lower part ) : Spatial schema retrieval was associated with enhanced functional coupling between the PCC and surrounding posterior midline regions , such as precuneus . The MPFC , HC , PHC , fusiform gyrus , and superior temporal gyrus also showed enhanced coupling with the PCC , along with the left AG . A similar network emerged during non-spatial schema retrieval . Again , there were no significant connectivity differences between the two conditions ( tested with a paired-sample t-test ) . To sum up , while the MPFC mainly showed increased neocortical coupling during spatial schema retrieval , the PCC was connected to an extensive network of regions during retrieval of both schema conditions ( Figure 4B; Table 2 , lower part ) . This network consistently involved MTL , MPFC , PCC , and left AG and constitutes a set of brain regions that was previously reported to underlie successful memory retrieval ( Rugg and Vilberg , 2013; Watrous et al . , 2013; King et al . , 2015 ) . Schemas are thought to be facilitated by a distributed system that stores memory components as separate representational units ( Bartlett , 1932; Schacter et al . , 1998 ) . Here , we asked where such components are neuronally represented . Crucially , our design allowed us to individually capture schema components , defined as ( 1 ) rule-based associations , and ( 2 ) low-level visual features . We employed multi-voxel pattern analysis ( MVPA ) in combination with a whole-brain searchlight procedure on day 2 ( Materials and methods , Multi-voxel pattern analysis ) , and separated schema components by discriminating ( 1 ) the schema conditions ( while collapsing across visual features ) , and ( 2 ) the visual features ( while collapsing across schema conditions; Figure 1—figure supplement 1 ) . First , we identified voxel patterns that discriminated between schema conditions ( spatial vs . non-spatial ) , and that served as a marker for representations of rule-based associations . By keeping visual input between both conditions constant , we considered multi-voxel schema patterns that go beyond any visual representation of the different circle pairs ( for example , a yellow and blue circle on the right half of the screen predict “sun” when applying the spatial schema , but the same circle pair predicts “rain” when applying the non-spatial schema; Figure 1B ) . Rule-based associations were represented in the right ventrolateral prefrontal cortex , left middle occipital gyrus , and left AG ( Figure 5A; Table 3 , upper part ) . 10 . 7554/eLife . 09668 . 011Figure 5 . Multi-voxel representations of schema components . ( A ) The searchlight MVPA revealed distributed representations of both schema components ( rule-based associations , low-level visual features ) . These representational levels converged within the AG ( yellow ) . Three horizontal slices are shown as cut-outs and are magnified to appreciate the overlap ( Table 3 ) . ( B ) Only the multi-voxel patterns of low-level visual features were shared between day 1 and day 2 . For display purposes , all maps were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 3 . L – left . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 01110 . 7554/eLife . 09668 . 012Table 3 . Multi-voxel representations of schema components . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 012MNIBrain regionxyzZ valueCluster sizeMVPA day 2 , rule-based associations R lateral orbitofrontal gyrus4242-184 . 1475L middle occipital gyrus-30-75324 . 13102L angular gyrus-38-70323 . 73MVPA day 2 , low-level visual features L cuneus0-82816630MVPA day 1 , low-level visual features R lingual gyrus2-78-215599Clusters that significantly discriminated schema component representations ( rule-based associations , low-level visual features ) . Bold font indicates the type of MVPA analysis ( day 1 , training the classifier on day 2 and testing it on day 1; day 2 , training the classifier on day 2 and testing it on day 2 using cross-validation; Materials and methods , Multi-voxel pattern analysis ) . MNI coordinates represent the location of peak voxels . We report the first two local maxima ( > 8 mm apart ) within each cluster ( rule-based associations ) , and the local maximum for the low-level visual feature MVPAs . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster sizes: day 2 , rule-based associations , 74 voxels; day 2 , low-level visual features , 72 voxels; day 1 , low-level visual features , 70 voxels ) . L – left , R – right . To determine brain regions that solely represent the low-level visual features of both schemas we next trained a classifier that dissociated the different circle pairs ( circle pairs 1 and 2 vs . circle pairs 3 and 4 , cancelling out the respective rule-based associations; Figure 1—figure supplement 1B ) . This allowed us to target the sum of visual features that formed the necessary basis to successfully apply one of the two schemas ( namely color and position ) . In line with our expectation to identify discrimination performance primarily in the visual system , we revealed that low-level visual features were represented in the lingual gyrus , fusiform gyrus , middle occipital gyrus , cuneus , and the AG ( Figure 5A; Table 3 , middle part ) . Since a distributed schema memory system is expected to rely on inter-connected networks of neocortical representations ( Wang and Morris , 2010 ) , we expected the convergence of both schema components within the retrieval network ( see above , Figure 4 ) . These components consisted of rule-based associations and low-level visual features – whereby a combination of both was necessary to solve a given trial successfully . Most importantly , we hypothesized a functional role of the MPFC or AG in retrieval-related schema integration . As can be seen from Figure 5A ( magnified cut-outs ) , both levels of schema components overlapped within the left AG . We did not find retrieval-related convergence of schema components within the MPFC . Next , we reasoned that if schema components converged in the AG only after consolidation , their multi-voxel representations should only partly generalize from day 2 to day 1 . That is , training a classifier on day 2 and testing it on data from day 1 ( Figure 1—figure supplement 1C ) should not yield discrimination performance above chance level for rule-based associations . Although low-level visual features were connected to higher-level information and were thus also regarded as a schema component , circle pairs were visually presented on the screen on both days . Therefore , we expected significant discrimination performance for low-level visual features in occipital cortex regions . As expected , representations of low-level visual features generalized from day 2 to day 1 , indicated through significant discrimination performance in occipital cortex ( Figure 5B; Table 3 , lower part ) . For rule-based associations , none of the runs on day 1 showed discrimination performance significantly above chance level , implying that the multi-voxel representations of the spatial and non-spatial schema conditions were not shared between days . However , this does not preclude the involvement of the AG in schema retrieval prior to 24-hour-consolidation , but may be caused by representational differences between the days . Therefore , we additionally trained and tested a classifier on data from day 1 . Again , we did not find representations of rule-based associations within the AG and thus no retrieval-related convergence of schema components on day 1 ( Materials and methods , Complementary analysis: AG involvement in schema retrieval on day 1 ) , suggesting that the left AG recombines schema components only after a 24-hour-delay . Using MVPA , we demonstrated the retrieval-related convergence of schema components within the left AG after a 24-hour-delay . To support this convergence , the AG should show increased functional coupling with regions that separately represent the different components . We tested this assumption using PPI ( Materials and methods , Connectivity analysis ) and created a mask of the overlap between both schema components during retrieval on day 2 ( Figure 6 , right middle panel , marked in white; Results , Multi-voxel representations of schema components; Figure 5A ) . This mask was used as a seed region . Spatial schema retrieval ( compared to the perceptual baseline ) was associated with enhanced functional coupling between the left AG and its locally surrounding lateral parietal cortex . Further , we observed increased connectivity with the HC , PHC , MPFC , PCC , and fusiform gyrus ( similar effects were observed for non-spatial schema retrieval; Figure 6 , Table 4 ) . Connectivity profiles between the two conditions did not differ significantly ( tested with a paired-sample t-test ) . The fusiform finding appears particularly relevant , since the fusiform gyrus was shown to represent the low-level visual features of the schema material ( see above , and Figure 5A ) . This corroborates our assumption that retrieval-related convergence within the AG is accomplished via increased functional connectivity among a distributed set of regions that each hold specific schema components . Moreover , the left AG was coupled to the retrieval network we identified earlier ( see above , and Figure 4 ) . The consistency of our PPI and MVPA results is further demonstrated in Figure 7 . 