scholarly journals Uncovering the computational mechanisms underlying many-alternative choice

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Armin W Thomas ◽  
Felix Molter ◽  
Ian Krajbich
Keyword(s):  
Choice Experiment ◽  
Response Times ◽  
Empirical Relation ◽  
Optimal Choice ◽  
Evidence Accumulation ◽  
The Gaze ◽  
Subjective Value ◽  
Choice Sets ◽  
Alternative Choice ◽  
Probabilistic Version

How do we choose when confronted with many alternatives? There is surprisingly little decision modelling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally provides slightly better fits to choices and response times, while the gaze-driven evidence accumulation and comparison model provides the best overall account of the data when also considering the empirical relation between gaze allocation and choice.

scholarly journals Uncovering the Computational Mechanisms Underlying Many-Alternative Choice

2020 ◽  
Author(s):  
Armin Thomas ◽  
Felix Molter ◽  
Ian Krajbich
Keyword(s):  
Everyday Life ◽  
Choice Experiment ◽  
Optimal Choice ◽  
The Other ◽  
Decision Modeling ◽  
Evidence Accumulation ◽  
Subjective Value ◽  
Choice Sets ◽  
Alternative Choice ◽  
Probabilistic Version

How do we choose when confronted with many alternatives? There is surprisingly little decision modeling work with large choice sets, despite their prevalence in everyday life. Even further, there is an apparent disconnect between research in small choice sets, supporting a process of gaze-driven evidence accumulation, and research in larger choice sets, arguing for models of optimal choice, satisficing, and hybrids of the two. Here, we bridge this divide by developing and comparing different versions of these models in a many-alternative value-based choice experiment with 9, 16, 25, or 36 alternatives. We find that human choices are best explained by models incorporating an active effect of gaze on subjective value. A gaze-driven, probabilistic version of satisficing generally outperforms the other models, though gaze-driven evidence accumulation and comparison performs comparably well with 9 alternatives and is overall most accurate in capturing the relation between gaze allocation and choice.

scholarly journals Gaze Amplifies Value in Decision Making

2018 ◽  
Vol 30 (1) ◽  
pp. 116-128 ◽  
Author(s):  
Stephanie M. Smith ◽  
Ian Krajbich
Keyword(s):  
Response Times ◽  
Data Sets ◽  
Choice Data ◽  
Data Set ◽  
Choice Process ◽  
The Gaze ◽  
Large Sets ◽  
Subjective Value ◽  
Amplifying Effect ◽  
Familiar Stimuli

When making decisions, people tend to choose the option they have looked at more. An unanswered question is how attention influences the choice process: whether it amplifies the subjective value of the looked-at option or instead adds a constant, value-independent bias. To address this, we examined choice data from six eye-tracking studies ( Ns = 39, 44, 44, 36, 20, and 45, respectively) to characterize the interaction between value and gaze in the choice process. We found that the summed values of the options influenced response times in every data set and the gaze-choice correlation in most data sets, in line with an amplifying role of attention in the choice process. Our results suggest that this amplifying effect is more pronounced in tasks using large sets of familiar stimuli, compared with tasks using small sets of learned stimuli.

scholarly journals GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour

2019 ◽  
Author(s):  
Felix Molter ◽  
Armin W. Thomas ◽  
Hauke R. Heekeren ◽  
Peter N. C. Mohr
Keyword(s):  
Model Space ◽  
Choice Data ◽  
Choice Behaviour ◽  
Individual Level ◽  
Evidence Accumulation ◽  
The Gaze ◽  
Choice Sets ◽  
The Individual ◽  
Future Work ◽  
Bayesian Statistical Modeling

AbstractRecent empirical findings have indicated that gaze allocation plays a crucial role in simple decision behaviour. Many of these findings point towards an influence of gaze allocation onto the speed of evidence accumulation in an accumulation-to-bound decision process (resulting in generally higher choice probabilities for items that have been looked at longer). Further, researchers have shown that the strength of the association between gaze and choice behaviour is highly variable between individuals, encouraging future work to study this association on the individual level. However, only few decision models exist that easily allow studying the gaze-choice association on the individual level, due to the high cost of developing and implementing them. The model space is particularly scarce for choice sets with more than two choice alternatives. Here, we present GLAMbox, a Python-based toolbox that is built upon PyMC3 and allows the easy application of the gaze-weighted linear accumulator model (GLAM) to experimental choice data. The GLAM assumes gaze-dependent evidence accumulation in a linear stochastic race that extends to decision scenarios with many choice alternatives. GLAMbox enables Bayesian parameter estimation of the GLAM for individual, pooled or hierarchical models, provides an easy-to-use interface to predict choice behaviour and visualize choice data, and benefits from all of PyMC3’s Bayesian statistical modeling functionality. Further documentation, resources and the toolbox itself are available at https://github.com/glamlab/glambox.

