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In real-life situations involving risk and uncertainty, optimal policy hinges on selecting a course of action characterized by the highest expected value (i.e., future outcomes weighted by their probabilities). Nevertheless, a vast body of findings from economic and psychological studies indicate that people rarely follow this principle. The attemp...
The main aim of this study is to replicate the effect shown by Traczyk et al. (2018), where individuals with higher statistical numeracy, compared to individuals with lower statistical numeracy, employed a more effortful choice strategy when outcomes were meaningful. I hypothesize that participants with higher numeracy will be more likely to make c...
Centre of Behavioural and Cognitive Sciences, University of Allahabad. References where, G(y)= fat-tailed distribution μ = Location parameter σ = Scale parameter ξ = Shape parameter α = Tail index x= Y −μ σ when, In perceptual decision making, Drift Diffusion model(DDM) widely use to fit choices and associated reaction time data, where it is known...
DDM widely use to ﬁt accuracy of choices and associated reaction time data, where it is known to implement theoretically optimal algorithms. This is certainly the case in frequentist environments or environments where series of less probable events have impact over our action policy. However, in reality this assumption is hardly always satisﬁed, an...
Every day we make various decisions that may result in consequences of different weights. For example, a trivial decision of whether to take an umbrella before leaving home or not becomes meaningful if we expect heavy rainfall. If our personal goal is to make a good impression during an interview for the desired job, the decision to take the umbrella on a cloudy day is simple, and we do not have to deliberate on its costs and benefits. However, if our personal goal is to have a good time with old friends, we may seriously consider whether it is worth carrying the cumbersome umbrella, at the same time accepting the risk of leaving it in a restaurant or getting wet on the way to the meeting. The main goal of the current project is to show that skilled decision makers (i.e., people with high statistical numeracy) are able to learn the importance of various choice problems (similar to those mentioned above), which allow them to select choice strategy adaptively and make superior decisions. We predict that people with high numeracy, in comparison to people with low numeracy, will employ and manifest recurring irrationality that would result in making superior decisions in the long run. Specifically, depending on the task structure, the characteristics of the environment, and personal goals, people with high numeracy will make a greater number of fast decisions based on heuristics when it pays off. That is, when a choice problem is perceived as trivial, they will make fast decisions, but at the same time, they will use a more comprehensive strategy and deliberate longer to solve personally meaningful choice problems. We hypothesize that even if decisions based on simple and fast heuristics are suboptimal according to normative standards, repeated satisfactory choices made within a limited time frame will lead to better overall outcomes than optimal choices predicted by rational choice theory. In this sense, the recurring irrationality is adaptively rational.
The goal is to investigate whether non-parametric psychometric function provides fit as good as, if not better than, parametric psychometric function. Furthermore,to check which said model predict more accurate reaction time data. lastly, to verify legitimacy of assumption such as- internal representation of the stimulus is normally distributed in current study context.
I'm trying to challenge the basic assumption of Speed-accuracy trade-off that exits in perceptual decision making domain. Presently, every model that has been proposed in this domain can not predict or accommodate fast and accurate decision making. In the study we are interested if reward contingent fast response obey speed-accuracy trade-off in perceptual decision making in condition where noise is distributed in fat-tailed fashion.