Memory formation as selection: an eligibility-based architecture

Abstract

Memory is often discussed in terms of encoding strength, retention, and retrieval success. Yet these dimensions do not fully explain how remembered information acquires structure within a larger mnemonic system. Strongly retained or highly accessible traces need not be broadly integrated, and their persistence or vividness alone does not establish that they will support abstraction, flexible transfer, or cross-context inference (Preston and Eichenbaum, 2013;Schlichting and Preston, 2015). What remains insufficiently specified, therefore, is not only why some information persists, but how persistent information becomes organized within the long-term architecture of memory.Existing accounts have clarified important components of this problem. Work on attentional allocation, salience, precision weighting, and neuromodulatory gain has described how incoming signals may receive different processing and learning priority (Aston-Jones and Cohen, 2005;Friston, 2010;Feldman and Friston, 2010;Hasselmo, 2006). In parallel, relational memory theories have emphasized that memory depends not only on persistence, but on the binding of events, contexts, and higher-order regularities into structures that support flexible cognition (Eichenbaum, 2004;Preston and Eichenbaum, 2013;Eichenbaum and Cohen, 2014). These accounts address important aspects of selection, plasticity, and relational binding, but do not by themselves specify how differences in persistence and structural incorporation accumulate into different forms of longterm mnemonic organization.The present article treats selection and mnemonic architecture as recursively coupled. The properties of incoming activity, the current state of the system, and its pre-existing organization jointly constrain which patterns become eligible for plastic modification. Selective stabilization concerns the degree to which eligible activity acquires persistence and recurrent availability, whereas relational integration concerns whether and how stabilized activity becomes incorporated into broader preexisting representational structure. Through repeated learning episodes, stabilization and integration modify this structure and thereby alter the conditions of future selection.Selective stabilization and relational integration are not proposed as alternative labels for episodic and semantic memory. Established memory categories describe forms or systems of memory, whereas the present constructs describe cross-cutting architectural dimensions that may operate within and across those categories (Renoult et al., 2019). The framework is concerned with whether selected information persists and how it contributes to broader mnemonic organization, rather than with replacing conventional classifications of memory.The argument developed here is conceptual and deliberately non-exhaustive. The article does not propose a new synaptic learning rule, nor does it aim to replace established computational or neurobiological theories of memory. Instead, it offers a systems-level architecture for describing how eligibility, selective stabilization, and relational integration may interact across repeated learning episodes to produce enduring differences in mnemonic organization. Neurobiological mechanisms are treated as constraints and possible implementations of the processes described, not as one-to-one correlates of the proposed architectural dimensions. The goal is to clarify an explanatory level between transient processing priorities and the long-term structural organization of memory.An Eligibility-Based Architecture for Memory FormationMemory formation is often described as successful encoding followed by stabilization or consolidation over time. Such descriptions address how a trace may persist, but do not fully explain why only some ongoing patterns acquire access to enduring modification or how the current organization of the system biases that access. Research on memory allocation and synaptic tagging similarly indicates that activation may create an opportunity for lasting change without guaranteeing that such change will occur (Redondo and Morris, 2011;Josselyn and Frankland, 2018). The present framework uses selection to identify this transition from transient activity to potential structural consequence.Selection is not treated as an independent gate acting on neutral input. It emerges from the interaction among the properties of incoming activity, the current state of the system, and its pre-existing representational organization. Intense or novel activity may acquire learning priority partly because of its input properties, while existing organization may preferentially recruit patterns through their relation to prior representations and established processing pathways (Aston-Jones and Cohen, 2005;Friston, 2010;Feldman and Friston, 2010). These influences are neither mutually exclusive nor fixed in their relative contribution. Selection is therefore neither purely stimulus-driven nor solely mnemonic, but dependent on the interaction between ongoing activity and the system in which it occurs.Within this account, selection refers to the processes through which active patterns acquire eligibility, whereas eligibility refers to the resulting temporary, state-dependent susceptibility to plastic modification. An active pattern may be processed without becoming eligible, and an eligible pattern may still fail to achieve enduring stabilization. Eligibility should therefore not be equated with memory formation or persistence. It functions as a bridge between transient processing and the possibility of lasting structural change. Although this formulation is compatible with biological accounts of synaptic tagging and eligibility traces, eligibility is used here at a broader functional level and does not designate a specific synaptic mechanism or a new learning rule (Redondo and Morris, 2011;Gerstner et al., 2018).The framework does not require selection to be exclusive to memory. Attentional selection and salience detection operate across cognitive domains and may bias which patterns become eligible for plastic modification. Their mnemonic consequences, however, depend on the current state and prior organization of the system. As mnemonic architecture develops, its established representational structure may increasingly constrain which patterns acquire learning priority, without making selection an exclusively mnemonic operation. The relevant question is therefore not whether memory possesses a unique selection mechanism, but how transient processing priorities become translated into persistent and differently organized mnemonic change.Eligibility alone is not sufficient to determine the outcome of memory formation. Once a pattern has become eligible, two further questions arise: the degree to which it becomes selectively stabilized and recurrently available, and whether and how stabilized activity becomes incorporated into broader preexisting representational structure. The following sections develop these dimensions as selective stabilization and relational integration.Once an active pattern has acquired eligibility, it may or may not produce a persistent mnemonic consequence. Selective stabilization denotes the degree to which eligible activity becomes enduring and recurrently available, thereby retaining the capacity to influence subsequent processing. It does not refer to the upstream processes through which learning priority is assigned. The qualifier selective indicates that stabilization depends on prior eligibility and on the conditions under which eligible activity is maintained; it does not imply the operation of a second, independent selection mechanism.Eligibility creates the possibility of plastic modification but does not guarantee the durability of its consequences. Some