scholarly journals Ventromedial prefrontal cortex compression during concept learning

2017 ◽  
Author(s):  
Michael L. Mack ◽  
Alison R. Preston ◽  
Bradley C. Love
Keyword(s):  
Prefrontal Cortex ◽  
Dimensionality Reduction ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Neural Representation ◽  
Data Driven ◽  
General Mechanism ◽  
Specific Information ◽  
Model Predictions ◽  
Data Driven Approach

AbstractPrefrontal cortex (PFC) is thought to support the ability to focus on goal-relevant information by filtering out irrelevant information, a process akin to dimensionality reduction. Here, we test this dimensionality reduction hypothesis by combining a data-driven approach to characterizing the complexity of neural representation with a theoretically-supported computational model of learning. We find strong evidence of goal-directed dimensionality reduction within human ventromedial PFC during learning. Importantly, by using model predictions of each participant’s attentional strategies during learning, we find that that the degree of neural compression predicts an individual’s ability to selectively attend to concept-specific information. These findings suggest a domain-general mechanism of learning through compression in ventromedial PFC.

scholarly journals Ventromedial prefrontal cortex compression during concept learning

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael L. Mack ◽  
Alison R. Preston ◽  
Bradley C. Love
Keyword(s):  
Prefrontal Cortex ◽  
Dimensionality Reduction ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Neural Representation ◽  
Data Driven ◽  
General Mechanism ◽  
Specific Information ◽  
Data Driven Approach ◽  
Computational Predictions

AbstractPrefrontal cortex (PFC) is thought to support the ability to focus on goal-relevant information by filtering out irrelevant information, a process akin to dimensionality reduction. Here, we test this dimensionality reduction hypothesis by relating a data-driven approach to characterizing the complexity of neural representation with a theoretically-supported computational model of learning. We find evidence of goal-directed dimensionality reduction within human ventromedial PFC during learning. Importantly, by using computational predictions of each participant’s attentional strategies during learning, we find that that the degree of neural compression predicts an individual’s ability to selectively attend to concept-specific information. These findings suggest a domain-general mechanism of learning through compression in ventromedial PFC.

scholarly journals A data-driven approach to stimulus selection reveals the importance of visual properties in the neural representation of objects.

2017 ◽  
Vol 17 (10) ◽  
pp. 29
Author(s):  
David Coggan ◽  
David Watson ◽  
Tom Hartley ◽  
Daniel Baker ◽  
Timothy Andrews
Keyword(s):  
Neural Representation ◽  
Data Driven ◽  
Stimulus Selection ◽  
Data Driven Approach ◽  
Visual Properties

scholarly journals Cognitive insights from tertiary sulci in prefrontal cortex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Willa I. Voorhies ◽  
Jacob A. Miller ◽  
Jewelia K. Yao ◽  
Silvia A. Bunge ◽  
Kevin S. Weiner
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Cognitive Skills ◽  
Brain Structures ◽  
Data Driven ◽  
Behavioral Studies ◽  
Data Driven Approach ◽  
Sulcal Morphology ◽  
The Relationship

AbstractThe lateral prefrontal cortex (LPFC) is disproportionately expanded in humans compared to non-human primates, although the relationship between LPFC brain structures and uniquely human cognitive skills is largely unknown. Here, we test the relationship between variability in LPFC tertiary sulcal morphology and reasoning scores in a cohort of children and adolescents. Using a data-driven approach in independent discovery and replication samples, we show that the depth of specific LPFC tertiary sulci is associated with individual differences in reasoning scores beyond age. To expedite discoveries in future neuroanatomical-behavioral studies, we share tertiary sulcal definitions with the field. These findings support a classic but largely untested theory linking the protracted development of tertiary sulci to late-developing cognitive processes.

scholarly journals Selection and suppression of visual information in the macaque prefrontal cortex

2020 ◽  
Author(s):  
F. Di Bello ◽  
S. Ben Hadj Hassen ◽  
E. Astrand ◽  
S. Ben Hamed
Keyword(s):  
Prefrontal Cortex ◽  
Spatial Attention ◽  
Visual Information ◽  
Sensory Information ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Attentional Selection ◽  
Prefrontal Cortical ◽  
Cognitive Framework ◽  
Task Irrelevant

AbstractIn everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Visual selection can be implemented by both long-term (learning-based spatial prioritization) and short term (dynamic spatial attention) mechanisms. On the other hand, distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by actively suppressing the processing of distractors. The distinctive neuronal signature of each of these four processes is largely unknown. Likewise, how selection and suppression mechanisms interact to drive perception has never been explored neither at the behavioral nor at the neuronal level. Here, we apply machine-learning decoding methods to prefrontal cortical (PFC) activity to monitor dynamic spatial attention with an unprecedented spatial and temporal resolution. This leads to several novel observations. We first identify independent behavioral and neuronal signatures for learning-based attention prioritization and dynamic attentional selection. Second, we identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on both learning-based attention prioritization and dynamic attentional selection. Overall, we thus provide a unified neuro-cognitive framework describing how the prefrontal cortex implements spatial selection and distractor suppression in order to flexibly optimize behavior in dynamic environments.

scholarly journals Prefrontal control of visual distraction

2017 ◽  
Author(s):  
Joshua D. Cosman ◽  
Geoffrey F. Woodman ◽  
Jeffrey D. Schall
Keyword(s):  
Prefrontal Cortex ◽  
Target Selection ◽  
Event Related Potentials ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Sensory Cortex ◽  
Extrastriate Cortex ◽  
Neural Firing ◽  
Related Potentials ◽  
Task Irrelevant

