Common Mechanisms for Working Memory and Attention: The Case of Perseveration with Visible Solutions

Stedron, Jennifer Merva; Sahni, Sarah D.; Munakata, Yuko

doi:10.1162/0898929053467622

Cited by 71 publications

(55 citation statements)

References 36 publications

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“…45 Researchers have begun to demonstrate associations between attentional control characteristics and cognitive regulatory tasks involving working memory and inhibitory control in preschool children, 28,36,[47][48][49] as well as in the neural network modeling of cognitive tasks in infants. 50 It is clear that from early in life, attentional control and working memory not only are coupled with each other, but also underlie many other aspects of cognitive performance associated with school readiness. 27,47 As with other regulatory abilities, working memory and cognitive regulation demonstrate great changes during infancy and early childhood.…”

Section: Development Of Cognitive Regulatory Behaviorsmentioning

confidence: 99%

Biological Systems and the Development of Self-Regulation: Integrating Behavior, Genetics, and Psychophysiology

Bell

Deater‐Deckard

2007

Journal of Developmental &Amp; Behavioral Pediatrics

142

124

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Self-regulation is the ability to control inner states or responses with respect to thoughts, emotions, attention, and performance. As such, it is a critical aspect of development and fundamental to personality and behavioral adjustment. In this review, we focus on attentional, cognitive, and emotional control as we discuss the genetic mechanisms and brain mechanisms that contribute to individual differences in self-regulation. We conclude with a discussion of the implications for deviations in the development of this complex construct and suggestions for future research. (J Dev Behav Pediatr 28:409-420, 2007) Index terms: self-regulation, controlled attention, temperament, brain mechanisms, genetic mechanisms Whatexpl ains the remarkable variation between children in how well they adapt to their ever changing environments? In part, the answer lies in self-regulation, a critical aspect of development in infancy, early childhood, and beyond. A set of developing regulatory processes appears to be fundamental to individual differences in personality development and behavioral adjustment 1-3 and includes biological components such as physiology, neurobiology, and genes, and situational components such as parenting, environment, and experience. 4,5 As such, self-regulation can be examined at many different levels and can have multiple definitions. 6 This necessarily makes self-regulation a complex construct and developmentalists have typically simplified the study of self-regulation by focusing only on one or two of these conceptual levels at a time, such as the regulation of emotion. 2,7 There is general agreement, however, that self-regulation operates at the physiological, attentional, emotional, cognitive, and behavioral levels. 1,8,9 In this review, we define self-regulation as the ability to control inner states or responses with respect to thoughts, emotions, attention, and performance. 6 Thus, our conceptualization of self-regulation could be described as cognitive control and emotion control. Within this conceptualization, we highlight attentional control as the mechanism for early developing self-regulation in both the cognitive and emotional domains. 10 -12 As such, we go beyond the typical focus on relationships between either cognitive functioning or attentional control and consequential emotion regulation 5,7 and provide a more dynamic view of self-regulatory processes. 11 Our focus in this review is on the biological components of developing self-regulation, as the situational and contextual components have been reviewed elsewhere. 6,7 We begin by highlighting the psychobiological framework that we use in conceptualizing regulatory processes. Then we discuss biological mechanisms of selfregulation associated with the development of specific cognitive and emotion behaviors. Because of our focus on psychobiology, we next examine genetic and brain mechanisms of self-regulation processes by integrating the functions associated with central nervous system and autonomic nervous system networks tha...

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Section: Development Of Cognitive Regulatory Behaviorsmentioning

confidence: 99%

Biological Systems and the Development of Self-Regulation: Integrating Behavior, Genetics, and Psychophysiology

Bell

Deater‐Deckard

2007

Journal of Developmental &Amp; Behavioral Pediatrics

142

124

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“…Theories of the developmental origins of cognitive control converge in positing that children engage these same proactive processes, but in a weaker form, with less strength or stability (9,10), less resistance toward habitual responses (1, 2), or degraded complexity (8,11). For example, according to one influential theory (8), developmental change in cognitive control is driven by ''age-related improvements in the complexity and scope of children's intentional, top-down processes.''…”

mentioning

confidence: 99%

“…10). Finally, neural network models have simulated developmental improvements in cognitive control via parametric increases in the strength of recurrent connections for maintaining task-relevant representations (9).…”

mentioning

confidence: 99%

Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control

Chatham

Frank²,

Munakata³

2009

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

275

292

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The capacity to anticipate and prepare for future events is thought to be critical for cognitive control. Dominant accounts of cognitive control treat the developing system as merely a weaker version of the adult system, progressively strengthening over time. Using the AX Continuous Performance Task (AX-CPT) in combination with high-resolution pupillometry, we find that whereas 8-year-old children resemble adults in their proactive use of cognitive control, 3.5-year-old children exhibit a qualitatively different, reactive form of cognitive control, responding to events only as they unfold and retrieving information from memory as needed in the moment. These results demonstrate the need to reconsider the origins of cognitive control and the basis for children's behaviors across domains.cognitive development ͉ context processing ͉ pupillometry ͉ reactive control ͉ proactive control

