scholarly journals Recurrent circuit dynamics underlie persistent activity in the macaque frontoparietal network

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Eric Hart ◽  
Alexander C Huk
Keyword(s):  
Computational Models ◽  
Theoretical Models ◽  
Relevant Information ◽  
Persistent Activity ◽  
Frontoparietal Network ◽  
Recurrent Excitation ◽  
Level Data ◽  
Local Functional ◽  
Oculomotor Response ◽  
Saccade Task

During delayed oculomotor response tasks, neurons in the lateral intraparietal area (LIP) and the frontal eye fields (FEF) exhibit persistent activity that reflects the active maintenance of behaviorally relevant information. Despite many computational models of the mechanisms of persistent activity, there is a lack of circuit-level data from the primate to inform the theories. To fill this gap, we simultaneously recorded ensembles of neurons in both LIP and FEF while macaques performed a memory-guided saccade task. A population encoding model revealed strong and symmetric long-timescale recurrent excitation between LIP and FEF. Unexpectedly, LIP exhibited stronger local functional connectivity than FEF, and many neurons in LIP had longer network and intrinsic timescales. The differences in connectivity could be explained by the strength of recurrent dynamics in attractor networks. These findings reveal reciprocal multi-area circuit dynamics in the frontoparietal network during persistent activity and lay the groundwork for quantitative comparisons to theoretical models.

scholarly journals A Functional Coupling of Brain and Behavior During Social Categorization of Faces

2021 ◽  
pp. 014616722097668
Author(s):  
Hannah I. Volpert-Esmond ◽  
Bruce D. Bartholow
Keyword(s):  
Theoretical Models ◽  
Social Categorization ◽  
Relevant Information ◽  
Event Related Potential ◽  
Functional Coupling ◽  
Flow Models ◽  
Stimulus Evaluation ◽  
Level Data ◽  
Brain And Behavior ◽  
And Behavior

Considerable research has focused on how people derive information about others’ social category memberships from their faces. Theoretical models posit that early extraction of task-relevant information from a face should determine the efficiency with which that face is categorized, but evidence supporting this idea has been elusive. Here, we used a novel trial-level data analytic approach to examine the relationship between two event-related potential components—the P2, indexing early attention to category-relevant information, and the P3, indexing stimulus evaluation—and the speed of overt categorization judgments. As predicted, a larger face-elicited P2 on a particular trial was associated with faster overt race or gender categorization of that face. Moreover, this association was mediated by P3 latency, indicating that extraction of more category-relevant information early in processing facilitated stimulus evaluation. These findings support continuous flow models of information processing and the long-theorized functional significance of face-elicited neurophysiological responses for social categorization.

scholarly journals Severe Sexual Abuse Reduces Frontoparietal Network Activity during Model-Based Reinforcement Learning Updates

2019 ◽  
Author(s):  
Allison Letkiewicz ◽  
Amy L. Cochran ◽  
Josh M. Cisler
Keyword(s):  
Sexual Abuse ◽  
Reinforcement Learning ◽  
Learning Styles ◽  
Computational Models ◽  
Learning Strategy ◽  
Network Activity ◽  
Model Based ◽  
Model Free ◽  
Frontoparietal Network ◽  
Assaultive Trauma

Trauma and trauma-related disorders are characterized by altered learning styles. Two learning processes that have been delineated using computational modeling are model-free and model-based reinforcement learning (RL), characterized by trial and error and goal-driven, rule-based learning, respectively. Prior research suggests that model-free RL is disrupted among individuals with a history of assaultive trauma and may contribute to altered fear responding. Currently, it is unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships to prospectively predict action-related outcomes, is also impaired among individuals who have experienced trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n=60) completed a three-arm bandit RL task during fMRI acquisition. Two computational models compared the degree to which each participant’s task behavior fit the use of a model-free versus model-based RL strategy. Overall, a greater portion of participants’ behavior was better captured by the model-based than model-free RL model. Although assaultive trauma did not predict learning strategy use, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates, which was not accounted for by PTSD. Given the significant impact that sexual trauma has on mental health and other aspects of functioning, it is plausible that altered model-based RL is an important route through which clinical impairment emerges.