10 . 7554/eLife . 09668 . 013Figure 6 . Schema convergence networks . Functional connectivity of the left AG seed ( defined as cluster of overlapping schema components; based on our MVPA result , Figure 5A , here marked in white ) during schema retrieval ( compared to the perceptual baseline ) . General retrieval effects are shown in purple . For display purposes , maps were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . L – left . Significant clusters are noted in Table 4 . Additionally , connectivity results are projected onto a surface-based flatmap . Relevant structures are labeled: AG , angular gyrus; FG , fusiform gyrus; IFG , inferior frontal gyrus; INS , insula; MPFC , medial prefrontal cortex; PCC , posterior cingulate cortex; PHC , parahippocampal cortex; STG , superior temporal gyrus . Regions of the retrieval network are highlighted in bold font . Dashed lines are inserted to aid orientation: a , border between medial and lateral prefrontal cortices; b , central sulcus; c , superior temporal gyrus; d , border between ventromedial and -lateral temporal cortices . LH – left hemisphere , RH – right hemisphere . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 01310 . 7554/eLife . 09668 . 014Table 4 . Schema convergence networks . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 014MNIBrain regionxyzZ valueCluster sizeSpatial > perceptual baseline L middle occipital gyrus-30-80325 . 90553R middle frontal gyrus4222455 . 37182L middle frontal gyrus-3218525 . 19380Cerebellum15-75-284 . 961553R inferior temporal gyrus55-58-124 . 94259L inferior frontal gyrus-402024 . 61922R angular gyurs42-65504 . 56701L fusiform gyrus-32-35-254 . 50352L middle frontal gyrus-1845284 . 50509L superior parietal gyrus-15-60184 . 39261R superior parietal gyrus20-55184 . 19109R precuneus8-55404 . 12152R insular cortex38-804 . 0895R superior temporal gyrus65-1824 . 03107R inferior frontal gyrus5235223 . 95456Non-spatial > perceptual baseline L superior frontal gyrus0-5485 . 8722582L inferior frontal gyrus-381555 . 77Cerebellum-15-75-385 . 76L angular gyrus-32-78425 . 63R fusiform gyrus48-55-225 . 54L inferior frontal gyrus-352825 . 52R middle temporal gyrus62-12-125 . 43R fusiform gyrus45-45-225 . 42L middle frontal gyrus-3812525 . 42R superior frontal gyrus228525 . 42L superior temporal gyrus-52-5-85 . 40R superior frontal gyrus88525 . 32Cerebellum-25-60-355 . 32L middle temporal gyrus-62-5825 . 31R superior frontal gyrus828405 . 30Clusters that showed a significant increase in AG connectivity during schema retrieval . Retrieval was compared to the perceptual baseline . The seed was defined as overlap between schema components , as determined with MVPA ( Figure 5A ) . Bold font indicates contrasts . MNI coordinates represent the location of peak voxels . We report the local maximum of each cluster . For the non-spatial schema condition we report the first 15 local maxima ( > 8 mm apart ) . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster sizes: spatial , 88 voxels; non-spatial , 83 voxels ) . L – left , R – right . 10 . 7554/eLife . 09668 . 015Figure 7 . Spatial relationship between schema retrieval networks and schema component representations . Results from connectivity analyses ( seeds MPFC , PCC , AG ) , and MVPA are shown as a 3D rendering . During schema retrieval , MPFC and PCC were functionally connected with the same AG region ( left part ) . Furthermore , MVPA revealed distributed representations of different schema components that converged within the left AG during retrieval ( right and surrounded in white ) . To aid orientation , dashed lines schematically indicate the intraparietal sulcus ( IPS ) . Asterisks mark identical locations within the AG across the different methodological approaches . Additionally , we show a horizontal cut at the level of the AG to demonstrate sub-surface effects . LH – left hemisphere . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 015 Schemas provide knowledge structures that help new but related information to be integrated more rapidly ( Tse et al . , 2007; van Kesteren et al . , 2014 ) . Therefore , our schema material should facilitate transfer to related task material . We tested this assumption during a transfer test at the end of day 2 ( Figure 1A; Materials and methods , Procedure ) . Here , the stimulus set was changed into circle pairs with different colors while keeping the same pair-wise arrangement ( Materials and methods , Material and task ) . By changing the color of the stimulus set , the transfer test only required transfer of the non-spatial schema condition . This allowed us to match the difficulty between old and new non-spatial rule-based associations while a change in position would have lead to an increase in difficulty for the spatial schema condition . Performance during encoding trials was significantly lower in the first run of the transfer test ( main effect of run: F ( 1 , 22 ) = 12 . 2 , P = 0 . 002; Figure 8A , left ) . Here , subjects performed worse in generalizing the non-spatial schema ( main effect of schema: F ( 1 , 22 ) = 6 . 2 , P = 0 . 021; interaction run × schema: F ( 1 , 22 ) = 5 , P = 0 . 036; t ( 22 ) = 2 . 8 , P = 0 . 01 ) , but had already adapted schemas to the new stimulus set during run 2 ( P = 0 . 803 ) . RTs did not show any differences between runs ( P = 0 . 681 ) , schemas ( P = 0 . 5 ) , or any run × schema interactions ( P = 0 . 477; Figure 8A , right ) . 10 . 7554/eLife . 09668 . 016Figure 8 . Transfer test . ( A ) Schema encoding: left , % of correct responses; right , average reaction time ( s ) . ( B ) Schema retrieval: left , % of correct responses; middle , average reaction time ( s ) ; right , % of high-confident ratings ( i . e . “sure”-responses ) . Error bars denote ± s . e . m . * marks significance at P < 0 . 05 , ** marks significance at P < 0 . 001 . ( C ) Multi-voxel patterns of rule-based associations and low-level visual features were shared between day 2 and the transfer test . Magnified cut-outs of the horizontal slice are provided to appreciate the overlap between schema components . The AG cluster showing schema convergence during day 2 is depicted in blue ( Figure 5A ) . For display purposes , all maps were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 5 . L – left . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 016 During retrieval , subjects performed significantly worse in applying the non-spatial schema ( no main effect of run: P = 0 . 441; main effect of schema: F ( 1 , 22 ) = 5 . 9 , P = 0 . 023; no run × schema interaction: P = 0 . 1; Figure 8B , left ) , and were less confident ( no main effect of run: P = 0 . 17; main effect of schema: F ( 1 , 22 ) = 5 . 2 , P = 0 . 033; no run × schema interaction: P = 0 . 105; Figure 8B , right ) . However , during the final run of the transfer test , correct responses were delivered faster for the non-spatial schema ( t ( 22 ) = 5 . 1 , P < 0 . 0005; no main effect of run: P = 0 . 44; main effect of schema: F ( 1 , 22 ) = 12 . 2 , P < 0 . 005; run × schema interaction: F ( 1 , 22 ) = 5 . 9 , P < 0 . 05; Figure 8B , middle ) . To investigate whether non-spatial schema knowledge was transferred from initial schema acquisition to new learning , we started out by comparing non-spatial schema performance , RTs , and retrieval confidence between the initial runs of day 1 and the transfer test . Performance during schema encoding did not differ between the study phases ( P = 0 . 894 ) , but subjects responded significantly faster during the transfer test as compared to day 1 ( t ( 21 ) = 5 . 7 , P < 0 . 0005 ) . Similarly , subjects responded faster when retrieving non-spatial schema material during the transfer test ( t ( 21 ) = 3 . 