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“…From a broad perspective, an informal analysis by showed that approximately 75% of the published developmental computational models have been connectionist. These models have accounted for some key developmental phenomena, such as tradeoffs in brain development (McClelland, McNaughton, & O'Reilly, 1995;O'Reilly & Munakata, 2000), nonlinear patterns of development (Plunkett & Marchman, 1993;Rogers & McClelland, 2004;, perseveration (Munakata, 1998;Stedron, Sahni, & Munakata, 2005), and the emergence of semantic knowledge (Rogers & McClelland, 2004). Second, this article focuses on connectionist models in the interest of depth over breadth.…”

Section: Why Focus On Pdp Models?mentioning

confidence: 99%

“…Development is modeled by increasing the network's ability to sustain activity over time. For example, in a model of the A-not-B error, Munakata (1998) and Stedron et al (2005) manipulated the persistence of hidden unit activity by changing the strength of the recurrent weights on these units. These recurrent weights were designed to reactivate the hidden units whose activity decayed over time.…”

Section: Commonly Modeled Phenomenamentioning

confidence: 99%

Connectionist modeling of developmental changes in infancy: Approaches, challenges, and contributions.

Yermolayeva¹,

Rakison²

2014

Psychological Bulletin

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Connectionist models have been applied to many phenomena in infant development including perseveration, language learning, categorization, and causal perception. In this article, we discuss the benefits of connectionist networks for the advancement of theories of early development. In particular, connectionist models contribute novel testable predictions, instantiate the theorized mechanism of change, and create a unifying framework for understanding infant learning and development. We relate these benefits to the 2 primary approaches used in connectionist models of infant development. The first approach employs changes in neural processing as the basis for developmental changes, and the second employs changes in infants' experiences. The review sheds light on the unique hurdles faced by each approach as well as the challenges and solutions related to both, particularly with respect to the identification of critical model components, parameter specification, availability of empirical data, and model comparison. Finally, we discuss the future of modeling work as it relates to the study of development. We propose that connectionist networks stand to make a powerful contribution to the generation and revision of theories of early child development. Furthermore, insights from connectionist models of early development can improve the understanding of developmental changes throughout the life span. Keywords: connectionist modeling, neural network modeling, parallel distributed processing, infant developmentSince the groundbreaking work of Rumelhart and McClelland in the 1980s, there has been an increasing interest in and research on the application of connectionist models to early human development. Connectionist models are instantiations of theories about the mechanisms that underpin particular behaviors. Building computational models allows for the exploration of the interaction of numerous factors both internal and external to the organism that typically contribute to a behavior, which often can be too complex to specify through verbal theory alone . These computational models provide researchers with a number of ways to explicitly test theoretical assumptions and develop novel and testable predictions.However, in our view, for many developmental scientists the contribution of models to an integrated understanding of development is far from clear. Although a given model might provide output that is similar to the behavior of infants, it often remains to be seen whether the model's results and the behavioral results occur for the same reasons. For example, a network may simulate effectively infants' ability to discriminate between two objects that differ along multiple features, but it is possible that the features used by the network for discrimination are different from those used by infants. Computational models have also been criticized for using overly technical terms and notations that may be offputting to nonexperts (Klahr, 2004); for not being explicit about the source of their starting states (Oakes, Newcom...

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Common Mechanisms for Working Memory and Attention: The Case of Perseveration with Visible Solutions

Cited by 71 publications

References 36 publications

Biological Systems and the Development of Self-Regulation: Integrating Behavior, Genetics, and Psychophysiology

Biological Systems and the Development of Self-Regulation: Integrating Behavior, Genetics, and Psychophysiology

Pupillometric and behavioral markers of a developmental shift in the temporal dynamics of cognitive control

Connectionist modeling of developmental changes in infancy: Approaches, challenges, and contributions.

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