eligible patterns may remain weak or transient, whereas others may acquire sufficient stability to support later accessibility and recurrent influence on processing. Selective stabilization is therefore graded and conceptually separates susceptibility to plastic change from the persistence of the resulting modification.Attentional allocation, salience-related processing, and precision weighting may contribute to determining which patterns become eligible, but they do not themselves constitute selective stabilization. Neuromodulatory systems should likewise not be confined to the transition from activity to eligibility, because they may constrain encoding and plastic modification across several stages. Cholinergic and noradrenergic influences are therefore treated as biologically plausible constraints on the proposed architecture, rather than as one-to-one correlates of selective stabilization or relational integration. Their mnemonic consequences depend on the current state and prior organization of the system in which they operate (Aston-Jones and Cohen, 2005;Hasselmo, 2006).Selective stabilization addresses the persistence and recurrent availability of eligible activity, not the pattern or extent of its incorporation into broader representational structure. A strongly stabilized pattern may remain accessible and locally robust while being only weakly integrated with other representations. This further architectural dimension is addressed by relational integration.Whereas selective stabilization concerns the persistence and recurrent availability of eligible activity, relational integration concerns its structural fate within the mnemonic system. It denotes whether, to what extent, and in what pattern stabilized activity becomes incorporated into broader pre-existing representational structure. Through such incorporation, stabilized patterns may become linked to prior episodes, contextual cues, conceptual relations, and higher-order regularities rather than remaining comparatively isolated. In this context, how refers to the pattern and scope of structural incorporation, not to a single underlying biological mechanism.Relational integration is therefore not equivalent to encoding strength or retention. A pattern may be strongly stabilized, readily accessible, and behaviorally influential while remaining only weakly connected to wider representational structure. Measures of persistence, vividness, or retrieval accessibility cannot by themselves establish the degree of relational integration. Its distinctive contribution lies in allowing retained information to participate in broader organization and thereby support flexible retrieval, abstraction, transfer, and cross-context inference (Eichenbaum, 2004;Preston and Eichenbaum, 2013;Schlichting and Preston, 2015).At a systems level, relational integration is compatible with accounts of hippocampal relational binding and interactions between the hippocampus, prefrontal systems, and distributed cortical representations (Preston and Eichenbaum, 2013;Eichenbaum and Cohen, 2014). These systems provide plausible biological constraints and possible implementations, but relational integration is not identified with a particular region or circuit. Nor is it another term for semantic memory. Episodic and semantic memory describe established forms or systems of memory, whereas relational integration describes a cross-cutting architectural dimension that may operate within and across them.Across repeated learning episodes, differences in relational integration may accumulate into distinct tendencies of long-term mnemonic organization. Broad incorporation may contribute to distributed relational structure, whereas selective stabilization without corresponding integration may produce persistent but comparatively isolated representations. These outcomes are graded organizational tendencies rather than fixed memory types. Their cumulative expression at the level of the wider mnemonic system is considered in the following section.Long-term mnemonic organization emerges from the repeated interaction of eligibility with the architectural dimensions of selective stabilization and relational integration, rather than from any one component in isolation. Individual learning episodes may produce relatively local changes, but differences in which patterns become persistent and in how they are incorporated accumulate over time into large-scale representational structure. This relationship is recursive: the resulting architecture subsequently constrains which activity becomes eligible and how future information can be stabilized and incorporated.At this level of description, global memory organization refers to the large-scale relational patterning of stabilized representations: the degree and manner in which they become interconnected, mutually constraining, and available for reconfiguration beyond the conditions of their original acquisition. This formulation is compatible with network accounts emphasizing the interplay between integration and segregation, while remaining focused on the organization of mnemonic representations rather than proposing a direct mapping onto a particular anatomical network (Bullmore and Sporns, 2009;Sporns, 2013).When eligible patterns are sufficiently stabilized and broadly incorporated into pre-existing representational structure, repeated learning may bias the system toward distributed relational organization. In this organizational tendency, representations remain differentiated while participating in wider relational structure, allowing retained information to support abstraction, generalization, flexible retrieval, and transfer across contexts (Preston and Eichenbaum, 2013;Schlichting and Preston, 2015). This outcome does not require equality between selective stabilization and relational integration or a static equilibrium between them. It reflects their effective conjunction across repeated learning episodes.By contrast, when selective stabilization repeatedly occurs without correspondingly broad relational integration, memory organization may become fragmented or weakly integrated. Under these conditions, representations may remain persistent, vivid, or readily accessible while being only narrowly connected to wider mnemonic structure. Such memories can retain substantial behavioral influence without supporting the same degree of abstraction, generalization, or cross-context inference (Eichenbaum, 2004;Schlichting and Preston, 2015). Fragmentation in this sense does not imply forgetting, weak encoding, or pathology. It denotes a graded organizational tendency in which locally robust representations contribute only to a limited extent to global relational structure.Experiences with similar levels of retention may therefore have different architectural consequences.One stabilized pattern may become incorporated into a distributed relational system and participate in flexible recombination, whereas another may persist primarily as a locally strong but comparatively isolated representation. Over time, the accumulation of such differences modifies the architecture that constrains subsequent selection and learning. Global memory organization is thus both an outcome of prior stabilization and integration and a condition shaping their future operation.Global memory organization does not fully specify the internal structure of a mnemonic system.Architectures with different large-scale organizational tendencies may also contain representational clusters that vary in their local density, stability, and degree of specialization. The term local-core deepening refers to the progressive strengthening of internal relational organization within a circumscribed representational domain across repeated learning episodes. It describes a secondary structural feature of the emerging architecture, rather than an additional mechanism or