Representation Of Goals Research Articles

Conjunctive reward-place coding properties of dorsal distal CA1 hippocampus cells.

Autonomous motivated spatial navigation in animals or robots requires the association between spatial location and value. Hippocampal place cells are involved in goal-directed spatial navigation and the consolidation of spatial memories. Recently, Gauthier and Tank (Neuron 99(1):179-193, 2018. https://doi.org/10.1016/j.neuron.2018.06.008) have identified a subpopulation of hippocampal cells selectively activated in relation to rewarded goals. However, the relationship between these cells' spiking activity and goal representation remains elusive. We analyzed data from experiments in which rats underwent five consecutive tasks in which reward locations and spatial context were manipulated. We found CA1 populations with properties continuously ranging from place cells to reward cells. Specifically, we found typical place cells insensitive to reward locations, reward cells that only fired at correct rewarded feeders in each task regardless of context, and "hybrid cells" that responded to spatial locations and change of reward locations. Reward cells responded mostly to the reward delivery rather than to its expectation. In addition, we found a small group of neurons that transitioned between place and reward cells properties within the 5-task session. We conclude that some pyramidal cells (if not all) integrate both spatial and reward inputs to various degrees. These results provide insights into the integrative coding properties of CA1 pyramidal cells, focusing on their abilities to carry both spatial and reward information in a mixed and plastic manner. This conjunctive coding property prompts a re-thinking of current computational models of spatial navigation in which hippocampal spatial and subcortical value representations are independent.

Retraction Notice to: How birds outperform humans inmulti-component behavior.

Recent years have witnessed an astonishing flurry of studies demonstrating that some bird species show higher-order cognitive processes on par with primates [1-3]. As birds have no neocortex, cortical processing cannot be a requirement for higher order cognition [1,4]. Although birds have more neurons than expected from their small brain weights [5], their absolute neuron count is still lower compared to cortical neuron numbers of primates. How, then, is it possible that pigeons reach performance levels in, for example, abstract numerical competence and orthographic processing, that are comparable to that of macaques [6]? While the subpallium is very similar, the organization of the pallium differs tremendously between birds and mammals [1]; moreover, the avian pallium is characterized by small, extremely tightly packed neurons [5]. It is conceivable that signal processing could be faster in such a brain as a result of a higher speed of propagation of activation between neighboring assemblies, resulting in faster switch times between neighboring networks and neuronal representations of behavioral goals. This is important, as behavioral goals in real-life situations are often achieved by a series of sub-tasks [7,8], and especially when sub-tasks supersede each other and show little overlap in processing resources, neocortical (pallial) structures are involved [7,8]. We now report that pigeons are on par with humans when a task demands simultaneous processing resources; importantly, pigeons show faster responses than humans when sub-tasks are separated such that fast switches between processes are required.

Abstract goal representation in visual search by neurons in the human pre-supplementary motor area.

The medial frontal cortex is important for goal-directed behaviours such as visual search. The pre-supplementary motor area (pre-SMA) plays a critical role in linking higher-level goals to actions, but little is known about the responses of individual cells in this area in humans. Pre-SMA dysfunction is thought to be a critical factor in the cognitive deficits that are observed in diseases such as Parkinson's disease and schizophrenia, making it important to develop a better mechanistic understanding of the pre-SMA's role in cognition. We simultaneously recorded single neurons in the human pre-SMA and eye movements while subjects performed goal-directed visual search tasks. We characterized two groups of neurons in the pre-SMA. First, 40% of neurons changed their firing rate whenever a fixation landed on the search target. These neurons responded to targets in an abstract manner across several conditions and tasks. Responses were invariant to motor output (i.e. button press or not), and to different ways of defining the search target (by instruction or pop-out). Second, ∼50% of neurons changed their response as a function of fixation order. Together, our results show that human pre-SMA neurons carry abstract signals during visual search that indicate whether a goal was reached in an action- and cue-independent manner. This suggests that the pre-SMA contributes to goal-directed behaviour by flexibly signalling goal detection and time elapsed since start of the search, and this process occurs regardless of task. These observations provide insights into how pre-SMA dysfunction might impact cognitive function.

