The basal ganglia as the selection mechanism in a cognitive task ∗

This paper builds on our existing, biologically constrained, model of the basal ganglia, which was originally constructed under the premise that these subcortical structures perform action selection. Here we show how this same model, when used in conjunction with a connectionist model of processing in the Stroop task, can provide an improved account of human performance on that task. Our model accounts for a wide variety of phenomenon, and provides a framework for connecting Stroop processing with the neuroanatomical basis of action selection. This work validates modelling the basal ganglia as the vertebrate solution to the action selection problem and demonstrates the importance of action selection issues to understanding performance on cognitive tasks. Proposals are made concerning the desirable properties a selection mechanism must possess. 1 The basal ganglia as a vertebrate solution to the selection problem ‘A selection problem arises whenever two or more competing systems seek simultaneous access to a restricted system. ’[Redgraveet al., 1999] It has been proposed that the basal ganglia is the vertebrate solution to the selection problem[Redgraveet al., 1999]. In other words, that it resolves the competition between dif ferent neural command centres requesting behavioural control. This need for selection is most clear, and has been most thoroughly empirically explored, in terms of motor expression, but it is expected that similar functional architectu re, and comparable functional requirements, underly selectio n in different domains. The basal ganglia has external and internal connectivity that makes it suitable for performing the role of a selection mechanism. It receives inputs from virtually the entire cer ebral cortex, limbic system structures such as the hippocamp us and the amygdala, and, notably, the anterior cingulate cort ex [Masterman and Cummings, 1997; Redgrave et al., 1999]. ∗This work was supported in part by EPSRC grant EP/C516303/1 to Kevin Gurney. The main input nucleus of the basal ganglia is the striatum, which provides the first processing of incoming signals from cortex. The projection neurons of the striatum (medium spiny neurons) are by default quiescent (in a down-state), a nd do not do not respond to low levels of input. Only after a substantial and coordinated excitatory input do they move to an up-state, in which they produce significant output which may subsequently be affected by smaller changes in input [Wilson, 1995]. Outputs from the basal ganglia project back to the cortex, via the thalamus, and to premotor areas of the brainstem. The output nucleus of the Basal ganglia is the globus pallidus, i nternal segment (GPi). Neurons here are are tonically active , inhibiting their target structures from enacting behaviou r. Actions are enabled by the selective release of that inhibitio n. It is posited that signals from cortex indicate the ’salienc ’ — i.e. the importance and urgency — of possible actions to the basal ganglia[Redgraveet al., 1999]. Sufficiently large saliences result in the selective disinhibition of channel s associated with that action, and thus the release of the action [Chevalier and Deniau, 1990 ]. 1.1 Modelling confirms that the basal ganglia can perform action selection We have constructed a neuronal network model of the basal ganglia, constrained by the known anatomy and physiology, and based on the selection hypothesis [Gurneyet al., 2001a; 2001b]. Analysis and simulation of this model [Gurneyet al., 2001b] shows that the basal ganglia display the properties of a good selection mechanism[Redgraveet al., 1999]: the highest salience rapidly promotes appropriate channel selecti on; once selection has been made competitors do not distort that selection; however, significant changes in the salience inp uts result in rapid and clean channel switching. Embedding this model into its anatomical context provided by cortex and thalamic circuits, improves the selecti on behaviour and gives a more complete understanding of the functional role the different nuclei involved may be playin g [Humphries and Gurney, 2002 ]. Further, using these models in robot controllers shows that the selection behaviour is of sufficient efficiency and sophistication to be behavioura lly adequate in realistic environments[Girard et al., 2003; Montes-Gonzalezet al., 2000]. The simulation work presented below uses the basal ganglia model exactly as presented elsewhere [Gurneyet al., 2001b; Humphries and Gurney, 2002 ]. We focus on the benefits of using this biologically plausible mechanism, which has been demonstrated to possess ethologically realistic s election properties. The internal structure of the basal gan gli model is only discussed as far as is necessary to illustrate why it works as it does in the context of the current work. 1.2 Using the basal ganglia model in a cognitive task This paper is concerned with a different extension of the model— into the domain of cognitive selection and performance, as measured by reaction times. In particular we consider a celebrated cognitive task that involves a selection conflict — the Stroop Task[Stroop, 1935] — and take as our starting point the most successful computational model of performance on this task to date[Cohenet al., 1990] which we extend by integration with our existing model of basal ganglia function [Gurney et al., 2001a; 2001b; Humphries and Gurney, 2002 ]. The purposes of this extension are threefold. Firstly, it al lows an additional test of the basal ganglia model of action selection. The model was constructed using the known functional neuroanatomy and guided by the selection hypothesis . It was not explicitly designed to simulate reaction times, n or was it constrained by human cognitive performance. However, any action selection mechanism should also be able to act as a response selection mechanism in cognitive tasks. Therefore the performance of the model in this domain is a good test of its validity. Secondly, some aspects of human Stroop performance remain inadequately addresses by exist ing models, leaving the possibility open that a model containing new elements may improve the possible account and shed light on why previous models have not been so successful. Additionally, making connection to the possible under lying neurobiology enriches the account possible of Stroop processing. In particular, we anticipate that features of t he basal ganglia model such as allowing arbitrary numbers of inputs and making provision for dopaminergic modulation of signal processing will provide opportunities for future experimental and modelling investigations. Thirdly, integr ating cognitive and systems-neuroscience models sheds light on i ssues of selection from both levels of analysis. We will attem pt to use our combined model to derive some general constraints on models of selection. 2 Modelling the Stroop Task