An official website of the United States government
Amodio argues that social cognition research has for many decades relied on imprecise dual-process models that build on questionable assumptions about how people learn and represent information. He presents an alternative framework for explaining social behavior as the product of multiple dissociable memory systems, based on the idea that cognitive neuroscience has revealed evidence for the existence of separate systems underlying distinct forms of learning and memory. Although we applaud Amodio’s attempt to build bridges between social cognition, learning psychology, and neuroscience, we believe that his interactive memory systems model rests on shaky grounds. In our view, the most significant limitation is the idea that behavioral dissociations provide strong evidence for multiple memory systems with functionally distinct learning mechanisms. A major problem with this idea is that behavioral dissociations can arise from processes during the retrieval and use of stored information, which does not require any assumptions about distinct memory systems or distinct forms of learning.
more »
« less
A Multiple-Memory Systems Framework for Examining Attention and Memory Interactions in Infancy
Abstract Visual attention both guides and is guided by learning and memory systems. In this article, we use a multiple-memory systems framework to examine the interplay between attention and memory that begins in early postnatal life. We review how attention and memory interact to support infant development with respect to perceptual learning about objects and features, item-in-context spatial memory, and reinforcement and reward learning. We argue that the multiple-memory systems approach offers a useful organizational structure for research on interactions between attention and memory.
more »
« less
- Award ID(s):
- 2051819
- PAR ID:
- 10658920
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Child Development Perspectives
- Volume:
- 15
- Issue:
- 2
- ISSN:
- 1750-8592
- Format(s):
- Medium: X Size: p. 132-138
- Size(s):
- p. 132-138
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Multiple types of memory guide attention: Both long-term memory (LTM) and working memory (WM) effectively guide visual search. Furthermore, both types of memories can capture attention automatically, even when detrimental to performance. It is less clear, however, how LTM and WM cooperate or compete to guide attention in the same task. In a series of behavioral experiments, we show that LTM and WM reliably cooperate to guide attention: Visual search is faster when both memories cue attention to the same spatial location (relative to when only one memory can guide attention). LTM and WM competed to guide attention in more limited circumstances: Competition only occurred when these memories were in different dimensions – particularly when participants searched for a shape and held an accessory color in mind. Finally, we found no evidence for asymmetry in either cooperation or competition: There was no evidence that WM helped (or hindered) LTM-guided search more than the other way around. This lack of asymmetry was found despite differences in LTM-guided and WM-guided search overall, and differences in how two LTMs and two WMs compete or cooperate with each other to guide attention. This work suggests that, even if only one memory is currently task-relevant, WM and LTM can cooperate to guide attention; they can also compete when distracting features are salient enough. This work elucidates interactions between WM and LTM during attentional guidance, adding to the literature on costs and benefits to attention from multiple active memories.more » « less
-
Abstract Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.more » « less
-
In explaining how humans selectively attend, common frameworks often focus on how attention is allocated relative to an idealized allocation based on properties of the task. However, these perspectives often ignore different types of constraints that could help explain why attention was allocated in a particular way. For example, many computational models of learning are well equipped to explain how attention should ideally be allocated to minimize errors within the task, but these models often assume all features are perfectly encoded or that the only learning goal is to maximize accuracy. In this article, we argue for a more comprehensive view by using computational modeling to understand the complex interactions that occur between selective attention and memory. Our central thesis is that although selective attention directs attention to relevant dimensions, relevance can be established only through memories of previous experiences. Hence, attention is initially used to encode features and create memories, but thereafter, attention operates selectively on the basis of what is kept in memory. Through this lens, deviations from ideal performance can still be viewed as goal-directed selective attention, but the orientation of attention is subject to the constraints of the individual learner.more » « less
-
Abstract In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.more » « less