GrHDP Solution for Optimal Consensus Control of Multiagent Discrete-Time Systems

This paper develops a new online learning consensus control scheme for multiagent discrete-time systems by goal representation heuristic dynamic programming (GrHDP) techniques. The agents in the whole system are interacted with each other through a communication graph structure. Therefore, each agent can only receive the information from itself and its neighbors. Our goal is to design the GrHDP method to achieve consensus control which makes all the agents track the desired dynamics and simultaneously makes the performance indices reach Nash equilibrium. The new local internal reinforcement signals and local performance indices are provided for each agent and the corresponding distributed control laws are designed. Then, GrHDP algorithm is developed to solve the multiagent consensus control problem with the proof of convergence. It is shown that the designed local internal reinforcement signals are bounded signals and the local performance indices can monotonically converge to their optimal values. Moreover, the desired distributed control laws can also achieve optimal. Two simulation studies, including one with four agents and another with ten agents, are applied to validate the theoretical analysis and also demonstrate the effectiveness of the proposed method.

How Tool-Use Shapes Body Metric Representation: Evidence From Motor Training With and Without Robotic Assistance.

Previous evidence has shown that tool-use can reshape one’s own body schema, extending peripersonal space and modulating the representation of related body parts. Here, we investigated the role of tool action in shaping the body metric representation, by contrasting two different views. According to a first view, the shaping would rely on the mere execution of tool action, while the second view suggests that the shaping induced by tool action on body representation would primarily depend on the representation of the action goals to be accomplished. To this aim, we contrasted a condition in which participants voluntarily accomplish the movement by representing the program and goal of a tool action (i.e., active tool-use training) with a condition in which the tool-use training was produced without any prior goal representation (i.e., passive tool-use training by means of robotic assistance). If the body metric representation primarily depends on the coexistence between goal representation and bodily movements, we would expect an increase of the perceived forearm length in the post- with respect to the pre-training phase after the active training phase only. Healthy participants were asked to estimate the midpoint of their right forearm before and after 20 min of tool-use training. In the active condition, subjects performed “enfold-and-push” movements using a rake to prolong their arm. In the passive condition, subjects were asked to be completely relaxed while the movements were performed with robotic assistance. Results showed a significant increase in the perceived arm length in the post- with respect to the pre-training phase only in the active task. Interestingly, only in the post-training phase, a significant difference was found between active and passive conditions, with a higher perceived arm length in the former than in the latter. From a theoretical perspective, these findings suggest that tool-use may shape body metric representation only when action programs are motorically represented and not merely produced. From a clinical perspective, these results support the use of robots for the rehabilitation of brain-damaged hemiplegic patients, provided that robot assistance during the exercises is present only “as-needed” and that patients’ motor representation is actively involved.

Gendered Career Choices: Paths Toward Studying a Degree in Physical Activity and Sport Science.

Drawing on Social Cognitive Career Theory (SCCT), we examined factors affecting interest in pursuing a degree in Physical Activity and Sport Science (PASS) among Spanish teenage students. Although women were awarded 55.1% of all bachelor degrees in Spain in 2017–2018, female enrollment in PASS degrees is decreasing and currently stands below 20% across the country. To better understand the under-representation of women in this field, 4146 students (50.2% girls; mean age = 16.82 years; SD = 0.837) participated in a survey designed to measure a series of SCCT constructs: interest in studying a PASS degree, career outcome expectations, goal representations, and perceived social supports. With these data, we tested a set of path analysis models to explain gender differences in interest in studying a degree in PASS. These models tested the assumption that interest in PASS would mediate the paths from outcome expectations and social supports to goal representations. Model 1 assumed that interest would partially mediate the path from outcome expectations to goal representations, Model 2 assumed complete mediation, and Model 3 assumed absence of mediation. All models were tested separately for boys and girls. Our results provide information on how male and female students set personal goals based on expected career outcomes and show that this process is affected by gender stereotypes. The lack of interest by young women in studying a degree in PASS (only 7.8% of girls expressed this interest compared with 19.0% of boys), together with the gender differences observed in perceived social supports, outcome expectations, and goal representations, have several important theoretical and practical implications. The present research suggests that interventions that foster positive outcome expectations and social support are necessary to increase interest in studying PASS among teenage girls.