scholarly journals An Exploratory Analysis of Museum Attributes from the Perspective of Tourists and Residents: The Case of Thyssen-Bornemisza National Museum, Madrid, Spain

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 64
Author(s):  
Alicia Orea-Giner ◽  
Jorge Calero-Sanz ◽  
Carmen De-Pablos-Heredero ◽  
Trinidad Vacas-Guerrero
Keyword(s):  
Choice Experiment ◽  
Local Community ◽  
National Museum ◽  
Visitor Experience ◽  
Convenience Sample ◽  
Cultural Sustainability ◽  
Museum Management ◽  
Visitor Perceptions ◽  
Subjective Value ◽  
Attribute Evaluation

Attribute evaluation provides an understanding of the perceived quality and subjective value of the museum visitor experience. The principal contribution of this paper is to analyze the attributes perceived by tourists and the local community (Madrid residents) of the Thyssen-Bornemisza National Museum (Madrid, Spain), utilizing the results from choice experiment and willingness-to-pay questionnaires. To analyze in depth the assessment regarding the museum attributes and the visitor perceptions of them, the relevance-determination model was applied. Data collection was achieved with a questionnaire using a convenience sample of international tourists and the local community, providing a total of 775 valid surveys. The results of the application of the relevance-determination analysis (RDA) show that there are two types of attributes: higher-impact core and lower-importance attributes. The attributes with the highest subjective value perceived by interviewed tourists and interviewed residents are the location, the building, and the permanent collection. These results show that there are substantial differences between the perception and appreciation of these attributes by interviewed residents and interviewed tourists. The results provide valuable information that can be applied in practice to devise strategies for economic and socio-cultural sustainability aimed at improving decision-making in museum management.

Experimental Analysis of Predecision Information Seeking

1969 ◽  
Vol 6 (2) ◽  
pp. 192-197 ◽  
Author(s):  
John E. Swan
Keyword(s):  
Experimental Analysis ◽  
Consumer Choice ◽  
Information Seeking ◽  
Choice Experiment ◽  
Optimal Choice ◽  
Brand Information

A simulated consumer choice experiment showed that where the same brands appeared across a set of trials, prechoice information seeking declined as the subjects evidently learned to choose by brand. Information seeking was also lower for satisfactory, as compared with optimal, choice.

scholarly journals Should I stay or should I go? Evidence accumulation drives decision making in human drivers

2020 ◽  
Author(s):  
Arkady Zgonnikov ◽  
David Abbink ◽  
Gustav Markkula
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Response Times ◽  
Process Models ◽  
Cognitive Mechanisms ◽  
Naturalistic Decision Making ◽  
Dynamic Constraints ◽  
Evidence Accumulation ◽  
Out Of Sample

Laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. Yet it is unclear whether the cognitive processes implicated in simple, isolated decisions in the lab are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Here we aim to address the gap between modern cognitive models of decision making and studies of naturalistic decision making in drivers, which so far have provided only limited insight into the underlying cognitive processes. We investigate drivers' decision making during unprotected left turns, and model the cognitive process driving these decisions. Our model builds on the classical drift-diffusion model, and emphasizes, first, the drift rate linked to the relevant perceptual quantities dynamically sampled from the environment, and, second, collapsing decision boundaries reflecting the dynamic constraints imposed on the decision maker’s response by the environment. We show that the model explains the observed decision outcomes and response times, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions, effectively providing a way to predict human drivers’ behavior in real time. Our results reveal the cognitive mechanisms of gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help us to understand human behavior in complex real-world tasks.

scholarly journals Response threshold variance as a basis of collective rationality

2017 ◽  
Vol 4 (4) ◽  
pp. 170097 ◽  
Author(s):  
Tatsuhiro Yamamoto ◽  
Eisuke Hasegawa
Keyword(s):  
Decision Maker ◽  
Choice Experiment ◽  
Swarm Robotics ◽  
Basic Mechanism ◽  
Optimal Choice ◽  
Collective Decision ◽  
Response Threshold ◽  
Collective Rationality ◽  
Graded Responses ◽  
Graded Response

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only ‘yes/no’ responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond ‘yes’ to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies.

scholarly journals Dissecting the impact of depression on decision-making

2019 ◽  
Vol 50 (10) ◽  
pp. 1613-1622 ◽  
Author(s):  
Victoria M. Lawlor ◽  
Christian A. Webb ◽  
Thomas V. Wiecki ◽  
Michael J. Frank ◽  
Madhukar Trivedi ◽  
...  
Keyword(s):  
Decision Making ◽  
Cognitive Deficits ◽  
Response Times ◽  
Major Depressive ◽  
Evidence Accumulation ◽  
Two Samples ◽  
Sensorimotor Deficit ◽  
The Impact ◽  
To Receive ◽  
Trial Participants

