1. Background
By understanding the cognitive processes of decision-making, one would not only be able to determine the applied strategy but also predict the subsequent behavior (1, 2). The ability to strike a balance between immediate and future consequences of choices is defined as decision-making (3, 4). Decision behavior research deals with formulation of theories surrounding cognitive processes by utilizing models that describe human thinking in the process of decision-making and judgment (5, 6). Substance use as a choice with short-term rewards and long-term negative consequences (7) is considered a decision-making fault. Although decision-making in substance use has been widely studied, the focus has been on the outcome rather than the process and stages of decision-making (8). In mice studies, for example, have mainly measured memory rather than investigating the process of decision-making (9). By applying Aristotle’s concept of Akrasia, some studies have had a philosophical approach to will and responsibility of substance users (10-12). Others, however, believe that disturbed adaptation to a particular situation and the consequent emotions might result in a decision to use (13-15). The stress of poverty and lack of natural resources have also been identified as sources of anxiety, reduced self-control, and subsequent substance use (16-19). Based on all those models an urge for short-term boosters such as overeating, substance use, or procrastination, help the person stay away from excessive emotions or stabilize them (13-16, 19, 20).
Descriptive approaches to decision-making vis-â-vis traditional normative models have already been discussed by scholars (5, 21, 22). Choice-based models, as a subset of descriptive approaches, measure the relationship between “attributing” values and “alternative” options of decision-making (23, 24). The structural model describes the final outcome of the decision with no reference to the processes of decision-making. Process tracing, however, is a different descriptive approach that emphasizes on the process of decision-making by focusing on quantity, type, time, and sequence of information acquisition (21, 25). Process tracing models are well-suited for studying human decision-making and understanding of cognitive processes from a psychological perspective (26-28).
Several criteria have been developed to examine the information acquisition behavior in decision-making situations (6). The following aspects of decision-making processes are more commonly measured by researchers (29).
• Timing of decision-making: The operational definition for measurement of timing of a decision consists of the time taken to make a decision on each screen or round of a game.
• Task-based complexity: Indicates the general aspects of a decision process, including (a) Multiplying of alternatives and attributes (amount of information), (b) Time pressure, (c) Information display style (sequential vs. simultaneous), and (d) Response method (selection vs. scoring).
• Decision-making strategies: By definition, a sequence of actions applied to transfer a primary form of knowledge to a pool of the ultimate situation knowledge, where the decision-maker is satisfied that the decision-making issue is resolved, is a decision-making strategy (30). While a decision-making strategy describes the process, based on which, the decision-maker proceeds in a selection among certain alternatives, a multi-attribute evaluative process describes the interaction between information acquisition and the final choice.
2. Objectives
This study aimed to evaluate decision-making processes in everyday life of individuals with a history of substance use according to above-mentioned criteria.
3. Materials and Methods
A culturally modified version of Mouselab software (31, 32) that operated as a game was applied for measurement of decision-making attributes, the details of which are published elsewhere (33). An application of the software is to test patterns of cumulative data acquisition in multivariate and multi-attribute situations (34). Based on the narrative of the game, a young male was determined to select a girl, out of four, to marry (i.e., alternatives). The selection had to be in accordance with preferred criteria based on four characteristics of the girls (i.e., attributes), each available as a binary coding of yes/no (i.e., task-based complexity). The game interface was, therefore, designed as a multi-attribute and multi-variate matrix of 4 × 4, with the binary data being hidden under the cells available at a cost (i.e., task-based complexity). The design and sorting of the game were randomized. The game was not a gamble and each round had an actual correct answer.
In our setting, the cost of each information unit -opening a box- was IRR (Iranian Rial) 450 and each time step exhausted IRR 780 (i.e., timing). The starting credit was IRR 900000 for 60 rounds (15000 for each round). By purchase of information (number of opened boxes × IRR 450 = cost of information acquired), and time spent (number of time steps × IRR 780) (time pressure). The subject entered the final stage of decision-making process, the choice. If the choice was correct the subject would be provided by a feedback credit bar [IRR 15000 - (cost of information + time pressure)] (i.e., decision-making strategy). However, if the subject would make a wrong choice, he/she would gain no reward from the specific round of the game (i.e., task-based complexity). By moving to the next round, the remaining credit was calculated as the total credit at the beginning of the previous round - amount lost on the current round (i.e., task-based complexity).
The participants in this study included a group of 17 volunteers with a history of at least one year of substance use and no history of chronic medical diseases and a control group of 17 healthy individuals with no history of substance use. The number of subjects in each group was determined according to similar studies. All subjects were male. For sampling, a snowball method with two chains was applied.