1 , P = 0 . 006 ) , but retrieval performance and confidence did not differ significantly ( retrieval performance: P = 0 . 312; retrieval confidence: P = 0 . 244 ) . In conclusion , subjects responded faster during schema encoding and retrieval in the transfer test as compared to the initial run on day 1 . We take this as indirect evidence that subjects applied schema knowledge to solve novel but related material . In our final analysis , we tested the convergence of schema component representations during the transfer test . This analysis was grounded on the assumption that subjects would employ stable , consolidated schema knowledge to solve new task material ( as suggested by our behavioral results above ) . If this was the case , the converging signatures of schema components should be similar between day 2 ( prior to the transfer test ) and the transfer test . Thus , training a classifier on data from day 2 and testing it on neural data from the transfer test ( Figure 1—figure supplement 1D ) should yield representations of rule-based associations and low-level visual features within the AG . MVPA was performed as described previously ( Materials and methods , Multi-voxel pattern analysis ) . In line with our prediction , and in contrast to day 1 ( see above ) , rule-based associations were represented within the left middle occipital gyrus and AG ( Figure 8C; Table 5 , upper part ) . As on day 2 , multi-voxel representations of low-level visual features were mainly found in occipital regions and AG ( Figure 8C; Table 5 , lower part ) . Most importantly , both levels of information converged in the left AG , and the precise location of convergence overlapped with our previous result ( Figure 8C , marked in blue; and see Figure 5A ) . Therefore , neural signatures of schema components were similar between day 2 and the transfer test , suggesting that subjects applied schema material to new and related information . Furthermore , this confirms our finding that the left AG recombines schema components after consolidation . 10 . 7554/eLife . 09668 . 017Table 5 . Transfer test: multi-voxel representations of schema components . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 017MNIBrain regionxyzZ valueCluster sizeMVPA transfer test , rule-based associations L superior occipital gyrus-15-78223 . 63211L angular gyrus-35-72353 . 57L superior occipital gyrus-12-90203 . 23MVPA transfer test , low-level visual features L lingual gyrus-12-82811692Clusters that significantly discriminated schema component representations ( rule-based associations , low-level visual features ) during the transfer test . Bold font indicates the type of MVPA analysis . MNI coordinates represent the location of peak voxels . We report the first three local maxima ( >8 mm apart ) within each cluster ( rule-based associations ) , and the local maximum for the low-level visual features analysis . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 005 ( rule-based associations ) or P < 0 . 001 ( low-level visual features ) and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster sizes: rule-based associations , 172 voxels; low-level visual features , 60 voxels ) . L – left , R – right .
In this study , we investigated the retrieval dynamics of well-controlled , rule-based schemas and identified representations of their constituting components . These components consisted of rule-based associations and low-level visual features . Most importantly , both levels of information converged within the left AG after 24-hour-consolidation . Memory networks are subject to reconfiguration as consolidation progresses . This process promotes the involvement of neocortical structures relevant for schema operations while downscaling MTL engagement ( Frankland and Bontempi , 2005; Takashima et al . , 2006; Takehara-Nishiuchi and McNaughton , 2008 ) , possibly reflecting the abstraction and integration of information into pre-existing knowledge structures ( Lewis and Durrant , 2011 ) . To start out , we observed increased activation within MPFC , PCC , and higher-level sensory cortices during schema retrieval after 24 hours ( Figure 3C ) . The MPFC is considered to play a pivotal role for schema-related mnemonic function ( Tse et al . , 2011; Kroes and Fernandez , 2012 ) , which is supported by lesion studies in both rodents ( Richards et al . , 2014 ) and humans ( Ghosh et al . , 2014; Warren et al . , 2014 ) . Also the PCC ( including precuneus and retrosplenial cortex ) , is regarded as central to memory processes ( Maguire et al . , 1999; for a review , see Vann et al . , 2009 ) . Together , the MPFC , PCC , MTL , and AG constitute a network of brain regions that act in concert during retrieval of ( episodic ) memories ( Rugg and Vilberg , 2013; Watrous et al . , 2013; King et al . , 2015 ) . Here , we observed that this network is also associated with the retrieval of rule-based schema memories ( Figure 4 ) . Considering the associative character of both , episodic and schema memory , common neural substrates seem plausible . Similar to previous schema studies with human subjects ( van Kesteren et al . , 2010a; van Buuren et al . , 2014 ) , we did not find a disengagement of hippocampal activation during retrieval of consolidated schema material . However , the hippocampus showed increased coupling with the retrieval network across days . Additionally , and in line with previous results ( Takashima et al . , 2009 ) , we found a decrease in hippocampal-neocortical coupling after 24 hours ( Materials and methods , Complementary analysis: hippocampal connectivity during schema retrieval ) . The different components of a schema memory are assumed to be stored as distributed signatures ( Bartlett , 1932; Schacter et al . , 1998; Wang and Morris , 2010 ) . At the same time , such a distributed memory system argues for the need to "bind" information in order to merge and recombine associative schema components upon retrieval . The novel feature of our experimental design allowed us to isolate the different schema components that consisted of rule-based associations and low-level visual features of the task material , while controlling for various , potentially confounding factors ( such as complexity and attentional demands; see Guerin et al . , 2012 ) . We found that rule-based associations were represented in the left AG and right ventrolateral prefrontal cortex – the latter potentially imposing top-down control on rule-based retrieval mechanisms ( Reverberi et al . , 2012 ) . Low-level visual features of the task material were represented in occipital regions , AG , and fusiform gyrus . Crucially , both schema components converged within the left AG on day 2 ( Figure 5A ) . Their multi-voxel representations , however , generalized only partly across days ( Figure 5B ) , and the AG did not recombine schema components during retrieval on day 1 ( Materials and methods , Complementary analysis: AG involvement in schema retrieval on day 1 ) . That is , while low-level visual features showed shared representations between days and were detectable on day 1 , representations of rule-based associations were not . Although the lower amount of retrieval trials on day 1 and the classification across two separate fMRI sessions might have affected the analyses , the coherence of our results suggests a change in the underlying representations , in particular for rule-based associations , that emerges after 24-hour-consolidation . Therefore , we conclude that the AG supports the integration of consolidated schema components during retrieval . This is corroborated by studies showing increased involvement of a parietal network in the processing of remote mnemonic content ( for a review , see Gilmore et al . , 2015 ) . Apart from theories that discuss the role of the AG in terms of mnemonic search and decision making ( Wagner et al . , 2005; Gilmore et al . , 2015 ) , the AG has been related to the "binding" of information . This is suggested by feature-integration theory ( Treisman and Gelade , 1980 ) , "cortical binding of relational activity" ( CoBRA; a model presented by Shimamura , 2011 ) , or accounts that identify the AG as heteromodal association cortex that recombines semantic information ( Binder et al . , 2009 ) . A recent study by Price and colleagues ( 2015 ) demonstrated that the combination of semantic concepts is modulated by activation in the left AG ( e . g . , more activation for meaningful than non-meaningful combinations ) , and that subjects with lower cortical