Volume 25, Issue 4 (Winter 2024)                   Advances in Cognitive Sciences 2024, 25(4): 124-139 | Back to browse issues page

Ethics code: IR.IUMS.REC.1398.1201

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Tajrishi H, Zabbah S, Hatami J, Ebrahimpour R. Perceptual decision-making in realistic situations with the integration of visual evidence and social cues: The diffusion model as an analytical tool. Advances in Cognitive Sciences 2024; 25 (4) :124-139
URL: http://icssjournal.ir/article-1-1632-en.html
1- Cognitive Science Modeling, Shahid Beheshti University, Tehran, Iran
2- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
3- Psychology Department, Faculty of Psychology, University of Tehran, Tehran, Iran & Institute for Cognitive Science Studies, Pardis, Iran
4- 5. Center for Cognitive Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran, Iran
Abstract:   (541 Views)
Introduction
Perceptual decision-making is a pivotal aspect of our daily lives, as various decision-making processes involve accumulating evidence from sensory perception, personal preferences, social cues, and other sources. In the realm of social psychology, the impact of social information on decision-making is a subject of interest, studied under the umbrella of social influence. Social influence involves changes in cognition, attitudes, or behavior due to external factors (1), and conformity refers to the frequency with which individuals mimic social cues, regardless of their accuracy. Social influence arises from two primary motivations: "Informational influence", where individuals seek group-derived information because they believe it to be more accurate, and "Normative influence", driven by the desire to align with group norms (2, 3). Well-designed experiments are essential to draw meaningful conclusions and disentangle normative and informational influences. Classic conformity studies have highlighted the challenges associated with both types of social influence. It is necessary to distinguish between them to ensure the interpretation of research findings, obstructing a comprehensive understanding of decision-making in social contexts.
This research focuses on minimizing the factors producing normative effects and investigating the impact of informational social influence on decision-making when social information is presented after distinct sensory cues. The present study aimed  to understand how last-minute social information may impact the sensory gathering process and, subsequently, influence decision-making.
Methods
The experimental task in this study comprised six stages, with five involving extensive training, followed by the main task. The main experiment was divided into four sessions, each consisting of one block.
In this psychophysical task, participants were exposed to two 120-millisecond sensory stimuli originating from Random Dot Motion (RDM), featuring different degrees of evidence strength and motion direction. Afterward, a graphical cue was displayed, using shapes and colors familiar to participants from their training. Notably, this graphical cue was solely provided to indicate the other participants’ responses, intentionally devoid of any information regarding the identity of the respondents, to minimize normative influence. They were informed that these cues represented responses from other participants who had encountered the same RDMs. However, the responses were not from actual participants; instead, the researchers generated them using a signal detection model. To create the social data, the researchers relied on participant behavior in sensory trials as a foundation. They employed the model to generate responses from four participants, each with distinct performance and confidence levels. This study employed the Generalized Drift Diffusion Model (GDDM) to provide a flexible framework for decision-making research. GDDM allows for a broad range of experimental designs and empirical hypotheses, accommodating customized distributions for parameters such as starting position, decision threshold, and drift rate (4). This flexibility enhances our ability to investigate the influence of social information on decision processes. The present analysis required the development of customized parameters due to the unique experimental conditions, including the presentation of discrete evidence and the incorporation of social information. The model used in this study propose that the drift rate, reflecting the speed of information uptake, remains consistent throughout each trial, each consisting of two pulses. However, its effect varies between pulses depending on their coherence levels. Specifically, This research derive this measure of drift rate from the initial pulse of information and compute it separately for each pulse based on its coherence level. In this manner, the researchers manage the transition between discrete pulses of information. By adopting this definition of the starting point as the ratio of the preference at time 0 to the size of the decision boundary (5) and considering that the starting point shifts to favor a more frequent or highly rewarded response when trials are manipulated (6, 7), it becomes possible to apply it to social cues. This accounts for the occurrence of a bias or preference in evidence accumulation toward social cues. However,  extending its timing to presentinga social cue is essential. By integrating this concept into the model, this study can effectively explain how social cues influence decision bias.
The task is run using the Psychtoolbox in MATLAB, and behavioral analysis takes place within MATLAB. Statistical tests are carried out using JASP, and the GDDM model is executed through the PyDDM, a drift-diffusion modeling framework for Python.
Results
Nine participants successfully completed all tasks. During the training phase, an average of 2100 data points were collected from each participant, and in the main test phase, 7200 trials were recorded. Additionally, 5400 trials were recorded in the isolated task. Participants notably increased their accuracy in group tasks compared to solo tasks, mainly when their partners were more precise.  However, the overall mean accuracy of participants remained significantly lower than that of their partners.
The extent of adapting to social cues is another crucial factor in participant behavior. As anticipated, participants exhibited greater conformity with more accurate partners. Statistical tests revealed a significant positive correlation between the influence of social partners. It was found to be significant only when comparing high-accuracy and low-accuracy partners to validate the significance of conformity behavior and identify differences between conditions. No significant difference in conformity was observed between partners with high-and low-confidence social information.
The obtained findings, as modeled through DDM, reveal significant differences in crucial decision-making parameters between the isolated phase and the social task. Specifically, when comparing parameters such as the drift rate (the pace of evidence accumulation), the decision threshold (the determiner of decision endpoints), and the starting point (illustrating how social information complements sensory evidence), the researchers observed an increase in the decision threshold and starting point, and a decrease in the drift rate in the social task compared to the isolated task. Further investigation into variations among partners reveals that the disparity in accuracy improvement between high and low-accuracy partners when social information is introduced at the final stage leads to an increase in the starting point.
Conclusion
This study delves into the intricate dynamics of decision-making in social contexts, specifically focusing  on the impact of social information presented after distinct sensory cues. The pursuit of understanding how last-minute social information affects the sensory gathering process and, consequently, influences decision-making has unveiled valuable insights into the multifaceted world of social influence on human behavior.
The obtained results shed light on the role of social cues in enhancing decision accuracy. Participants exhibited a significant improvement in accuracy when operating in social blocks as opposed to isolated ones, primarily when their partners exhibited higher accuracy. Nevertheless, the mean accuracy of participants remained persistently lower than that of their high-accuracy partners, highlighting the unique complexities involved in integrating social information into perceptual decision-making.
Furthermore, the current study unveiled the pivotal influence of conformity on participant behavior. Conformity was more pronounced when participants interacted with partners who demonstrated higher accuracy. This not only provides evidence of the impact of informational social influence but also illustrates the nature of conformity, revealing that individuals may adapt to the cues of more accurate peers.
The present study investigates the influence of both visual and social cues on decision-making, seeking to bridge the gap between theoretical models and real-life social interactions. Understanding the interplay between perception, cognition, and social behavior during decision-making enhances our ability to model and predict decision processes in various social domains.
Ethical Considerations
Compliance with ethical guidelines
This research complied approved experimental protocols by the Ethics Committee of the Iran University of Medical Science (Approval ID: IE392343). All participants provided written informed consent, assuring the collected data’s confidentiality and scientific purpose-bound use. Participants were given the option to withdraw from the experiment at any stage.
Authors’ contributions
Hoora Tajrishi: Conceptualization, data collection, analysis, visualization, writing original draft, review, and editing. Sajjad Zabbah: Conceptualization, supervision, writing reviews, and editing. Javad Hatami: Supervision, writing reviews, and editing. Reza Ebrahimpour: Conceptualization, supervision, writing reviews, and editing.
Funding
This study was funded by the  Cognitive Sciences & Technologies Council, Iran (9970).
Acknowledgments
The authors would like to express their sincere gratitude to all the participants for their time and cooperation, without which this research would not have been achievable. The authors are also thankful to their colleagues who generously offered their assistance and valuable input, especially during the early stages of task development and debugging.
Conflicts of interest
The authors declared no conflicts of interest.
 
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Type of Study: Research |
Received: 2023/11/7 | Accepted: 2024/04/8 | Published: 2024/04/22

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