# Getting Rid of the Usability/Security Trade-Off: A Behavioral Approach
## 4 Behavior Analysis: A Primer
The token economy is a rather complex reinforcement system based on the accumulation of objects, namely tokens that can be eventually exchanged for goods, services, or privileges (the \"back-up\" reinforcements). The operating principle of the token economy is similar to the monetary system; it is a set of rules that determines the value of an object without an intrinsic value (just like a coin or a banknote). As tokens become exchangeable, the value of terminal reinforcements and the schedules employed to obtain both tokens and back-up reinforcers constitute the variable the experimenter can manipulate. [PERSON] and [PERSON] [49] were the first to implement the token economy as a behavior modification strategy. The first study was conducted on psychiatric patients, and the token economy was used as a reinforcement system to modify a range of behaviors. Subsequent studies [50] have supported and accumulated data on the efficacy of the token economy by applying it to settings other than mental health [51, 52]. Today, the token economy is the strategy chosen for the treatment of autism spectrum disorders [53]. The token economy consists of six primary elements [54, 55]:  
* Target behavior: the behavior required for receiving tokens. This behavior must be objective and measurable. * Token conditioning: the procedure through which the token is conditioned as reinforcement. * Back-up reinforcement selection: the method by which the activities that can be acquired through the token exchange are identified. * Token production schedule: schedule of reinforcement through which tokens are released. * Exchange production schedule: a schedule that defines when tokens can be exchanged for back-up reinforcement. * Token exchange schedule: schedules that determine the cost of back-up reinforcement in terms of tokens. Moreover, interventions based on the token economy allow assessing the effectiveness of the treatment at the level of the single individual. The effects that are reported in the psychological literature are almost always based on studies that examine average scores from measurements obtained on a high number of subjects and analyzed using techniques based on inferential statistics. Of course, there are different circumstances in which this is desirable, but single-subject designs [56] allow the examination of the time course of a phenomenon. Effects can be studied in more detail by comparing the baseline (i.e., how the subject behaved before treatment) with what happens after the introduction of the treatment and, subsequently, its removal. We believe that this research strategy may be more useful to better understand the dynamics of the interaction with security systems. As an example, consider the following plot (Figure 1) showing the performance of a 23-year-old female subject in discriminating between suspicious and non-suspicious emails in an experimental study designed to be realistic but not real. The d-prime metric was used as the dependent variable. In Signal Detection Theory, the discriminability index (d-prime) is the separation between the means of the signal and the noise distributions. The task was to read 90 emails (30% of them were suspicious) and correctly categorize them. The \"system\" required the user to enter a password for each categorization. This was necessary to create discomfort for the subject. The subject performed the task for three days (Baseline), then entered the Treatment condition (three days), in which she received a bonus point for each correct identification (token). Whenever she correctly categorized 10 emails, she received access to the benefit (backup reinforcement) of not entering the password for the following 10 emails (and so on, until the end of the task). After three days of Treatment, the subject entered the Baseline condition again (no tokens collected, no back-up reinforcement for three more days).