scholarly journals Multi-attribute Decision-making is Best Characterized by an Attribute-Wise Reinforcement Learning Model

2017 ◽  
Author(s):  
Shaoming Wang ◽  
Bob Rehder
Keyword(s):  
Computational Models ◽  
Theoretical Models ◽  
Decision Makers ◽  
Common Finding ◽  
Cue Competition ◽  
Recency Effects ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
The Common ◽  
Alternative Choice

AbstractChoice alternatives often consist of multiple attributes that vary in how successfully they predict reward. Some standard theoretical models assert that decision makers evaluate choices either by weighting those attribute optimally in light of previous experience (so-called rational models), or adopting heuristics that use attributes suboptimally but in a manner that yields reasonable performance at minimal cost (e.g., the take-the-best heuristic). However, these models ignore both the possibility that decision makers might learn to associate reward with whole stimuli (a particular combination of attributes) rather than individual attributes and the common finding that decisions can be overly influenced by recent experiences and exhibit cue competition effects. Participants completed a two-alternative choice task where each stimulus consisted of three binary attributes that were predictive of reward, albeit with different degrees of reliability. Their choices revealed that, rather than using only the “best” attribute, they made use of all attributes but in manner that reflected the classic cue competition effect known as overshadowing. The time needed to make decisions increased as the number of relevant attributes increased, suggesting that reward was associated with attributes rather than whole stimuli. Fitting a family of computational models formed by crossing attribute use (optimal vs. only the best), representation (attribute vs. whole stimuli), and recency (biased or not), revealed that models that performed better when they made use of all information, represented attributes, and incorporated recency effects and cue competition. We also discuss the need to incorporate selective attention and hypothesis-testing like processes to account for results with multiple-attribute stimuli.

scholarly journals Submillimeter Array Observations of Magnetic Fields in Star Forming Regions

2010 ◽  
Vol 6 (S270) ◽  
pp. 103-106
Author(s):  
R. Rao ◽  
J.-M. Girart ◽  
D. P. Marrone
Keyword(s):  
Magnetic Field ◽  
Star Formation ◽  
Magnetic Fields ◽  
Computational Models ◽  
Dominant Role ◽  
Theoretical Models ◽  
Mass Star ◽  
Star Forming ◽  
Star Forming Regions ◽  
Low Mass

AbstractThere have been a number of theoretical and computational models which state that magnetic fields play an important role in the process of star formation. Competing theories instead postulate that it is turbulence which is dominant and magnetic fields are weak. The recent installation of a polarimetry system at the Submillimeter Array (SMA) has enabled us to conduct observations that could potentially distinguish between the two theories. Some of the nearby low mass star forming regions show hour-glass shaped magnetic field structures that are consistent with theoretical models in which the magnetic field plays a dominant role. However, there are other similar regions where no significant polarization is detected. Future polarimetry observations made by the Submillimeter Array should be able to increase the sample of observed regions. These measurements will allow us to address observationally the important question of the role of magnetic fields and/or turbulence in the process of star formation.

ARTIFICIAL INTELLIGENCE IN DENTISTRY: A MILESTONE

2021 ◽  
pp. 49-52
Author(s):  
Gaurvi Vikram Kamra ◽  
Ankur Sharma
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Treatment Planning ◽  
Computational Models ◽  
Improve Patient Care ◽  
Relevant Information ◽  
Documented Information ◽  
Improve Patient