AbstractBackgroundCognitive deficits in depressed adults may reflect impaired decision-making. To investigate this possibility, we analyzed data from unmedicated adults with Major Depressive Disorder (MDD) and healthy controls as they performed a probabilistic reward task. The Hierarchical Drift Diffusion Model (HDDM) was used to quantify decision-making mechanisms recruited by the task, to determine if any such mechanism was disrupted by depression.MethodsData came from two samples (Study 1: 258 MDD, 36 controls; Study 2: 23 MDD, 25 controls). On each trial, participants indicated which of two similar stimuli was presented; correct identifications were rewarded. Quantile-probability plots and the HDDM quantified the impact of MDD on response times (RT), speed of evidence accumulation (drift rate), and the width of decision thresholds, among other parameters.ResultsRTs were more positively skewed in depressed v. healthy adults, and the HDDM revealed that drift rates were reduced—and decision thresholds were wider—in the MDD groups. This pattern suggests that depressed adults accumulated the evidence needed to make decisions more slowly than controls did.ConclusionsDepressed adults responded slower than controls in both studies, and poorer performance led the MDD group to receive fewer rewards than controls in Study 1. These results did not reflect a sensorimotor deficit but were instead due to sluggish evidence accumulation. Thus, slowed decision-making—not slowed perception or response execution—caused the performance deficit in MDD. If these results generalize to other tasks, they may help explain the broad cognitive deficits seen in depression.

Experimental design issues in choice-based conjoint applied to patient choice in healthcare

2020 ◽  
Vol 9 (2) ◽  
pp. 141-147
Author(s):  
Pallavi Chitturi ◽  
Alexandra Carides
Keyword(s):  
Experimental Design ◽  
Drug Development ◽  
Optimal Choice ◽  
Patient Choice ◽  
Pareto Optimal ◽  
Design Issues ◽  
Choice Sets ◽  
Attribute Levels ◽  
Decision Alternatives ◽  
Good Research

Choice-based conjoint (CBC) is used to understand how individuals develop preferences for decision alternatives. When decision alternatives can be described in terms of attributes, researchers want to determine the value respondents attach to various attribute levels. Popular in psychology, marketing, economics and other areas, CBC is now finding applications in healthcare to understand patient choice in healthcare policy, drug development, doctor–patient communications, etc. However, a lack of standard methodologies has served as a barrier to its use in healthcare. Therefore, there is a need to identify good research practices for CBC in healthcare. We review recent advances in CBC such as Pareto optimal choice sets, information per profile and reducing choice set sizes, as applied to patient choice.

scholarly journals Evidence Accumulation Modelling Reveals that Gaussian Noise Accounts for Inhibition of Return

2020 ◽  
Author(s):  
Tal Seidel Malkinson ◽  
Alexia Bourgeois ◽  
Nicolas Wattiez ◽  
Pierre Pouget ◽  
Paolo Bartolomeo
Keyword(s):  
Gaussian Noise ◽  
Inhibition Of Return ◽  
Response Times ◽  
Information Criterion ◽  
Model Parameters ◽  
Noise Parameter ◽  
Evidence Accumulation ◽  
Cortical Regions ◽  
Different Response ◽  
The Right

AbstractInhibition of return (IOR) refers to the slowing of response times (RTs) for stimuli repeated at previously inspected locations, as compared with novel ones. However, the exact processing stage(s) at which IOR occurs, and its nature across different response modalities, remain debated. We tested predictions on these issues originating from the FORTIOR model (fronto-parietal organization of response times in IOR; Seidel Malkinson & Bartolomeo, 2018), and from evidence accumulation models. We reanalysed RT data from a target-target IOR paradigm (Bourgeois et al.,2013a, 2013b) by using a LATER-like evidence accumulation model (Carpenter & Williams, 1995), to test the predictions of FORTIOR, and specifically whether IOR could occur at sensory/attentional stages of processing, or at stages of decision and action selection. We considered the following conditions: manual or saccadic response modality, before or after TMS perturbation over four cortical regions. Results showed that the Gaussian noise parameter best explained both manual and saccadic IOR, suggesting that in both response modalities IOR may result from slower accumulation of evidence for repeated locations. Additionally, across stimulated regions, TMS affected only manual RTs, lowering them equally in the conditions with repeated targets (Return) and non-repeated targets (Non-return). Accordingly, the modelling results show that TMS stimulation did not significantly alter the pattern between model parameters, with the Gaussian noise parameter remaining the parameter best explaining the Return - Non-return RT difference. Moreover, TMS over the right intra-parietal sulcus (IPS) perturbed IOR by shortening the Return RT. When directly testing this effect by modelling the TMS impact in the Return condition, the Bayesian information criterion of the Gaussian noise parameter was the smallest, but this effect did not reach significance. These results support the hypothesis that target-target IOR is a predominantly sensory/attentional phenomenon, and may be modulated by activity in fronto-parietal networks.

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