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.

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.

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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.

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Author details

1. Isabella C Wagner

   1. Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

   Contribution

   ICW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   i.wagner@donders.ru.nl

   Competing interests

   The authors declare that no competing interests exist.

2. Mariët van Buuren

   Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands

   Contribution

   MvB, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Marijn CW Kroes

   1. Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
   2. Center for Neural Science, New York University, New York, United States
   3. Department of Psychology, New York University, New York, United States

   Contribution

   MCWK, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Tjerk P Gutteling

   Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

   Contribution

   TPG, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Marieke van der Linden

   Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands

   Contribution

   MvdL, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Richard G Morris

   Centre for Cognitive and Neural Systems, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   RGM, Conception and design, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Guillén Fernández

   Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands

   Contribution

   GF, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

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