51
|
Optimal policy for multi-alternative decisions. Nat Neurosci 2019; 22:1503-1511. [PMID: 31384015 DOI: 10.1038/s41593-019-0453-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 06/19/2019] [Indexed: 01/05/2023]
Abstract
Everyday decisions frequently require choosing among multiple alternatives. Yet the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions. This strategy requires evidence accumulation to nonlinear, time-dependent bounds that trigger choices. A geometric symmetry in those boundaries allows the optimal strategy to be implemented by a simple neural circuit involving normalization with fixed decision bounds and an urgency signal. The model captures several key features of the response of decision-making neurons as well as the increase in reaction time as a function of the number of alternatives, known as Hick's law. In addition, we show that in the presence of divisive normalization and internal variability, our model can account for several so-called 'irrational' behaviors, such as the similarity effect as well as the violation of both the independence of irrelevant alternatives principle and the regularity principle.
Collapse
|
52
|
Abstract
Parameter estimation in evidence-accumulation models of choice response times is demanding of both the data and the user. We outline how to fit evidence-accumulation models using the flexible, open-source, R-based Dynamic Models of Choice (DMC) software. DMC provides a hands-on introduction to the Bayesian implementation of two popular evidence-accumulation models: the diffusion decision model (DDM) and the linear ballistic accumulator (LBA). It enables individual and hierarchical estimation, as well as assessment of the quality of a model's parameter estimates and descriptive accuracy. First, we introduce the basic concepts of Bayesian parameter estimation, guiding the reader through a simple DDM analysis. We then illustrate the challenges of fitting evidence-accumulation models using a set of LBA analyses. We emphasize best practices in modeling and discuss the importance of parameter- and model-recovery simulations, exploring the strengths and weaknesses of models in different experimental designs and parameter regions. We also demonstrate how DMC can be used to model complex cognitive processes, using as an example a race model of the stop-signal paradigm, which is used to measure inhibitory ability. We illustrate the flexibility of DMC by extending this model to account for mixtures of cognitive processes resulting from attention failures. We then guide the reader through the practical details of a Bayesian hierarchical analysis, from specifying priors to obtaining posterior distributions that encapsulate what has been learned from the data. Finally, we illustrate how the Bayesian approach leads to a quantitatively cumulative science, showing how to use posterior distributions to specify priors that can be used to inform the analysis of future experiments.
Collapse
|
53
|
Bergner AS, Oppenheimer DM, Detre G. VAMP (Voting Agent Model of Preferences): A computational model of individual multi-attribute choice. Cognition 2019; 192:103971. [PMID: 31234078 DOI: 10.1016/j.cognition.2019.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 10/26/2022]
Abstract
This paper proposes an original account of decision anomalies and a computational alternative to existing dynamic models of multi-attribute choice. To date, most models attempting to account for the "Big Three" decision anomalies (similarity, attraction, and compromise effects) are variants of evidence accumulation models, or rational Bayesian analysis. This paper provides an existence proof of a new approach in the form of a multi-agent system based on the principles of voting geometry. Assuming there are a number of neural systems (agents) within an individual's brain, the Big Three decision anomalies can arise as a natural consequence of aggregating preferences across these agents. We operationalize these principles in VAMP, (Voting Agent Model of Preferences), and compare its performance to existing computational models as well as to empirical data. This provides a fundamentally different lens for understanding decision anomalies in multi-attribute choice.
Collapse
|
54
|
Noguchi T, Stewart N. Multialternative decision by sampling: A model of decision making constrained by process data. Psychol Rev 2019; 125:512-544. [PMID: 29952622 PMCID: PMC6022729 DOI: 10.1037/rev0000102] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Sequential sampling of evidence, or evidence accumulation, has been implemented in a variety of models to explain a range of multialternative choice phenomena. But the existing models do not agree on what, exactly, the evidence is that is accumulated. They also do not agree on how this evidence is accumulated. In this article, we use findings from process-tracing studies to constrain the evidence accumulation process. With these constraints, we extend the decision by sampling model and propose the multialternative decision by sampling (MDbS) model. In MDbS, the evidence accumulated is outcomes of pairwise ordinal comparisons between attribute values. MDbS provides a quantitative account of the attraction, compromise, and similarity effects equal to that of other models, and captures a wider range of empirical phenomena than other models.
Collapse
Affiliation(s)
- Takao Noguchi
- Department of Experimental Psychology, University College London
| | | |
Collapse
|
55
|
Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions. Trends Cogn Sci 2019; 23:251-263. [DOI: 10.1016/j.tics.2018.12.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 12/06/2018] [Accepted: 12/10/2018] [Indexed: 11/16/2022]
|
56
|
Response-time data provide critical constraints on dynamic models of multi-alternative, multi-attribute choice. Psychon Bull Rev 2019; 26:901-933. [DOI: 10.3758/s13423-018-1557-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
57
|
|
58
|
Preference accumulation as a process model of desirability ratings. Cogn Psychol 2019; 109:47-67. [PMID: 30611104 DOI: 10.1016/j.cogpsych.2018.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 11/29/2018] [Accepted: 12/20/2018] [Indexed: 11/23/2022]
Abstract
In desirability rating tasks, decision makers evaluate objects on a continuous response scale. Despite their prominence, full process models of these rating tasks have not been developed. We investigated whether a preference accumulation process, a process often used to model discrete choice, might explain ratings as well. According to our model, attributes from each option are sampled and evaluated stochastically. The evaluations are integrated over time, forming a preference. Preferences for options compete with each other, and accumulated preferences can decay. The model makes precise predictions regarding the statistical distribution of desirability ratings, as well as their dependence on deliberation time and on context. We test and confirm these predictions in two experimental studies. Additionally, quantitative model fits indicate that participants are better described by our proposed model, relative to a model without dynamism, competition, or stochastic attribute sampling. Our results show that the descriptive power of models of preference accumulation extends beyond discrete choice, and that the assumptions of this framework accurately characterize the core cognitive processes at play in the construction of preference and the evaluation of objects.