Left dorsolateral prefrontal cortex supports context-dependent prioritisation of off-task thought

When environments lack compelling goals, humans often let their minds wander to thoughts with greater personal relevance; however, we currently do not understand how this context-dependent prioritisation process operates. Dorsolateral prefrontal cortex (DLPFC) maintains goal representations in a context-dependent manner. Here, we show this region is involved in prioritising off-task thought in an analogous way. In a whole brain analysis we established that neural activity in DLPFC is high both when ‘on-task’ under demanding conditions and ‘off-task’ in a non-demanding task. Furthermore, individuals who increase off-task thought when external demands decrease, show lower correlation between neural signals linked to external tasks and lateral regions of the DMN within DLPFC, as well as less cortical grey matter in regions sensitive to these external task relevant signals. We conclude humans prioritise daydreaming when environmental demands decrease by aligning cognition with their personal goals using DLPFC.

Pattern detection and design rationale traceability: an integrated approach to software design quality

Design rationale is the synthesis of the structure that is consistent with the model of an artefact, decisions, alternative approaches, and the reasoning behind it. Hence, bypassing it will result in poor quality systems engineering. Ambiguous representation of design rationale goals is just one of the many limitations that contributes to the intricacy of design patterns, thereby this research aims to introduce an approach to support the structuring, evaluation, and analysis of design patterns. The authors have proposed a method and implemented an associated tool with a mechanism that validates the class relationships and properties of design patterns comprehensively, resulting in a reliable pattern detection result. The preliminary results of the proposed research show that although detection and evaluation of a design pattern require some overhead using the approach, it does contribute to the confidence of the developers and gives affirmation that the design pattern implemented in the source code achieves its defined rationale goals based on the gang of four definition of design patterns through the reliable pattern detection output and straightforward evaluation result. The conclusion could be drawn that through the proposed approach, the preliminary result resolves the intricacy of design rationale traceability while achieving software quality is permissible.

Hippocampal theta phases organize the reactivation of large-scale electrophysiological representations during goal-directed navigation.

Humans are adept in simultaneously following multiple goals, but the neural mechanisms for maintaining specific goals and distinguishing them from other goals are incompletely understood. For short time scales, working memory studies suggest that multiple mental contents are maintained by theta-coupled reactivation, but evidence for similar mechanisms during complex behaviors such as goal-directed navigation is scarce. We examined intracranial electroencephalography recordings of epilepsy patients performing an object-location memory task in a virtual environment. We report that large-scale electrophysiological representations of objects that cue for specific goal locations are dynamically reactivated during goal-directed navigation. Reactivation of different cue representations occurred at stimulus-specific hippocampal theta phases. Locking to more distinct theta phases predicted better memory performance, identifying hippocampal theta phase coding as a mechanism for separating competing goals. Our findings suggest shared neural mechanisms between working memory and goal-directed navigation and provide new insights into the functions of the hippocampal theta rhythm.

Boldness moderates the effects of external threat on performance within a task-switching paradigm.

