Comparison of magnitude-sensitive sequential sampling models in a simulation-based study

Bose, Thomas; Pirrone, Angelo; Reina, Andreagiovanni; Marshall, James A. R.

doi:10.1016/j.jmp.2019.102298

Cited by 5 publications

(18 citation statements)

References 71 publications

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“…Note that the hypothesis of magnitude dependent noise received further experimental support in Brunton, Botvinick and Brody (2013), Louie, Khaw and Glimcher (2013), Lu and Dosher (2008). Similarly, other models in which magnitude sensitivity arises as a function of the accumulation dynamics or as a function of collapsing thresholds have been proposed (Bose et al, 2020;Pais et al, 2013). Interestingly, these models are agnostic about visual fixations in their original formulations, and for these models magnitude sensitivity arises as a function of information processing and/or accumulation dynamics.…”

Section: Discussionmentioning

confidence: 93%

“…It has been widely argued elsewhere (Bose et al, 2020;Pirrone, Azab et al, 2018;Ratcliff et al, 2018;Teodorescu et al, 2016) that magnitude sensitivity is a key factor that allows to overcome model mimicry between computational models of choice; only a limited number of models and mechanisms allow magnitude sensitivity. Here, we further strengthen the evidence that attentional discounting provides a principled account of magnitude sensitivity in value-based decision making.…”

Section: Discussionmentioning

confidence: 99%

“…Traditionally, only the difference between alternatives was taken under consideration in two-alternatives decision making and magnitude was deemed as 'irrelevant' (Ashby, Jekel, Dickert & Glöckner, 2016;Bogacz et al, 2006). Recently, it has been proposed and demonstrated that the overall magnitude of the alternatives affects decision making too, both in perceptual and value-based decision making, for organisms at different levels of biological complexity, from unicellular organisms to monkeys and humans (Bose, Pirrone, Reina & Marshall, 2020;Dussutour, Ma & Sumpter, 2019;Hunt et al, 2012;Kvam & Pleskac, 2016;Pais et al, 2013;Pirrone, Azab, Hayden, Stafford & Marshall, 2018;Pirrone, Stafford & Marshall, 2014;Pirrone, Wen & Li, 2018;Ratcliff et al, 2018;Steverson, Chung, Zimmermann, Louie & Glimcher, 2019;Teodorescu, Moran & Usher, 2016).…”

Section: Magnitude Sensitivitymentioning

confidence: 99%

“…We decided to use equal alternatives only since those conditions allow to estimate the effects of fixation advantage and magnitude on choice, in the absence of additional decisional confounds due to value difference. The focus on equal alternatives has characterised previous studies about magnitude sensitivity (Bose et al, 2020;Pirrone, Azab et al, 2018;Pirrone & Gobet, 2020;Pirrone et al, 2014;Pirrone, Wen & Li, 2018).…”

Section: Behavioural Analysesmentioning

confidence: 99%

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Is attentional discounting in value-based decision making magnitude sensitive?

Pirrone

Gobet

2021

Journal of Cognitive Psychology

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Choices in value-based decision making are affected by the magnitude of the alternatives (i.e., the summed values of the options). Magnitude sensitivity has been instrumental in discriminating between computational models of choice. Smith and Krajbich (2019) have shown that the attentional drift-diffusion model (aDDM) can account for magnitude sensitivity. This is because the discount parameter on the value of the nonfixated alternative ensures faster choices for high-magnitude alternatives, even in the case of high-magnitude equal alternatives compared to low-magnitude equal alternatives. Their result highlights the importance of visual fixations as a mechanism for magnitude sensitivity. This rationale relies on the untested assumption that the discount parameter is constant across magnitude levels. However, the discount parameter could vary as a function of the magnitude of the alternatives in unpredicted ways; this would suggest that the ability of the aDDM to account for magnitude sensitivity has been misinterpreted by previous research. Here, we reanalyse previous datasets and we directly test whether attentional discounting scales with the magnitude of the alternatives. Our analyses show that attentional discounting does not vary with magnitude. This result further strengthens the aDDM and the role that visual fixations could play as an explanation of magnitude sensitivity.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Magnitude Sensitivitymentioning

confidence: 99%

Section: Behavioural Analysesmentioning

confidence: 99%

See 2 more Smart Citations

Is attentional discounting in value-based decision making magnitude sensitive?

Pirrone

Gobet

2021

Journal of Cognitive Psychology

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“…In particular, research using GP in decision-making could apply the methodology used here to data from human/animal studies in both laboratory and ecological settings. In those scenarios, the true data-generating process is unknown, and several qualitatively similar models could have generated such models, an aspect known as model mimicry (Bose et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Modeling Value-Based Decision-Making Policies Using Genetic Programming

Pirrone

Gobet

2020

Swiss Journal of Psychology

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Abstract. An important way to develop models in psychology and cognitive science is to express them as computer programs. However, computational modeling is not an easy task. To address this issue, some have proposed using artificial-intelligence (AI) techniques, such as genetic programming (GP) to semiautomatically generate models. In this paper, we establish whether models used to generate data can be recovered when GP evolves models accounting for such data. As an example, we use an experiment from decision-making which addresses a central question in decision-making research, namely, to understand what strategy, or “policy,” agents adopt in order to make a choice. In decision-making, this often means understanding the policy that best explains the distribution of choices and/or reaction times of two-alternative forced-choice decisions. We generated data from three models using different psychologically plausible policies and then evaluated the ability and extent of GP to correctly identify the true generating model among the class of virtually infinite candidate models. Our results show that, regardless of the complexity of the policy, GP can correctly identify the true generating process. Given these results, we discuss implications for cognitive science research and computational scientific discovery as well as possible future applications.

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