Collapse
|
59
|
Ai S, Yin Y, Chen Y, Wang C, Sun Y, Tang X, Lu L, Zhu L, Shi J. Promoting subjective preferences in simple economic choices during nap. eLife 2018; 7:e40583. [PMID: 30520732 PMCID: PMC6294547 DOI: 10.7554/elife.40583] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 12/06/2018] [Indexed: 02/05/2023] Open
Abstract
Sleep is known to benefit consolidation of memories, especially those of motivational relevance. Yet, it remains largely unknown the extent to which sleep influences reward-associated behavior, in particular, whether and how sleep modulates reward evaluation that critically underlies value-based decisions. Here, we show that neural processing during sleep can selectively bias preferences in simple economic choices when the sleeper is stimulated by covert, reward-associated cues. Specifically, presenting the spoken name of a familiar, valued snack item during midday nap significantly improves the preference for that item relative to items not externally cued. The cueing-specific preference enhancement is sleep-dependent and can be predicted by cue-induced neurophysiological signals at the subject and item level. Computational modeling further suggests that sleep cueing accelerates evidence accumulation for cued options during the post-sleep choice process in a manner consistent with the preference shift. These findings suggest that neurocognitive processing during sleep contributes to the fine-tuning of subjective preferences in a flexible, selective manner.
Collapse
Affiliation(s)
- Sizhi Ai
- National Institute on Drug DependencePeking UniversityBeijingChina
- Department of Cardiology, Heart Center, Henan Key Laboratory of NeurorestoratologyThe First Affiliated Hospital of Xinxiang Medical UniversityWeihuiChina
| | - Yunlu Yin
- School of Psychological and Cognitive SciencesPeking UniversityBeijingChina
- Faculty of Business and EconomicsThe University of Hong KongHong Kong SARChina
| | - Yu Chen
- National Institute on Drug DependencePeking UniversityBeijingChina
| | - Cong Wang
- Peking-Tsinghua Center for Life SciencesPeking UniversityBeijingChina
- Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
| | - Yan Sun
- National Institute on Drug DependencePeking UniversityBeijingChina
| | - Xiangdong Tang
- Sleep Medicine Center, State Key Laboratory of BiotherapyWest China Hospital, Sichuan UniversityChengduChina
| | - Lin Lu
- National Institute on Drug DependencePeking UniversityBeijingChina
- Peking-Tsinghua Center for Life SciencesPeking UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth HospitalPeking UniversityBeijingChina
| | - Lusha Zhu
- School of Psychological and Cognitive SciencesPeking UniversityBeijingChina
- Peking-Tsinghua Center for Life SciencesPeking UniversityBeijingChina
- IDG/McGovern Institute for Brain ResearchPeking UniversityBeijingChina
- Key Laboratory of Machine Perception, Ministry of Education; Beijing Key Laboratory of Behavior and Mental HealthPeking UniversityBeijingChina
| | - Jie Shi
- National Institute on Drug DependencePeking UniversityBeijingChina
- Beijing Key Laboratory on Drug Dependence ResearchBeijingChina
- The State Key Laboratory of Natural and Biomimetic DrugsBeijingChina
- The Key Laboratory for Neuroscience of the Ministry of Education and HealthPeking UniversityBeijingChina
| |
Collapse
|
60
|
Davis-Stober CP, Doignon JP, Fiorini S, Glineur F, Regenwetter M. Extended Formulations for Order Polytopes through Network Flows. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2018; 87:1-10. [PMID: 30906069 PMCID: PMC6426318 DOI: 10.1016/j.jmp.2018.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mathematical psychology has a long tradition of modeling probabilistic choice via distribution-free random utility models and associated random preference models. For such models, the predicted choice probabilities often form a bounded and convex polyhedral set, or polytope. Polyhedral combinatorics have thus played a key role in studying the mathematical structure of these models. However, standard methods for characterizing the polytopes of such models are subject to a combinatorial explosion in complexity as the number of choice alternatives increases. Specifically, this is the case for random preference models based on linear, weak, semi- and interval orders. For these, a complete, linear description of the polytope is currently known only for, at most, 5-8 choice alternatives. We leverage the method of extended formulations to break through those boundaries. For each of the four types of preferences, we build an appropriate network, and show that the associated network flow polytope provides an extended formulation of the polytope of the choice model. This extended formulation has a simple linear description that is more parsimonious than descriptions obtained by standard methods for large numbers of choice alternatives. The result is a computationally less demanding way of testing the probabilistic choice model on data. We sketch how the latter interfaces with recent developments in contemporary statistics.
Collapse
|
61
|
Gluth S, Spektor MS, Rieskamp J. Value-based attentional capture affects multi-alternative decision making. eLife 2018; 7:e39659. [PMID: 30394874 PMCID: PMC6218187 DOI: 10.7554/elife.39659] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 10/01/2018] [Indexed: 12/23/2022] Open
Abstract
Humans and other animals often violate economic principles when choosing between multiple alternatives, but the underlying neurocognitive mechanisms remain elusive. A robust finding is that adding a third option can alter the relative preference for the original alternatives, but studies disagree on whether the third option's value decreases or increases accuracy. To shed light on this controversy, we used and extended the paradigm of one study reporting a positive effect. However, our four experiments with 147 human participants and a reanalysis of the original data revealed that the positive effect is neither replicable nor reproducible. In contrast, our behavioral and eye-tracking results are best explained by assuming that the third option's value captures attention and thereby impedes accuracy. We propose a computational model that accounts for the complex interplay of value, attention, and choice. Our theory explains how choice sets and environments influence the neurocognitive processes of multi-alternative decision making.
