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The visual environment and attention in decision making

Jacob L. Orquin, Erik S. Lahm, Hrvoje Stojić
2021 Psychological bulletin  
Visual attention is a fundamental aspect of most everyday decisions, and governments and companies spend vast resources competing for the attention of decision makers. In natural environments, choice options differ on a variety of visual factors, such as salience, position, or surface size. However, most decision theories ignore such visual factors, focusing on cognitive factors such as preferences as determinants of attention. To provide a systematic review of how the visual environment guides
more » ... attention we meta-analyze 122 effect sizes on eye movements in decision making. A psychometric meta-analysis and Top10 sensitivity analysis show that visual factors play a similar or larger role than cognitive factors in determining attention. The visual factors that most influence attention are positioning information centrally, ρ = .43 (Top10 = .67), increasing the surface size, ρ = .35 (Top10 = .43), reducing the set size of competing information elements, ρ = .24 (Top10 = .24), and increasing visual salience, ρ = .13 (Top10 = .24). Cognitive factors include attending more to preferred choice options and attributes, ρ = .36 (Top10 = .31), effects of task instructions on attention, ρ = .35 (Top10 = .21), and attending more to the ultimately chosen option, ρ = .59 (Top10 = .26). Understanding real-world decision making will require the integration of both visual and cognitive factors in future theories of attention and decision making. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
doi:10.1037/bul0000328 pmid:34843300 fatcat:blnzd7rwcfbs3im7e5vuvblo6e

Diversity of preferences can increase collective welfare in sequential exploration problems [article]

Pantelis P. Analytis, Hrvoje Stojic, Alexandros Gelastopoulos and Mehdi Moussaïd
2017 arXiv   pre-print
In search engines, online marketplaces and other human-computer interfaces large collectives of individuals sequentially interact with numerous alternatives of varying quality. In these contexts, trial and error (exploration) is crucial for uncovering novel high-quality items or solutions, but entails a high cost for individual users. Self-interested decision makers, are often better off imitating the choices of individuals who have already incurred the costs of exploration. Although imitation
more » ... akes sense at the individual level, it deprives the group of additional information that could have been gleaned by individual explorers. In this paper we show that in such problems, preference diversity can function as a welfare enhancing mechanism. It leads to a consistent increase in the quality of the consumed alternatives that outweighs the increased cost of search for the users.
arXiv:1703.10970v2 fatcat:6zgy5ps7pngkfmaoywdrz2vyiq

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow [article]

John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried
2021 arXiv   pre-print
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-efficient than model-free methods in terms of agent-environment interactions, because the model enables to extrapolate to unseen situations. In the more recent past, model-based methods have shown superior results compared to model-free methods in some challenging domains with non-linear state transitions. At the same
more » ... me, it has become apparent that RL is not market-ready yet and that many real-world applications are going to require model-based approaches, because model-free methods are too sample-inefficient and show poor performance in early stages of training. The latter is particularly important in industry, e.g. in production systems that directly impact a company's revenue. This demonstrates the necessity for a toolbox to push the boundaries for model-based RL. While there is a plethora of toolboxes for model-free RL, model-based RL has received little attention in terms of toolbox development. Bellman aims to fill this gap and introduces the first thoroughly designed and tested model-based RL toolbox using state-of-the-art software engineering practices. Our modular approach enables to combine a wide range of environment models with generic model-based agent classes that recover state-of-the-art algorithms. We also provide an experiment harness to compare both model-free and model-based agents in a systematic fashion w.r.t. user-defined evaluation metrics (e.g. cumulative reward). This paves the way for new research directions, e.g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.
arXiv:2103.14407v2 fatcat:c7mzfbm7w5g4vapexxzj4p66li

Gender effects for loss aversion: Yes, no, maybe?

