Speaker
Description
Understanding how the visual system extracts and represents information is a fundamental prerequisite for studying cognitive processes that depend on vision, such as time perception. Yet, defining and controlling the amount of information in, for instance, natural images is a daunting task, owing to their rich, high-dimensional statistical structure, which is further complicated by their semantic content. To overcome this problem, Victor & Conte (2012) have introduced binary maximum-entropy textures, a class of artificial images with tunable local multipoint correlation patterns. While these textures have found experimental applications (e.g., Hermundstad et al., 2014), a rigorous measure for their information content is lacking, limiting their use and theoretical value. We address this issue using the theory of higher-order spin models. Following Beretta et al. (2018), we compute the intrinsic statistical information of the textures as the geometric complexity of the underlying spin model. The main result is that all textures defined by a single correlation pattern are equivalent in terms of their intrinsic complexity. In contrast, visual sensitivity differs per pattern type (Hermundstad et al., 2014), indicating that the visual system has adopted an internal model that goes beyond the intrinsic structure of these images. We are developing measures of complexity that account for this. In addition, we are generalising our results to textures that mix patterns, along with efficient algorithms for generating them. As an application of this, we conduct time perception experiments, showing that the more extractable information a texture contains, the longer its perceived duration is. Taken together, we extend both the theoretical understanding and experimental use of the textures for investigating how stimulus complexity affects vision-dependent processes.
| Preferred Presentation | No specific preference |
|---|