Collapse
Affiliation(s)
| | - Mikhail S Spektor
- Department of PsychologyUniversity of BaselBaselSwitzerland
- Department of PsychologyUniversity of FreiburgFreiburgGermany
| | - Jörg Rieskamp
- Department of PsychologyUniversity of BaselBaselSwitzerland
| |
Collapse
|
62
|
Bhatia S, Stewart N. Naturalistic multiattribute choice. Cognition 2018; 179:71-88. [DOI: 10.1016/j.cognition.2018.05.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 05/28/2018] [Accepted: 05/29/2018] [Indexed: 10/28/2022]
|
63
|
The comparison process as an account of variation in the attraction, compromise, and similarity effects. Psychon Bull Rev 2018; 26:934-942. [PMID: 30264240 DOI: 10.3758/s13423-018-1531-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Context effects are changes in preference that occur when alternatives are added to a choice set. Models that account for context effects typically assume a within-dimension comparison process; however, the presentation format of a choice set can influence comparison strategies. The present study jointly tests the influence of presentation format on the attraction, compromise, and similarity effects in a within-subjects design. Participants completed a series of choices designed to elicit each of the three context effects, with either a by-alternative or by-dimension format. Whereas the by-alternative format elicited a standard similarity effect, but null attraction and reverse compromise effects, the by-dimension format elicited standard attraction and compromise effects, but a reverse similarity effect. These novel results are supported by a re-analysis of the eye-tracking data collected by Noguchi and Stewart (Cognition, 132(1), 44-56, 2014) and demonstrate that flexibility in the comparison process should be incorporated into theories of preferential choice.
Collapse
|
64
|
Li V, Michael E, Balaguer J, Herce Castañón S, Summerfield C. Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making. Proc Natl Acad Sci U S A 2018; 115:E8825-E8834. [PMID: 30166448 PMCID: PMC6156680 DOI: 10.1073/pnas.1805224115] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
When making decisions, humans are often distracted by irrelevant information. Distraction has a different impact on perceptual, cognitive, and value-guided choices, giving rise to well-described behavioral phenomena such as the tilt illusion, conflict adaptation, or economic decoy effects. However, a single, unified model that can account for all these phenomena has yet to emerge. Here, we offer one such account, based on adaptive gain control, and additionally show that it successfully predicts a range of counterintuitive new behavioral phenomena on variants of a classic cognitive paradigm, the Eriksen flanker task. We also report that blood oxygen level-dependent signals in a dorsal network prominently including the anterior cingulate cortex index a gain-modulated decision variable predicted by the model. This work unifies the study of distraction across perceptual, cognitive, and economic domains.
Collapse
Affiliation(s)
- Vickie Li
- Department of Experimental Psychology, University of Oxford, OX2 6GG Oxford, United Kingdom;
| | - Elizabeth Michael
- Department of Psychology, University of Cambridge, CB2 3EB Cambridge, United Kingdom
| | - Jan Balaguer
- Department of Experimental Psychology, University of Oxford, OX2 6GG Oxford, United Kingdom
| | - Santiago Herce Castañón
- Department of Experimental Psychology, University of Oxford, OX2 6GG Oxford, United Kingdom
- Department of Psychology and Educational Sciences, University of Geneva, 1202 Geneva, Switzerland
| | | |
Collapse
|
65
|
Turner BM, Van Zandt T. Approximating Bayesian Inference through Model Simulation. Trends Cogn Sci 2018; 22:826-840. [PMID: 30093313 DOI: 10.1016/j.tics.2018.06.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Revised: 06/13/2018] [Accepted: 06/14/2018] [Indexed: 12/01/2022]
Abstract
The ultimate test of the validity of a cognitive theory is its ability to predict patterns of empirical data. Cognitive models formalize this test by making specific processing assumptions that yield mathematical predictions, and the mathematics allow the models to be fitted to data. As the field of cognitive science has grown to address increasingly complex problems, so too has the complexity of models increased. Some models have become so complex that the mathematics detailing their predictions are intractable, meaning that the model can only be simulated. Recently, new Bayesian techniques have made it possible to fit these simulation-based models to data. These techniques have even allowed simulation-based models to transition into neuroscience, where tests of cognitive theories can be biologically substantiated.
Collapse
Affiliation(s)
- Brandon M Turner
- Department of Psychology, Ohio State University, Columbus, OH 43210, USA.
| | - Trisha Van Zandt
- Department of Psychology, Ohio State University, Columbus, OH 43210, USA
| |
Collapse
|
66
|
Evans NJ, Steyvers M, Brown SD. Modeling the Covariance Structure of Complex Datasets Using Cognitive Models: An Application to Individual Differences and the Heritability of Cognitive Ability. Cogn Sci 2018; 42:1925-1944. [PMID: 29873105 DOI: 10.1111/cogs.12627] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Revised: 03/03/2018] [Accepted: 05/02/2018] [Indexed: 11/27/2022]
Abstract
Understanding individual differences in cognitive performance is an important part of understanding how variations in underlying cognitive processes can result in variations in task performance. However, the exploration of individual differences in the components of the decision process-such as cognitive processing speed, response caution, and motor execution speed-in previous research has been limited. Here, we assess the heritability of the components of the decision process, with heritability having been a common aspect of individual differences research within other areas of cognition. Importantly, a limitation of previous work on cognitive heritability is the underlying assumption that variability in response times solely reflects variability in the speed of cognitive processing. This assumption has been problematic in other domains, due to the confounding effects of caution and motor execution speed on observed response times. We extend a cognitive model of decision-making to account for relatedness structure in a twin study paradigm. This approach can separately quantify different contributions to the heritability of response time. Using data from the Human Connectome Project, we find strong evidence for the heritability of response caution, and more ambiguous evidence for the heritability of cognitive processing speed and motor execution speed. Our study suggests that the assumption made in previous studies-that the heritability of cognitive ability is based on cognitive processing speed-may be incorrect. More generally, our methodology provides a useful avenue for future research in complex data that aims to analyze cognitive traits across different sources of related data, whether the relation is between people, tasks, experimental phases, or methods of measurement.