Ranoua Bouchouicha, Lachlan Deer, Ashraf Galal Eid, Peter McGee, Daniel Schoch, Hrvoje Stojic, Jolanda Ygosse-Battisti, Ferdinand M. Vieider
2019 Journal of Risk and Uncertainty  
Gender effects in risk taking have attracted much attention by economists, and remain debated. Loss aversion-the stylized finding that a given loss carries substantially greater weight than a monetarily equivalent gain-is a fundamental driver of risk aversion. We deploy four definitions of loss aversion commonly used in the literature to investigate gender effects. Even though the definitions only differ in subtle ways, we find women to be more loss averse than men according to one definition,
more » ... hile another definition results in no gender differences, and the remaining two definitions point to women being less loss averse than men. Conceptually, these contradictory effects can be organized by systematic measurement error resulting from model misspecifications relative to the true underlying decision process.
doi:10.1007/s11166-019-09315-3 fatcat:gh7c4z2mnbbkthgxu45mj2hhe4

Ranking-based rich-get-richer processes [article]

Pantelis P. Analytis, Alexandros Gelastopoulos, Hrvoje Stojic
2021 arXiv   pre-print
We study a discrete-time Markov process X_n∈ℝ^d, for which the distribution of the future increments depends only on the relative ranking of its components (descending order by value). We endow the process with a rich-get-richer assumption and show that, together with a finite second moments assumption, it is enough to guarantee almost sure convergence of X_n / n. We characterize the possible limits if one is free to choose the initial state, and give a condition under which the initial state
more » ... irrelevant. Finally, we show how our framework can account for ranking-based Pólya urns and can be used to study ranking-algorithms for web interfaces.
arXiv:1910.01066v5 fatcat:oaohnynxkratxnlehgrvxh4tfy

Uncertainty in learning, choice, and visual fixation

Hrvoje Stojić, Jacob L. Orquin, Peter Dayan, Raymond J. Dolan, Maarten Speekenbrink
2020 Proceedings of the National Academy of Sciences of the United States of America  
| Stojić et al.  ...  Downloaded by guest on November 3, 2020 | Stojić et al.  ... 
doi:10.1073/pnas.1911348117 pmid:31980535 pmcid:PMC7022187 fatcat:2ixka2zgx5gzfnerynie4n3pb4

An empirical evaluation of active inference in multi-armed bandits [article]

Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel, Stefan J. Kiebel
2021 arXiv   pre-print
A key feature of sequential decision making under uncertainty is a need to balance between exploiting--choosing the best action according to the current knowledge, and exploring--obtaining information about values of other actions. The multi-armed bandit problem, a classical task that captures this trade-off, served as a vehicle in machine learning for developing bandit algorithms that proved to be useful in numerous industrial applications. The active inference framework, an approach to
more » ... ial decision making recently developed in neuroscience for understanding human and animal behaviour, is distinguished by its sophisticated strategy for resolving the exploration-exploitation trade-off. This makes active inference an exciting alternative to already established bandit algorithms. Here we derive an efficient and scalable approximate active inference algorithm and compare it to two state-of-the-art bandit algorithms: Bayesian upper confidence bound and optimistic Thompson sampling. This comparison is done on two types of bandit problems: a stationary and a dynamic switching bandit. Our empirical evaluation shows that the active inference algorithm does not produce efficient long-term behaviour in stationary bandits. However, in the more challenging switching bandit problem active inference performs substantially better than the two state-of-the-art bandit algorithms. The results open exciting venues for further research in theoretical and applied machine learning, as well as lend additional credibility to active inference as a general framework for studying human and animal behaviour.
arXiv:2101.08699v4 fatcat:swdx2m5l3zfdxgbkczjejbpa7e

Analysis of task-based functional MRI data preprocessed with fMRIPrep [article]

Oscar Esteban, Rastko Ciric, Karolina Finc, Ross W Blair, Christopher J Markiewicz, Craig A Moodie, James D Kent, Mathias Goncalves, Elizabeth DuPre, Daniel EP Gomez, Zhifang Ye, Taylor Salo (+15 others)
2019 bioRxiv   pre-print
Functional magnetic resonance imaging (fMRI) is widely used to investigate the neural correlates of cognition. fMRI non-invasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets
more » ... m many sources. Here we showcase fMRIPrep, a robust preprocessing tool for virtually any human BOLD (blood-oxygen level dependent) fMRI dataset that addresses the reproducibility concerns of the established protocols for fMRI preprocessing. Based on standardizations of the input and output data specifications, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.
doi:10.1101/694364 fatcat:tjpgk4fg6fhjxpagqpatc7iadu

Not everything looks like a nail: Learning to select appropriate decision strategies in multiple environments

Hrvoje Stojic, Henrik Olsson, Maarten Speekenbrink
Stojić, Olsson, and Analytis (2016) find that differences in speed of learning could account for the inter-individual variation in strategy adoption within conditions.  ... 
doi:10.17605/ fatcat:cuvy6v4wbva6tcxknidcgotmze

