Mental compression of spatial sequences in human working memory using numerical and geometrical primitives

Reactivating ordinal position information from auditory sequence memory in human brains

“…In contrast, the content code fails to show the encoding-to-maintaining representational generalization (Barak et al, 2010; Lundqvist et al, 2016; Quentin et al, 2019; Trübutschek et al, 2017; Yue et al, 2019). Content and structure are postulated to be signified in a factorized manner (Al Roumi et al, 2021; Liu et al, 2019; Ninokura et al, 2004), thereby a sequence is stored as a combination of stable structure (e.g., ordinal position) and varied content (e.g., tone pitch), on a trial by trial basis. The stable characteristics of structure would potentially facilitate memory formation, as the structure keeps steady to incorporate varied contents (Behrens et al, 2018; Friston & Buzsáki, 2016; Shahnazian et al, 2021).…”

“…We finally asked whether the neural code for ordinal position is the same or different between encoding and retention stages, by employing a cross-time encoding-tomaintaining generalization analysis (King & Dehaene, 2014). Although previous studies convergingly support stable representations for structure (Al Roumi et al, 2021;Fan et al, 2021;Kalm et al, 2017), the stable codes might arise from different possibilities, as illustrated in Figure 4A. For example, it might be specific temporal segment, in short or long duration, during encoding (y-axis, vertical span from A to D) that carries the structure code, which would be reactivated during retention in a sustained way (x-axis, horizontal span from A to B), referred to as "Stable-sustained representation" (Figure 4A, left).…”

Functional neuroimaging in psychiatry and the case for failing better

“…In the context of functional neuroimaging, such a ''representation-rich'' approach might involve multivariate decoding or RSA (Behrens et al, 2018;Diedrichsen and Kriegeskorte, 2017;Liu et al, 2022). For example, in visually evoked MEG data, decoding and RSA can uncover temporal windows that contain information pertaining to abstract features of task structure (e.g., ordinal position in a learned sequence, or geometric primitives used for compositional cognition) (Liu et al, 2019;Luyckx et al, 2019;Nour et al, 2021;Al Roumi et al, 2021) (Figure 1C), where this type of information emerges after learning (Nour et al, 2021) and is reinstated spontaneously during subsequent rest periods (Liu et al, 2019).…”

Working Memory for Spatial Sequences: Developmental and Evolutionary Factors in Encoding Ordinal and Relational Structures

“…We then asked whether the three groups could spontaneously extract these spatial patterns and use this information to inform sequence encoding (e.g., using the relational structures between locations to encode sequences in a more succinct form; Amalric et al, 2017;Wang et al, 2019;Al Roumi et al, 2021). Note that during either training in monkeys or behavioral testing in humans, there was no explicit instruction to use such spatial patterns.…”