Collapse
Affiliation(s)
| | - Mark Steyvers
- Department of Cognitive Sciences, University of California, Irvine
| | | |
Collapse
|
67
|
|
68
|
Spektor MS, Kellen D, Hotaling JM. When the Good Looks Bad: An Experimental Exploration of the Repulsion Effect. Psychol Sci 2018; 29:1309-1320. [PMID: 29792774 DOI: 10.1177/0956797618779041] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
When people are choosing among different options, context seems to play a vital role. For instance, adding a third option can increase the probability of choosing a similar dominating option. This attraction effect is one of the most widely studied phenomena in decision-making research. Its prevalence, however, has been challenged recently by the tainting hypothesis, according to which the inferior option contaminates the attribute space in which it is located, leading to a repulsion effect. In an attempt to test the tainting hypothesis and explore the conditions under which dominated options make dominating options look bad, we conducted four preregistered perceptual decision-making studies with a total of 301 participants. We identified two factors influencing individuals' behavior: stimulus display and stimulus design. Our results contribute to a growing body of literature showing how presentation format influences behavior in preferential and perceptual decision-making tasks.
Collapse
Affiliation(s)
- Mikhail S Spektor
- 1 Faculty of Psychology, University of Basel.,2 Department of Psychology, University of Freiburg
| | | | - Jared M Hotaling
- 1 Faculty of Psychology, University of Basel.,4 School of Psychology, University of New South Wales
| |
Collapse
|
69
|
Abstract
Choice option similarity is a key contextual variable in multiattribute choice. Based on theories of preference accumulation, we predicted that decision times would be longer when the available choice options were similar compared with when they were dissimilar, controlling for the relative desirabilities of the options. We tested for the relationship between similarity and decision time in an experiment involving incentivised binary choices between items of equivalent desirability and found that our predictions were confirmed. Our results show how the effects of contextual factors on key decision variables can be accurately predicted by existing computational theories of decision-making.
Collapse
Affiliation(s)
- Sudeep Bhatia
- 1 Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy L Mullett
- 2 Behavioral Science Group, Warwick Business School, The University of Warwick, Coventry, UK
| |
Collapse
|
70
|
Abstract
To adjudicate between deterministic and probabilistic accounts of the meaning of conditionals, we examined the influence of context on the reading of general conditionals. Context was varied with the contrast context, where participants judged uncertain conditionals after certain conditionals, and the control context, where participants judged only uncertain conditionals. Experiment 1 had participants to judge whether a set of truth table cases was possible for the conditional. Experiment 2 had participants to judge whether the conditional was true for a set of truth table cases. The findings are as follows. Possibility and truth judgments showed a similar response pattern. The reading of general conditionals varied with conditional contexts. The predominant reading was deterministic in the contrast context but was probabilistic in the control context. Conditional contexts yielded a significant contrast effect. Meanwhile, conditional probability P( q| p) made a smaller difference to the acceptance rate in the contrast context than in the control context. The overall pattern is beyond both the deterministic and probabilistic accounts. Alternatively, we propose a dynamic-threshold account for the relative reading of general conditionals.
Collapse
|
71
|
Abstract
Models of human decision-making aim to simultaneously explain the similarity, attraction, and compromise effects. However, evidence that people show all three effects within the same paradigm has come from studies in which choices were averaged over participants. This averaging is only justified if those participants show qualitatively similar choice behaviors. To investigate whether this was the case, we repeated two experiments previously run by Trueblood (Psychonomic Bulletin & Review, 19(5), 962-968, 2012) and Berkowitsch, Scheibehenne, and Rieskamp (Journal of Experimental Psychology, 143(3), 1331-1348, 2014). We found that individuals displayed qualitative differences in their choice behavior. In general, people did not simultaneously display all three context effects. Instead, we found a tendency for some people to show either the similarity effect or the compromise effect but not both. More importantly, many individuals showed strong dimensional biases that were much larger than any effects of context. This research highlights the dangers of averaging indiscriminately and the necessity for accounting for individual differences and dimensional biases in decision-making.
Collapse
|
72
|
|
73
|
Bhatia S, Loomes G. Noisy preferences in risky choice: A cautionary note. Psychol Rev 2017; 124:678-687. [PMID: 28569526 PMCID: PMC5619393 DOI: 10.1037/rev0000073] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 04/12/2017] [Accepted: 04/12/2017] [Indexed: 11/08/2022]
Abstract
We examine the effects of multiple sources of noise in risky decision making. Noise in the parameters that characterize an individual's preferences can combine with noise in the response process to distort observed choice proportions. Thus, underlying preferences that conform to expected value maximization can appear to show systematic risk aversion or risk seeking. Similarly, core preferences that are consistent with expected utility theory, when perturbed by such noise, can appear to display nonlinear probability weighting. For this reason, modal choices cannot be used simplistically to infer underlying preferences. Quantitative model fits that do not allow for both sorts of noise can lead to wrong conclusions. (PsycINFO Database Record
Collapse
Affiliation(s)
- Sudeep Bhatia
- Department of Psychology, University of Pennsylvania
| | - Graham Loomes
- Behavioural Science Group, Warwick Business School, University of Warwick
| |
Collapse
|
74
|
Rigoli F, Mathys C, Friston KJ, Dolan RJ. A unifying Bayesian account of contextual effects in value-based choice. PLoS Comput Biol 2017; 13:e1005769. [PMID: 28981514 PMCID: PMC5645156 DOI: 10.1371/journal.pcbi.1005769] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 10/17/2017] [Accepted: 09/11/2017] [Indexed: 11/18/2022] Open
Abstract
Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects). These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects) by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling.
