A Simplified Model of Choice Behavior under Uncertainty

Abstract

The Iowa Gambling Task (IGT) has been standardized as a clinical assessment tool (Bechara, 2007). Nonetheless, numerous research groups have attempted to modify IGT models to optimize parameters for predicting the choice behavior of normal controls and patients. A decade ago, most researchers considered the expected utility (EU) model (Busemeyer and Stout, 2002) to be the optimal model for predicting choice behavior under uncertainty. However, in recent years, studies have demonstrated that models with the prospect utility (PU) function are more effective than the EU models in the IGT (Ahn et al., 2008). Nevertheless, after some preliminary tests based on our behavioral dataset and modeling, it was determined that the Ahn et al. (2008) PU model is not optimal due to some incompatible results. This study aims to modify the Ahn et al. (2008) PU model to a simplified model and used the IGT performance of 145 subjects as the benchmark data for comparison. In our simplified PU model, the best goodness-of-fit was found mostly as the value of α approached zero. More specifically, we retested the key parameters α, λ, and A in the PU model. Notably, the influence of the parameters α, λ, and A has a hierarchical power structure in terms of manipulating the goodness-of-fit in the PU model. Additionally, we found that the parameters λ and A may be ineffective when the parameter α is close to zero in the PU model. The present simplified model demonstrated that decision makers mostly adopted the strategy of gain-stay loss-shift rather than foreseeing the long-term outcome. However, there are other behavioral variables that are not well revealed under these dynamic-uncertainty situations. Therefore, the optimal behavioral models may not have been found yet. In short, the best model for predicting choice behavior under dynamic-uncertainty situations should be further evaluated.

Recollecting Cross-Cultural Evidences: Are Decision Makers Really Foresighted in Iowa Gambling Task?

“…The preference for bad deck B shown by normal participants in the IGT was first demonstrated by Wilder et al (1998) , and the phenomenon has since been documented by other researchers ( Toplak et al, 2005 ; Fernie and Tunney, 2006 ; Lin et al, 2007 , 2013 ; Takano et al, 2010 ; Steingroever et al, 2013 ). Prior studies have defined participants’ preferences for bad deck B and good deck D in the IGT as the “gain-loss frequency effect” ( Lin et al, 2007 ; Chiu et al, 2008 ), as the preference is associated with the high-frequency gain structure (i.e., nine gains, one loss) of both bad deck B and good deck D. The observed preference also implies that, under uncertainty conditions, control participants will use a “gain-stay loss-randomize” strategy, meaning that the probability of choosing the same deck will increase when participants face continuous gains, whereas the choice will be randomized when they face loss ( Chiu et al, 2008 ; Worthy et al, 2013b ; Lin et al, 2016 ). This strategy has been employed in recent IGT-related model studies ( Worthy et al, 2013b ; Lin et al, 2016 ).…”

The Prominent Deck B Phenomenon in Schizophrenia: An Empirical Study on Iowa Gambling Task

“…Moreover, a series of relevant IGT studies have found that controls also preferred the disadvantageous deck B (Bark et al, 2005;Rodríguez-Sánchez et al, 2005;Fernie, 2007;Takano et al, 2010;Chiu et al, 2012;Steingroever et al, 2013). The PDB phenomenon of controls has gradually made an impact on the evaluation and development of IGT (Zhang et al, 2017;Chiu et al, 2018), including verifying IGT validity (Buelow and Suhr, 2009;Lin et al, 2013), constructing IGT decision-making models (Ahn et al, 2008;Lin et al, 2016), examining markers for sleep deprivation (Seeley et al, 2014(Seeley et al, , 2016, and examining the clinical application of IGT (Upton et al, 2012). Therefore, researchers have increasingly emphasized the association between the PDB phenomenon and IGT, suggesting that GLF plays an essential role in decisionmaking in healthy and neuropsychiatric individuals.…”

Uncertainty Modulates the Effect of Transcranial Stimulation Over the Right Dorsolateral Prefrontal Cortex on Decision-Making Under Threat

“…Under incredible threat, the subjective probability of rejection would be affected by immediate emotion such as fear in decision-making (Loewenstein and Lerner, 2003; Lin et al, 2016; Chiu et al, 2018). Fear of rejection caused by the threatener’s incredible threat likely prevented the subject from thinking rationally (Schotter et al, 1994; Van Dijk and Vermunt, 2000; Fellner and Güth, 2003) since the probability of future rejection was uncertain.…”