When people are instructed to locate the vanishing location of a moving target, systematic errors forward in the direction of motion (M-displacement) and downward in the direction of gravity (O-displacement) are found. These phenomena came to be linked with the notion that physical invariants are embedded in the dynamic representations generated by the perceptual system. We explore the nature of these invariants that determine the representational mechanics of projectiles. By manipulating the retention intervals between the target's disappearance and the participant's responses, while measuring both M- and O-displacements, we were able to uncover a representational analogue of the trajectory of a projectile. The outcomes of three experiments revealed that the shape of this trajectory is discontinuous. Although the horizontal component of such trajectory can be accounted for by perceptual and oculomotor factors, its vertical component cannot. Taken together, the outcomes support an internalization of gravity in the visual representation of projectiles.

Download full-text PDF

Source
http://dx.doi.org/10.1037/a0031777DOI Listing

Publication Analysis

Top Keywords

representational dynamics
4
dynamics remembered
4
remembered projectile
4
projectile locations
4
locations people
4
people instructed
4
instructed locate
4
locate vanishing
4
vanishing location
4
location moving
4

Similar Publications

Greater neural delay discounting on reward evaluation in anhedonia.

Int J Clin Health Psychol

January 2025

Department of Psychology, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.

Background/objective: Recent years have witnessed a surge of interest in dissecting the anticipatory and the consummatory aspects of anhedonia in terms of temporal dynamics. However, few research has directly examined reward valuation as a function of time in anhedonia.

Method: Using a delay discounting task, this event-related potential study examined the neural representation of rewards available immediately or in six months in a high-anhedonia group ( = 40) and a low-anhedonia group ( = 40) recruited from a nonclinical sample.

View Article and Find Full Text PDF

RNA velocities and generalizations emerge as powerful approaches for extracting time-resolved information from high-throughput snapshot single-cell data. Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data.

View Article and Find Full Text PDF

Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts.

View Article and Find Full Text PDF

Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infectious disease forecasting model based on physics-informed neural networks (PINNs), an emerging area of scientific machine learning.

View Article and Find Full Text PDF

"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".

J Neurosci Methods

January 2025

School of Electrical and Computer Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA.

Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.

New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!