Collapse
Affiliation(s)
- Francesco Rigoli
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom
- City, University of London, Northampton Square, London, United Kingdom
| | - Christoph Mathys
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Karl J. Friston
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom
| | - Raymond J. Dolan
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| |
Collapse
|
75
|
Multi-attribute, multi-alternative models of choice: Choice, reaction time, and process tracing. Cogn Psychol 2017; 98:45-72. [PMID: 28843070 DOI: 10.1016/j.cogpsych.2017.08.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 08/01/2017] [Accepted: 08/08/2017] [Indexed: 11/22/2022]
Abstract
The first aim of this research is to compare computational models of multi-alternative, multi-attribute choice when attribute values are explicit. The choice predictions of utility (standard random utility & weighted valuation), heuristic (elimination-by-aspects, lexicographic, & maximum attribute value), and dynamic (multi-alternative decision field theory, MDFT, & a version of the multi-attribute linear ballistic accumulator, MLBA) models are contrasted on both preferential and risky choice data. Using both maximum likelihood and cross-validation fit measures on choice data, the utility and dynamic models are preferred over the heuristic models for risky choice, with a slight overall advantage for the MLBA for preferential choice. The response time predictions of these models (except the MDFT) are then tested. Although the MLBA accurately predicts response time distributions, it only weakly accounts for stimulus-level differences. The other models completely fail to account for stimulus-level differences. Process tracing measures, i.e., eye and mouse tracking, were also collected. None of the qualitative predictions of the models are completely supported by that data. These results suggest that the models may not appropriately represent the interaction of attention and preference formation. To overcome this potential shortcoming, the second aim of this research is to test preference-formation assumptions, independently of attention, by developing the models of attentional sampling (MAS) model family which incorporates the empirical gaze patterns into a sequential sampling framework. An MAS variant that includes attribute values, but only updates the currently viewed alternative and does not contrast values across alternatives, performs well in both experiments. Overall, the results support the dynamic models, but point to the need to incorporate a framework that more accurately reflects the relationship between attention and the preference-formation process.
Collapse
|
76
|
Attraction Effect in Risky Choice Can Be Explained by Subjective Distance Between Choice Alternatives. Sci Rep 2017; 7:8942. [PMID: 28827699 PMCID: PMC5567099 DOI: 10.1038/s41598-017-06968-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 06/21/2017] [Indexed: 11/09/2022] Open
Abstract
Individuals make decisions under risk throughout daily life. Standard models of economic decision making typically assume that people evaluate choice options independently. There is, however, substantial evidence showing that this independence assumption is frequently violated in decision making without risk. The present study extends these findings to the domain of decision making under risk. To explain the independence violations, we adapted a sequential sampling model, namely Multialternative Decision Field Theory (MDFT), to decision making under risk and showed how this model can account for the observed preference shifts. MDFT not only better predicts choices compared with the standard Expected Utility Theory, but it also explains individual differences in the size of the observed context effect. Evidence in favor of the chosen option, as predicted by MDFT, was positively correlated with brain activity in the medial orbitofrontal cortex (mOFC) and negatively correlated with brain activity in the anterior insula (aINS). From a neuroscience perspective, the results of the present study show that specific brain regions, such as the mOFC and aINS, not only code the value or risk of a single choice option but also code the evidence in favor of the best option compared with other available choice options.
Collapse
|
77
|
From information processing to decisions: Formalizing and comparing psychologically plausible choice models. Cogn Psychol 2017; 96:26-40. [DOI: 10.1016/j.cogpsych.2017.05.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 05/26/2017] [Accepted: 05/27/2017] [Indexed: 11/19/2022]
|
78
|
The Attraction Effect Modulates Reward Prediction Errors and Intertemporal Choices. J Neurosci 2017; 37:371-382. [PMID: 28077716 DOI: 10.1523/jneurosci.2532-16.2016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 10/24/2016] [Accepted: 11/09/2016] [Indexed: 02/06/2023] Open
Abstract
Classical economic theory contends that the utility of a choice option should be independent of other options. This view is challenged by the attraction effect, in which the relative preference between two options is altered by the addition of a third, asymmetrically dominated option. Here, we leveraged the attraction effect in the context of intertemporal choices to test whether both decisions and reward prediction errors (RPE) in the absence of choice violate the independence of irrelevant alternatives principle. We first demonstrate that intertemporal decision making is prone to the attraction effect in humans. In an independent group of participants, we then investigated how this affects the neural and behavioral valuation of outcomes using a novel intertemporal lottery task and fMRI. Participants' behavioral responses (i.e., satisfaction ratings) were modulated systematically by the attraction effect and this modulation was correlated across participants with the respective change of the RPE signal in the nucleus accumbens. Furthermore, we show that, because exponential and hyperbolic discounting models are unable to account for the attraction effect, recently proposed sequential sampling models might be more appropriate to describe intertemporal choices. Our findings demonstrate for the first time that the attraction effect modulates subjective valuation even in the absence of choice. The findings also challenge the prospect of using neuroscientific methods to measure utility in a context-free manner and have important implications for theories of reinforcement learning and delay discounting. SIGNIFICANCE STATEMENT Many theories of value-based decision making assume that people first assess the attractiveness of each option independently of each other and then pick the option with the highest subjective value. The attraction effect, however, shows that adding a new option to a choice set can change the relative value of the existing options, which is a violation of the independence principle. Using an intertemporal choice framework, we tested whether such violations also occur when the brain encodes the difference between expected and received rewards (i.e., the reward prediction error). Our results suggest that neither intertemporal choice nor valuation without choice adhere to the independence principle.