thickness in this region perform worse in this combinatorial task ( Price et al . , 2015 ) . Further support for the "binding" notion comes from lesion studies . Typically , the impact of focal AG lesions is subtle . While patients with parietal lesions perform equally well as healthy controls in a recall task ( Simons et al . , 2008 ) , a disruption of angular gyrus processing by transcranial magnetic stimulation reduces confidence ( Yazar et al . , 2014 ) . Additionally , lesions in parietal cortex can cause so-called "illusory-conjunctions errors" where previously studied objects are identified , but mistakes are made when recombining information ( Friedman-Hill et al . , 1995; Kesner , 2012 ) . This pattern of findings ( intact retrieval , but impaired confidence and recombination of information ) might be explained by limited damage to a distributed network that stores different memory components in respective brain structures ( Bartlett , 1932; Schacter et al . , 1998; Wang and Morris , 2010 ) . In the present study , we showed that low-level visual features of the stimulus material were represented within the fusiform gyrus ( Figure 5A ) . Additionally , the fusiform gyrus was functionally connected to the AG ( Figure 6 ) , as well as with the remaining retrieval network ( Figure 4 ) . This supports the assumption of strengthened cortico-cortical connections during schema retrieval ( Marr , 1970; Frankland and Bontempi , 2005 ) that might act as a back-up in cases of AG disruption . If associative memory is truly dependent on the "binding" function of lateral parietal cortex , disruption should lead to an increase in memory conjunctions errors . Thus , memories should be retrieved , but 1 ) recombined in an incorrect manner , or 2 ) the combination of different memory features should not be possible at all . Future research could test this by experimentally inducing memory conjunction errors ( Reinitz et al . , 1992 ) . We were not able to identify schema representations within the remaining regions of the retrieval network ( MTL , MPFC , and PCC ) . However , information might be represented at a finer spatial scale that cannot be detected by MVPA as done here . Also , our scan parameters were not optimized for the decoding of representations within the MTL ( for example , see Hassabis et al . , 2009 ) , and the repeated retrieval of schema memories might have decreased our power to detect representations in the MPFC ( Woolgar et al . , 2011 ) . This region , for example , was previously shown to hold remote , retrieval-related representations of specific autobiographical memories ( Bonnici et al . , 2012 ) . Further , hippocampal cells that were active during the encoding of contextual fear memories were shown to be reactivated during retrieval ( Tanaka et al . , 2014 ) . Silencing these cells rendered memory retrieval impossible . In either case , autobiographical and contextual fear memories certainly differ from more abstract schema memories . With the nature of these memory representations being so different , schema memories might simply not be represented within the MPFC , PCC , or MTL during retrieval . Across different studies , definitions of the term "schema" so far ranged from simple ( Preston and Eichenbaum , 2013 ) and more complex , experimentally-controlled associations ( Tse et al . , 2007 , 2011; van Buuren et al . , 2014 ) , to schemas that required the integration of new information into pre-existing real-world knowledge ( van Kesteren et al . , 2014 ) . Van Kesteren and colleagues ( 2014 ) , for example , assumed with their design that prior knowledge guides congruency judgments of object-scene pairs , which in turn influences schema memory . However , this prior knowledge is difficult to control for as it is highly individual and thus may additionally involve self-referential , autobiographical memory processing . Here , we defined schemas as artificial sets of rules ( Kumaran et al . , 2009 ) . While other studies may have greater ecological validity ( Maguire et al . , 1999; van Buuren et al . , 2014; van Kesteren et al . , 2014 ) , we explicitly tailored this task to enable our analysis . This constitutes a crucial and novel feature of our design . By training and testing subjects on schema material across consecutive days , we achieved near-ceiling performance that allowed us to reliably train and test a classifier . To the best of our knowledge , we are the first to dissociate the multi-voxel representations of different schema components and to demonstrate their convergence during retrieval . Lastly , we show that new but related trials during the transfer test are solved by applying the schemas ( Figure 8 ) and take this as evidence that our material provided a mental framework for subjects , allowing the rapid assimilation of new , related information ( Tse et al . , 2007 ) . This is an important point in which schemas differ from so-called "task-sets" ( Sakai and Passingham , 2006; Bengtsson et al . , 2009; Collins and Frank , 2013 ) . The creation of , or integration into a "categorical structure" is where the essence of schema lies . Taking into account the range of schema definitions , it is currently unclear where the border between simple sets of rules and schemas should be drawn . To determine if our approach potentially constitutes a schema , we applied a set of criteria that was recently proposed by Ghosh & Gilboa ( 2014 ) . According to them , the necessary features for a schema memory are: ( 1 ) an associative network structure , ( 2 ) formation on the basis of multiple episodes , ( 3 ) the lack of unit detail , and ( 4 ) adaptability . Based on these criteria , our approach provides a very basic form of schematic memory: ( 1 ) our material has an associative structure , although simple; ( 2 ) schemas are not defined based on specific episodic information , material is learned fast but across multiple instances; ( 3 ) specific features are predictive while others are not; and ( 4 ) schemas could be expanded and adapted to new material ( Figure 8 ) . To conclude , we manipulated the content of well-controlled , rule-based schema memories and were able to probe the functional dynamics during retrieval . We identified distributed representations of schema components that comprised rule-based associations and low-level visual features . These components converged within the left AG . Most importantly , this retrieval-related convergence was found only after 24-hour-consolidation and when transferring consolidated schemas to new but related task material . As such , the left AG might fulfill a role similar to the hippocampus during the retrieval of recent episodic memories ( Frankland and Bontempi , 2005 ) . In essence , we substantially expand current models of memory retrieval and provide neuroimaging evidence for a mechanistic framework in which the left AG acts as a convergence zone that may support the integration of distributed schema components .
Thirty-seven neurologically healthy , right-handed subjects ( 23 female , age range = 18–29 years , mean = 22 ) volunteered in this study . Eleven subjects were excluded from the study due to failure to learn the correct schemas after the first session . In particular , these excluded subjects made the following assumption: Encoding circle pair 3 ( Figure 1B , left part , third from top ) depicts two circles in horizontal arrangement; yellow on the left , blue in the center . When applying the spatial schema , this circle pair would yield the outcome ”sun” . During a retrieval trial ( for example , circle pair 3 in Figure 1B , right part , that shows a yellow circle on the upper left and a blue circle at the lower left ) , the correct answer should again be ”sun” . Thus , spatial retrieval trials can be solved by acknowledging the horizontal position of one of the circles . Instead , eleven subjects solved such trials by mentally rotating the horizontal circle pair between encoding and retrieval with a 90° angle . Consequently , the inferred ( correct ) trial outcome was ”sun” ( since “the yellow circle was placed left of the blue circle” ) , or ( incorrectly ) “rain” ( since “the yellow circle was placed right of the blue circle” ) . This strategy resulted in a large amount of incorrect answers to spatial schema trials ( day 1 , % correct retrieval responses , mean ± s . e . m . : excluded subjects: 45 . 8 ± 1 . 7; included subjects: 89 . 6 ± 3 . 4 ) , while the non-spatial schema was learned correctly ( excluded subjects: 79 . 7 ± 3 . 1; included subjects: 93 . 