Refine
Has Fulltext
- no (13)
Document Type
- Article (10)
- Other (2)
- Conference Proceeding (1)
Language
- English (13)
Is part of the Bibliography
- yes (13) (remove)
Keywords
- eye movements (5)
- modeling (2)
- saliency (2)
- spatial statistics (2)
- Bayesian inference (1)
- Beta-binomial model (1)
- Confidence intervals (1)
- Credible intervals (1)
- Eye movements (1)
- Inhibition of return (1)
- Non-stationarity (1)
- Overdispersion (1)
- Psychometric function (1)
- Psychophysical methods (1)
- Saliency (1)
- Visual attention (1)
- Visual scanpath (1)
- attention (1)
- dynamic models (1)
- dynamical model (1)
- fixation locations (1)
- fixations (1)
- image-computable (1)
- likelihood (1)
- model (1)
- model comparison (1)
- model fitting (1)
- natural scenes (1)
- pair correlation function (1)
- point process (1)
- psychophysics (1)
- saccades (1)
- scene perception (1)
- scene viewing (1)
- spatial correlations (1)
- spatial vision (1)
- visual attention (1)
- visual scanpath (1)
- visual search (1)
Institute
Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.
Scene viewing is used to study attentional selection in complex but still controlled environments. One of the main observations on eye movements during scene viewing is the inhomogeneous distribution of fixation locations: While some parts of an image are fixated by almost all observers and are inspected repeatedly by the same observer, other image parts remain unfixated by observers even after long exploration intervals. Here, we apply spatial point process methods to investigate the relationship between pairs of fixations. More precisely, we use the pair correlation function, a powerful statistical tool, to evaluate dependencies between fixation locations along individual scanpaths. We demonstrate that aggregation of fixation locations within 4 degrees is stronger than expected from chance. Furthermore, the pair correlation function reveals stronger aggregation of fixations when the same image is presented a second time. We use simulations of a dynamical model to show that a narrower spatial attentional span may explain differences in pair correlations between the first and the second inspection of the same image.