scholarly journals Quantifying the benefits of using decision models with response time and accuracy data

2020 ◽  
Vol 52 (5) ◽  
pp. 2142-2155 ◽  
Author(s):  
Tom Stafford ◽  
Angelo Pirrone ◽  
Mike Croucher ◽  
Anna Krystalli
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Drift Rate ◽  
Behavioral Science ◽  
Decision Models ◽  
Group Differences ◽  
Decision Modeling ◽  
Trade Offs ◽  
Typical Experiment ◽  
Speed Accuracy

Abstract Response time and accuracy are fundamental measures of behavioral science, but discerning participants’ underlying abilities can be masked by speed–accuracy trade-offs (SATOs). SATOs are often inadequately addressed in experiment analyses which focus on a single variable or which involve a suboptimal analytic correction. Models of decision-making, such as the drift diffusion model (DDM), provide a principled account of the decision-making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioral variables. For plausible parameters of a typical between-groups experiment, we simulate experimental data, for both real and null group differences in participants’ ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time, and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modeling, but to use established decision models to demonstrate and quantify the benefits of decision modeling for experimentalists. We show, in terms of reduction of required sample size, how decision modeling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed–accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment.

scholarly journals Quantifying the benefits of using decision models with response time and accuracy data

2019 ◽  
Author(s):  
Tom Stafford ◽  
Angelo Pirrone ◽  
Mike Croucher ◽  
Anna Krystalli
Keyword(s):  
Decision Making ◽  
Response Time ◽  
Drift Rate ◽  
Decision Models ◽  
Group Differences ◽  
Decision Modelling ◽  
Trade Offs ◽  
Specificity And Sensitivity ◽  
Typical Experiment ◽  
Speed Accuracy

Response time and accuracy are fundamental measures of behavioural science, but discerning participants’ underlying abilities can be masked by speed-accuracy trade-offs (SATOs). Although a well-known possibility, SATOs are often inadequately addressed in experiment analyses which focus on a single variable (e.g. psychophysics paradigms analysing accuracy alone), or which involve a suboptimal analytic correction (e.g. dividing accuracy by response time). Models of decision making, such as the drift diffusion model (DDM), provide a principled account of the decision making process, allowing the recovery of SATO-unconfounded decision parameters from observed behavioural variables. For plausible parameters of a typical between-groups experiment we simulate experimental data, for both real and null group differences in participants’ ability to discriminate stimuli (represented by differences in the drift rate parameter of the DDM used to generate the simulated data), for both systematic and null SATOs. We then use the DDM to fit the generated data. This allows the direct comparison of the specificity and sensitivity for testing of group differences of different measures (accuracy, reaction time and the drift rate from the model fitting). Our purpose here is not to make a theoretical innovation in decision modelling, but to use established decision models to demonstrate and quantify the benefits of decision modelling for experimentalists. We show, in terms of reduction of required sample size, how decision modelling can allow dramatically more efficient data collection for set statistical power; we confirm and depict the non-linear speed-accuracy relation; and we show how accuracy can be a more sensitive measure than response time given decision parameters which reasonably reflect a typical experiment. Our results are supported by an online interactive data explorer.

scholarly journals Quorums enable optimal pooling of independent judgements

2018 ◽  
Author(s):  
James Marshall ◽  
Ralf H.J.M. Kurvers ◽  
Jens Krause ◽  
Max Wolf
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Collective Decision ◽  
Majority Voting ◽  
Single Shot ◽  
Collective Decision Making ◽  
Trade Offs ◽  
Speed Accuracy