Collapse
|
79
|
Abstract
People with autism spectrum conditions (ASC) show reduced sensitivity to
contextual stimuli in many perceptual and cognitive tasks. We investigated
whether this also applies to decision making by examining adult participants’
choices between pairs of consumer products that were presented with a third,
less desirable “decoy” option. Participants’ preferences between the items in a
given pair frequently switched when the third item in the set was changed, but
this tendency was reduced among individuals with ASC, which indicated that their
choices were more consistent and conventionally rational than those of control
participants. A comparison of people who were drawn from the general population
and who varied in their levels of autistic traits revealed a weaker version of
the same effect. The reduced context sensitivity was not due to differences in
noisy responding, and although the ASC group took longer to make their
decisions, this did not account for the enhanced consistency of their choices.
The results extend the characterization of autistic cognition as relatively
context insensitive to a new domain, and have practical implications for
socioeconomic behavior.
Collapse
|
80
|
Davis-Stober CP, Brown N, Park S, Regenwetter M. Recasting a biologically motivated computational model within a Fechnerian and random utility framework. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2017; 77:156-164. [PMID: 28827888 PMCID: PMC5562386 DOI: 10.1016/j.jmp.2016.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The selective integration model of Tsetsos et al. (2016a) is a biologically motivated computational framework that aims to model intransitive preference and choice. Tsetsos et al. (2016a) concluded that a noisy system can lead to violations of transitivity in otherwise rational agents optimizing a task. We show how their model can be interpreted from a Fechnerian perspective and within a random utility framework. Specifically, we spell out the connection between the selective integration model and two probabilistic models of transitive preference, weak stochastic transitivity and the triangle inequalities, tested by Tsetsos et al. (2016a).
Collapse
Affiliation(s)
- Clintin P Davis-Stober
- Department of Psychological Sciences, 219 McAlester Hall, University of Missouri at Columbia, Columbia, MO 65211, USA
| | - Nicholas Brown
- Department of Psychological Sciences, 210 McAlester Hall, University of Missouri at Columbia, Columbia, MO 65211, USA,
| | - Sanghyuk Park
- Department of Psychological Sciences, 210 McAlester Hall, University of Missouri at Columbia, Columbia, MO 65211, USA,
| | - Michel Regenwetter
- Department of Psychology, 603, E. Daniel Street, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA,
| |
Collapse
|
81
|
Guo L, Trueblood JS, Diederich A. Thinking Fast Increases Framing Effects in Risky Decision Making. Psychol Sci 2017; 28:530-543. [DOI: 10.1177/0956797616689092] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Every day, people face snap decisions when time is a limiting factor. In addition, the way a problem is presented can influence people’s choices, which creates what are known as framing effects. In this research, we explored how time pressure interacts with framing effects in risky decision making. Specifically, does time pressure strengthen or weaken framing effects? On one hand, research has suggested that framing effects evolve through the deliberation process, growing larger with time. On the other hand, dual-process theory attributes framing effects to an intuitive, emotional system that responds automatically to stimuli. In our experiments, participants made decisions about gambles framed in terms of either gains or losses, and time pressure was manipulated across blocks. Results showed increased framing effects under time pressure in both hypothetical and incentivized choices, which supports the dual-process hypothesis that these effects arise from a fast, intuitive system.
Collapse
Affiliation(s)
- Lisa Guo
- Institute for Mathematical Behavioral Sciences, University of California, Irvine
| | | | | |
Collapse
|
82
|
Farmer GD, Warren PA, El-Deredy W, Howes A. The Effect of Expected Value on Attraction Effect Preference Reversals. JOURNAL OF BEHAVIORAL DECISION MAKING 2016; 30:785-793. [PMID: 29081595 PMCID: PMC5637901 DOI: 10.1002/bdm.2001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Revised: 10/19/2016] [Accepted: 10/19/2016] [Indexed: 11/11/2022]
Abstract
The attraction effect shows that adding a third alternative to a choice set can alter preference between the original two options. For over 30 years, this simple demonstration of context dependence has been taken as strong evidence against a class of parsimonious value‐maximising models that evaluate alternatives independently from one another. Significantly, however, in previous demonstrations of the attraction effect alternatives are approximately equally valuable, so there was little consequence to the decision maker irrespective of which alternative was selected. Here we vary the difference in expected value between alternatives and provide the first demonstration that, although extinguished with large differences, this theoretically important effect persists when choice between alternatives has a consequence. We use this result to clarify the implications of the attraction effect, arguing that although it robustly violates the assumptions of value‐maximising models, it does not eliminate the possibility that human decision making is optimal. © 2016 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd.