4 ± 1 . 1 ) . We excluded subjects based on their poor performance , which perfectly correlated with incorrect written schema explanations ( Materials and methods , Procedure ) . Additionally , two subjects aborted the experiment during the first session , and one was excluded due to technical problems ( power breakdown ) . This left 23 subjects for analyses ( 16 female , age range = 18–29 years , mean = 22 ) . All subjects had normal or corrected-to-normal vision and gave written informed consent . The study was conducted according to protocol approved by the institutional review board ( CMO Region Arnhem-Nijmegen , The Netherlands ) . Subjects learned to apply two sets of rules ( i . e . schemas; spatial , non-spatial ) in a deterministic weather prediction task in which colored circle pairs were associated with a fictive weather outcome ( “sun” , ”rain” ) . Circle pairs could be solved with two different schemas regarding 1 ) the horizontal position of one circle ( spatial schema; for example , “a circle on the left predicts sun” ) , or 2 ) the color of one circle ( non-spatial schema; for example , “a blue circle predicts rain” ) . Thus , identical circle pairs could yield different weather outcomes when applying either spatial or non-spatial schemas ( Figure 1B; see Materials and methods , Procedure for specific instructions to the subjects ) . Colored circles were matched for size and color intensity , and formed two different stimulus sets ( yellow , blue , red; or green , orange , pink ) . While one set was used for schema encoding and retrieval across day 1 and 2 , the other set was presented during the transfer test at the end of day 2 ( Figure 1A ) . The order of stimulus sets was balanced across subjects . Stimulus material was created with Adobe Illustrator CS4 ( Adobe , Inc . ) and stimulus presentation was controlled using the Psychophysics Toolbox ( Brainard , 1997 ) . Colored circles were presented in pairs at two possible orientations on the screen ( left , right ) , and formed four distinct circle pairs during encoding and retrieval trials of the experiment ( four circle pairs during encoding trials and four circle pairs during retrieval trials; Figure 1B ) . All circle pairs were presented during the experiment . During encoding trials , circles were presented in horizontal pairs . To make a clear distinction between both trial types , circles were presented in vertical pairs during retrieval . Thus , retrieval trials required the application of schematic knowledge to related information . To control for perceptual input , we created horizontal and vertical circle pairs that matched the spatial layout of encoding and retrieval trials but consisted of two-colored circles ( perceptual baseline; Figure 1—figure supplement 2C ) . Subjects were instructed that these control trials would not follow any underlying schema and the response they needed to make was marked randomly . The experiment consisted of two fMRI sessions on consecutive days ( Figure 1A ) , specifically designed for MVPA approaches ( Coutanche and Thompson-Schill , 2012 ) . Sessions were approximately 24 hours apart ( ± 2 hours ) . Prior to the first scan session subjects were instructed to pay attention to spatial or non-spatial features ( “The spatial rule concerns the position of a certain object , whereas the non-spatial rule concerns the color of a certain object . ” ) but the exact stimulus-schema-outcome mappings were not provided . Further , they received a short training and familiarization with randomized feedback . This was followed by seven runs inside the MR scanner , each lasting approximately 9 min . Each run was structured in eight blocks of five trials each , whereby two blocks of encoding trials were always followed by two blocks of retrieval trials . Encoding and retrieval blocks contained trials of either spatial or non-spatial schema types , with one perceptual control trial randomly intermixed . All runs during the experiment consisted of equal amounts of encoding and retrieval trials , spatial and non-spatial trials , and trials with ”sun"/"rain” outcomes . After completing day 1 , subjects were asked to give a short written explanation of the two schemas ( for example , “Please describe the spatial rule in your own words . ” ) . Additionally , subjects were shown the different circle pairs and were asked to indicate the outcomes when applying one or the other schema . Answers were scored as correct if they contained the correct association between schema , color/position , and outcome . During day 2 subjects completed seven runs of retrieval blocks only . This yielded a total of 560 trials across both sessions ( 280 trials on day 1 , of which 140 were encoding trials; 280 retrieval trials on day 2 ) . The transfer test took place at the end of day 2 . It comprised two runs inside the MR scanner that contained both encoding and retrieval trials ( same structure as on day 1 , see above; 80 trials across two runs , of which 40 were encoding trials and 40 were retrieval trials ) . The stimulus set was changed into circle pairs with different colors while keeping the same pair-wise arrangement ( Materials and methods , Material and task ) . Each block , irrespective of trial ( encoding , retrieval ) or schema type ( spatial , non-spatial ) shared the same timing parameters ( Figure 1—figure supplement 2 ) . At the beginning of a block , subjects were cued to use a specific schema type for solving all following trials . This was indicated by the word ”spatial"/"non-spatial” printed in white font on a black computer screen ( 2 s ) . After a variable delay of 1 . 5–2 s , circle pairs were presented ( 3 s ) and subjects had to think of the associated weather outcome . Then , after another short delay of 1–1 . 5 s , the response options ( indicated by the letters ”S" ( "sun" ) or "R” ( "rain” ) ) were shown ( 2 s ) . To prevent fixed response-to-outcome mappings , response positions were randomly switched and subjects had to make a button press with their left or right index fingers . For encoding trials the correct answer was shown ( 2 s ) and the next trial started after a short delay ( 1 . 5–2 s ) . No feedback was presented during retrieval trials , but subjects were asked to rate their confidence instead . Here , the options ”not sure"/"sure” were presented on the screen ( 2 s ) . The confidence option ”sure” should be chosen when being approximately 90% confident that the previous response was correct . Perceptual control trials followed the same timing as all other trials . Here , one response option was marked . After the successive presentation of five trials a black screen was shown ( 10–12 s ) and a new block started . Performance and RTs of schema encoding data were tested with a run ( 1 to 7 ) × schema ( spatial , non-spatial ) repeated measures ANOVA; retrieval performance , RTs , and confidence were each analyzed with a day ( day 1 , day 2 ) × run ( 1 to7 ) × schema ( spatial , non-spatial ) ANOVA for repeated measures . Significant interaction effects were investigated with post-hoc ANOVAs and paired-sample t-tests . For the transfer test , behavioral data of schema encoding and retrieval were analyzed as above , but employing run ( 1 , 2 ) × schema ( spatial , non-spatial ) ANOVAs for repeated measures . Greenhouse-Geisser correction was applied when appropriate and alpha was set to 0 . 05 throughout . Learning quickly increased performance ( main effect of run: F ( 2 . 5 , 42 . 9 ) = 11 . 6 , P < 0 . 0005 ) , which did not differ between schemas ( no main effect of schema: P = 0 . 209; no run × schema interaction: P = 0 . 441; Figure 9A ) . Similarly , RTs decreased across runs ( main effect of run: F ( 6 , 102 ) = 5 . 4 , P < 0 . 0005 ) and did not differ between conditions ( no main effect of schema: P = 0 . 927; no run × schema interaction: P = 0 . 360; see Figure 9B ) . 10 . 7554/eLife . 09668 . 018Figure 9 . Behavioral performance during schema encoding . ( A ) Data represents the % of correct responses , and ( B ) the average reaction time ( s ) . Shaded error bars denote ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 018 We found a significant day x run interaction ( F ( 2 . 9 , 49 . 5 ) = 4 . 8 , P < 0 . 0005; main effect of day: F ( 1 , 17 ) = 4 . 6 , P = 0 . 046; main effect of run: F ( 3 . 6 , 60 . 3 ) = 2 . 8 , P = 0 . 038; no main effect of schema: P = 0 . 3; no day × schema interaction: P = 0 . 145; no run × schema interaction: P = 0 . 066; no day × run × schema interaction: P = 0 . 