Set shifting involves the capacity to effectively and efficiently direct mental resources in the service of dynamically changing goal representations. This capacity is important in everyday life and may be vital in situations where processing resources needed for adaptive action may be diverted by cues for external danger or threat (e.g., first responding, military combat, trauma surgery). Although considerable research has investigated performance in set-shifting tasks, little work exists on how the presence of external threats may affect the capacity to flexibly deploy cognitive resources. Even less is known about individual difference factors that might moderate such effects. The current study addressed these gaps in the literature through use of a novel task-switching procedure in which participants (N = 77) performed two tasks in alternation under shock-threat and no-shock ("safe") conditions. Results indicated that behavioral performance was impacted by the presence of threat. However, these effects were moderated by individual differences in threat reactivity as indexed by both self-report and physiological measures. Our findings serve to clarify the impact of explicit threat on set-shifting performance. In addition, they encourage further use of the threat/task-switching paradigm as a laboratory model for studying individual differences in performance under conditions of pressure or peril. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Application of the GORITE BDI Framework to Human-Autonomy Teaming: A Case Study

Human-Autonomy Teaming (HAT) is of growing interest in the military sector, particularly in its application to war gaming using semi-automated computer generated forces (CGF). In these applications, one or more operators manage multiple semi-autonomous game entities. If effective collaboration (teaming) is to occur between operators and entities, then having effective interaction models is essential if the levels of trust and explanatory capability required for military operations are to be delivered. The Situation Awareness-Based Agent Transparency (SAT) Model has been identified as providing a suitable conceptual framework for such models. However, while the SAT model is informed by the Belief-Desire-Intention (BDI) model of agency, to date there has been no implementation of an interaction model at the level of desires and intentions, i.e. goals. In this paper, we propose that GORITE, a novel BDI framework that employs explicit goal representations and a shared data context for goal execution, provides a suitable platform for the development of SAT-enabled agents. The feasibility of this proposition is demonstrated through the development of a simple but representative CGF case study.

An analysis of the representation of sustainable development goals in textbook maps and atlases in educational contexts

ABSTRACTMaps are a central medium for teaching content in the classroom, especially for geography lessons. Maps hold great potential in educational contexts, as they are helpful for the visualization of complex facts. The United Nations General Assembly’s sustainable development goals (SDGs) are complex contents whose teaching is central to education in sustainable development. In order to evaluate the potential of maps for the teaching of SDG-relevant contexts, it is necessary to analyze the extent to which the contents of SDGs have thus far been implemented in educational materials. Qualitative content analysis was used as the methodological basis with a subsequent quantitative evaluation of a number of maps (N = 1.527). The main results represent priority implementation of SDG-related content in map materials in textbooks for older age groups, as well as in atlases. An underrepresentation was observed in temporal aspects and in changes. Implications for the design of educational materials are derived from these observations.

Resilient Wide-Area Damping Control Using GrHDP to Tolerate Communication Failures

This paper proposes a goal representation heuristic dynamic programming (GrHDP)-based resilient wide-area damping controller (WADC) for voltage source converter high voltage direct current (VSC-HVDC) employing redundant wide-area signals as input signals to tolerate communication failure. A supervisory fuzzy logic module is proposed and added in the resilient WADC to adjust the learning rate of GrHDP online when encountering communication failure. Moreover, the resilient WADC does not need the accurate model of the power system and has the adaptability to the variation of operation conditions and communication failures. Case studies are conducted in a 10-machine 39-bus system with one VSC-HVDC transmission line. Simulation results show that the resilient WADC can counteract the negative impact of communication failures on control performance under a wide range of system operating conditions.

A baseline assessment of the United Nations Sustainable Development Goals (SDGs) within European Student Union web environments

The world of student unions is under-researched, particularly relating to global issues and challenges, despite them holding a large proportion of the future workforce. At present, the European Students Union (ESU) has a duty of care to approximately 15 million European students, with subsequent policies and information. This paper addresses the disparity between these areas, with a specific focus on the 2015 United Nations Sustainable Development Goals - in partnership with the UK National Union of Students, under the Dissertations for Good Programme. An innovative web mining method was created and refined over a 10-week data collection period on ESU public web domains using websites from January 2014-18. A pre-determined selection of seventeen key terms were used to quantify the overall knowledge and understanding of the Sustainable Development Goals on a country by country basis, with the UK National Union of Students as a control during the study. This resulted in the successful analysis of 39 ESUs, showing a wide range of keyword abundance, knowledge depth and breadth across member nations. 89% of all key term sites were located within the UK & Switzerland unions, creating 86% of all key terms found – highlighting disparity against developments in European Education for Sustainable Development (ESD). To conclude, this research has highlighted both the lack of visual representation of the Sustainable Development Goals to students and therefore, the variation in education quality across nations, allowing for potential inequality of sustainability skillsets required by graduates on a global scale to tackle global challenges.

