Do children with autism use inner speech and visuospatial resources for the service of executive control? Evidence from suppression in dual tasks

Three experiments used dual‐task suppression methodology to study the use of inner speech and visuospatial resources for mediating central executive performance by children with autism (CWA) and group‐matched typically developing (TD) controls. Expt 1 revealed that CWA did not recruit inner speech to facilitate arithmetic task‐switching performance: there was no effect of articulatory suppression (AS) on completion time for CWA compared to the TD group. Expt 2 revealed that suppression of visuospatial resources disrupted the task‐switching performance of both CWA and TD groups. It also confirmed that the task‐switching performance of CWA was significantly slowed by visuospatial compared to AS. Expt 3 showed that CWA also did not employ inner speech, compared to visuospatial resources, for implementing planning movements. Overall, compared to the mixture of representations used by the TD group for problem solving, CWA seemed to use visuospatial working memory resources but not inner speech to service executive control.     

Controlled & automatic processing: behavior,theory, and biological mechanisms

This paper provides an overview of developments in a dual processing theory of automatic and controlled processing that began with the empirical and theoretical work described by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977) over a quarter century ago. A review of relevant empirical findings suggests that there is a set of core behavioral phenomena reflecting differences between controlled and automatic processing that must be addressed by a successful theory. These phenomena relate to: consistency in training, serial versus parallel processing, level of effort, robustness to stressors, degree of control, effects on long‐term memory, and priority encoding. We detail a computational model of controlled processing, CAP2, that accounts for these phenomena as emergent properties of an underlying hybrid computational architecture. The model employs a large network of distributed data modules that can categorize, buffer, associate, and prioritize information. Each module is a connectionist network with input and output layers, and each module communicates with a central Control System by outputting priority and activity report signals, and by receiving control signals. The Control System is composed of five processors including a Goal Processor, an Attention Controller, an Activity Monitor, an Episodic Store, and a Gating & Report Relay. The transition from controlled to automatic processing occurs in this model as the data modules become capable of transmitting their output without mediation by the Control System. We describe recent progress in mapping the components of this model onto specific neuroanatomical substrates, briefly discuss the potential for applying functional neuroimaging techniques to test the model's predictions, and its relation to other models.