Modeling strategies in Stroop with a general architecture of executive control

Modeling strategies in Stroop with a general architecture of executive control Tomasz Smolen (bertrand@o2.pl) Adam Chuderski (a.chuderski@emapa.pl) Institute of Psychology, Jagiellonian University al. Mickiewicza 3, 31-120 Krakow, Poland long periods of time is metabolically costly and often cogni- tively inefficient. Early observations indicated that control is amplified after errors. However, results like Gratton effect (i.e., the interference cost in a flanker task is 20 ms smaller in trials following incongruent stimuli, compared to ones following congruent stimuli; Gratton, Coles, & Donchin, 1992), usually observed even if errors are rare, suggested that control can be dynamically modulated on some other basis. Botvinick et al.’s (2001) conflict monitoring theory states that specialized brain mechanism (anterior cingulate cortex; ACC) performs online computing of the level of conflict between alternative responses and it increases the strength of top-down control as such a conflict arises. A more general idea is that ACC learns and reacts to a level of “risk” – conflict related error likelihood and its real-world consequences (Brown & Braver, 2007). Both cited models, however, evaluate only response representations in performing need-for-control monitoring, while conflicts can also be found between covert cognitive processes, which just influence next steps of cognition. Another new mecha- nism implemented in DUCCA is such a conflict monitoring procedure, which evaluates conflicts in cognitive processing (e.g., between opposing goals), which need not lead directly to any response. Finally, DUCCA is aimed at taking into account the indi- vidual differences in control. Even healthy people differ in efficiency of control, which seem to be correlated with working memory capacity and fluid intelligence (Chuderski & Necka, 2010). Moreover, humans are able to regulate their mode of control by switching between top-down, pro- active control and bottom-up, reactive one (Braver et al., 2007). All these differences can be expressed as differences in values of DUCCA internal control parameters, which yield qualitative changes in its simulated behaviour. Abstract This paper presents a preliminary work on a new architecture of executive control (DUCCA), aimed at integration and extension of some leading approaches to executive control. We present DUCCA assumptions and operation and use the architecture to simulate a few effects observed in Stroop-like task, a hallmark test of how control deals with interference. The focus of DUCCA is on how strategical use of general executive mechanisms contributes to Stroop effect. We explain also what is usually neglected in Stroop modeling: the significant individual differences in task performance. Introduction Executive control is implemented via numerous brain me- chanisms and on different levels of neuronal organization. However, a few general flexible control mechanisms, which are involved in most of situations that require control, were also proposed in control literature (Anderson, Fincham, Qui, & Stocco, 2008; Braver, Gray & Burgess, 2007; Kane & Engle, 2003; Koechlin & Summerfield, 2007). The goal of this paper is to present a new model of executive control, called Dual Cognitive Control Architecture (DUCCA), which integrates several recent theoretical approaches to control and extends them with a few original control mechanisms. The model explains crucial effects related to interference control in Stroop-like tasks with an appeal only to general mechanisms of control, while abstracting from specific (e.g., semantical or stimulus-related) ones. The first general function of control regards using con- textual, episodic, or goal information in order to change the probability distribution of alternative actions into a one that maximizes their task-relevance (Anderson et al., 2008; Koechlin & Summerfield, 2007). Such a function is imple- mented in cognitive models of executive processing in two ways. In most of connectionist models, a network carrying out non-executive processing is supplemented with task (or goal) units, which modulate processing by propagating addi- tional activation to nodes relevant to a respective task (e.g., Altmann & Davidson, 2001; Cohen, Dunbar & McClelland, 1990; Verguts & Notebaert, 2008). The control in symbolic architectures is usually implemented as control signals, stor- ed in a goal or working memory buffer, which are matched to possible actions in order to select a next operation (Anderson et al., 2008; Meyer & Kieras, 1997). The first original aspect of DUCCA is that it integrates these two approaches into the unitary, general mechanism of top-down control, which may either directly select an action or just modulate a chance of its selection. The second important function of executive control deals with regulation of its strength, as maintaining control for Overview of DUCCA Cognitive operations DUCCA is modeled as a hybrid production system. Coordi- nation of the working of its modules is inspired by ACT-R architecture (Anderson et al., 2008). However, as the system is focused on executive functioning, “ordinary” cognitive operations have been very simplified. The system stores in- formation received from the environment in a visual atten- tion module, which recognizes 25 (5×5) locations on the computer screen and attends to one of them (via a focus of visual attention) at a time. Model can read symbols and some of their features (e.g., colors) from the focus. Long- term declarative knowledge is organized as a semantic network, which consists of information chunks of defined