The concept of "articial intelligence" (AI) refers to machines that are capable of executing human-like tasks. AI can also be dened as a eld concerned with computational models that can reason and act intelligently. Perspicacious software for data computation has become a necessity as the amount of documented information and patient data has increased dramatically. The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview. AI has been used in a variety of ways, from processing of data and locating relevant information to using neural networks for diagnosis and the introduction of augmented reality and virtual reality in dental education. AI-based apps will improve patient care by relieving the dental workforce of tedious routine duties, improving population health at lower costs, and eventually facilitating individualized, anticipatory, prophylactic, and collaborative dentistry. The convergence of AI and digitization has ushered in a new age in dentistry, with tremendously promising future prospects.The applicability, limitations, and potential future of AI-based dental diagnoses, treatment planning, and conduct are described in this concise narrative overview.

scholarly journals Persistent Spatial Information in the FEF during Object-based Short-term Memory Does Not Contribute to Task Performance

2014 ◽  
Vol 26 (6) ◽  
pp. 1292-1299 ◽  
Author(s):  
Kelsey L. Clark ◽  
Behrad Noudoost ◽  
Tirin Moore
Keyword(s):  
Task Performance ◽  
Spatial Information ◽  
Short Term Memory ◽  
Spatial Working Memory ◽  
Memory Performance ◽  
Persistent Activity ◽  
Short Term ◽  
Object Based ◽  
Saccade Task

We previously reported the existence of a persistent spatial signal in the FEF during object-based STM. This persistent activity reflected the location at which the sample appeared, irrespective of the location of upcoming targets. We hypothesized that such a spatial signal could be used to maintain or enhance object-selective memory activity elsewhere in cortex, analogous to the role of a spatial signal during attention. Here, we inactivated a portion of the FEF with GABAa agonist muscimol to test whether the observed activity contributes to object memory performance. We found that, although RTs were slowed for saccades into the inactivated portion of retinotopic space, performance for samples appearing in that region was unimpaired. This contrasts with the devastating effects of the same FEF inactivation on purely spatial working memory, as assessed with the memory-guided saccade task. Thus, in a task in which a significant fraction of FEF neurons displayed persistent, sample location-based activity, disrupting this activity had no impact on task performance.

An Attention-Based Framework for Context Identification in Autonomous Robots

2017 ◽  
Author(s):  
Maria Alessandra Montironi ◽  
Harry H. Cheng
Keyword(s):  
Information Content ◽  
Computational Models ◽  
Markov Models ◽  
Relevant Information ◽  
Recursive Estimation ◽  
Sensor Data ◽  
Infrared Range ◽  
Contextual Knowledge ◽  
Visual Sensor

Being able to correctly assess the context it is currently acting in is a very important ability for every autonomous robot performing a task in a real world scenario such as navigating, manipulating an object or interacting with a user. Sensors are the primary interface with the external world and the means through which contextual knowledge is generated. Humans and animals use cognitive processes such as attention to selectively process perceived task-relevant information and to recognize the context they are currently acting in. Biologically inspired computational models of attention have been developed in recent years to be used as interpretation keys of mainly visual sensor data. This paper presents a new framework for situation assessment that expands existing computational models of attention by providing a unified methodology to interpret and combine data from different sources. The method utilizes probabilistic state estimation techniques such as Bayesian recursive estimation, Kalman filter, and hidden Markov models to interpret features extracted from sensor data and formulate hypotheses about different aspects of the task the robot is performing or of the environment it is currently acting in. The concept of Bayesian surprise is also used to mark the information content of each new hypothesis. A weight that takes into account the confidence in the estimate that generated the hypothesis, its information content, and the quality of the data is then calculated. The methodology presented in this paper is general and allows to consistently apply the framework to data from different types of sensors and to then combine their hypotheses. Once formulated, hypotheses can then be used for context-based reasoning and plan adaptation. The framework was implemented on a small two-wheel differential drive robot equipped with a camera, an ultrasonic and two infrared range sensors. Three different sets of results that evaluate the performance of different features of the framework are presented. First, the method has been applied to detect a target object and to distinguish it from similar objects. Second, the hypotheses strength calculation method has been characterized by isolating the effect of belief, surprise, and of the quality of the data. Third, the combination of hypotheses from different modules has been evaluated in the context of environment classification.

