Why do we do what we do? The Attention–Readiness–Motivation framework

Suri, Gaurav; Shine, James M.; Gross, James J.

doi:10.1111/spc3.12382

Cited by 7 publications

(7 citation statements)

References 62 publications

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“…Related to our third proposition, there is strong empirical evidence that suggests that increased attention with respect to a particular option, increases the probability of selection for that option (Milosavljevic et al, 2012; Krajbich et al, 2010; Shimojo et al, 2003; Suri et al, 2018; Suri & Gross, 2015; for a review see Orquin & Loose, 2013). This empirical evidence is bolstered by influential drift-diffusion accounts of decision-making (Krajbich, 2019) according to which decision-makers accumulate evidence in favour of an alternative when attending to it, and that the speed of accumulation is a function of the value of the alternative relative to other alternatives in the choice set, which is called the bias rate or drift rate.…”

Section: Biased Attention As a Driver Of Sqbmentioning

confidence: 82%

The role of attention in status quo bias

Suri

Miceli

2022

Quarterly Journal of Experimental Psychology

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In many decision making contexts, people often persist with their previous selections. This predisposition to choose to maintain a current (or previous) choice is referred to as the status quo bias (SQB). In this work, we propose that increased attention towards the status quo option – enabled by its visual salience – is a previously underappreciated driver of SQB. We base this hypothesis on three propositions: 1) the status-quo bias option is often more visually salient relative to the non-status quo options on offer, 2) greater visual salience of an option biases attention towards that option, and 3) increased attention towards an option leads to that option being selected at greater rates. We examined the attention hypothesis among 6,854 participants in four studies. Study 1 and Study 2 showed that increasing the visual salience of a non-status quo option (i.e., the type of visual salience often garnered by the status quo option) increased the selection rate of that option. Study 3 directly tested the hypothesis by lessening the visual salience of the status quo option. Doing so eliminated SQB. Study 4 replicated and extended the findings of Study 3 in a real-world decision context. Collectively, these studies suggest that the selection of the status quo may often be related to its saliency relative to other available options.

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Section: Biased Attention As a Driver Of Sqbmentioning

confidence: 82%

The role of attention in status quo bias

Suri

Miceli

2022

Quarterly Journal of Experimental Psychology

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“…We close this work with the observation that while the study of three addend sums is firmly rooted in the mathematical cognition literature, it has the potential to open a new domain for research into value-based decision making (Rangel, Camerer, & Montague, 2008;Suri, Shine, & Gross, 2018) and in other domains. Researchers in several sub-disciplines study contexts in which decision-making process unfolds via a comparison of the costs and benefits on offer.…”

Section: Discussionmentioning

confidence: 87%

Three addend addition: Who goes out of order and why?

2021

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Single-digit, three addend sums of the type a + b + c offer a rich opportunity to directly observe the range of strategies that different participants may use because they afford the possibility of measuring a partial sum (i.e., a + b or a + c or b + c). For example, while computing the sum 9 + 7 + 1, do participants go in order by first adding 9 + 7 and then adding 1, or do they incur the cost of going out of order by adding 9 + 1 in order to obtain the partial sum of 10, which makes the subsequent addition of 7 less effortful? Informed by findings in simple and complex arithmetic, we investigated the problem types and participant characteristics that can predict out of order switching behavior in such three-addend sums. To test our hypotheses, we tasked participants, first in an online study, and then in an in-person study to complete 120 single-digit, three addend problems. We found that participants switched the order of addition to prioritize efficiency gains in contexts in which the partial sum addends were small or equal to each other, or when doing so led to a partial sum of 10, or led to a partial sum that is equal to the third remaining integer. Response latency data confirmed that participants were deriving efficiencies in the manner we expected. Related to individual differences, our findings showed that participants with higher levels of math education were most likely to seek efficiency benefits whenever they were on offer.

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“…Participants were shown either a negative image or a neutral image (Suri & Gross, 2015; Suri, Sheppes, & Gross, 2015; Suri, Shine, & Gross, 2018) from a database of affective images, classified into positive, neutral, and negative categories. In each of 40 trials, they were shown a negative or neutral image as a default.…”

Section: Simulations Of Empirical Findings Using Iacmentioning

confidence: 99%

Value-based decision making: An interactive activation perspective.

Suri¹,

Gross²,

McClelland³

2020

Psychological Review

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Prominent theories of value-based decision making have assumed that choices are made via the maximization of some objective function (e.g., expected value) and that the process of decision making is serial and unfolds across modular subprocesses (e.g., perception, valuation, and action selection). However, the influence of a large number of contextual variables that are not related to expected value in any direct way and the ubiquitous reciprocity among variables thought to belong to different subprocesses suggest that these assumptions may not always hold. Here, we propose an interactive activation framework for value-based decision making that does not assume that objective function maximization is the only consideration affecting choice or that processing is modular or serial. Our framework holds that processing takes place via the interactive propagation of activation in a set of simple, interconnected processing elements. We use our framework to simulate a broad range of well-known empirical phenomena-primarily focusing on decision contexts that feature nonoptimal decision making and/or interactive (i.e., not serial or modular) processing. Our approach is constrained at Marr's (1982) algorithmic and implementational levels rather than focusing strictly on considerations of optimality at the computational theory level. It invites consideration of the possibility that choice is emergent and that its computation is distributed.

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Why do we do what we do? The Attention–Readiness–Motivation framework

Cited by 7 publications

References 62 publications

The role of attention in status quo bias

The role of attention in status quo bias

Three addend addition: Who goes out of order and why?

Value-based decision making: An interactive activation perspective.

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