A Robotics-Based Approach to Modeling of Choice Reaching Experiments on Visual Attention

Abstract

The paper presents a robotics-based model for choice reaching experiments on visual attention. In these experiments participants were asked to make rapid reach movements toward a target in an odd-color search task, i.e., reaching for a green square among red squares and vice versa (e.g., Song and Nakayama, 2008). Interestingly these studies found that in a high number of trials movements were initially directed toward a distractor and only later were adjusted toward the target. These “curved” trajectories occurred particularly frequently when the target in the directly preceding trial had a different color (priming effect). Our model is embedded in a closed-loop control of a LEGO robot arm aiming to mimic these reach movements. The model is based on our earlier work which suggests that target selection in visual search is implemented through parallel interactions between competitive and cooperative processes in the brain (Heinke and Humphreys, 2003; Heinke and Backhaus, 2011). To link this model with the control of the robot arm we implemented a topological representation of movement parameters following the dynamic field theory (Erlhagen and Schoener, 2002). The robot arm is able to mimic the results of the odd-color search task including the priming effect and also generates human-like trajectories with a bell-shaped velocity profile. Theoretical implications and predictions are discussed in the paper.

Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information

“…In Section 3 we will present a neurobiologically inspired robotics model of this evidence. The model is called CoRLEGO (Choice reaching with a LEGO robot arm) and was first published by Strauss and Heinke (2012) . Section 4 will present an extension of CoRLEGO to accommodate novel findings from Woodgate et al.…”

How active perception and attractor dynamics shape perceptual categorization: A computational model

“…The idea that categorization is a situated activity based on active perception and agentenvironment interactions is present in several models using evolutionary robotics techniques (Gigliotta and Nolfi, 2008;Mirolli et al, 2010;Nolfi and Marocco, 2002;Tuci et al, 2010). Furthermore, the importance of situated interactions for categorization has been recognized in dynamical systems (Quinton et al, 2013;Schoener, 2008;Spivey, 2007;Strauss and Heinke, 2012;Tipper et al, 2000) and probabilistic models (Maye and Engel, 2011). Our model incorporates aspects of these proposals while retaining key aspects of the evidence-accumulation framework discussed earlier.…”

The neural dynamics of goal-directed arm movements: A developmental perspective

“…Optimal control models are also autonomous only to a limited extent in that they compute the optimal motor command but do not address how movements are started or stopped and how the updating of the movement occurs as a process when the target shifts [31]. The model closest to ours [28] shares the commitment to a spatial representation of motor plans and of movement timing, and is technically in the same language of dynamic field theory. It differs in the details of the timing mechanism and does not address the muscular level.…”