When working memory may be just working, not memory.

The N-back task is often considered to be a canonical example of a task that relies on working memory (WM), requiring both maintenance of representations of previously presented stimuli and also processing of these representations. In particular, the set-size effect in this task (e.g., poorer performance on three-back than two-back judgments), as in others, is often interpreted as indicating that the task relies on retention and processing of information in a limited-capacity WM system. Here, we consider an alternative possibility: that retention in episodic memory (EM) rather than WM can account for both set-size and lure effects in the N-back task. Accordingly, performance in the N-back task may reflect engagement of the processing ("working") function of WM but not necessarily limits in either that processing ability nor in retention ("memory"). To demonstrate this point, we constructed a neural network model that was augmented with an EM component, but lacked any capacity to retain information across trials in WM, and trained it to perform the N-back task. We show that this model can account for the set-size and lure effects obtained in an N-back study by M. J. Kane et al. (2007), and that it does so as a result of the well-understood effects of temporal distinctiveness on EM retrieval, and the processing of this information in WM. These findings help illuminate the ways in which WM may interact with EM in the service of cognitive function and add to a growing body of evidence that tasks commonly assumed to rely on WM may alternatively (or additionally) rely on EM. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Decomposing Neural Representational Patterns of Discriminatory and Hedonic Information during Somatosensory Stimulation.

The ability to interrogate specific representations in the brain, determining how, and where, difference sources of information are instantiated can provide invaluable insight into neural functioning. Pattern component modeling (PCM) is a recent analytic technique for human neuroimaging that allows the decomposition of representational patterns in brain into contributing subcomponents. In the current study, we present a novel PCM variant that tracks the contribution of prespecified representational patterns to brain representation across areas, thus allowing hypothesis-guided employment of the technique. We apply this technique to investigate the contributions of hedonic and nonhedonic information to the neural representation of tactile experience. We applied aversive pressure (AP) and appetitive brush (AB) to stimulate distinct peripheral nerve pathways for tactile information (C-/CT-fibers, respectively) while patients underwent functional magnetic resonance imaging (fMRI) scanning. We performed representational similarity analyses (RSAs) with pattern component modeling to dissociate how discriminatory versus hedonic tactile information contributes to population code representations in the human brain. Results demonstrated that information about appetitive and aversive tactile sensation is represented separately from nonhedonic tactile information across cortical structures. This also demonstrates the potential of new hypothesis-guided PCM variants to help delineate how information is instantiated in the brain.

Learning to perform role-filler binding with schematic knowledge.

Through specific experiences, humans learn the relationships that underlie the structure of events in the world. Schema theory suggests that we organize this information in mental frameworks called "schemata," which represent our knowledge of the structure of the world. Generalizing knowledge of structural relationships to new situations requires role-filler binding, the ability to associate specific "fillers" with abstract "roles." For instance, when we hear the sentence Alice ordered a tea from Bob, the role-filler bindings customer:Alice, drink:tea and barista:Bob allow us to understand and make inferences about the sentence. We can perform these bindings for arbitrary fillers-we understand this sentence even if we have never heard the names Alice, tea, or Bob before. In this work, we define a model as capable of performing role-filler binding if it can recall arbitrary fillers corresponding to a specified role, even when these pairings violate correlations seen during training. Previous work found that models can learn this ability when explicitly told what the roles and fillers are, or when given fillers seen during training. We show that networks with external memory learn to bind roles to arbitrary fillers, without explicitly labeled role-filler pairs. We further show that they can perform these bindings on role-filler pairs that violate correlations seen during training, while retaining knowledge of training correlations. We apply analyses inspired by neural decoding to interpret what the networks have learned.

Is Activity Silent Working Memory Simply Episodic Memory?

Working memory (WM) maintains task-relevant information in a state ready for processing. While traditional theories assume that sustained neuronal activity is responsible for WM, the Activity Silent WM (ASWM) account proposes that maintenance can also be supported by short-term synaptic weight changes. Here, we argue that the evidence for ASWM can be explained more parsimoniously by the involvement of episodic memory (EM) in WM tasks. Like ASWM, EM relies on rapid synaptic modification that is also activity silent; however, while ASWM posits transient synaptic modifications, EM traces persist over longer time periods. We discuss how, despite this difference, well-established EM mechanisms can account for the key findings attributed to ASWM, and describe predictions of this account.