Are you sure about that? On the origins of confidence in concept learning

Hrvoje Stojic, Eran Eldar, Hassan Bassam, Peter Dayan, Raymond J. Dolan
2018 2018 Conference on Cognitive Computational Neuroscience   unpublished
Probabilistic learning as a basis of confidence We modelled function learning using Gaussian process regression (Rasmussen & Williams, 2006; Schulz, Tenenbaum, Duvenaud, Speekenbrink, & Gershman, 2017; Stojic  ... 
doi:10.32470/ccn.2018.1197-0 fatcat:2i5q4viydranlpalcxgt3khqme

Joint criminal enterprise – Bosnia and Herzegovina in Croatia's great project
Udruženi zločinački poduhvat – Bosna i Hercegovina u hrvatskom velikodržavnom projektu

Meldijana Arnaut Haseljić, Institute for the Study of Crimes against Humanity and International Law, University of Sarajevo
2020 Historijski pogledi  
In addition, Prlić, Stojić, Petković and Ćorić were convicted of rape and inhuman treatment (sexual abuse).  ...  The International Criminal Tribunal for the former Yugoslavia (ICTY / ICTY) has indicted Jadranko Prlić, Bruno Stojić, Slobodan Praljak, Milivoj Petković, Valentin Ćorić and Berislav Pušić.  ...  Bruno Stojić bio je načelnik Odjela obrane HVO od 3. jula 1993. do 15. novembra 1993.  ... 
doi:10.52259/historijskipogledi.2020.3.4.240 fatcat:p3mnw2h6jfcmte5jqww3lh72c4

Influence of dietary mannan oligosaccharide and clinoptilolite on hematological, biochemical and gut histological parameters in weaned pigs

Hrvoje Valpotic
2017 Periodicum biologorum  
Impact of mannan and zeolite on porcine systemic and gut health parameters Hrvoje Valpotić et al.  ...  Impact of mannan and zeolite on porcine systemic and gut health parameters Hrvoje Valpotić et al. Period biol, Vol 119, No 1, 2017.  ...  Impact of mannan and zeolite on porcine systemic and gut health parameters Hrvoje Valpotić et al. Period biol, Vol 119, No 1, 2017.  ... 
doi:10.18054/pb.v119i1.4407 fatcat:bcioxvgxnzfznit3pqedb7q2qm

Zeolite clinoptilolite nanoporous feed additive for animals of veterinary importance: potentials and limitations

Hrvoje Valpotic
2017 Periodicum biologorum  
Hrvoje Valpotić et al. Dietary clinoptilolite in animal biotechnology and veterinary medicine Dietary clinoptilolite in animal biotechnology and veterinary medicine Hrvoje Valpotić et al.  ...  However, 24 and 48 hours after birth differences between groups were significant (P<0.001) Stojić et al.  ...  Dschaak et al. (94) Hrvoje Valpotić et al. Dietary clinoptilolite in animal biotechnology and veterinary medicine duction (24) .  ... 
doi:10.18054/pb.v119i3.5434 fatcat:rlvlchyjznd65pu5uvon6mf57m

Bibliography of the Vjeverica Book Series
Bibliografija biblioteke Vjeverice

Vesna Radošević
2016 Libri et Liberi  
Mirko Stojić. 120 str. 1 301. Balog, Zvonimir. 1991. Ljubav za početnike. Ilus. Ninoslav Kunc. 174 str. 1 302. (Eko) Hirtz, Miroslav. 1991. Novele iz životinjskoga svijeta.  ...  Mary Shepard. 169 str. 1 Hitrec, Hrvoje. Smogovci. Twain, Mark. Kraljević i prosjak. 293. -296. 297. (300.) Pilić, Sanja. 1990. O mamama sve najbolje. Ilus.  ... 
doi:10.21066/carcl.libri.2016-05(01).0009 fatcat:dhs2kcx4afabbl2l3mihrqdghy

Brain activity forecasts video engagement in an internet attention market

Lester C. Tong, M. Yavuz Acikalin, Alexander Genevsky, Baba Shiv, Brian Knutson
2020 Proceedings of the National Academy of Sciences of the United States of America  
We thank spanlab, Ali Hortacsu, Alex Peysakhovich, Hrvoje Stojic, Carolyn Yoon, and three anonymous reviewers for feedback on previous drafts.  ... 
doi:10.1073/pnas.1905178117 pmid:32152105 fatcat:wohlj4duzzdizhzcdcjc3om2ie
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