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

Abstract

We present an extension of a neurobiologically inspired robotics model, termed CoRLEGO (Choice reaching with a LEGO arm robot). CoRLEGO models experimental evidence from choice reaching tasks (CRT). In a CRT participants are asked to rapidly reach and touch an item presented on the screen. These experiments show that non-target items can divert the reaching movement away from the ideal trajectory to the target item. This is seen as evidence attentional selection of reaching targets can leak into the motor system. Using competitive target selection and topological representations of motor parameters (dynamic neural fields) CoRLEGO is able to mimic this leakage effect. Furthermore if the reaching target is determined by its colour oddity (i.e. a green square among red squares or vice versa), the reaching trajectories become straighter with repetitions of the target colour (colour streaks). This colour priming effect can also be modelled with CoRLEGO. The paper also presents an extension of CoRLEGO. This extension mimics findings that transcranial direct current stimulation (tDCS) over the motor cortex modulates the colour priming effect (Woodgate et al., 2015). The results with the new CoRLEGO suggest that feedback connections from the motor system to the brain’s attentional system (parietal cortex) guide visual attention to extract movement-relevant information (i.e. colour) from visual stimuli. This paper adds to growing evidence that there is a close interaction between the motor system and the attention system. This evidence contradicts the traditional conceptualization of the motor system as the endpoint of a serial chain of processing stages. At the end of the paper we discuss CoRLEGO’s predictions and also lessons for neurobiologically inspired robotics emerging from this work.

Task instructions modulate unit–decade binding in two-digit number representation

“…It may be the case that target digits with longer length (i.e., two digits versus a single digit) might increase perceptual load, which in turn increases the time for response initiation. This interpretation of the data would be consistent with previous research indicating that initiation time and reach curvature originate from two dissociable processes (e.g., Erb, Moher, Sobel, & Song, 2016;Ruitenberg et al, 2016;Strauss, Woodgate, Sami, & Heinke, 2015). Specifically, it is thought that initiation times might be driven by an early action planning stage that is tied to cue-related encoding processes, whereas MT and AUC might reflect mainly top-down (controlled) processes which provide for revision and correction of the decision during the movement execution phase (e.g., Faulkenberry et al, 2016;Sobel, Puri, & Faulkenberry, 2016;Sobel, Puri, Faulkenberry, & Dague, 2017).…”

“…This interpretation of the data fits well with a dynamic view of decision-making which describes decision process as a continuous competition between response alternatives in which initial choice can be revised at any time during the movement as soon as new information becomes available (Spivey et al, 2005). This explanation would also be consistent with earlier findings favoring a distinction between effects of action planning and online control (Erb et al, 2016;Ruitenberg et al, 2016;Strauss et al, 2015).…”

Task instructions modulate unit-decade binding in two-digit number representation

“…It may be the case that target digits with longer length (i.e., two digits versus a single digit) might increase perceptual load, which in turn increases the time for response initiation. This interpretation of the data would be consistent with previous research indicating that initiation time and reach curvature originate from two dissociable processes (e.g., Erb, Moher, Sobel, & Song, 2016;Ruitenberg et al, 2016;Strauss, Woodgate, Sami, & Heinke, 2015). Specifically, it is thought that initiation times might be driven by an early action planning stage that is tied to cue-related encoding processes, whereas MT and AUC might reflect mainly top-down (controlled) processes which provide for revision and correction of the decision during the movement execution phase (e.g., Faulkenberry et al, 2016;Sobel, Puri, & Faulkenberry, 2016;Sobel, Puri, Faulkenberry, & Dague, 2017).…”

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

“…Beyond the toy example used here, DFT architectures have exploited the scaling properties of DFT to push both toward generating motor behaviors in autonomous robots (Knips et al, 2014; Strauss et al, 2015; Zibner et al, 2015) and toward higher cognitive function, such as grounding spatial language (Richter et al, 2014a), parsing action sequences (Lobato et al, 2015), or task learning (Sousa et al, 2015). These architectures are fairly complex.…”