Representation Of Goals Research Articles

A computational account of self-control

Self-control is core to human well-being. However, the lack of a well-specified, computationally tractable framework related to self-control makes it difficult to clarify underlying mechanisms, interpret relevant empirical phenomena, or develop interventions helpful in promoting self-control. To help address this gap, we invite consideration of the Comparison with Goal States Model (CGSM) for self-control. The CGSM amplifies activations related to available options whose representations are similar to representations of relevant goals and diminishes activations related to available options whose representations are dissimilar to representations of relevant goals. For example, influenced by healthy eating goals, the CGSM would amplify activations related to an apple and diminish activations related to a cookie, leading to an eventual preference for the apple, even though the cookie might be initially preferred. The CGSM successfully explicates observations related to reaction time in food choice, dynamics reflected in mouse-tracking trajectories, and showcases a mechanism by which hyperbolic discount curves in temporal discounting contexts might emerge. We use the CGSM to propose theoretical constraints on the nature of self-control and describe how multiple strategies have the potential to promote self-control.

Mapping cognitive activity from electrocorticography field potentials in humans performing NBack task

Objective. Advancements in data science and assistive technologies have made invasive brain-computer interfaces (iBCIs) increasingly viable for enhancing the quality of life in physically disabled individuals. Intracortical microelectrode implants are a common choice for such a communication system due to their fine temporal and spatial resolution. The small size of these implants makes the implantation plan critical for the successful exfiltration of information, particularly when targeting representations of task goals that lack robust anatomical correlates. Approach. Working memory processes including encoding, retrieval, and maintenance are observed in many areas of the brain. Using human electrocorticography (ECoG) recordings during a working memory experiment, we provide proof that it is possible to localize cognitive activity associated with the task and to identify key locations involved with executive memory functions. Results. From the analysis, we could propose an optimal iBCI implant location with the desired features. The general approach is not limited to working memory but could also be used to map other goal-encoding factors such as movement intentions, decision-making, and visual-spatial attention. Significance. Deciphering the intended action of a BCI user is a complex challenge that involves the extraction and integration of cognitive factors such as movement planning, working memory, visual-spatial attention, and the decision state. Examining field potentials from ECoG electrodes while participants engaged in tailored cognitive tasks can pinpoint location with valuable information related to anticipated actions. This manuscript demonstrates the feasibility of identifying electrodes involved in cognitive activity related to working memory during user engagement in the NBack task. Devoting time in meticulous preparation to identify the optimal brain regions for BCI implant locations will increase the likelihood of rich signal outcomes, thereby improving the overall BCI user experience.

A neural circuit architecture for rapid learning in goal-directed navigation

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

A Goal Interaction Graph Planning Framework for Conversational Recommendation

Multi-goal conversational recommender system (MG-CRS) which is more in line with realistic scenarios has attracted a lot of attention. MG-CRS can dynamically capture the demands of users in conversation, continuously engage their interests, and make recommendations. The key of accomplishing these tasks is to plan a reasonable goal sequence which can naturally guide the user to accept the recommended goal. Previous works have demonstrated that mining the correlations of goals from the goal sequences in the dialogue corpus is helpful for recommending the goal that the user is interested in. However, they independently model correlations for each level of goal (i.e., goal type or entity) and neglect the order of goals appear in the dialogue. In this paper, we propose a goal interaction graph planning framework which constructs a directed heterogeneous graph to flexibly model the correlations between any level of goals and retain the order of goals. We design a goal interaction graph learning module to model the goal correlations and propagate goal representations via directed edges, then use an encoder and a dual-way fusion decoder to extract the most relevant information with the current goal from the conversation and domain knowledge, making the next-goal prediction fully exploit the prior goal correlations and user feedback. Finally we generate engaging responses based on the predicted goal sequence to complete the recommendation task. Experiments on two benchmark datasets show that our method achieves significant improvements in both the goal planning and response generation tasks.