Hand forces and placement are modulated and covary during anticipatory control of bimanual manipulation.

Dexterous object manipulation relies on the feedforward and feedback control of kinetics (forces) and kinematics (hand shaping and digit placement). Lifting objects with an uneven mass distribution involves the generation of compensatory moments at object lift-off to counter object torques. This is accomplished through the modulation and covariation of digit forces and placement, which has been shown to be a general feature of unimanual manipulation. These feedforward anticipatory processes occur before performance-specific feedback. Whether this adaptation is a feature unique to unimanual dexterous manipulation or general across unimanual and bimanual manipulation is not known. We investigated the generation of compensatory moments through hand placement and force modulation during bimanual manipulation of an object with variable center of mass. Participants were instructed to prevent object roll during the lift. Similar to unimanual grasping, we found modulation and covariation of hand forces and placement for successful performance. Thus this motor adaptation of the anticipatory control of compensatory moment is a general feature across unimanual and bimanual effectors. Our results highlight the involvement of high-level representation of manipulation goals and underscore a sensorimotor circuitry for anticipatory control through a continuum of force and placement modulation of object manipulation across a range of effectors. NEW & NOTEWORTHY This is the first study, to our knowledge, to show that successful bimanual manipulation of objects with asymmetrical centers of mass is performed through the modulation and covariation of hand forces and placements to generate compensatory moments. Digit force-to-placement modulation is thus a general phenomenon across multiple effectors, such as the fingers of one hand, and both hands. This adds to our understanding of integrating low-level internal representations of object properties into high-level task representations.

Tutorial and simulations with ADAM: an adaptation and anticipation model of sensorimotor synchronization.

Interpersonal coordination of movements often involves precise synchronization of action timing, particularly in expert domains such as ensemble music performance. According to the adaptation and anticipation model (ADAM) of sensorimotor synchronization, precise yet flexible interpersonal coordination is supported by reactive error correction mechanisms and anticipatory mechanisms that exploit systematic patterns in stimulus timing to plan future actions. Here, we provide a tutorial introduction to the computational architecture of ADAM and present a series of single- and dual-virtual agent simulations that examine the model parameters that produce ideal synchronization performance in different tempo conditions. In the single-agent simulations, a virtual agent synchronized responses to steady tempo sequence or a sequence containing gradual tempo changes. Parameters controlling basic reactive error (phase) correction were sufficient for producing ideal synchronization performance at the steady tempo, whereas parameters controlling anticipatory mechanisms were necessary for ideal performance with a tempo-changing sequence. In the dual-agent simulations, two interacting virtual agents produced temporal sequences from either congruent or incongruent internal performance templates specifying a steady tempo or tempo changes. Ideal performance was achieved with reactive error correction alone when both agents implemented the same performance template (either steady tempo or tempo change). In contrast, anticipatory mechanisms played a key role when one agent implemented a steady tempo template and the other agent implemented a tempo change template. These findings have implications for understanding the interplay between reactive and anticipatory mechanisms when agents possess compatible versus incompatible representations of task goals during human-human and human-machine interaction.