065 ) , followed up by separate run ( 1–7 ) × schema ( spatial , non-spatial ) ANOVAs for both days . Only for day 1 we found a significant main effect of run ( F ( 6 , 102 ) = 6 . 1 , P < 0 . 0005 ) , and a run × schema interaction ( interaction: F ( 6 , 102 ) = 2 . 8 , P = 0 . 013; no main effect of schema: P = 0 . 165 ) . Post-hoc paired-sample t-tests revealed significantly shorter RTs for the non-spatial schema during run 1 ( t ( 21 ) = 2 . 5 , P = 0 . 022; Figure 2B , left ) . On day 2 , we did not find any significant differences between runs or schema conditions ( no main effect of run: P = 0 . 718; no main effect of schema: P = 0 . 749; no run × schema interaction: P = 0 . 849; Figure 2B , right ) . Retrieval was accompanied by a two-point confidence rating ( “not sure”/”sure” ) . A three-way interaction between the factors day ( day 1 , day 2 ) , run ( 1 to 7 ) , and schema ( spatial , non-spatial ) ( F ( 3 . 8 , 65 . 3 ) = 4 . 2 , P = 0 . 005 ) was observed , suggesting that the difference in confidence ratings between days ( main effect of day: F ( 1 , 17 ) = 12 . 7 , P = 0 . 002 ) was caused by differences over runs or between schemas ( main effect of run: F ( 3 , 51 . 1 ) = 5 . 4 , P = 0 . 003; main effect of schema: F ( 1 , 17 ) = 7 . 7 , P = 0 . 013; interaction day × run: F ( 3 , 50 . 3 ) = 8 . 3 , P < 0 . 0005; interaction day × schema: F ( 1 , 17 ) = 16 . 7 , P = 0 . 001; interaction run × schema: F ( 6 , 102 ) = 3 . 7 , P = 0 . 002 ) . To further test this , we employed a repeated measure ANOVA for each day with run and schema as factors . Only for day 1 we found an increase in retrieval confidence across runs ( main effect of run: F ( 2 . 7 , 45 . 4 ) = 7 . 5 , P = 0 . 001; main effect of schema: F ( 1 , 170 ) = 12 . 2 , P = 0 . 003 ) , and this increase differed between conditions ( interaction run × schema: F ( 3 . 4 , 58 ) = 4 . 8 , P = 0 . 003 ) . Post-hoc paired-sample t-tests revealed lower confidence during retrieval of spatial , as compared to non-spatial , rule-based schema memories within the first run ( t ( 21 ) = -4 . 2 , P < 0 . 0005; Figure 2C , left ) . As can be seen , lower retrieval confidence for the spatial schema during the initial run of day 1 was also accompanied by lower retrieval performance and slower reaction times ( Figure 2A and B , left ) . However , subjects quickly gained confidence . On day 2 , retrieval confidence was at ceiling level and did not differ significantly between runs or schemas ( no main effect of run: P = 0 . 187; no main effect of schema: P = 0 . 397; no run × schema interaction: P = 0 . 549; Figure 2C , right ) . Brain imaging data were acquired with a 3 Tesla MRI scanner ( Trio Tim , Siemens , Erlangen , Germany ) using a 32-channel head coil . For each run we obtained 256 T2*-weighted BOLD images with the following parameters: gradient multi-echo EPI sequence ( Poser et al . , 2006 ) , TR = 2100 ms , TEs = 7 . 6 , 19 . 9 , 32 , 44 ms , flip angle = 80° , FOV = 200 × 200 mm , matrix = 80 × 80 , 39 ascending axial slices , 10% slice gap , voxel size = 2 . 5 mm isotropic . Structural scans were acquired using a Magnetization-Prepared Rapid Gradient Echo ( MP-RAGE ) sequence with the following parameters: TR = 2300 ms , TE = 3 . 03 ms , flip angle = 8° , FOV = 256 × 256 mm , voxel size = 1 mm isotropic . All imaging data were analyzed using SPM8 ( http://www . fil . ion . ucl . ac . uk/spm/ ) in combination with Matlab ( Matlab 2010b , The Mathworks , Inc . , Natick , MA , USA ) . Due to technical problems with the gradient multi-echo EPI sequence , only echoes from echo-times 19 . 9 , 32 , 44 ms were used for analyses . Images from multiple echo-times were combined by first performing motion correction on the first echo ( 19 . 9 ms ) , estimating iterative rigid body realignment to minimize the residual sum of squares between the first echo of the first scan and all remaining scans . These estimated parameters were applied to all other echoes , thereby realigning all echoes to the first echo of the first scan . Then , the three echo images of each scan were combined into single images by calculating the weighted sum of the three echo times . The first six volumes were discarded to allow for T1-equilibration . A total of seven runs in five subjects exceeded the limit of 2 . 5 mm movement and were excluded from further analysis . The combined EPI volumes from both fMRI sessions were slice-time corrected to the middle slice and realigned to the mean image . The structural image was co-registered to the mean image using mutual information optimization , and segmented into gray matter , white matter and cerebrospinal fluid . MVPA was performed in the native space of each subject . For univariate analysis , images were further spatially normalized to the Montreal Neurological Institute ( MNI ) EPI template using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra ( DARTEL; Ashburner , 2007 ) , and smoothed with a 3D Gaussian kernel ( 8 mm full-width at half maximum , FWHM ) . The BOLD response for all correct trials of the different conditions was modeled as separate regressors time-locked to the onset of the presentation of the circle pairs ( day 1: spatial encoding , non-spatial encoding , spatial retrieval , non-spatial retrieval; day 2: spatial retrieval , non-spatial retrieval ) . Additional regressors were included to model the perceptual baseline trials ( day 2: perceptual baseline encoding , perceptual baseline retrieval; day 2: perceptual baseline retrieval ) , response periods ( collapsed across response and feedback/confidence ratings ) , cues and incorrect trials ( summarized as one regressor of no interest ) . All events were estimated as a boxcar function ( circle pairs: 3 s , responses: 4 s , cues: 2 s ) and convolved with a canonical hemodynamic response function ( HRF ) . The efficiency of this design was verified prior to the start of the study , based on data from piloting . In addition , six realignment parameters and two regressors consisting of the mean signal of white matter and CSF were included in the design matrix . Next , a high-pass filter with a cutoff at 128 s was applied . To address general effects of schema retrieval , we collapsed across spatial and non-spatial retrieval trials at a first level , created a general schema retrieval condition , and contrasted this against the perceptual baseline ( schema retrieval > perceptual baseline ) . Schema consolidation was tested by entering these contrast images into a second level random-effects day ( day 1 , day 2 ) × run ( 1 to7 ) factorial design . Activation was tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons . We used Psychophysiological Interaction analyses ( PPI; Friston et al . , 1997 ) to probe functional coupling during schema retrieval . Two PPI analyses were performed per seed region ( i . e . contrasts spatial schema retrieval > perceptual baseline and non-spatial schema retrieval > perceptual baseline ) . MPFC ( x = -2 , y = 35 , z = -2 ) and PCC ( x = 2 , y = -45 , z = 22 ) seeds ( Results , Schema retrieval networks: MPFC and PCC ) were defined as brain regions involved in the retrieval of consolidated schema memories ( day 2 > day 1; consolidation contrast , Results , Schema encoding; Figure 3C ) , and coordinates of peak activations were chosen considering previous effects within these regions ( Kumaran et al . , 2009 ) , as well as anatomical boundaries ( Nieuwenhuis and Takashima , 2011 ) . For these coordinates , a sphere with a radius of 8 mm was placed around the peak activations . The left AG seed ( Results , Schema convergence networks ) was delineated with a mask resulting from the convergence of schema components ( 1437 voxels; Figure 5A ) . The hippocampal seed region ( Materials and methods , Complementary analysis: hippocampal connectivity during schema retrieval ) was defined as bilateral hippocampus and was based on the Automatic Anatomical Labeling ( AAL ) atlas ( http://fmri . wfubmc . edu/software/pickatlas ) . Next , time courses of each seed region were extracted . The interaction between time course and psychological factor ( i . e . , spatial schema retrieval > perceptual baseline × regional time course , and non-spatial schema retrieval > perceptual baseline × regional time course ) was computed and activity positively related to this interaction was investigated . To test for group effects on day 2 , individual contrast images were entered into a second level analysis and activity explained by the PPI regressor was tested with one-sample t-tests . To reveal the functional network