scholarly journals Parietal representations of stimulus features are amplified during memory retrieval and flexibly aligned with top-down goals

2018 ◽  
Author(s):  
Serra E. Favila ◽  
Rosalie Samide ◽  
Sarah C. Sweigart ◽  
Brice A. Kuhl

AbstractIn studies of human episodic memory, the phenomenon of reactivation has traditionally been observed in regions of occipitotemporal cortex (OTC) involved in sensory experience. However, reactivation also occurs in lateral parietal cortex (LPC), and recent evidence indicates that reactivation of stimulus-specific information may be stronger in LPC than in OTC. These observations raise a number of questions about the nature of memory representations in LPC and their relation to representations in OTC. Here, we report two fMRI experiments that quantify stimulus feature information (color and object category) within LPC and OTC, separately during perception and memory retrieval, in male and female human subjects. Across both experiments, we show a clear dissociation between OTC and LPC: while feature information in OTC is relatively stronger during perception than memory, feature information in LPC is relatively stronger during memory than perception. Thus, while OTC and LPC represent common stimulus features, they preferentially represent this information during different stages. We show that this transformation of feature information across regions co-occurs with stimulus-level reinstatement within LPC and high-level OTC. In Experiment 2, we consider whether feature information in LPC during memory retrieval is flexibly and dynamically shaped by top-down goals. Indeed, we find that dorsal LPC preferentially represents retrieved feature information that addresses current goals. In contrast, ventral LPC represents retrieved features independent of current goals. Collectively, these findings provide insight into the nature and significance of mnemonic representations in LPC and constitute an important bridge between putative mnemonic and control functions of parietal cortex.

2018 ◽  
Vol 38 (36) ◽  
pp. 7809-7821 ◽  
Author(s):  
Serra E. Favila ◽  
Rosalie Samide ◽  
Sarah C. Sweigart ◽  
Brice A. Kuhl

GeroPsych ◽  
2020 ◽  
Vol 33 (1) ◽  
pp. 15-29 ◽  
Author(s):  
Sarah Peters ◽  
Signy Sheldon

Abstract. We examined whether interindividual differences in cognitive functioning among older adults are related to episodic memory engagement during autobiographical memory retrieval. Older adults ( n = 49, 24 males; mean age = 69.93; mean education = 15.45) with different levels of cognitive functioning, estimated using the Montreal Cognitive Assessment (MoCA), retrieved multiple memories (generation task) and the details of a single memory (elaboration task) to cues representing thematic or event-specific autobiographical knowledge. We found that the MoCA score positively predicted the proportion of specific memories for generation and episodic details for elaboration, but only to cues that represented event-specific information. The results demonstrate that individuals with healthy, but not unhealthy, cognitive status can leverage contextual support from retrieval cues to improve autobiographical specificity.


2021 ◽  
Vol 43 (1) ◽  
pp. 1-46
Author(s):  
David Sanan ◽  
Yongwang Zhao ◽  
Shang-Wei Lin ◽  
Liu Yang

To make feasible and scalable the verification of large and complex concurrent systems, it is necessary the use of compositional techniques even at the highest abstraction layers. When focusing on the lowest software abstraction layers, such as the implementation or the machine code, the high level of detail of those layers makes the direct verification of properties very difficult and expensive. It is therefore essential to use techniques allowing to simplify the verification on these layers. One technique to tackle this challenge is top-down verification where by means of simulation properties verified on top layers (representing abstract specifications of a system) are propagated down to the lowest layers (that are an implementation of the top layers). There is no need to say that simulation of concurrent systems implies a greater level of complexity, and having compositional techniques to check simulation between layers is also desirable when seeking for both feasibility and scalability of the refinement verification. In this article, we present CSim 2 a (compositional) rely-guarantee-based framework for the top-down verification of complex concurrent systems in the Isabelle/HOL theorem prover. CSim 2 uses CSimpl, a language with a high degree of expressiveness designed for the specification of concurrent programs. Thanks to its expressibility, CSimpl is able to model many of the features found in real world programming languages like exceptions, assertions, and procedures. CSim 2 provides a framework for the verification of rely-guarantee properties to compositionally reason on CSimpl specifications. Focusing on top-down verification, CSim 2 provides a simulation-based framework for the preservation of CSimpl rely-guarantee properties from specifications to implementations. By using the simulation framework, properties proven on the top layers (abstract specifications) are compositionally propagated down to the lowest layers (source or machine code) in each concurrent component of the system. Finally, we show the usability of CSim 2 by running a case study over two CSimpl specifications of an Arinc-653 communication service. In this case study, we prove a complex property on a specification, and we use CSim 2 to preserve the property on lower abstraction layers.


2010 ◽  
Vol 182 (3) ◽  
pp. 191-199 ◽  
Author(s):  
Martin Lepage ◽  
Marc Pelletier ◽  
Amélie Achim ◽  
Alonso Montoya ◽  
Matthew Menear ◽  
...  

2018 ◽  
Author(s):  
Tao He ◽  
Matthias Fritsche ◽  
Floris P. de Lange

AbstractVisual stability is thought to be mediated by predictive remapping of the relevant object information from its current, pre-saccadic locations to its future, post-saccadic location on the retina. However, it is heavily debated whether and what feature information is predictively remapped during the pre-saccadic interval. Using an orientation adaptation paradigm, we investigated whether predictive remapping occurs for stimulus features and whether adaptation itself is remapped. We found strong evidence for predictive remapping of a stimulus presented shortly before saccade onset, but no remapping of adaptation. Furthermore, we establish that predictive remapping also occurs for stimuli that are not saccade targets, pointing toward a ‘forward remapping’ process operating across the whole visual field. Together, our findings suggest that predictive feature remapping of object information plays an important role in mediating visual stability.


2020 ◽  
Author(s):  
Gina F. Humphreys ◽  
JeYoung Jung ◽  
Matthew A. Lambon Ralph

AbstractSeveral decades of neuropsychological and neuroimaging research have highlighted the importance of lateral parietal cortex (LPC) across a myriad of cognitive domains. Yet, despite the prominence of this region the underlying function of LPC remains unclear. Two domains that have placed particular emphasis on LPC involvement are semantic memory and episodic memory retrieval. From each domain, sophisticated models have been proposed as to the underlying function, as well as the more domain-general that LPC is engaged by any form of internally-directed cognition (episodic and semantic retrieval both being examples if this process). Here we directly address these alternatives using a combination of fMRI and DTI white-matter connectivity data. The results show that ventral LPC (angular gyrus) was positively engaged during episodic retrieval but disengaged during semantic memory retrieval. In addition, the level of activity negatively varied with task difficulty in the semantic task whereas episodic activation was independent of difficulty. In contrast, dorsal LPC (intraparietal sulcus) showed domain general activation that was positively correlated with task difficulty. In terms of structural connectivity, a dorsal-ventral and anterior-posterior gradient of connectivity was found to different processing networks (e.g., mid-angular gyrus (AG) connected with episodic retrieval). We propose a unifying model in which LPC as a whole might share a common underlying function (e.g., multimodal buffering) and variations across subregions arise due to differences in the underlying white matter connectivity.


2017 ◽  
Author(s):  
Falk Lieder ◽  
Amitai Shenhav ◽  
Sebastian Musslick ◽  
Tom Griffiths

The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.


2005 ◽  
Vol 5 (8) ◽  
pp. 781-781
Author(s):  
A. E. Ipata ◽  
A. L. Gee ◽  
J. W. Bisley ◽  
M. E. Goldberg
Keyword(s):  

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