scholarly journals Temporal Properties of Abstract Shape Representation

2016 ◽  
Vol 16 (12) ◽  
pp. 789
Author(s):  
Nicholas Baker ◽  
Philip Kellman
PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254719
Author(s):  
Nicholas Baker ◽  
Philip J. Kellman

How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a set of boundary points into a constant curvature representation. The model includes two free parameters that relate to the degree to which the visual system encodes shapes with high fidelity vs. the importance of simplicity in shape representations. We conducted two experiments to estimate these parameters empirically. Experiment 1 tested the limits of observers’ ability to discriminate a contour made up of two constant curvature segments from one made up of a single constant curvature segment. Experiment 2 tested observers’ ability to discriminate contours generated from cubic splines (which, mathematically, have no constant curvature segments) from constant curvature approximations of the contours, generated at various levels of precision. Results indicated a clear transition point at which discrimination becomes possible. The results were used to fix the two parameters in our model. In Experiment 3, we tested whether outputs from our parameterized model were predictive of perceptual performance in a shape recognition task. We generated shape pairs that had matched physical similarity but differed in representational similarity (i.e., the number of segments needed to describe the shapes) as assessed by our model. We found that pairs of shapes that were more representationally dissimilar were also easier to discriminate in a forced choice, same/different task. The results of these studies provide evidence for constant curvature shape representation in human visual perception and provide a testable model for how abstract shape descriptions might be encoded.


2020 ◽  
Vol 63 (4) ◽  
pp. 1270-1281
Author(s):  
Leah Fostick ◽  
Riki Taitelbaum-Swead ◽  
Shulamith Kreitler ◽  
Shelly Zokraut ◽  
Miriam Billig

Purpose Difficulty in understanding spoken speech is a common complaint among aging adults, even when hearing impairment is absent. Correlational studies point to a relationship between age, auditory temporal processing (ATP), and speech perception but cannot demonstrate causality unlike training studies. In the current study, we test (a) the causal relationship between a spatial–temporal ATP task (temporal order judgment [TOJ]) and speech perception among aging adults using a training design and (b) whether improvement in aging adult speech perception is accompanied by improved self-efficacy. Method Eighty-two participants aged 60–83 years were randomly assigned to a group receiving (a) ATP training (TOJ) over 14 days, (b) non-ATP training (intensity discrimination) over 14 days, or (c) no training. Results The data showed that TOJ training elicited improvement in all speech perception tests, which was accompanied by increased self-efficacy. Neither improvement in speech perception nor self-efficacy was evident following non-ATP training or no training. Conclusions There was no generalization of the improvement resulting from TOJ training to intensity discrimination or generalization of improvement resulting from intensity discrimination training to speech perception. These findings imply that the effect of TOJ training on speech perception is specific and such improvement is not simply the product of generally improved auditory perception. It provides support for the idea that temporal properties of speech are indeed crucial for speech perception. Clinically, the findings suggest that aging adults can be trained to improve their speech perception, specifically through computer-based auditory training, and this may improve perceived self-efficacy.


2017 ◽  
Author(s):  
Darren Rhodes

Time is a fundamental dimension of human perception, cognition and action, as the perception and cognition of temporal information is essential for everyday activities and survival. Innumerable studies have investigated the perception of time over the last 100 years, but the neural and computational bases for the processing of time remains unknown. First, we present a brief history of research and the methods used in time perception and then discuss the psychophysical approach to time, extant models of time perception, and advancing inconsistencies between each account that this review aims to bridge the gap between. Recent work has advocated a Bayesian approach to time perception. This framework has been applied to both duration and perceived timing, where prior expectations about when a stimulus might occur in the future (prior distribution) are combined with current sensory evidence (likelihood function) in order to generate the perception of temporal properties (posterior distribution). In general, these models predict that the brain uses temporal expectations to bias perception in a way that stimuli are ‘regularized’ i.e. stimuli look more like what has been seen before. Evidence for this framework has been found using human psychophysical testing (experimental methods to quantify behaviour in the perceptual system). Finally, an outlook for how these models can advance future research in temporal perception is discussed.


Author(s):  
Tengfei Li ◽  
Jing Liu ◽  
Haiying Sun ◽  
Xiang Chen ◽  
Lipeng Zhang ◽  
...  

AbstractIn the past few years, significant progress has been made on spatio-temporal cyber-physical systems in achieving spatio-temporal properties on several long-standing tasks. With the broader specification of spatio-temporal properties on various applications, the concerns over their spatio-temporal logics have been raised in public, especially after the widely reported safety-critical systems involving self-driving cars, intelligent transportation system, image processing. In this paper, we present a spatio-temporal specification language, STSL PC, by combining Signal Temporal Logic (STL) with a spatial logic S4 u, to characterize spatio-temporal dynamic behaviors of cyber-physical systems. This language is highly expressive: it allows the description of quantitative signals, by expressing spatio-temporal traces over real valued signals in dense time, and Boolean signals, by constraining values of spatial objects across threshold predicates. STSL PC combines the power of temporal modalities and spatial operators, and enjoys important properties such as finite model property. We provide a Hilbert-style axiomatization for the proposed STSL PC and prove the soundness and completeness by the spatio-temporal extension of maximal consistent set and canonical model. Further, we demonstrate the decidability of STSL PC and analyze the complexity of STSL PC. Besides, we generalize STSL to the evolution of spatial objects over time, called STSL OC, and provide the proof of its axiomatization system and decidability.


Sign in / Sign up

Export Citation Format

Share Document