What do I do next? The influence of two self-cueing strategies on children’s engagement of proactive control

Setting goals in advance of upcoming tasks is a critical aspect of proactive control. Using this mode of control is particularly challenging in young children, and increases in efficiency during school years. We tested the extent to which two self-cueing strategies can help kindergarteners, first- and fourth graders to set the goal in advance in a cued task-switching paradigm. Whether requesting verbal labeling of the task cue is necessary to induce proactive control was also investigated. Children were assigned to one of three conditions: they were required to be silent, or to identify the relevant task goal by labeling out loud the task name or by pointing at one of two pictorial representations of the task goals. Error rates showed that both strategies helped kindergarteners and first graders to engage in advance preparation. This finding suggests that prompting children to build an explicit representation of the goal is critical in boosting proactive control. It is discussed in terms of the mechanisms underlying this mode of control.

Design and real-time implementation of data-driven adaptive wide-area damping controller for back-to-back VSC-HVDC

This paper proposes a data-driven adaptive wide-area damping controller (D-WADC) for back-to-back VSC-HVDC to suppress the low frequency oscillation in a large-scale interconnected power system. The proposed D-WADC adopts a dual-loop control structure to make full use of the active and reactive power control of VSC-HVDC to improve the damping of the power system. A data-driven algorithm named the goal representation heuristic dynamic programming is employed to design the proposed D-WADC, which means the design procedure only requires the input and output data rather than the mathematic model of the concerned power system. Thus, the D-WADC can adapt to the change of operating condition through online weight modification. Besides, the adaptive delay compensator (ADC) is added to effectively compensate the stochastic delay involved in the wide-area feedback signal. Case studies are conducted based on the simplified model of a practical power system and the 16-machine system with a back-to-back VSC-HVDC. Both the simulation and hardware-in-loop experiment results verify that the proposed D-WADC can effectively suppress the low-frequency oscillation under a wide range of operating conditions, disturbances, and stochastic communication delays.

Shared right-hemispheric representations of sensorimotor goals in dynamic task environments.

Functional behaviour affords that we form goals to integrate sensory information about the world around us with suitable motor actions, such as when we plan to grab an object with a hand. However, much research has tested grasping in static scenarios where goals are pursued with repetitive movements, whereas dynamic contexts require goals to be pursued even when changes in the environment require a change in the actions to attain them. To study grasp goals in dynamic environments here, we employed a task where the goal remained the same but the execution of the movement changed; we primed participants to grasp objects either with their right or left hand, and occasionally they had to switch to grasping with both. Switch costs should be minimal if grasp goal representations were used continuously, for example, within the left dominant hemisphere. But remapped or re-computed goal representations should delay movements. We found that switching from right-hand grasping to bimanual grasping delayed reaction times but switching from left-hand grasping to bimanual grasping did not. Further, control experiments showed that the lateralized switch costs were not caused by asymmetric inhibition between hemispheres or switches between usual and unusual tasks. Our results show that the left hemisphere does not serve a general role of sensorimotor grasp goal representation. Instead, sensorimotor grasp goals appear to be represented at intermediate levels of abstraction, downstream from cognitive task representations, yet upstream from the control of the grasping effectors.

Towards Goal-Directed Navigation Through Combining Learning Based Global and Local Planners.

Robot navigation is a fundamental problem in robotics and various approaches have been developed to cope with this problem. Despite the great success of previous approaches, learning-based methods are receiving growing interest in the research community. They have shown great efficiency in solving navigation tasks and offer considerable promise to build intelligent navigation systems. This paper presents a goal-directed robot navigation system that integrates global planning based on goal-directed end-to-end learning and local planning based on reinforcement learning (RL). The proposed system aims to navigate the robot to desired goal positions while also being adaptive to changes in the environment. The global planner is trained to imitate an expert’s navigation between different positions by goal-directed end-to-end learning, where both the goal representations and local observations are incorporated to generate actions. However, it is trained in a supervised fashion and is weak in dealing with changes in the environment. To solve this problem, a local planner based on deep reinforcement learning (DRL) is designed. The local planner is first implemented in a simulator and then transferred to the real world. It works complementarily to deal with situations that have not been met during training the global planner and is able to generalize over different situations. The experimental results on a robot platform demonstrate the effectiveness of the proposed navigation system.