SummaryAvoiding distraction by salient irrelevant stimuli is critical to accomplishing daily tasks. Regions of prefrontal cortex control attention by enhancing the representation of task-relevant information in sensory cortex, which can be measured directly in modulation of both single neurons and averaging of the scalp-recorded electroencephalogram [1,2]. However, when irrelevant information is particularly conspicuous, it may distract attention and interfere with the selection of behaviorally relevant information. Many studies have shown that that distraction can be minimized via top-down control [3–5], but the cognitive and neural mechanisms giving rise to this control over distraction remain uncertain and vigorously debated [6–8]. Bridging neurophysiology to electrophysiology, we simultaneously recorded neurons in prefrontal cortex and event-related potentials (ERPs) over extrastriate visual cortex to track the processing of salient distractors during a visual search task. Critically, we observed robust suppression of salient distractor representations in both cortical areas, with suppression arising in prefrontal cortex before being manifest in the ERP signal over extrastriate cortex. Furthermore, only prefrontal neurons that participated in selecting the task-relevant target also showed suppression of the task-irrelevant distractor. This suggests a common prefrontal mechanism for target selection and distractor suppression, with input from prefrontal cortex being responsible for both selecting task-relevant and suppressing task-irrelevant information in sensory cortex. Taken together, our results resolve a long-standing debate over the mechanisms that prevent distraction, and provide the first evidence directly linking suppressed neural firing in prefrontal cortex with surface ERP measures of distractor suppression.

scholarly journals Cognitive insights from evolutionarily new brain structures in prefrontal cortex

2020 ◽  
Author(s):  
Willa I. Voorhies ◽  
Jacob A. Miller ◽  
Jewelia K. Yao ◽  
Silvia A. Bunge ◽  
Kevin S. Weiner
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Processes ◽  
Cognitive Skills ◽  
Short Range ◽  
Brain Structures ◽  
Data Driven ◽  
Neural Signals ◽  
Data Driven Approach ◽  
Reasoning Skills ◽  
The Relationship

ABSTRACTWhile the disproportionate expansion of lateral prefrontal cortex (LPFC) throughout evolution is commonly accepted, the relationship between evolutionarily new LPFC brain structures and uniquely human cognitive skills is largely unknown. Here, we tested the relationship between variability in evolutionarily new LPFC tertiary sulci and reasoning skills in a pediatric cohort. A novel data-driven approach in independent discovery and replication samples revealed that the depth of specific LPFC tertiary sulci predicts individual differences in reasoning skills beyond age. These findings support a classic, yet untested, theory linking the protracted development of tertiary sulci to late-developing cognitive processes. We conclude by proposing a mechanistic hypothesis relating the depth of LPFC tertiary sulci to anatomical connections. We suggest that deeper LPFC tertiary sulci reflect reduced short-range connections in white matter, which in turn, improve the efficiency of local neural signals underlying cognitive skills such as reasoning that are central to human cognitive development.

scholarly journals Data-Based Value Co-Creation in Smart Service Systems: A Reinterpretation of Customer Journey

2020 ◽  
Vol 15 (6) ◽  
pp. 212
Author(s):  
Mara Grimaldi ◽  
Maria Vincenza Ciasullo ◽  
Orlando Troisi ◽  
Paola Castellani
Keyword(s):  
New Technologies ◽  
Relevant Information ◽  
Service Systems ◽  
Data Driven ◽  
Enabling Factors ◽  
Service Creation ◽  
Data Driven Approach ◽  
Smart Service ◽  
Smart Technologies

Industry 4.0 is characterized by the key role of new technologies in the development of relationships between companies and their stakeholders. Thus, the most recent theories on service redefine organizations as complex service systems that create and co-create value thanks to the interactions between actors, enhanced by smart technologies and ICTs. In particular, the concept of service systems- introduced in Service Science- seems to be suitable for the exploration of how service design, and the processes of innovation sharing and emergence, can be strengthened thanks to the application of smart technologies. Despite the adoption of a system logic, service systems, and their conceptualization, need to be reinterpreted according to a perspective that applies a total and all-encompassing view to the processes of value generation and to the interpretation of the information and data exchanged (data-driven decision-making). Therefore, the study proposes a conceptual model that integrates the key enabling factors of value co-creation in service systems with the main strategic drivers introduced in data-driven approach to redefine the entire service experience as a service journey. In this continuous information flow, providers, customers and users share and combine data streams, to be turned into relevant information and value, through an integrated and interacting set of touch points that connect the different stages of service creation, delivery and co-creation.

scholarly journals Tracking Child Language Development With Neural Network Language Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Kenji Sagae
Keyword(s):  
Neural Network ◽  
Language Development ◽  
Child Language ◽  
Structural Characteristics ◽  
Language Models ◽  
Data Driven ◽  
Evaluation Methodology ◽  
Specific Information ◽  
Data Driven Approach ◽  
Cross Lingual

Recent work on the application of neural networks to language modeling has shown that models based on certain neural architectures can capture syntactic information from utterances and sentences even when not given an explicitly syntactic objective. We examine whether a fully data-driven model of language development that uses a recurrent neural network encoder for utterances can track how child language utterances change over the course of language development in a way that is comparable to what is achieved using established language assessment metrics that use language-specific information carefully designed by experts. Given only transcripts of child language utterances from the CHILDES Database and no pre-specified information about language, our model captures not just the structural characteristics of child language utterances, but how these structures reflect language development over time. We establish an evaluation methodology with which we can examine how well our model tracks language development compared to three known approaches: Mean Length of Utterance, the Developmental Sentence Score, and the Index of Productive Syntax. We discuss the applicability of our model to data-driven assessment of child language development, including how a fully data-driven approach supports the possibility of increased research in multilingual and cross-lingual issues.

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