computational modeling

The aim of this research is studying of 2-sided message’s effects on persuasiveness of anti-drug messages by computational modeling method. It’s been done for getting more effective and more persuasive messages. Persuasiveness of messages is measured be perceived argument strength of them which is determined by audiences. In this research, according to formative researches, a method for measuring the perceived argument strength was developed. Then 3 types of messages with 3 different arguments and frames was used which 2 types of 2-sided messages (Anti/Pro and Pro/Anti) and 1 type of single-side messages was examined. The messages were displayed to the audiences through on-line questionnaires. Audiences were involved 10 persons with drug abusing experiences and 36 persons without any drug related experiences. In the questionnaires, audiences were asked about effectiveness of messages on them and their close friends. The comparison of means and standard deviations was done for 3 types of messages which ranked according their perceived argument strengths by 2 groups (with/without drug experiences) of audiences. In order to modeling the effects of factors other than (types of message and drug experience), mixed effects models were run to the data. The results showed that 2-sided messages are more persuasive than 1-sided messages and 2-sided messages with anti/pro frames are most effective messages to audiences with or without drug abusing experiences. In this type of messages, if first side of message be consistent with audience’s beliefs, then it will act like mental anchor and increases effectiveness of messages.

In many cases, when making real-life decisions, the evidence does not come to the person on a continuous basis; instead, the individual receives separate pieces of evidence during a time period. While subjects are able to integrate information perfectly from separate pieces of evidence to improve their accuracy, the confidence formation is controversial.
 

To specify confidence formation, we tested 12 healthy adults using a Random Dot Motions (RDM) paradigm. Participants had to distinguish the predominant direction of motion of moving dots by saccadic eye movement after receiving one or two pieces of 120ms of information (cue). Both direction and strength of the motion changed trial by trial. However, the direction of both pieces in double-piece trials was the same and participants were aware of that. The interval of two pieces (up to 1 s) was selected randomly. Color-coded targets helped subjects, indicating confidence simultaneously. In addition to empirical analysis, we applied a set of computational models to discuss how accuracy and confidence have changed after receiving an extra short RDM stimulus. First, we fitted a standard signal detection model to participantschr choices and confidence to directly estimate each participantchr confidence criteria and metacognition sensitivity. Second, based on the taskchr  nature and considering the single-piece trialschrdata, a perfect accumulator model and an optimized model were fitted to participants’ choices and confidence. These models were used to predict the accuracy, confidence, and metacognition of double-piece trials. We evaluated the models both quantitatively —Maximum Likelihood Estimation— and qualitatively —parameter recovery exercises in which data were simulated from models’ parameters.
 

The results showed that participants integrate the decision evidence, invariant to gap interval to form their decision and confidence after receiving the second piece of information. Although previous studies showed a positive relation between accuracy and confidence, double-piece trialschr confidence did not improve along with improved accuracy in the current study. Instead, our data showed that receiving an extra short piece of information improves participantschr  confidence in correct choices but worsens participantschr confidence in incorrect decisions. Results from computational models showed, the perfect accumulator could not predict the confidence of double-piece trials. Moreover, by optimizing the perfect accumulator, the model could predict the confidence in double-piece trials well. The evaluation of the models both quantitatively and qualitatively confirmed the superiority of the latter model. Based on the reported data and computational modeling results, participants reallocate their confidence criteria slightly after receiving the second pulse to report their confidence more accurately.
 

The investigations indicated that although participants integrate the decision evidence perfectly and improve their accuracy and decision metacognition, their confidence did not improve significantly. In that way, providing brief pieces of information over time is more effective in persuading participants to make a more sustainable choice and prevent changes in decisions. These findings have implications for many areas such as marketing and behavioral economics; providing limited information over time helps people make more stable choices in purchasing goods or receiving services. Also, in situations such as elections, if the information presented in favor of a candidate offered over time and in several phases, it can have a greater impact and improve decision stability.

Investigating how individuals' decision-making is influenced by other cognitive elements and using computational modeling of decision can help us to better appreciate this cognitive function, as well as better decision-making quality.  Thepresent study aimed to present a new cognitive model in the field of decision-making and to examine the model’s efficacy in predicting the decisions of those participating in the Iowa decision test compared to other classical decision- making models.

In this study, 56 subjects, including 20 men and 36 womenwith an average age of 43.52, were asked to participate in the Iowa gambling task. The results of the model used were then compared with the results of the three well-known decision- making models, including expected value, expected utility, and prospect model.. The flexibility of this model by calculating the impact factor of each concept for each individual, allows us to model each person's decision-making individually and identify the concepts, which are most effective in each individual's decision-making.

The obtained results revealed that the expected value, expected utility, prospect, and the proposed cognitive connectionist models predicted 36.04%, 42.46%, 49.18%, and 73.02% of subjects’ decisions, respectively.

As a result, it can be argued that the proposed cognitive connectionist model has more potential for modeling examinee’s behavior in the Iowa test. It also provided a reasonable way to study the essential cognitive elements that can affect the participant’s decisions.