Collapse
Affiliation(s)
- George D Farmer
- Division of Neuroscience and Experimental Psychology University of Manchester Manchester UK
| | - Paul A Warren
- Division of Neuroscience and Experimental Psychology University of Manchester Manchester UK
| | - Wael El-Deredy
- Division of Neuroscience and Experimental Psychology University of Manchester Manchester UK.,School of Biomedical Engineering University of Valparaíso Valparaíso Chile
| | - Andrew Howes
- School of Computer Science University of Birmingham Birmingham UK
| |
Collapse
|
83
|
Roads BD, Mozer MC. Improving Human‐Machine Cooperative Classification Via Cognitive Theories of Similarity. Cogn Sci 2016; 41:1394-1411. [DOI: 10.1111/cogs.12400] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 03/01/2016] [Accepted: 04/05/2016] [Indexed: 11/29/2022]
Affiliation(s)
- Brett D. Roads
- Department of Computer Science and Institute of Cognitive Science University of Colorado
| | - Michael C. Mozer
- Department of Computer Science and Institute of Cognitive Science University of Colorado
| |
Collapse
|
84
|
Howes A, Warren PA, Farmer G, El-Deredy W, Lewis RL. Why contextual preference reversals maximize expected value. Psychol Rev 2016; 123:368-91. [PMID: 27337391 PMCID: PMC4918408 DOI: 10.1037/a0039996] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2014] [Revised: 06/27/2015] [Accepted: 09/30/2015] [Indexed: 12/03/2022]
Abstract
Contextual preference reversals occur when a preference for one option over another is reversed by the addition of further options. It has been argued that the occurrence of preference reversals in human behavior shows that people violate the axioms of rational choice and that people are not, therefore, expected value maximizers. In contrast, we demonstrate that if a person is only able to make noisy calculations of expected value and noisy observations of the ordinal relations among option features, then the expected value maximizing choice is influenced by the addition of new options and does give rise to apparent preference reversals. We explore the implications of expected value maximizing choice, conditioned on noisy observations, for a range of contextual preference reversal types-including attraction, compromise, similarity, and phantom effects. These preference reversal types have played a key role in the development of models of human choice. We conclude that experiments demonstrating contextual preference reversals are not evidence for irrationality. They are, however, a consequence of expected value maximization given noisy observations. (PsycINFO Database Record
Collapse
Affiliation(s)
| | - Paul A Warren
- School of Psychological Sciences, University of Manchester
| | - George Farmer
- School of Psychological Sciences, University of Manchester
| | - Wael El-Deredy
- School of Psychological Sciences, University of Manchester
| | | |
Collapse
|
85
|
Bhatia S. Decision Making in Environments with Non-Independent Dimensions. JOURNAL OF BEHAVIORAL DECISION MAKING 2016. [DOI: 10.1002/bdm.1964] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
86
|
Konovalov A, Krajbich I. Over a Decade of Neuroeconomics: What Have We Learned? ORGANIZATIONAL RESEARCH METHODS 2016. [DOI: 10.1177/1094428116644502] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
At its inception, neuroeconomics promised to revolutionize economics. That promise has not yet been realized, and neuroeconomics has seen limited penetration into mainstream economics. Nevertheless, it would be a mistake to declare that neuroeconomics has failed. Quite to the contrary, the yearly rate of neuroeconomics papers has roughly doubled since 2005. While the number of direct applications to economics remains limited, due to the infancy of the field, we have learned an amazing amount about how the brain makes decisions. In this article, we review some of the major topics that have emerged in neuroeconomics and highlight findings that we believe will form the basis for future applications to economics. When possible, we focus on existing applications to economics and future directions for that research.
Collapse
Affiliation(s)
- Arkady Konovalov
- Department of Economics, The Ohio State University, Columbus, OH, USA
| | - Ian Krajbich
- Department of Economics, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
87
|
Bhatia S, Mullett TL. The dynamics of deferred decision. Cogn Psychol 2016; 86:112-51. [PMID: 26970689 DOI: 10.1016/j.cogpsych.2016.02.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 02/10/2016] [Accepted: 02/16/2016] [Indexed: 11/20/2022]
Abstract
Decision makers are often unable to choose between the options that they are offered. In these settings they typically defer their decision, that is, delay the decision to a later point in time or avoid the decision altogether. In this paper, we outline eight behavioral findings regarding the causes and consequences of choice deferral that cognitive theories of decision making should be able to capture. We show that these findings can be accounted for by a deferral-based time limit applied to existing sequential sampling models of preferential choice. Our approach to modeling deferral as a time limit in a sequential sampling model also makes a number of novel predictions regarding the interactions between choice probabilities, deferral probabilities, and decision times, and we confirm these predictions in an experiment. Choice deferral is a key feature of everyday decision making, and our paper illustrates how established theoretical approaches can be used to understand the cognitive underpinnings of this important behavioral phenomenon.
Collapse
|
88
|
Holmes WR, Trueblood JS, Heathcote A. A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model. Cogn Psychol 2016; 85:1-29. [PMID: 26760448 DOI: 10.1016/j.cogpsych.2015.11.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2015] [Revised: 09/25/2015] [Accepted: 11/28/2015] [Indexed: 11/15/2022]
Abstract
In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.
