Can two dots form a Gestalt? Measuring emergent features with the capacity coefficient.
2016; 126: 19-33
While there is widespread agreement among vision researchers on the importance of some local aspects of visual stimuli, such as hue and intensity, there is no general consensus on a full set of basic sources of information used in perceptual tasks or how they are processed. Gestalt theories place particular value on emergent features, which are based on the higher-order relationships among elements of a stimulus rather than local properties. Thus, arbitrating between different accounts of features is an important step in arbitrating between local and Gestalt theories of perception in general. In this paper, we present the capacity coefficient from Systems Factorial Technology (SFT) as a quantitative approach for formalizing and rigorously testing predictions made by local and Gestalt theories of features. As a simple, easily controlled domain for testing this approach, we focus on the local feature of location and the emergent features of Orientation and Proximity in a pair of dots. We introduce a redundant-target change detection task to compare our capacity measure on (1) trials where the configuration of the dots changed along with their location against (2) trials where the amount of local location change was exactly the same, but there was no change in the configuration. Our results, in conjunction with our modeling tools, favor the Gestalt account of emergent features. We conclude by suggesting several candidate information-processing models that incorporate emergent features, which follow from our approach.
View details for DOI 10.1016/j.visres.2015.04.019
View details for PubMedID 25986994
- A tutorial on General Recognition Theory JOURNAL OF MATHEMATICAL PSYCHOLOGY 2016; 73: 94-109
- The Formation of Social Conventions in Real-Time Environments PLOS ONE 2016; 11 (3)
Conducting real-time multiplayer experiments on the web
BEHAVIOR RESEARCH METHODS
2015; 47 (4): 966-976
Group behavior experiments require potentially large numbers of participants to interact in real time with perfect information about one another. In this paper, we address the methodological challenge of developing and conducting such experiments on the web, thereby broadening access to online labor markets as well as allowing for participation through mobile devices. In particular, we combine a set of recent web development technologies, including Node.js with the Socket.io module, HTML5 canvas, and jQuery, to provide a secure platform for pedagogical demonstrations and scalable, unsupervised experiment administration. Template code is provided for an example real-time behavioral game theory experiment which automatically pairs participants into dyads and places them into a virtual world. In total, this treatment is intended to allow those with a background in non-web-based programming to modify the template, which handles the technical server-client networking details, for their own experiments.
View details for DOI 10.3758/s13428-014-0515-6
View details for Web of Science ID 000364511400005
View details for PubMedID 25271089
- Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems ENTROPY 2013; 15 (6): 2246-2276