V. General Discussion
V.4 The modulating effects of the rewarded dimension
One aspect of our findings that needs to be discussed is that the improved performance we observed is confined to situations in which response speed rather than accuracy is rewarded. The question arises why this is so. The evolutionary perspective offers some hints:
in the real world, it is often far more important for the survival and the reproduction of an
organism to react in a quick and dirty manner rather than in a slow but one hundred percent accurate manner. In other words, organisms are generally better off when they rely on computational mechanisms that are correct most of the time (but far from one hundred percent) and extremely fast, because they just do not have infinite time available for making decisions. Gigerenzer and his colleagues coined the term fast and frugal heuristics for this kind of mechanisms (Gigerenzer & Goldstein, 1996; Gigerenzer & Todd, 1999). More generally, these mechanisms belong to the class of satisficing mechanisms, a term coined by Herbert Simon (Simon, 1956; Simon, 1982). The notion references the fact that in the real world, information-processing systems rarely need to optimize. All they need to be capable of is to produce satisfying/sufficient results. One famous example to illustrate this fact is mate choice (Gigerenzer & Todd, 1999): nobody has the time to evaluate all potential mates and then pick the best one. Rather, once a potential mate lives up to certain standards, he or she is likely to be chosen. It may not be the best of all choices, but given that picking a mate in this fashion saves a lot of the limited resource ‘time’, it is a satisfying and clearly environmentally rational choice.
So the key to understanding the effect that the rewarded dimension has lies in the fact that humans evolved in a threatening, hostile environment in which they were subject to all kinds of threat all the time. Therefore, in most cases, it is far more conducive to an organism’s well-being and his chances of propagating his or her genes to set the speed-accuracy tradeoff in such a way that a relatively high false alarm rate is allowed, as an error would potentially result in the organism’s death. Accordingly, humans presumably evolved to err on the side of reacting quickly to uncertain information of threat, rather than waiting for more information to be accumulated, as it could be too late for the right decision right then. The bottom-line is that individuals have to make decisions with limited amounts of time and knowledge, and as errors are potentially extremely costly, they evolved to accept a higher rate of false alarms for the sake of avoiding these costly errors. This is in line with the concept of bounded rationality (Simon, 1982). There are already some modern models of choice that acknowledge the idea that there might have been an evolutionary pressure towards speed of decisions (Bogacz, Usher, Zhang, & McClelland, 2007). The explanation is also consistent with the idea of the existence of two decision-making systems in the human brain that are responsible for making decisions in situations of threat (LeDoux, 1996; Morris, Öhman, & Dolan, 1999), one of them subcortical, and the other cortical. These two systems differ in terms of their speed-accuracy
tradeoff: whereas the subcortical pathway allows for fast but inaccurate decisions, the cortical pathway delivers slower but more accurate decisions. As Trimmer and his colleagues (Trimmer et al., 2008) have shown, the operations of the subcortical pathway can be modeled by using simple signal detection theory, so the idea that organisms trade speed for accuracy to come to satisficing decisions may be pretty accurate.
However, there is another possible account: it could be that the response deadline might prompt participants to finish the information accumulation process too early. Generally, higher accuracy is achieved by trading off response speed for the sake of more time to accumulate information (Bogacz et al., 2009). However, with a response deadline impending, this is rather difficult to achieve adequately, as the time limit interferes with the need to take more time to accumulate more information, especially if you can never be sure when the time limit will be reached.
There is yet another alternative account for the result that it makes a difference whether speed or accuracy is punished harder. This account draws more on the phenomenon of ‘choking on the money’ (Mobbs, et al., 2009). It is reasonable to assume that the classification of numbers as either odd or even is highly overlearned in educated people like university students. A characteristic of such highly automatized behavior is that it is performed without the involvement of consciousness, or conscious attention (Norman &
Shallice, 1986). In fact, conscious attention can be harmful to the performance of automated behavior. Two well-known examples that illsutrate this fact are trying to walk consciously, or tying one’s shoes in a conscious, step-by-step manner. However, when the stakes are high, for example, in a professional level soccer penalty shootout, people try to do particularly well, and therefore start monitoring their own actions more closely than they normally would.
While it introspectively feels to be the right thing to do, in reality, for highly skilled players, it can actually result in a considerable performance decrement, a phenomenon well known as choking under pressure (cf. Baumeister, 1984; Jackson & Beilock, 2007). A similar phenomenon might arise in the case when errors are attached to a greater monetary loss than slowness. In such a scenario, people might try to adapt to these conditions by consciously trying to avoid errors by monitoring their decisions. However, as the task (categorizing oddness of numbers) is highly overlearned, such conscious monitoring might result not in less, but actually in more errors than would be the case if they would simply relax their control processes and let the automatic processes take care of themselves.
However, it is also reasonable to assume that the results would look different if the stakes were higher. For example, in our flanker task, if errors would be punished very harshly (say, 100 points per error) and/or reward correct reactions highly, chances are that the participants would invest far more effort in adapting their response strategy to these constraints.