Perceptual crossing: the simplest online paradigm

Abstract

Researchers in social cognition increasingly realize that many phenomena cannot be understood by investigating offline situations only, focusing on individual mechanisms and an observer perspective. There are processes of dynamic emergence specific to online situations, when two or more persons are engaged in a real-time interaction that are more than just the sum of the individual capacities or behaviors, and these require the study of online social interaction. Auvray et al.'s (2009) perceptual crossing paradigm offers possibly the simplest paradigm for studying such online interactions: two persons, a one-dimensional space, one bit of information, and a yes/no answer. This study has provoked a lot of resonance in different areas of research, including experimental psychology, computer/robot modeling, philosophy, psychopathology, and even in the field of design. In this article, we review and critically assess this body of literature. We give an overview of both behavioral experimental research and simulated agent modeling done using the perceptual crossing paradigm. We discuss different contexts in which work on perceptual crossing has been cited. This includes the controversy about the possible constitutive role of perceptual crossing for social cognition. We conclude with an outlook on future research possibilities, in particular those that could elucidate the link between online interaction dynamics and individual social cognition.

Keywords: coordination; online interaction; perceptual crossing; social cognition.

Figure 1

Schematic illustration of Auvray et al.'s (2009) experimental set-up.

Figure 2

An illustration of the algorithm used in Evolutionary Robotics. Random strings (“genomes”) are interpreted as parameters for neural network robot control. Robot behavior is simulated and evaluated. The higher scoring agents' genome is recombined and copied with mutations to seed the next generation. Over thousands of repetitions of this cycle, behavior according to the evaluation criterion (“fitness function”) is optimized (source: Rohde, ; ch. 3).

Figure 3

Simulated agents performing perceptual crossing (Di Paolo et al., ; Rohde, ; ch. 6). (A) An example movement generated in simulated interaction. The two agents (thick lines, gray and black) subsequently interact, part, engage with the two kinds of distractor objects (thin lines) and eventually find each other and lock in interaction. (B,C) Even though from the observer perspective, interactions with another agent (B) and with the stationary object (C) look very different (top panels), the sensorimotor plots (sensor activation and motor outputs across time, bottom panels) look strikingly similar. This reveals why discriminating the other and the fixed objects is difficult for simulated agents.