Unsupervised Object Interaction Learning with Counterfactual Dynamics Models

We present COIL (Counterfactual Object Interaction Learning), a novel way of learning skills of object interactions on entity-centric environments. The goal is to learn primitive behaviors that can induce interactions without external reward or any supervision. Existing skill discovery methods are limited to locomotion, simple navigation tasks, or single-object manipulation tasks, mostly not inducing interaction between objects. Unlike a monolithic representation usually used in prior skill learning methods, we propose to use a structured goal representation that can query and scope which objects to interact with, which can serve as a basis for solving more complex downstream tasks. We design a novel counterfactual intrinsic reward through the use of either a forward model or successor features that can learn an interaction skill between a pair of objects given as a goal. Through experiments on continuous control environments such as Magnetic Block and 2.5-D Stacking Box, we demonstrate that an agent can learn object interaction behaviors (e.g., attaching or stacking one block to another) without any external rewards or domain-specific knowledge.

The effect of high-frequency rTMS over left DLPFC and fluid abilities on goal neglect

Goal neglect refers to when an aspect of task instructions is not utilised due to increased competition between goal representations, an attentional limit theoretically linked to working memory. In an attempt to alleviate goal neglect and to investigate the association between dorsolateral prefrontal cortex (DLPFC)-supported working memory and goal neglect, we used high-frequency repetitive transcranial magnetic stimulation to the left DLPFC whilst participants completed the letter-monitoring task, a measure of goal neglect, and an N3-back task, a working memory task known to be affected by rTMS of the left DLPFC, following 20 min of active and sham stimulation (run on separate days). We found increased accuracy on the N3-back task in addition to decreased goal neglect in the active compared to sham condition when controlling for age and fluid abilities (as assessed by matrix reasoning performance). Furthermore, analysis showed that active stimulation improvements on both the N3-back and letter-monitoring tasks were greater for those with higher fluid abilities. These findings provide support for the link between the DLPFC-support working memory and goal neglect. Increased performance on the N3-back task also supports the literature reporting a link between left DLPFC and verbal working memory. Results are evaluated in the context of potential use to alleviate symptoms of disorders related to goal neglect.

Common modular architecture across diverse cortical areas in early development

In order to deal with a complex environment, animals form a diverse range of neural representations that vary across cortical areas, ranging from largely unimodal sensory input to higher-order representations of goals, outcomes, and motivation. The developmental origin of this diversity is currently unclear, as representations could arise through processes that are already area-specific from the earliest developmental stages or alternatively, they could emerge from an initially common functional organization shared across areas. Here, we use spontaneous activity recorded with two-photon and widefield calcium imaging to reveal the functional organization across the early developing cortex in ferrets, a species with a well-characterized columnar organization and modular structure of spontaneous activity in the visual cortex. We find that in animals 7 to 14 d prior to eye-opening and ear canal opening, spontaneous activity in both sensory areas (auditory and somatosensory cortex, A1 and S1, respectively), and association areas (posterior parietal and prefrontal cortex, PPC and PFC, respectively) showed an organized and modular structure that is highly similar to the organization in V1. In all cortical areas, this modular activity was distributed across the cortical surface, forming functional networks that exhibit millimeter-scale correlations. Moreover, this modular structure was evident in highly coherent spontaneous activity at the cellular level, with strong correlations among local populations of neurons apparent in all cortical areas examined. Together, our results demonstrate a common distributed and modular organization across the cortex during early development, suggesting that diverse cortical representations develop initially according to similar design principles.

Supplementary Control for Quantized Discrete-Time Nonlinear Systems Under Goal Representation Heuristic Dynamic Programming.

This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neural network (NN)-based observer is first proposed to estimate the unknown system state in the simultaneous presence of quantized influence. In light of the estimated states and the ideal control inputs via a zero-sum game, a GrHDP algorithm with a reinforced term is developed to implement the supplementary control task, where some novel weight updating rules are constructed by virtue of an additional tunable parameter to improve the system performance. Furthermore, a set of conditions about the stability of estimated error dynamics of both observer states and updated NNs' weights are derived by resorting to the Lyapunov stability theory. Finally, the effectiveness of the developed method is verified by a power system and a numerical experiment.

Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation

Proactively and naturally guiding the dialog from the non-recommendation context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is crucial for the Conversational Recommender System (CRS). Prior studies mainly focus on planning the next dialog goal (e.g., chat on a movie star) conditioned on the previous dialog. However, we find the dialog goals can be simultaneously observed at different levels, which can be utilized to improve CRS. In this paper, we propose Dual-space Hierarchical Learning (DHL) to leverage multi-level goal sequences and their hierarchical relationships for conversational recommendation. Specifically, we exploit multi-level goal sequences from both the representation space and the optimization space. In the representation space, we propose the hierarchical representation learning where a cross attention module derives mutually enhanced multi-level goal representations. In the optimization space, we devise the hierarchical weight learning to reweight lower-level goal sequences, and introduce bi-level optimization for stable update. Additionally, we propose a soft labeling strategy to guide optimization gradually. Experiments on two real-world datasets verify the effectiveness of our approach. Code and data are available here.

Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning.

We propose an Information bottleneck (IB) for Goal representation learning (InfoGoal), a self-supervised method for generalizable goal-conditioned reinforcement learning (RL). Goal-conditioned RL learns a policy from reward signals to predict actions for reaching desired goals. However, the policy would overfit the task-irrelevant information contained in the goal and may be falsely or ineffectively generalized to reach other goals. A goal representation containing sufficient task-relevant information and minimum task-irrelevant information is guaranteed to reduce generalization errors. However, in goal-conditioned RL, it is difficult to balance the tradeoff between task-relevant information and task-irrelevant information because of the sparse and delayed learning signals, i.e., reward signals, and the inevitable task-relevant information sacrifice caused by information compression. Our InfoGoal learns a minimum and sufficient goal representation with dense and immediate self-supervised learning signals. Meanwhile, InfoGoal adaptively adjusts the weight of information minimization to achieve maximum information compression with a reasonable sacrifice of task-relevant information. Consequently, InfoGoal enables policy to generate a targeted trajectory toward states where the desired goal can be found with high probability and broadly explores those states. We conduct experiments on both simulated and real-world tasks, and our method significantly outperforms baseline methods in terms of policy optimality and the success rate of reaching unseen test goals. Video demos are available at infogoal.github.io.

EVALUATING GOAL THREAT IN FOOTBALL USING PLAYER AND BALL LOCATIONS

Goal scoring in football is relatively low but vitally important, hence research has considered how goals are created and scored with measures such as expected goals prevalent. The dynamical systems theoretical perspective, considers a collective system, such as football, as existing in two states, stable (no substantive advantage for either team) or unstable (advantage present). Hence, goal scoring events occur when the system has become unstable, with a “perturbation” the event causing the system state change. Here, a “goal threat” value was calculated every second (scaled from 0 to 100) using the XY coordinates of players and the ball, weighted in relation to proximity to the goal (a potential proxy for the degree of system instability). Video recordings and synchronised Amisco 2D representations of goals (n=64) scored in Swansea City AFC English Premier League 2012/2013 matches (n=20) were analysed using Dartfish v10 Pro software. Each goal was analysed from when the play was judged to be stable (no obvious goal scoring opportunity), or the start of possession, until the goal had been scored. Goals were not always preceded by high goal threat values (maximum goal threat values ranged from 13.4 to 99.0). The authors independently subjectively determined that perturbations occurred up to 7 seconds from when the goal threat value increased by at least 40%. Thus, perturbations were not directly related to goal scoring opportunities. This novel method provides a useful, quantifiable, and simple measure of goal threat that may also aid audience engagement and measure defensive effectiveness.

