L'origine de l'objectif est-elle importante? Effets motivationnels
d'objectifs autod{\'e}finis en production
- URL: http://arxiv.org/abs/2311.05626v1
- Date: Mon, 4 Sep 2023 12:08:39 GMT
- Title: L'origine de l'objectif est-elle importante? Effets motivationnels
d'objectifs autod{\'e}finis en production
- Abstract summary: We focus on a commonly used game element, goal setting, through the lens of self-determination theory and goal-setting theory.
One hundred two participants completed a repetitive material-handling task in one of three conditions.
Results showed that perceived autonomy (autonomous motivation) and performance were best when goals were self-set. Engagement, however, was equal between self-set and assigned goals.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Only 21% of employees consider themselves engaged at work. Moreover,
disengagement has been shown to be even more problematic when work is
repetitive in nature. Lack of engagement has been linked to variety of negative
outcomes for employees and companies (e.g., turnover, absenteeism, well-being,
safety incidents, productivity). Gamification, i.e., integrating game elements
into work systems, has been successfully used to increase engagement and
motivation, even when work tasks were mundane and repetitive. In the current
study, we focused on a commonly used game element, goal setting, through the
lens of self-determination theory and goal-setting theory. We argue that goals
given by an external source (e.g., company, experimenter) produce extrinsic
motivation, which improves engagement and performance only in the short term.
We posit that self-set goals lead to more autonomous motivation, and therefore
long-term engagement and performance. One hundred two participants completed a
repetitive material-handling task in one of three conditions (assigned goal,
self-set goal, no goal). Results showed that perceived autonomy (autonomous
motivation) and performance were best when goals were self-set. Engagement,
however, was equal between self-set and assigned goals. The results indicate
that self-set goals have the greatest potential to generate long-term positive
outcomes both for employees and companies.
Related papers
- Aspire: Can Models Self-Evolve from Vague Goals? [61.65007006747275]
We introduce ASPIRE, a benchmark for vague-goal-driven self-evolution.<n>ASPIRE supports both model-weight and agent-harness evolution in a unified interactive environment.<n>Our experiments show that vague goals redirect search effort toward goal interpretation.
arXiv Detail & Related papers (2026-08-31T17:14:59Z) - Reward function compression facilitates goal-dependent reinforcement learning [2.470815298095903]
Reinforcement learning agents learn from rewards, but humans can uniquely assign value to novel, abstract outcomes in a goal-dependent manner.<n>We propose that goal-dependent learning is initially supported by a capacity-limited working memory system.<n>Our work suggests that efficient goal-directed learning relies on compressing complex goal information into a stable reward function.
arXiv Detail & Related papers (2025-09-08T15:43:40Z) - Does the Order of Fine-tuning Matter and Why? [11.975836356680855]
We study the effect of fine-tuning multiple intermediate tasks and their ordering on target task performance.
Experimental results show that there is an impact of task ordering on target task performance by up to 6% of performance gain and up to 4% of performance loss.
arXiv Detail & Related papers (2024-10-03T19:07:14Z) - Carrot and Stick: Inducing Self-Motivation with Positive & Negative Feedback [8.894019620224112]
Self-motivation that drives people to reach their goals has not yet been studied from a computational perspective.
We propose CArrot and STICk dataset, consisting of 12,590 sentences with 5 different strategies for enhancing self-motivation.
arXiv Detail & Related papers (2024-06-24T10:55:31Z) - A Large Scale Survey of Motivation in Software Development and Analysis of its Validity [0.3683202928838613]
We identify 11 motivators from the literature and evaluate their relative effect on motivation.
Since motivation is an internal subjective feeling, we also analyze the validity of the answers.
arXiv Detail & Related papers (2024-04-12T07:51:21Z) - Generating Adversarial Examples with Task Oriented Multi-Objective
Optimization [21.220906842166425]
Adversarial training is one of the most efficient methods to improve the model's robustness.
We propose emphTask Oriented MOO to address this issue.
Our principle is to only maintain the goal-achieved tasks, while letting the spend more effort on improving the goal-unachieved tasks.
arXiv Detail & Related papers (2023-04-26T01:30:02Z) - EgoTaskQA: Understanding Human Tasks in Egocentric Videos [89.9573084127155]
EgoTaskQA benchmark provides home for crucial dimensions of task understanding through question-answering on real-world egocentric videos.
We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others.
We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos.
arXiv Detail & Related papers (2022-10-08T05:49:05Z) - Mutual Information State Intrinsic Control [91.38627985733068]
Intrinsically motivated RL attempts to remove this constraint by defining an intrinsic reward function.
Motivated by the self-consciousness concept in psychology, we make a natural assumption that the agent knows what constitutes itself.
We mathematically formalize this reward as the mutual information between the agent state and the surrounding state.
arXiv Detail & Related papers (2021-03-15T03:03:36Z) - Learning with AMIGo: Adversarially Motivated Intrinsic Goals [63.680207855344875]
AMIGo is a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals.
We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks.
arXiv Detail & Related papers (2020-06-22T10:22:08Z) - Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
Continually solving new, unsolved tasks is the key to learning diverse behaviors.
In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency.
We propose setting up an automatic curriculum for goals that the agent needs to solve.
We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
arXiv Detail & Related papers (2020-06-17T03:58:25Z) - Intrinsic Motivation for Encouraging Synergistic Behavior [55.10275467562764]
We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks.
Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.
arXiv Detail & Related papers (2020-02-12T19:34:51Z) - Mutual Information-based State-Control for Intrinsically Motivated
Reinforcement Learning [102.05692309417047]
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
In the natural world, intelligent organisms learn from internal drives, bypassing the need for external signals.
We propose to formulate an intrinsic objective as the mutual information between the goal states and the controllable states.
arXiv Detail & Related papers (2020-02-05T19:21:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.