Distributed Code for Semantic Relations Predicts Neural Similarity during Analogical Reasoning

2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Analogical Reasoning ◽  
Computational Models ◽  
Semantic Network ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Frontoparietal Network ◽  
Neural Activities ◽  
Core Set ◽  
Superior Parietal Cortex

The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coarsely coded as links in a semantic network or finely coded as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations are yet to be empirically established. Using sequential presentation of verbal analogies, we compared neural activities in making analogy judgments with predictions derived from alternative computational models of relational dissimilarity to adjudicate among rival accounts of how semantic relations are coded and compared in the brain. We found that a frontoparietal network encodes the three relation types included in the design. A computational model based on semantic relations coded as distributed representations over a pool of abstract relations predicted neural activities for individual relations within the left superior parietal cortex and for second-order comparisons of relations within a broader left-lateralized network.

scholarly journals Modelling auditory attention

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160101 ◽  
Author(s):  
Emine Merve Kaya ◽  
Mounya Elhilali
Keyword(s):  
Computational Models ◽  
Selection Process ◽  
Relevant Information ◽  
Auditory Attention ◽  
Cognitive Resources ◽  
Cocktail Party Problem ◽  
Modelling Studies ◽  
Pertinent Information ◽  
Current Article ◽  
Auditory Scenes

Sounds in everyday life seldom appear in isolation. Both humans and machines are constantly flooded with a cacophony of sounds that need to be sorted through and scoured for relevant information—a phenomenon referred to as the ‘cocktail party problem’. A key component in parsing acoustic scenes is the role of attention, which mediates perception and behaviour by focusing both sensory and cognitive resources on pertinent information in the stimulus space. The current article provides a review of modelling studies of auditory attention. The review highlights how the term attention refers to a multitude of behavioural and cognitive processes that can shape sensory processing. Attention can be modulated by ‘bottom-up’ sensory-driven factors, as well as ‘top-down’ task-specific goals, expectations and learned schemas. Essentially, it acts as a selection process or processes that focus both sensory and cognitive resources on the most relevant events in the soundscape; with relevance being dictated by the stimulus itself (e.g. a loud explosion) or by a task at hand (e.g. listen to announcements in a busy airport). Recent computational models of auditory attention provide key insights into its role in facilitating perception in cluttered auditory scenes. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals When Being in the Minority Pays Off

2017 ◽  
Vol 82 (1) ◽  
pp. 147-178 ◽  
Author(s):  
Amandine Ody-Brasier ◽  
Isabel Fernandez-Mateo
Keyword(s):  
Social Relationships ◽  
Intergroup Relations ◽  
Underlying Mechanism ◽  
Relevant Information ◽  
Price Setting ◽  
Local Constraints ◽  
Market Prices ◽  
Intergroup Processes ◽  
Level Data ◽  
Sociological Literature

Economic sociologists have studied how social relationships shape market prices by focusing mostly on vertical interactions between buyers and sellers. In this article, we examine instead the price consequences of horizontal relationships that arise from intergroup processes among sellers. Our setting is the market for Champagne grapes. Using proprietary transaction-level data, we find that female grape growers—a minority in the growers’ community—charge systematically higher prices than do male grape growers. We argue that the underlying mechanism for this unexpected pattern of results involves the relationships developed and maintained by minority members. Specifically, in-depth fieldwork reveals that female growers get together to compensate for their isolation from the majority. This behavior enables them to overcome local constraints on the availability of price-relevant information, constraints that stem from prevailing norms of market behavior: individualism and secrecy. We discuss the implications of these findings for the study of how relationships shape price-setting processes, for the sociological literature on intergroup relations, and for our understanding of inequality in markets.

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