Entrepreneurial opportunities as expressions of personal identities: interpretative engagement through personal value structures

ABSTRACT Entrepreneurial identities have a significant influence on new venture emergence. Yet, an entrepreneur’s (meaning-constructing) interpretive engagement with situations such as entrepreneurial opportunities remain relatively unexplained. This paper explores how entrepreneurs’ personal identities influence their interpretive engagement with entrepreneurial opportunities. We presented 34 entrepreneurs with three business scenarios and, using verbal protocols and content analysis techniques, inductively identified seven individual value types. We find that these value types vary among individual founders, revealing distinct personal identities (as personal value structures). These types are reflections of exogenously and endogenously oriented personal identities that depict how individuals perceive scenarios as situations that represent the environment as exogenous (scenarios as what is) or as endogenously constructed (scenarios as what could be). Therefore, we contribute to the entrepreneurship literature by highlighting the significance of personal identities as representations of entrepreneurial goals that give rise to different framings of situations as entrepreneurial opportunities as these become expressions of their personal identities.

Students' Personalized Targeted Homework Based On Knowledge Mapping

After the implementation of the dual reduction policy by the country, the direction of education development has changed. Future education requires teachers to develop intelligent teaching methods based on the different characteristics of different students. Based on intelligent teaching, corresponding ability goals are constructed, but the representation of such ability goals is difficult to present systematically and accurately. Using artificial intelligence and high school students as the core and carrier of learning, guided by the theory of multiple intelligences, and guided by subject knowledge graphs, students choose homework sheets that are suitable for themselves based on their actual learning situation. In the process of completing homework, they follow the principle of easy to difficult to avoid repetitive assignments. To fully stimulate students' interests and enable them to have better personalized learning abilities, ensuring that future teaching is more intelligent and real-time.

Students' Personalized Targeted Homework Based On Knowledge Mapping

After the implementation of the dual reduction policy by the country, the direction of education development has changed. Future education requires teachers to develop intelligent teaching methods based on the different characteristics of different students. Based on intelligent teaching, corresponding ability goals are constructed, but the representation of such ability goals is difficult to present systematically and accurately. Using artificial intelligence and high school students as the core and carrier of learning, guided by the theory of multiple intelligences, and guided by subject knowledge graphs, students choose homework sheets that are suitable for themselves based on their actual learning situation. In the process of completing homework, they follow the principle of easy to difficult to avoid repetitive assignments. To fully stimulate students' interests and enable them to have better personalized learning abilities, ensuring that future teaching is more intelligent and real-time.

Students' Personalized Targeted Homework Based On Knowledge Mapping

After the implementation of the dual reduction policy by the country, the direction of education development has changed. Future education requires teachers to develop intelligent teaching methods based on the different characteristics of different students. Based on intelligent teaching, corresponding ability goals are constructed, but the representation of such ability goals is difficult to present systematically and accurately. Using artificial intelligence and high school students as the core and carrier of learning, guided by the theory of multiple intelligences, and guided by subject knowledge graphs, students choose homework sheets that are suitable for themselves based on their actual learning situation. In the process of completing homework, they follow the principle of easy to difficult to avoid repetitive assignments. To fully stimulate students' interests and enable them to have better personalized learning abilities, ensuring that future teaching is more intelligent and real-time.

Forward effects from action observation: the role of attentional focus.

After viewing an image representing an action on an object, we recognize the forward states of the seen action faster than the backward states. The present study exploits a variant of a new experimental paradigm to investigate cognitive mechanisms underlying this effect. Participants viewed a series of still photos of unfolding actions on objects, each followed by a photo depicting either one of three (instead of two of the original paradigm) different and temporally distant moments after the image or one moment before the image, along with photos of different actions. Experiment 1 revealed the classical forward effects in this new context: when the task was to judge whether the action in the second photo was the same as in the first photo, evaluations were faster for all forward photos than for backward photos. In Experiment 2, we examined the role of participants' attention to the object alongside the role of attention to action kinematics in triggering these "forward effects" by manipulating participants' attentional focus. As the results showed, evaluations were faster for all forward photos when the focus was on the action kinematics, but when the focus was on the object, evaluations were faster only for the last forward photo showing the final action state. These results seem to suggest that focusing on the object triggers a representation of the action goal and thus modulates the mental simulation underlying action anticipation.