involved in general retrieval processes , irrespective of the distinct rule-based schemas , we made a second-level conjunction ( logical "and" ) of both ( ( spatial schema retrieval > perceptual baseline ) ∩ ( non-spatial schema retrieval > perceptual baseline ) ; Figure 4 , and 6 , shown in purple ) . Hippocampal connectivity was tested for changes over time and individual contrast images were thus submitted to a second level random-effects day ( day 1 , day 2 ) × run ( 1 to7 ) × schema ( spatial , non-spatial ) factorial design . All effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons . We started out by obtaining single-trial parameter estimates for later classification analyses . To this end , each schema retrieval trial was modeled as a separate regressor ( Mumford et al . , 2012 ) with remaining regressors appended identically to our first level estimation for univariate analysis ( see above ) . Runs were modeled independently . This yielded 140 single-trial t-maps for day 1 and 280 single-trial t-maps for day 2 per subject . For the transfer test , all trials ( encoding and retrieval ) were included in the analysis due to the limited amount of data . This resulted in 64 single-trial t-maps per subject . To dissociate the distributed representations of schema components , we implemented MVPA using the library for support vector machines ( LIBSVM , http://www . csie . ntu . edu . tw/~cjlin/libsvm/ ) . For all MVPA analyses , a spherical searchlight ( Kriegeskorte et al . , 2006 ) was centered at each voxel in turn , considering all surrounding voxels within a radius of 8 mm . Only searchlights that included more than 30 gray matter voxels were examined . Features ( i . e . , voxels ) were transformed into a pattern vector and a linear SVM classifier with a fixed regularization parameter C = 1 was trained to discriminate between schema components that consisted of ( 1 ) rule-based associations , and ( 2 ) low-level visual features of the task material ( Figure 1—figure supplement 1A ) . The Matlab code for all searchlight analyses is openly available via https://github . com/isabellawagner/searchlight-svm . First , we reasoned that subjects should show stable multi-voxel brain patterns of rule-based schema memories on day 2 . Training and testing a classifier on this data would thus allow us to identify the neural signatures of both schema components ( Figure 1—figure supplement 1B ) . Only correct and high-confidence data from day 2 was used for training and testing ( spatial vs . non-spatial; number of trials per category , mean ± s . d . : 104 ± 9 vs . 105 ± 7 ) . Discrimination performance was assessed using a 7-fold cross-validation regime during which the classifier was trained on data from six runs and tested on the remaining . This was repeated until every independent run was tested once . The average discrimination performance of each searchlight was assigned to its center voxel . Additionally , we trained a classifier to determine brain regions that solely distinguished between low-level visual features of the circle pairs rather than rule-based schema representations . Visual features necessary to predict a certain trial outcome consisted of the 1 ) position ( spatial rule ) , and 2 ) color ( non-spatial rule ) of one of the circles . For retrieval trials , this information was not orthogonal in our experiment ( a specific color appeared always on the left ) . By discriminating between the different circle pairs ( circle pairs 1 and 2 vs . circle pairs 3 and 4; Figure 1—figure supplement 1A; number of trials per category , mean ± s . d . : 104 ± 8 vs . 105 ± 7 ) we were able to capture the visual features that differed between them . Classifier predictions were obtained as described above . After completing the discrimination procedure for all possible searchlights within a volume , a 3D performance map was created . Individual performance maps were corrected for chance level by subtracting 50% ( binary discrimination ) from every voxel . Performance above chance implied the presence of discriminative information within this local voxel-pattern . Maps were normalized using DARTEL and smoothed with a 3 mm Gaussian kernel ( FWHM ) . To test for statistical significance at a group-level , we submitted individual performance maps to a one-sample t-test in SPM8 . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons . As a next step , we investigated the generalization of multi-voxel representations across days and study phases . We repeated the training step , again using only correct and high-confidence data of day 2 ( see above; discrimination between rule-based associations and between low-level visual features ) . However , we tested the classifier on neural data from day 1 ( Figure 1—figure supplement 1C ) and the transfer test ( Figure 1—figure supplement 1D ) . Discrimination performance significantly above chance level would thus indicate shared multi-voxel representations of schema components between day 2 and day 1/the transfer test . Throughout day 1 , subjects completed 7 runs that each contained 16 retrieval trials ( Materials and methods , Procedure ) . Since retrieval performance , RTs , and confidence across day 1 increased quickly and differed between runs ( Figure 2 ) , we predicted every run separately . Due to the small amount of data per run , we included all retrieval data ( disregarding correctness or confidence ) . The resulting whole-brain maps were post-processed ( see above ) and submitted to a second level ANOVA with run ( 1 to 7 ) as within-subjects factor . The transfer test consisted of two runs at the end of day 2 that each contained encoding and retrieval trials ( 8 trials each ) . We included thus all trials for testing the classifier ( disregarding the trial type , correctness , retrieval confidence , or run ) . As above , individual discrimination maps were post-processed and submitted to one-sample t-tests . Again , unless stated otherwise , all effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons . We performed additional MVPA analyses to test the AG involvement during schema retrieval on day 1 . If schema component representations converged within the left AG during retrieval on day 1 , this would point towards a general involvement of the AG , rather than a schema-specific involvement after 24-hour-consolidation . Therefore , we mimicked the previous MVPA of day 2 data . This contained the training and testing of two classifiers for each local searchlight pattern , using 7-fold cross-validation ( fully described in Materials and methods , Multi-voxel pattern analysis; rule-based associations: spatial vs . non-spatial; low-level visual features: circle pairs 1 and 2 vs . circle pairs 3 and 4; trials per category , mean ± s . d . : 54 ± 5 ) . Since day 1 contained only small amounts of retrieval trials ( 8 trials per condition , per run ) , we included all retrieval trials in the analysis ( irrespective of correctness or retrieval confidence ) . As for the day 2 analysis , individual performance maps were entered into one-sample t-tests in SPM8 . Unless stated otherwise , effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons . We did not find any significant representations of rule-based associations within the AG or any other brain region on day 1 . However , low-level visual features were represented as expected within occipital regions , extending into the AG , as well as within the right anterior temporal lobe ( Figure 10; Table 6 , upper part ) . Results appeared similar when we repeated this analysis in 14 subjects , selecting only correct retrieval trials with high confidence ratings ( excluding nine subjects that showed one or more runs without correct and high confidence trials on day 1; trials per category , mean ± s . d . : rule-based associations , 48 ± 7 vs . 50 ± 7; low-level visual features , 48 ± 8 vs . 50 ± 6 ) . Again , we did not find significant representations of rule-based associations , and low-level visual features were mostly represented in occipital regions ( Table 6 , lower part ) . 10 . 7554/eLife . 09668 . 019Figure 10 . Multi-voxel representations of low-level visual features on day 1 . Additional searchlight MVPA revealed distributed representations of low-level visual features , but not rule-based associations , during schema retrieval on day 1 ( Materials and methods , Complementary analysis: AG involvement in schema retrieval on day 1 ) . For display purposes , the map was resliced to a voxel dimension of 0 . 