Collapse
Affiliation(s)
- William R Holmes
- Department of Physics and Astronomy, Vanderbilt University, 37212, United States.,Department of Mathematics, University of Melbourne, Australia
| | - Jennifer S Trueblood
- Department of Psychology, Vanderbilt University, 37212, United States.,Department of Cognitive Sciences, University of California, Irvine, 92697, United States
| | | |
Collapse
|
89
|
Trueblood JS, Pettibone JC. The Phantom Decoy Effect in Perceptual Decision Making. JOURNAL OF BEHAVIORAL DECISION MAKING 2015. [DOI: 10.1002/bdm.1930] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
90
|
Evidence accumulation as a model for lexical selection. Cogn Psychol 2015; 82:57-73. [DOI: 10.1016/j.cogpsych.2015.07.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Revised: 07/28/2015] [Accepted: 07/29/2015] [Indexed: 11/21/2022]
|
91
|
|
92
|
Hotaling JM, Cohen AL, Shiffrin RM, Busemeyer JR. The Dilution Effect and Information Integration in Perceptual Decision Making. PLoS One 2015; 10:e0138481. [PMID: 26406323 PMCID: PMC4583276 DOI: 10.1371/journal.pone.0138481] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 08/30/2015] [Indexed: 12/02/2022] Open
Abstract
In cognitive science there is a seeming paradox: On the one hand, studies of human judgment and decision making have repeatedly shown that people systematically violate optimal behavior when integrating information from multiple sources. On the other hand, optimal models, often Bayesian, have been successful at accounting for information integration in fields such as categorization, memory, and perception. This apparent conflict could be due, in part, to different materials and designs that lead to differences in the nature of processing. Stimuli that require controlled integration of information, such as the quantitative or linguistic information (commonly found in judgment studies), may lead to suboptimal performance. In contrast, perceptual stimuli may lend themselves to automatic processing, resulting in integration that is closer to optimal. We tested this hypothesis with an experiment in which participants categorized faces based on resemblance to a family patriarch. The amount of evidence contained in the top and bottom halves of each test face was independently manipulated. These data allow us to investigate a canonical example of sub-optimal information integration from the judgment and decision making literature, the dilution effect. Splitting the top and bottom halves of a face, a manipulation meant to encourage controlled integration of information, produced farther from optimal behavior and larger dilution effects. The Multi-component Information Accumulation model, a hybrid optimal/averaging model of information integration, successfully accounts for key accuracy, response time, and dilution effects.
Collapse
Affiliation(s)
| | - Andrew L. Cohen
- Department of Psychology, University of Massachusetts, Amherst, Massachusetts, United States of America
| | - Richard M. Shiffrin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| | - Jerome R. Busemeyer
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
| |
Collapse
|
93
|
Berkowitsch NAJ, Scheibehenne B, Rieskamp J, Matthäus M. A generalized distance function for preferential choices. THE BRITISH JOURNAL OF MATHEMATICAL AND STATISTICAL PSYCHOLOGY 2015; 68:310-325. [PMID: 25677976 DOI: 10.1111/bmsp.12048] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Revised: 10/26/2014] [Indexed: 06/04/2023]
Abstract
Many cognitive theories of judgement and decision making assume that choice options are evaluated relative to other available options. The extent to which the preference for one option is influenced by other available options will often depend on how similar the options are to each other, where similarity is assumed to be a decreasing function of the distance between options. We examine how the distance between preferential options that are described on multiple attributes can be determined. Previous distance functions do not take into account that attributes differ in their subjective importance, are limited to two attributes, or neglect the preferential relationship between the options. To measure the distance between preferential options it is necessary to take the subjective preferences of the decision maker into account. Accordingly, the multi-attribute space that defines the relationship between options can be stretched or shrunk relative to the attention or importance that a person gives to different attributes describing the options. Here, we propose a generalized distance function for preferential choices that takes subjective attribute importance into account and allows for individual differences according to such subjective preferences. Using a hands-on example, we illustrate the application of the function and compare it to previous distance measures. We conclude with a discussion of the suitability and limitations of the proposed distance function.
Collapse
|
94
|
Christopoulos V, Bonaiuto J, Andersen RA. A biologically plausible computational theory for value integration and action selection in decisions with competing alternatives. PLoS Comput Biol 2015; 11:e1004104. [PMID: 25803729 PMCID: PMC4372613 DOI: 10.1371/journal.pcbi.1004104] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 12/29/2014] [Indexed: 11/18/2022] Open
Abstract
Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation. In high-pressure situations, such as driving on a highway or flying a plane, people have limited time to select between competing options while acting. Each option is usually accompanied with reward benefits (e.g., avoid traffic) and action costs (e.g., fuel consumption) that characterize the value of the option. The value and the availability of an option can change dynamically even during ongoing actions which compounds the decision-making challenge. How the brain dynamically integrates value information from disparate sources and selects between competing options is still poorly understood. In the current study, we present a neurodynamical framework to show how a distributed brain network can solve the problem of value integration and action selection in decisions with competing alternatives. It combines dynamic neural field theory with stochastic optimal control theory, and includes circuitry for perception, expected reward, effort cost and decision-making. It provides a principled way to explain both the neural and the behavioral findings from a series of visuomotor decision tasks in human and animal studies. For instance, the model shows how the competitive interactions between populations of neurons within and between sensorimotor regions can result in “spatial-averaging” movements, and how decision-variables influence neural activity and choice behavior.
Collapse
Affiliation(s)
- Vassilios Christopoulos
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
| | - James Bonaiuto
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Sobell Department of Motor Neuroscience and Movement Disorders, University College London, London, United Kingdom
| | - Richard A. Andersen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| |
Collapse
|
95
|
Bartels DM, Johnson EJ. Connecting cognition and consumer choice. Cognition 2015; 135:47-51. [DOI: 10.1016/j.cognition.2014.11.024] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Revised: 11/16/2014] [Accepted: 11/17/2014] [Indexed: 11/28/2022]
|
96
|
Affiliation(s)
- Daniel M. Oppenheimer
- Anderson School of Management, University of California, Los Angeles, California 90077;
| | - Evan Kelso
- Anderson School of Management, University of California, Los Angeles, California 90077;
| |
Collapse
|
97
|
Cognitive science contributions to decision science. Cognition 2014; 135:43-6. [PMID: 25500184 DOI: 10.1016/j.cognition.2014.11.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 11/12/2014] [Accepted: 11/13/2014] [Indexed: 10/24/2022]
Abstract
This article briefly reviews the history and interplay between decision theory, behavioral decision-making research, and cognitive psychology. The review reveals the increasingly important impact that psychology and cognitive science have on decision science. One of the main contributions of cognitive science to decision science is the development of dynamic models that describe the cognitive processes that underlay the evolution of preferences during deliberation phase of making a decision.
Collapse
|