The representation of sustainable development goals in a national curriculum: a content analysis of Taiwan’s 12-Year Basic Education Curriculum Guidelines

Despite the increasing focus on incorporating the Sustainable Development Goals (SDGs) into education through curriculum mainstreaming, related research remains lacking. This study explored the representation of SDGs in Taiwan’s national curriculum in terms of selection and organization. Through a content analysis of the social studies and natural sciences curriculum guidelines, the findings revealed that SDG-related content comprises a minor part of the guidelines and is not well organized, possibly due to limitations on discipline characteristics and curriculum types. A subtle but complex implicit curriculum reflects neoliberalism, which addresses SDG-related issues through market mechanisms and shifts the focus from power, politics, and citizenship to consumers’ choices and lifestyles. The results provide a reference for policymakers and educators in re-examining underrepresented SDGs and dimensions and organizing them coherently. They also bridge the gap between the international framework and national curricula, laying the foundation for international comparisons, discussions, and debates on ESD.

Cycles of goal silencing and reactivation underlie complex problem-solving in primate frontal and parietal cortex

While classic views proposed that working memory (WM) is mediated by sustained firing, recent evidence suggests a contribution of activity-silent states. Within WM, human neuroimaging studies suggest a switch between attentional foreground and background, with only the foregrounded item represented in active neural firing. To address this process at the cellular level, we recorded prefrontal (PFC) and posterior parietal (PPC) neurons in a complex problem-solving task, with monkeys searching for one or two target locations in a first cycle of trials, and retaining them for memory-guided revisits on subsequent cycles. When target locations were discovered, neither frontal nor parietal neurons showed sustained goal-location codes continuing into subsequent trials and cycles. Instead there were sequences of timely goal silencing and reactivation, and following reactivation, sustained states until behavioral response. With two target locations, goal representations in both regions showed evidence of transitions between foreground and background, but the PFC representation was more complete, extending beyond the current trial to include both past and future selections. In the absence of unbroken sustained codes, different neuronal states interact to support maintenance and retrieval of WM representations across successive trials.

No temporal decay of cognitive control in the congruency sequence effect.

Cognitive control theories describe the active maintenance of goal representations over temporal delays as central for adaptive behavior. Dynamic adaptations of goal representations are often measured as the congruency sequence effect (CSE), which describes a reduced congruency effect in trials following incongruent trials compared to congruent trials. Previous studies questioned active maintenance of CSEs and instead found that CSEs decrease rapidly over time (Duthoo et al., 2014; Egner et al., 2010). However, in these studies, CSEs can be attributed to both, control adaptations following conflict and binding effects due to repetition of stimulus (S) and response (R) features. In four experiments, we demonstrate that CSEs originating solely from control adaptions were not affected by temporal delays. Additional within-subject conditions partially replicated previous research showing a decrease in CSEs in task designs allowing for S-R binding effects and controlled for task complexity as a potential moderator. Together, results support theories which predict an active maintenance or retrieval of cognitive control. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Data-driven control for dynamic quantized nonlinear systems with state constraints based on barrier functions

In this study, the data-driven control problem is investigated for discrete-time nonlinear systems with state constraints and dynamic quantization. A dynamic quantizer is utilized to encode the control signal to relieve the impact of inherent quantization errors in comparison with traditional uniform quantizers. By employing three sets of neural networks (NNs), a data-based goal representation heuristic dynamic programming algorithm with the concurrent learning technique is offered to achieve the near-optimal control objective without model requirement. Specifically, by introducing the relaxed barrier function into the cost function, a barrier-based control strategy is proposed to generate the repulsive control force when the state moves to the constraint boundary. Subsequently, the recorded historical data is adequately leveraged to build a new weight updating rule, and persistent excitation of weight parameter estimation is hence removed. Furthermore, the Lyapunov method is utilized to demonstrate that the weights estimation errors of NNs are bounded. Finally, numerical simulation and power system examples are employed to demonstrate the validity of the control strategy.