Majority-voting and the Condorcet Jury Theorem pervade thinking about collective decision-making. Thus, it is typically assumed that majority-voting is the best possible decision mechanism, and that scenarios exist where individually-weak decision-makers should not pool information. Condorcet and its applications implicitly assume that only one kind of error can be made, yet signal detection theory shows two kinds of errors exist, ‘false positives’ and ‘false negatives’. We apply signal detection theory to collective decision-making to show that majority voting is frequently sub-optimal, and can be optimally replaced by quorum decision-making. While quorums have been proposed to resolve within-group conflicts, or manage speed-accuracy trade-offs, our analysis applies to groups with aligned interests undertaking single-shot decisions. Our results help explain the ubiquity of quorum decision-making in nature, relate the use of sub- and super-majority quorums to decision ecology, and may inform the design of artificial decision-making systems.Impact StatementTheory typically assumes that majority voting is optimal; this is incorrect – majority voting is typically sub-optimal, and should be replaced by sub-majority or super-majority quorum voting. This helps explain the prevalence of quorum-sensing in even the simplest collective systems, such as bacterial communities.

scholarly journals Big Data, Decision Models, and Public Health

2020 ◽  
Vol 17 (18) ◽  
pp. 6723
Author(s):  
Chien-Lung Chan ◽  
Chi-Chang Chang
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Medical Decision ◽  
Mining Machine ◽  
Special Issue ◽  
Trade Offs

Unlike most daily decisions, medical decision making often has substantial consequences and trade-offs. Recently, big data analytics techniques such as statistical analysis, data mining, machine learning and deep learning can be applied to construct innovative decision models. With complex decision making, it can be difficult to comprehend and compare the benefits and risks of all available options to make a decision. For these reasons, this Special Issue focuses on the use of big data analytics and forms of public health decision making based on the decision model, spanning from theory to practice. A total of 64 submissions were carefully blind peer reviewed by at least two referees and, finally, 23 papers were selected for this Special Issue.

scholarly journals Context-Dependent Urgency Influences Speed–Accuracy Trade-Offs in Decision-Making and Movement Execution

2014 ◽  
Vol 34 (49) ◽  
pp. 16442-16454 ◽  
Author(s):  
David Thura ◽  
Ignasi Cos ◽  
Jessica Trung ◽  
Paul Cisek
Keyword(s):  
Decision Making ◽  
Movement Execution ◽  
Trade Offs ◽  
Context Dependent ◽  
Speed Accuracy

scholarly journals Linking behavioural syndromes and cognition: a behavioural ecology perspective

2012 ◽  
Vol 367 (1603) ◽  
pp. 2762-2772 ◽  
Author(s):  
Andrew Sih ◽  
Marco Del Giudice
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Behavioural Ecology ◽  
Risk Sensitivity ◽  
Model Species ◽  
Behavioural Syndromes ◽  
Trade Offs ◽  
Behavioural Types ◽  
Cognitive Speed ◽  
Speed Accuracy

With the exception of a few model species, individual differences in cognition remain relatively unstudied in non-human animals. One intriguing possibility is that variation in cognition is functionally related to variation in personality. Here, we review some examples and present hypotheses on relationships between personality (or behavioural syndromes) and individual differences in cognitive style. Our hypotheses are based largely on a connection between fast–slow behavioural types (BTs; e.g. boldness, aggressiveness, exploration tendency) and cognitive speed–accuracy trade-offs. We also discuss connections between BTs, cognition and ecologically important aspects of decision-making, including sampling, impulsivity, risk sensitivity and choosiness. Finally, we introduce the notion of cognition syndromes, and apply ideas from theories on adaptive behavioural syndromes to generate predictions on cognition syndromes.

scholarly journals A Diffusion Model Approach for Understanding the Impact of 17 Interventions on the Race Implicit Association Test

2020 ◽  
pp. 014616722097448
Author(s):  
Jessica Röhner ◽  
Calvin K. Lai
Keyword(s):  
Decision Making ◽  
Implicit Association Test ◽  
Association Test ◽  
Implicit Measure ◽  
Implicit Association ◽  
Implicit Measures ◽  
Long Term Effects ◽  
Trade Offs ◽  
The Impact ◽  
Speed Accuracy

Performance on implicit measures reflects construct-specific and nonconstruct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: Change in performance could reflect changes in the constructs of interest or changes in other mental processes. We reanalyzed data from six studies ( N = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race implicit association test (IAT) performance. