Qualitative Article Summary
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Qualitative Article Summary
Summary of Introduction
The majority of data analyses are pegged on models. Psychologists utilize statistical models to translate different variables that have been observed into meaningful psychological constructs. The authors discussed delved into cognitive models and response time models. In the article, the authors’ main aim was to study the validity of the inferences that have been drawn from various cognitive models of response time data. The authors also argued that the validity of assumptions from these cognitive models could be influenced by other factors similar to those that affect statistical analysis CITATION Dut19 l 2057 (Dutilh et al., 2019). According to the authors, cognitive models have become a standard measurement tool used to translate the accuracy and speed of participants’ responses. The authors also look into the latent psychological factors of interest, such as the participants’ response bias, their abilities, and whether they are cautious in their responses.
Cognitive models provide a clear account of the psychological processes involved during data analysis. According to the authors, a cognitive model is a formalized theory that mimics the cognitive processes that result in the observed data. The formalization process enables the researchers to derive specific predictions about the information that has been observed. It also makes it possible for the researcher to reverse engineer different variables that have been observed from the data or research CITATION Arn15 l 2057 (Arnold, Bayen, & Broder, 2015). The cognitive models contain two main features: the ability to capture critical phenomena that behavioral data might have missed or ignored, and it reflects the magnitude of the assumed constructs CITATION Don11 l 2057 (Donkin, Brown, & Heathcote, 2011). One of the most common cognitive models is Ratcliff’s diffusion model, which transforms data accuracy and response times into constructs for various studies.
According to the article, other models are the evidence-accumulation models used to measure four main components. These components include boundary separations, non-decision times by the participants, accumulation rates, and starting points. Although the diffusion model is the most common model for response time data, researchers could utilize other alternative models. Some of these alternative models address the shortcomings of the diffusion model. They include the full diffusion model and linear ballistic accumulation (LBA) CITATION Dut19 l 2057 m Dut11 (Dutilh et al., 2019; Dutilh, Krypotos, & Wagenmakers, 2011). Like many of the other models used in statistical analysis, some of the shortcomings of cognitive models include undefined degrees of freedom for the researcher and discriminant and convergent validity.
Summary of Methods
The participants would shoot the direction of the apparent motion that several moving dots would constitute. Each stimulus contained 120 dots that were presented on a screen. During the first trial, all the dots were utilized randomly in an aperture. Afterward, the dots were then displaced at random intervals or according to the rules of the experiment.
For the easy stimuli, 20 percent of the dots were moved in the targeted direction. However, 90 percent of the dots were randomly placed in the aperture for the problematic stimuli, while the other 10 percent moved in the target direction. The set of stimuli had a duration of roughly 180 microseconds. The experiment also had an interval of between 0.5 and 1 second before the subsequent trial. To record the responses, the participants had to press buttons on a computer mouse.
Sample
The research involved 20 psychology students from the University of Basel, with 5 of them being male while the rest were females. The mean age of the participants was 26.7, while their standard deviation (SD) was 2.1. The 20 participants were required to perform a random dot motion task that utilized Python. This sampling method was picked because it allows a more accessible collection of data from the research and generalized results. The study design used three manipulations. The three manipulations included an accurate response to instructions, biasness, and difficulty or ease.
Measures
The manipulation trials were administered in blocks in which the total number of trials was 400 trials where they used five blocks of 80 trials each. This enabled the participants to familiarize themselves with response caution of the manipulations, bias, and stimuli. Before an upcoming block was displayed, the participants were allowed to get acquainted with the instructions. It required the participants to either pay attention to their accuracy and the total number of right and left stimuli.
Summary of Results
The first set of results indicated that the 17 contributors used the 17 procedures. However, the groups in the experiment did not use similar approaches to solve the problems at hand. Based on the results, there were high levels of agreement between the models. The diffusion models that were used had similar inferences along with the LBA models that were used. Despite the agreement within the models, there were differences between the diffusion and LBA models. It is critical to note that although there were different conclusions from the models, one could note that there was consensus in the inferences that were established. The diffusion model made 84 percent correct inferences. EZ2 indicated five false alarms and four misses from a group of 56 inferences. Therefore, the accuracy level was 71 percent.
The LBA models recorded an average inference
