Katherine R Storrs
Datos Biográficos
| ID | 1490288 |
|---|---|
| NOMBRE | Katherine R Storrs |
| NOMBRES | Katherine R |
| APELLIDO | Storrs |
| FIRMA | STORRS K R |
| AFILIACIONES | Justus-Liebig-Universität Gießen |
| ORCID | 0000-0001-9573-8654 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAS | 10 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2014 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2021 |
| ÍNDICE H | 1 |
Unsupervised learning predicts human perception and misperception of gloss
Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. …
Material perception for philosophers
Common everyday materials such as textiles, foodstuffs, soil or skin can have complex, mutable and varied appearances. Under typical viewing conditions, most observers can visually recognize materials effortlessly, and determine many of their properties without touching them. Visual material perception raises many fascinating questions for vision researchers, neuroscientists and philosophers, yet has received little attention compared to the perc…
Loss of control stimulates approach motivation
Loss of control stimulates approach motivation
Unsupervised learning predicts human perception and misperception of gloss
Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. …
Loss of control stimulates approach motivation
Unsupervised learning predicts human perception and misperception of gloss
Reflectance, lighting and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, such as surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this hypothesis, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgements. …
Material perception for philosophers
Common everyday materials such as textiles, foodstuffs, soil or skin can have complex, mutable and varied appearances. Under typical viewing conditions, most observers can visually recognize materials effortlessly, and determine many of their properties without touching them. Visual material perception raises many fascinating questions for vision researchers, neuroscientists and philosophers, yet has received little attention compared to the perc…
Cognitive psychology (3 obras) · Psychology (3 obras) · Aesthetic Perception and Analysis (2 obras) · Neuroscience (2 obras) · Perception (2 obras) · Visual perception and processing mechanisms (2 obras) · Aesthetics (1 obras) · Affect (linguistics (1 obras) · Arousal (1 obras) · Artificial Intelligence (1 obras)