4. Results
The mean age of the subjects in the user group was 35.94 (± 12.60) and in the control group was 33.8 (± 8.83) years, where a t-test showed no difference between the groups (P = 0.205). In terms of education, both groups had a high school diploma or more. The number of individuals with higher education or bachelor’s degree was higher in the control group (11 vs. 8).
4.1. Search for Information
An independent‐sample t‐test was conducted to compare the mean values of opened boxes variable between the group with a positive substance use history and control group (Table 1).
Table 1.Description and Analysis of Data Related to Search for Information (df = 32)
| Boxes Opened | A Positive History of Substance Use | Control Group | P valuea | ||
|---|---|---|---|---|---|
| Mean (SD) | Median (Interquartile Range) | Mean (SD) | Median (Interquartile Range) | ||
| Number | 475.29 (114.93) | 455 (184.50) | 407.35 (88.34) | 403 (95.50) | 0.06 |
| Percent | 0.49 (0.11) | 0.47 (0.11) | 0.42 (0.04) | 0.41 (0.10) | 0.06 |
| Average | 7.92 (1.91) | 7.60 (3.07) | 6.78 (1.47) | 6.71 (1.59) | 0.06 |
aEqual variances assumed.
4.2. Patterns of Move
Comparison of attribute-based and option-based transitions between the two groups showed no significant difference (df = 32, P = 0.96 for attribute-based transitions and df = 32, P = 0.30 for alternative-based transitions) (Table 2).
Table 2.Description and Analysis of Path Processing and Tracing Processing of Data (df = 32)
| Variables | A Positive History of Substance Use | Control Group | P Valuea | ||
|---|---|---|---|---|---|
| Mean (SD) | Median (Interquartile Range) | Mean (SD) | Median (Interquartile Range) | ||
| Attribute-based transition (n) | 165.05 (102.87) | 135 (111) | 163.76 (86.60) | 150 (137) | 0.96 |
| Option-based transition (n) | 211.52 (82.54) | 186 (108) | 182.47 (80.45) | 166 (109.50) | 0.30 |
aEqual variances assumed.
4.3. Time-Related Variables
As presented in Table 3, time spent on different stages of the game was significantly different between the two groups. There were two categories in this subset of variables. The first category is the amount of time participants held the mouse cursor over the 4 × 4 matrix on the computer interface in relation to the click time (sum of all the time that cursor was held on each of 16 boxes of the matrix) and the on-click (sum of all the time that cursor was held or moved on the game interface except over the buttons section). These two variables refer to the search and stop search times of a decision cycle. Both variables were significantly different between the two groups (P = 0.036 for click time and P = 0.028 for on-click time), indicating that participants with a history of substance use spent more time for search and stop search, compared to the control group. The other category refers to the time that participants move the mouse cursor into the buttons area, including mouseover or button time (the sum of all the times the cursor was held or a button was pressed) and mouse-out time (the sum of all the times the cursor was in the buttons area except the mouseover times). These two variables refer to the time that the participant spent on processing information and decision-making. The mouse-out time was also significantly different between the two groups (P = 0.025). However, the button time variable did not reveal a significant difference between the two groups. These results indicate that individuals with a positive history of substance use spent more time on information processing and decision-making stages, compared to the control group. However, the move-on and pressing the buttons was not different between the two groups. Furthermore, a significant difference was observed between the two groups in total time spent on playing the 60 rounds of the game (P = 0.029).
Table 3.Description and Analysis of Data Related to Time Spent in the Stages of the Game
| Variablesa | A Positive History of Substance Use | Control Group | P Value | ||
|---|---|---|---|---|---|
| Mean (SD) | Median (Interquartile Range) | Mean (SD) | Median (Interquartile Range) | ||
| Button time | 1333.49 (416.40) | 1285.90 (505.24) | 1182.38 (728.65) | 1011 (467.11) | 0.46b |
| Click time | 1291750.82 (498172) | 1308300 (790349) | 976378.82 (313708) | 970020 (375837) | 0.036c |
| On-click time | 1421200 (538161) | 1441200 (853027) | 1068600 (320865) | 1040100 (391291) | 0.028c |
| Mouse-out time | 1505200 (549685) | 1507700 (871134) | 1133800 (337383) | 1093400 (430519) | 0.025c |
| Total time | 4218100 (1584780) | 4255600 (2514510) | 3178700 (968620) | 3103500 (1246254) | 0.029c |
aAll times in milliseconds.
bEqual variances assumed.
cEqual variances not assumed.
4.4. Accuracy of Choice
Our results showed no significant difference in the correct and wrong choice variable between the two groups (P = 0.172). However, the credit balance throughout the game was significantly different between the two groups (P = 0.003) (Table 4).
