On, 2001; Townsend Ashby, 1983). We developed a special independent race model that describes the race between going and purchase NSC 697286 stopping as stochastic accumulators, and examined versions in which the go process and stop process shared capacity and did not share capacity. When we manipulated the difficulty of the go task and occasionally presented a single signal we found that the stop rate parameter was not influenced. This indicates that stopping did not share capacity with going in a standard stop task (Logan et al., 2014). Versions of the model in which going and stopping share capacity might fit the results of the present study better, so a future goal of our research program is to fit our diffusion race model to the present data. Capacity as measure of processing rate describes the consequences, but it does not necessarily describe why processing rates decrease when extra choice alternatives are added or when other processes enter the race. We hypothesize that limited capacity arises from competition between representations. Biased competition accounts of visual attention assume that visual processing is competitive: the stronger the response to a particular object, the weaker the response to other objects (e.g. Beck Kastner, 2009; Bundesen, 1990; Desimone Duncan, 1995; Duncan, 2006; Kastner Ungerleider, 2000). Thus, when extra stimuli are added, processing rates for the other stimuli will decrease. This competition can be biased in a top-down fashion, allowing people to focus on task-relevant information. In a similar vein, many models of action selection assume that multiple action options will compete, so that support for one option reduces the (relative) support for the alternatives (e.g. Cisek Kalaska, 2010; Logan Gordon, 2001; Usher McClelland, 2001). Again, this competition can be biased (e.g. Logan Gordon, 2001). More generally, competition between representations has been used to account for limitations in working memory capacity (e.g. Oberauer, 2009), and the broader difficulty of doing several things at once (Duncan, 2006). In sum, the biased competition idea seems to provide a general description of how the cognitive and neural system processes information, and for why concurrent processes sometimes appear to share limited capacity (but see e.g. Navon Miller, 2002). 3.3. Simple stopping as a prepared reflex? In selective stop tasks (including our selective stop hange task), ongoing processes interfere with each other. But several studies indicate that in the stop-signal task and stop?change tasks in which all signals are valid, the stop process does not interfere with ongoing go processing (except for a very brief period of interaction near the end of SSRT; Boucher et al., 2007; Logan et al., 2015). For example, manipulating the difficulty of the responseselection processes in the go task does not influence stopping performance when all signals are valid (Logan, 1981; Logan et al., 1984, 2014). Other studies showed that stopping in a standard stop-signal task or stop hange task does not suffer from dual-task interference (H ner Druey, 2006; Logan Burkell, 1986; Yamaguchi et al., 2012). So why did we observe strong dependence between going and stopping (violating the context independence UNC0642 web assumption of the independent race model)? Consistent with standard PRP models, we assume that various forms of action control, including stopping, rely on signal detection, selection of an appropriate action, and the acti.On, 2001; Townsend Ashby, 1983). We developed a special independent race model that describes the race between going and stopping as stochastic accumulators, and examined versions in which the go process and stop process shared capacity and did not share capacity. When we manipulated the difficulty of the go task and occasionally presented a single signal we found that the stop rate parameter was not influenced. This indicates that stopping did not share capacity with going in a standard stop task (Logan et al., 2014). Versions of the model in which going and stopping share capacity might fit the results of the present study better, so a future goal of our research program is to fit our diffusion race model to the present data. Capacity as measure of processing rate describes the consequences, but it does not necessarily describe why processing rates decrease when extra choice alternatives are added or when other processes enter the race. We hypothesize that limited capacity arises from competition between representations. Biased competition accounts of visual attention assume that visual processing is competitive: the stronger the response to a particular object, the weaker the response to other objects (e.g. Beck Kastner, 2009; Bundesen, 1990; Desimone Duncan, 1995; Duncan, 2006; Kastner Ungerleider, 2000). Thus, when extra stimuli are added, processing rates for the other stimuli will decrease. This competition can be biased in a top-down fashion, allowing people to focus on task-relevant information. In a similar vein, many models of action selection assume that multiple action options will compete, so that support for one option reduces the (relative) support for the alternatives (e.g. Cisek Kalaska, 2010; Logan Gordon, 2001; Usher McClelland, 2001). Again, this competition can be biased (e.g. Logan Gordon, 2001). More generally, competition between representations has been used to account for limitations in working memory capacity (e.g. Oberauer, 2009), and the broader difficulty of doing several things at once (Duncan, 2006). In sum, the biased competition idea seems to provide a general description of how the cognitive and neural system processes information, and for why concurrent processes sometimes appear to share limited capacity (but see e.g. Navon Miller, 2002). 3.3. Simple stopping as a prepared reflex? In selective stop tasks (including our selective stop hange task), ongoing processes interfere with each other. But several studies indicate that in the stop-signal task and stop?change tasks in which all signals are valid, the stop process does not interfere with ongoing go processing (except for a very brief period of interaction near the end of SSRT; Boucher et al., 2007; Logan et al., 2015). For example, manipulating the difficulty of the responseselection processes in the go task does not influence stopping performance when all signals are valid (Logan, 1981; Logan et al., 1984, 2014). Other studies showed that stopping in a standard stop-signal task or stop hange task does not suffer from dual-task interference (H ner Druey, 2006; Logan Burkell, 1986; Yamaguchi et al., 2012). So why did we observe strong dependence between going and stopping (violating the context independence assumption of the independent race model)? Consistent with standard PRP models, we assume that various forms of action control, including stopping, rely on signal detection, selection of an appropriate action, and the acti.