Time Perception: A Review on Psychological, Computational and Robotic Models

• URL: http://arxiv.org/abs/2007.11845v3
• Date: Fri, 25 Dec 2020 08:23:33 GMT
• Title: Time Perception: A Review on Psychological, Computational and Robotic Models
• Authors: Hamit Basgol, Inci Ayhan, Emre Ugur
• Abstract summary: We introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception. We summarize the emergent computational and robotic models of time perception. Most models of timing are developed for either sensory timing (i.e. ability to assess an interval) or motor timing (i.e. ability to reproduce an interval)
• Score: 2.223733768286313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Animals exploit time to survive in the world. Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication, and effective cooperation. Since time is an inseparable part of cognition, there is a growing interest in the artificial intelligence approach to subjective time, which has a possibility of advancing the field. The current survey study aims to provide researchers with an interdisciplinary perspective on time perception. Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and related abilities. Secondly, we summarize the emergent computational and robotic models of time perception. A general overview to the literature reveals that a substantial amount of timing models are based on a dedicated time processing like the emergence of a clock-like mechanism from the neural network dynamics and reveal a relationship between the embodiment and time perception. We also notice that most models of timing are developed for either sensory timing (i.e. ability to assess an interval) or motor timing (i.e. ability to reproduce an interval). The number of timing models capable of retrospective timing, which is the ability to track time without paying attention, is insufficient. In this light, we discuss the possible research directions to promote interdisciplinary collaboration in the field of time perception.

Related papers

• Temporal Interception and Present Reconstruction: A Cognitive-Signal Model for Human and AI Decision Making [0.0]
  This paper proposes a novel theoretical model to explain how the human mind and artificial intelligence can approach real-time awareness.<n>By investigating cosmic signal delay, neurological reaction times, and the ancient cognitive state of stillness, we explore how one may shift from reactive perception to a conscious interface with the near future.
  arXiv  Detail & Related papers  (2025-05-11T15:38:27Z)
• Classifying Subjective Time Perception in a Multi-robot Control Scenario Using Eye-tracking Information [3.8916312075738273]
  Accurately assessing an operator's mental state is critical for maintaining performance and well-being. We use subjective time perception as a sensitive, low-latency indicator of well-being and cognitive strain. We study how human physiological signals can be used to estimate a person's subjective time perception in a human-swarm interaction scenario.
  arXiv  Detail & Related papers  (2025-04-08T21:30:18Z)
• Predicting change in time production -- A machine learning approach to time perception [1.3091548163229116]
  We train a machine learning model to predict the direction of change in an individual's time production. We find that a participant's previous timing performance played a significant role in determining the direction of change in time production.
  arXiv  Detail & Related papers  (2024-12-17T10:41:19Z)
• Evidence of Cognitive Deficits andDevelopmental Advances in Generative AI: A Clock Drawing Test Analysis [17.5336703613751]
  This study explores how several recent GenAI models perform on the Clock Drawing Test (CDT), a neuropsychological assessment of visuospatial planning and organization. While models create clock-like drawings, they struggle with accurate time representation, showing deficits similar to mild-severe cognitive impairment.
  arXiv  Detail & Related papers  (2024-10-15T16:27:22Z)
• Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? [70.19200858203388]
  Temporal reasoning is fundamental for large language models to comprehend the world. CoTempQA is a benchmark containing four co-temporal scenarios. Our experiments reveal a significant gap between the performance of current LLMs and human-level reasoning.
  arXiv  Detail & Related papers  (2024-06-13T12:56:21Z)
• A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience [0.0]
  We highlight the influence of the temporal distance between a stimulus event and the moment when its experience is reported on the provided information's validity. We champion the existence of a temporal distance for each type of stimulus that maximizes the validity of self-reports.
  arXiv  Detail & Related papers  (2023-01-27T15:28:31Z)
• Neuro-Symbolic Spatio-Temporal Reasoning [16.29507250221851]
  Spatio-temporal knowledge is required beyond interacting with the physical world. Different attempts have been made to integrate this into AI systems. We propose a synergy between logical reasoning and machine learning grounded on spatial and temporal knowledge.
  arXiv  Detail & Related papers  (2022-11-28T17:21:41Z)
• A-ACT: Action Anticipation through Cycle Transformations [89.83027919085289]
  We take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms. A recent study on human psychology explains that, in anticipating an occurrence, the human brain counts on both systems. In this work, we study the impact of each system for the task of action anticipation and introduce a paradigm to integrate them in a learning framework.
  arXiv  Detail & Related papers  (2022-04-02T21:50:45Z)
• Bayesian sense of time in biological and artificial brains [1.52292571922932]
  The brain's ability to process the passage of time is one of the fundamental dimensions of our experience. How can we explain empirical data on human time perception using the Bayesian brain hypothesis? What insights can the agent-based machine learning models provide for the study of this subject?
  arXiv  Detail & Related papers  (2022-01-14T14:05:30Z)
• Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution [97.50813120600026]
  Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) Recent works have focused on an abstract reasoning task of this kind -- Raven's Progressive Matrices ( RPM) We propose a neuro-symbolic Probabilistic Abduction and Execution learner (PrAE) learner.
  arXiv  Detail & Related papers  (2021-03-26T02:42:18Z)
• AGENT: A Benchmark for Core Psychological Reasoning [60.35621718321559]
  Intuitive psychology is the ability to reason about hidden mental variables that drive observable actions. Despite recent interest in machine agents that reason about other agents, it is not clear if such agents learn or hold the core psychology principles that drive human reasoning. We present a benchmark consisting of procedurally generated 3D animations, AGENT, structured around four scenarios.
  arXiv  Detail & Related papers  (2021-02-24T14:58:23Z)
• Learning Temporal Dynamics from Cycles in Narrated Video [85.89096034281694]
  We propose a self-supervised solution to the problem of learning to model how the world changes as time elapses. Our model learns modality-agnostic functions to predict forward and backward in time, which must undo each other when composed. We apply the learned dynamics model without further training to various tasks, such as predicting future action and temporally ordering sets of images.
  arXiv  Detail & Related papers  (2021-01-07T02:41:32Z)
• Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data [7.780531445879182]
  We present a novel modeling framework that addresses a number of topical challenges and open questions in the psychological literature on modeling dynamic processes. How can we model and forecast ILD when the length of individual time series and the number of variables collected are roughly equivalent? Second, how can we best take advantage of the cross-sectional (between-person) information inherent to most ILD scenarios while acknowledging individuals differ both quantitatively and qualitatively?
  arXiv  Detail & Related papers  (2020-07-09T20:34:23Z)
• Towards a Neural Model for Serial Order in Frontal Cortex: a Brain Theory from Memory Development to Higher-Level Cognition [53.816853325427424]
  We propose that the immature prefrontal cortex (PFC) use its primary functionality of detecting hierarchical patterns in temporal signals. Our hypothesis is that the PFC detects the hierarchical structure in temporal sequences in the form of ordinal patterns and use them to index information hierarchically in different parts of the brain. By doing so, it gives the tools to the language-ready brain for manipulating abstract knowledge and planning temporally ordered information.
  arXiv  Detail & Related papers  (2020-05-22T14:29:51Z)

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.