5 mm isotropic and is shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 6 . L – left . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 01910 . 7554/eLife . 09668 . 020Table 6 . Multi-voxel representations of low-level visual features on day 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 020MNIBrain regionxyzZ valueCluster sizeMVPA day 1 , all trials , N = 23 R middle occipital gyrus12-881016470R inferior temporal gyrus4012-403 . 92180MVPA day 1 , correct and high confidence trials , N = 14 R middle occipital gyrus12-88106 . 2412975L postcentral gyrus-52-22404 . 3283L insular cortex-351024 . 2775R inferior frontal gyrus50503 . 8891Clusters that significantly discriminated the low-level visual features during retrieval on day 1 . Bold font indicates the type of MVPA analysis ( day 1 , training and testing the classifier on day 1 using cross-validation; Materials and methods , Complementary analysis: AG involvement in schema retrieval on day 1 ) . MNI coordinates represent the location of peak voxels . We report the local maximum of each cluster . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster sizes: upper part , 80 voxels; lower part , 65 voxels ) . L – left , R – right . First , we assessed retrieval effects across both days and schema conditions . Results showed increased functional coupling between the hippocampus and an extensive set of regions , comprising surrounding MTL structures , the MPFC , PCC , and lateral occipital cortex ( Figure 11A; Table 7 , upper part ) . We did not find a difference in hippocampal coupling between the two schema conditions ( no main effect of schema ) . Second , to investigate time effects in hippocampal connectivity , we chose a specific contrast between the days that allowed us to equate for differences in retrieval performance , confidence , and reaction times ( day 1 , runs 5–7 vs . day 2 , run 1; Results , Schema consolidation ) . Results showed decreased coupling between the bilateral hippocampus and MPFC , as well as lateral occipital cortex during schema retrieval on day 2 as compared to day 1 ( day 1 > day 2; Figure 11B; Table 7 , lower part ) . No region showed increased functional coupling with the hippocampus during retrieval on day 2 as compared to day 1 ( day 2 > day 1 ) . 10 . 7554/eLife . 09668 . 021Figure 11 . Hippocampal connectivity during schema retrieval . ( A ) Hippocampal connectivity during general schema retrieval ( compared to the perceptual baseline ) across both days ( day 1 & day 2 ) . Additionally , connectivity results are projected onto a surface-based flatmap . Relevant structures are labeled: AG , angular gyrus; ATL , anterior temporal lobe; IFG , inferior frontal gyrus; IOG , inferior occipital gyrus; MPFC , medial prefrontal cortex; MTS , medial temporal sulcus; PCC , posterior cingulate cortex; PHC , parahippocampal cortex; SFG , superior frontal gyrus . Regions of the retrieval network are highlighted in bold font . Dashed lines are inserted to aid orientation: a , border between medial and lateral prefrontal cortices; b , central sulcus; c , superior temporal gyrus; d , border between ventromedial and -lateral temporal cortices . LH – left hemisphere , RH – right hemisphere . ( B ) Decreased hippocampal-neocortical coupling during schema retrieval from day 1 to day 2 ( day 1 > day 2 ) . For display purposes , all maps were resliced to a voxel dimension of 0 . 5 mm isotropic and are shown at P < 0 . 001 , uncorrected . Significant clusters are noted in Table 7 . L – left . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 02110 . 7554/eLife . 09668 . 022Table 7 . Hippocampal connectivity during schema retrieval . DOI: http://dx . doi . org/10 . 7554/eLife . 09668 . 022MNIBrain regionxyzZ valueCluster sizeDay 1 & day 2 R precuneus2-55187 . 0618139Cerebellum-5-52-456 . 04602R angular gyrus52-60304 . 84339R superior parietal gyrus28-38584 . 62139L insular cortex-32-20204 . 5790R middle occipital gyrus32-80403 . 94111Day 1 > day 2 R cingulate gyrus222-55 . 12162R middle occipital gyrus35-9004 . 91279L middle occipital gyrus-38-88-24 . 43295R superior frontal gyrus285704 . 23108R middle frontal gyrus2258184 . 01144R cingulate gyrus540154 . 0187Clusters that showed a significant increase in hippocampal connectivity during schema retrieval ( Materials and methods , Complementary analysis: hippocampal connectivity during schema retrieval ) . Bold font indicates contrasts . Retrieval ( collapsed across spatial and non-spatial schema conditions ) was compared to the perceptual baseline . MNI coordinates represent the location of peak voxels . We report the local maximum of each cluster . Effects were tested for significance using cluster-inference with a cluster-defining threshold of P < 0 . 001 and a cluster-probability of P < 0 . 05 family-wise error ( FWE ) corrected for multiple comparisons ( critical cluster size = 76 voxels ) . L – left , R – right .
Mental schemas form associative knowledge structures that can promote the encoding and consolidation of new and related information . Schemas are facilitated by a distributed system that stores components separately , presumably in the form of inter-connected neocortical representations . During retrieval , these components need to be recombined into one representation , but where exactly such recombination takes place is unclear . Thus , we asked where different schema components are neuronally represented and converge during retrieval . Subjects acquired and retrieved two well-controlled , rule-based schema structures during fMRI on consecutive days . Schema retrieval was associated with midline , medial-temporal , and parietal processing . We identified the multi-voxel representations of different schema components , which converged within the angular gyrus during retrieval . Critically , convergence only happened after 24-hour-consolidation and during a transfer test where schema material was applied to novel but related trials . Therefore , the angular gyrus appears to recombine consolidated schema components into one memory representation .
To make sense of the world around us , we constantly try to work out the relationship of new information to other things that we already know , and sort our knowledge into pre-existing mental frameworks , or “schemas” . This makes learning new things that are related to a schema , as well as remembering this knowledge , easier . The process of making these mental connections is thought to involve an extensive brain network . Separate types of information are stored in different brain regions within this network , yet to link this information together , the brain must combine them into a single representation . Wagner et al . have now investigated which brain regions are involved in recombining separate information . Human volunteers were trained to interpret the positions or colors of pairs of circles with different rules . The combination of these separate types of information formed a mental schema that could be used as a “weather forecast” . The design of the experiment meant that measuring the brain activity of the volunteers during the task ( using a technique called functional magnetic resonance imaging ) allowed the brain regions involved in retrieving the different parts of such a schema to be distinguished . Twenty-four hours later volunteers returned to use the mental schemas that they had learned to predict the weather . Retrieving which weather conditions the circle pairs represented activated a network of regions in the volunteers’ brains . Further analysis revealed that some of these regions showed specific activity patterns in response to remembering information about only one element of the task ( for example , only the rules or only the visual information ) . However , the different aspects of the task all appeared to be integrated by a brain region called the angular gyrus . This suggests that the angular gyrus is responsible for combining separate memory parts and pieces of information into a single representation . It is able to do so by connecting to brain regions that code for such specific aspects , although this only occurs 24 hours after the mental schemas have been established . Future studies could investigate the result of damage to the angular gyrus: different pieces of information might not be combined , or could result in an incorrect memory during retrieval . Finally , since the angular gyrus has been related to a wealth of different mental processes , it remains a challenge for future research to "converge" these findings and to understand the underlying computations .