Table 4.Description and Analysis of Data Related to Accuracy of Choices and Credit Line
| Variables | Positive History of Substance Use | Control Group | P Valuea | ||
|---|---|---|---|---|---|
| Mean (SD) | Median (Interquartile Range) | Mean (SD) | Median (Interquartile Range) | ||
| Correct choice (n) | 41.52(10.74) | 44 (11) | 46.11 (8.25) | 48 (9.50) | 0.172 |
| Wrong choice (n) | 18.47 (10.74) | 16 (11) | 11.88 (8.25) | 12 (9.50) | 0.172 |
| Winning credit (IRR) | 28395.52 (6769.74) | 28173 (12535.50) | 35566.23 (6241.26) | 36744 (10270.50) | 0.003 |
aEqual variances assumed.
5. Discussion
Altogether, decision-making processes were similar between individuals with a positive and negative history of substance use in two processes of stop search and final decision. However, two other stages of decision-making process of searching for information and decision-making were significantly different between the two groups (Table 5).
Table 5.Differences Between Decision-Making Processes in Individuals with a Positive and Negative History of Substance Use
| Stages of Decision-Making | Difference |
|---|---|
| Searching for information | Significantly different |
| Stop searching | No difference |
| Decision-making | Significantly different |
| Final selection | No difference |
Lack of significant differences in accuracy of choice, coupled with the lack of a difference in the pathway between the two groups, suggests that these cognitive components might indicate that either the decision to use substances is not related to these two processes of decision-making, or that using substances does not result in considerable change in those processes. By considering sequence and time durations as stages of decision-making and making a choice, it can be concluded that the difference between the groups reveals that individuals with a positive history of substance use consumed more resources, i.e., the time required for each cognitive act corresponds to the energy consumed by brain (25, 35-37). Besides, one may claim that the two processes of searching for information and decision-making do not affect the accuracy of choice.
By measurement of ability to coordinate intent with the actualization of that intent (8, 38) it has been shown that, similar to other individuals, subjects who use substances are capable of determining the best possible strategy to execute and accomplish tasks. However, they might not be able to apply a decided strategy (39-41). It is said that individuals who use substances might suffer from cognitive control defects which restrict their ability to translate their strategies and goals into action (8). According to results of Iowa Gambling Test in subjects who use substances, they are shown to have a lower loss aversion capacity (42), meaning that they are not sensitive to negative feedback. Furthermore, it is argued that in individuals who use substances adaptive decision-making is impaired. In our study, the results were in agreement with such indications.
Some scholars have suggested that by limiting the time of a test, especially when the subject’s sense of the passage of time is intensified, the individual’s information processing is accelerated at the expense of searching for the information (43-46). However, Payne argued that by negatively associating the outcome reward to the passage of time, decision-making further speeds up in addition to already accelerated processing (6, 29). In contrast to conditioning process of incentive salience and craving (9, 47, 48), action-selection systems, that are responsible for declarative and episodic evaluation processes, emerge from simulation of events and future rewards, following a response-result pathway (49). In our game model, all those booster processes were in action. Therefore, it appears that not only accelerated processing of some stages may compensate for other defective processes (i.e., limited search for information), but also boosting of some processes, for example by negatively associating decision-making time to incentives, may assure the accuracy of choice. It has been demonstrated that individuals who use substances are more likely to tolerate risk and uncertainty in their preferences (8). According to the results of this study, subjects with a positive history of substance use appear to process information and make decisions similar to control subjects, provided they have the minimum required time.
The above argument that some processes of decision-making may have the capacity to compensate other defective stages raises the question of whether using substances is the result of defective decision-making or it is related to factors other than decision-making. By this argument, not only the generalizability of rational addiction theory must be cautioned, but also one should cast doubt on the common belief that rationality-based prevention programs that provide information about negative consequences of using substances would work as expected. Furthermore, the fact that in our study subjects who had used substances spent more time on making the same decisions compared to individuals with a negative history of using substances might be taken as a sign that negative consequences of substance use might have distorted some processes of decision-making without influencing the outcome.
5.1. Conclusions
The aim of this study was to investigate the pathological aspects of cognitive changes in decision-making and choice of substance-using behavior. The results indicated that despite the previously identified cognitive changes in individuals who use substances, those changes may not be general and occur in only specific aspects of decision-making process. The modality of using the time for proper decision-making in individuals with a history of substance use is different and extended from control subjects. However, as the final choice of individuals with a positive or negative history of substance use were not different, the impression that decision-making processes play as direct components of rational addiction theory (50-52) might need further deliberation. In other words, the behavior of using substances may not be a direct consequence of defective decision-making processes.
5.2. Limitations of the Study
A limitation of this research was the small number of samples. As the study took a long time, participation was dependent on incentives, but as researchers were compensating the incentives they had to limit the study to smaller samples. Wide inclusion criteria that did not differentiate between different types of substances of use (i.e., stimulants, narcotics, sedatives, etc.) may be regarded as another limitation of the study.
