Conflict Monitoring in a Dynamical Agent

Conflict Monitoring in a Dynamical Agent Ronnie Ward (rward@acm.org) Department of Computer Science, Texas AM evolutionary neural modeling. Introduction Conflict in distributed cognitive processing occurs when neural pathways associated with multiple stimuli intersect, and thus interfere with one other such that related task performance suffers (Botvinick, Braver, Barch, Carter & Cohen, 2001). For example, consider a frog eyeing two flies. By targeting a single fly for capture, the frog may succeed if it manages the conflicting input from the second fly. The case to be avoided is an intermediate response in which the frog attacks a midpoint between the flies. To avoid such performance problems, Botvinick et al. (2001) hypothesized the existence of what they call a top- down conflict-monitoring system where conflict is detected and control invoked to activate appropriate cognitive regulatory processes. To test the sufficiency of their hypothesis, they measured conflict in terms of a rising value of the Hopfield (1984) energy function in the response layer of a discrete interactive model of the Stroop task (Cohen & Hudson, 1994). Energy increased during incongruent trials suggesting that a potential monitoring mechanism could be activated by such a signal, and subsequently causes invocation of appropriate cognitive control elements. In discussing conflict monitoring, Botvinick et al. (2001) describe control as an active or passive mechanism, which is either on-line and modulated, or off-line based on conflict monitoring information. They modified the Cohen and Hudson (1984) Stroop model by adding recurrent links to task control nodes back from an inserted conflict-monitoring node. The feedback links strengthened involvement of the color-naming task unit, making response on incongruent trials faster. This correlates to experimentally observed response time improvement on incongruent trials in humans. They suggest that activation of the anterior cingulate cortex in humans is a result of conflict monitoring recruiting executive control to improve performance. Recent work by Beer (2003, 1996) proposed an artificial visual agent (VA) as an idealized model of a complete brain-body-environment system that exhibits minimally cognitive behavior. Unlike the connectionist modeling methodologies used by Botvinick et al. (2001) and others (Cohen, Dunbar & McClelland, 1990; Cohen et al., 1994), the VA is an embodied, situated and dynamical (ESD) agent operating in continuous time. ESD agents stress what Clark (1998) calls “the unexpected intimacy between the brain, body, and world”. As model systems, they emphasize the contextually bound nature of solutions to cognitive problems, and allow a tractable analysis of the type of cognitive processing going on in more complex systems. While VA is vastly different from a human, it is reasonable to investigate the nature of VA’s conflict monitoring, if any, in its cognitive control. The agent has been shown to demonstrate both memory and selective attention (Slocum, Downey, & Beer, 2000; Goldenberg, Garcowski, & Beer, 2004). Moreover, Ward and Ward (2004) found that VA exhibits reactive inhibition (Houghton & Tipper, 1994) such that greater inhibition is observed for more salient targets. VA offers an opportunity to analyze an evolved conflict management mechanism in a dynamical system established through artificial evolutionary processes using genetic algorithms. This paper examines the VA for conflict monitoring in a dual-task scenario where the agent must select actions in the presence of stimuli suggesting conflicting responses. First, we demonstrate that the agent exhibits cognitive conflict. Periods of conflict are located using a stable-state equation, which the agent solves during processing. These conflict periods are equated with disagreements in the source inputs to the agent’s control circuits, which are examined for explicit conflict monitoring, as are the intra- and inter-layer network connections in the agent.