Cognitive models of optimal sequential search with recall

Bhatia, Sudeep; He, Lisheng; Zhao, Wenjia; Analytis, Pantelis P.

doi:10.1016/j.cognition.2021.104595

Cited by 6 publications

(19 citation statements)

References 58 publications

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“…Some studies have suggested that people use biased decision thresholds and search too little because they tend to be risk averse (Bhatia et al, 2021; Schotter & Braunstein, 1981; Schunk, 2009). Unlike the risk-neutral best performing LTM, human decision makers are often found to be risk averse (e.g., Pedroni et al, 2017), preferring the safe over the risky option.…”

Section: Discussionmentioning

confidence: 99%

“…One study reported that there was no relationship between search length in an optimal stopping task and risk preferences elicited in a series of lottery tasks (Schunk, 2009). On the other side, a recent study about optimal sequential search with recall found evidence that in this paradigm, risk aversion is one of the main factors guiding people’s behavior (Bhatia et al, 2021). Moreover, Sonsino et al (2002) suggested that the probability of choosing a given option decreases with the relative complexity of that alternative.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, Seale and Rapoport (2000) hypothesized that stopping too early in the optimal stopping task could result from the existence of endogenous search costs, that is, psychological costs arising from considering and evaluating options (see also Bhatia et al, 2021). Although our experiment used a somewhat different design, participants may have been motivated to end the experiment as quickly as possible and considered the time spent observing and reviewing the prices as a kind of search cost.…”

Section: Discussionmentioning

confidence: 99%

“…The BOM, an optimal model with systematic biases, was less capable of accommodating people’s choices. A recent study introduced a model that assumes decision thresholds are based on the optimal thresholds but explains deviations thereof by psychological factors such as risk preference and effort search costs (Bhatia et al, 2021). Such models represent an important source for quantifying and understanding deviations from optimality especially in simpler search task with tractable optimal solutions.…”

Section: Discussionmentioning

confidence: 99%

“…However, it appears to be widely acknowledged that humans do not behave optimally in optimal stopping tasks (e.g., Baumann et al, 2020; Bhatia et al, 2021; Lee et al, 2004; Lee & Courey, 2021; Zwick et al, 2003), such that typical accounts often focus on describing deviations from optimality due to biases and noise (Baumann et al, 2020; Bhatia et al, 2021; Goldstein et al, 2020; Guan et al, 2015). Recent debates, however, have criticized the idea of capturing parametric deviations from optimal (bias) for neglecting the underlying psychological processes that drive behavior in the first place (for a general discussion, see Rahnev & Denison, 2018).…”

Section: Changes In Decision Environmentsmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive behavior in optimal sequential search.

Baumann¹,

Schlegelmilch²,

Helversen³

2023

Journal of Experimental Psychology: General

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Sequential decision making-making a decision where available options are encountered successively-is a hallmark of everyday life. Such decisions require deciding to accept or reject an alternative without knowing potential future options. Prior work focused on understanding choice behavior by developing decision models that capture human choices in such tasks. We investigated people's adaptive behavior in changing environments in light of their cognitive strategies. We present two studies in which we modified (a) outcome variance and (b) the time horizon and provide empirical evidence that people adapt to both context manipulations. Furthermore, we apply a recently developed threshold model of optimal stopping to our data to disentangle different cognitive processes involved in optimal stopping behavior. The results from Study 1 show that participants adaptively scaled the values of the sampling distribution to its variance, suggesting that the value of an option is perceived in relative rather than absolute terms. The results from Study 2 suggest that increasing the time horizon decreases the initial acceptance level, but less strongly than would be optimal. Furthermore, for longer sequences, participants more weakly adjusted this acceptance threshold over time than for shorter sequences. Further correlations between individual estimates in each condition indicate that individual differences between the participants' thresholds remain fairly stable between the conditions, pointing toward an additive effect of our manipulations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Changes In Decision Environmentsmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive behavior in optimal sequential search.

Baumann¹,

Schlegelmilch²,

Helversen³

2023

Journal of Experimental Psychology: General

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Search for the best alternative: An experimental approach

2023

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Effectively implementing any dimension of a company's strategy requires a careful balance between ensuring a sufficient number of opportunities have been explored and the execution has taken place in a timely manner for a specific initiative. Naturally, the importance of specific characteristics of an initiative will vary according to the strategic objective. Alternatives vary in their likelihood of success, how sensitive their value is to time, and the resources required to explore additional options. Extant research has established optimal rules for what constitutes a sufficient degree of exploration, and how to go about exploring alternatives. Yet, how individuals vary their behavior according to the characteristics of an initiative is not well understood. We use a controlled experiment to study how individuals' decisions are impacted by these characteristics. As expected, individuals perform significantly worse than optimal behavior would predict. However, the underlying drivers of this performance deviation critically depend on the characteristics of the initiative. Individuals who face simple initiatives that lack sensitivity to time tend to evaluate far too many options. In contrast, when a simple initiative's value is sensitive to time, individuals tend to evaluate far too few options. When the individuals face more difficult initiatives, they do not exhibit such extreme tendencies in terms of how many alternatives they evaluate. Rather, for the most challenging initiatives, individuals exhibit near optimal behavior in terms of how many alternatives they evaluate. Our findings help to provide insight to managers on where they should focus their attention in terms of pushing for more or less exploration.

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Diagnostic Value of Convolutional Neural Network Algorithm and High-Sensitivity Cardiac Troponin I Detection Under Machine Learning in Myocardial Infarction

Jiang

Lin³

2022

j biomed nanotechnol

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Background: It aimed to the diagnosis and examination of acute myocardial infarction (AMI) using echocardiography under improved convolutional neural network (CNN) algorithm and high-sensitivity (Sen) cardiac troponin I (hs-cTnI) detection. The application effect was also evaluated. Methods: Ninety AMI patients were recruited as the AMI group, and ninety healthy individuals who underwent physical examinations simultaneously were chosen as control (Ctrl) group. Improved CNN algorithm-based echocardiography combined with hs-cTnI detection was applied, and its diagnostic efficiency was evaluated. Results: The optimal dataset scale (ODS), optimal image scale (OIS) and average precision (AP) of the proposed algorithm were better than those of manual labeling, Canny algorithm, and structured edge (SE) algorithm (P < 0.05). The left ventricular ejection fraction (LVEF) of the patients in the AMI group was inferior to that of Ctrl group ((55.09±2.78)%) versus (65.01±3.19)%), the left ventricular end-diastolic dimension (LVEDD) was superior to that of Ctrl group ((54.89±6.56) mm vs. (45.98±5.77) mm), and the cTnI level was also superior to that of Ctrl group ((2.90±0.31) pg/L vs. (0.73±0.42) pg/L) (P < 0.05). The diagnostic Sen (91.89%), specificity (Spe) (81.25%), accuracy (Acc) (90.00%) and consistency (0.56) of echocardiography combined with hs-cTnI were superior to those of single echocardiography or cTnI detection (P < 0.05).

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Cognitive models of optimal sequential search with recall

Cited by 6 publications

References 58 publications

Adaptive behavior in optimal sequential search.

Adaptive behavior in optimal sequential search.

Search for the best alternative: An experimental approach

Diagnostic Value of Convolutional Neural Network Algorithm and High-Sensitivity Cardiac Troponin I Detection Under Machine Learning in Myocardial Infarction

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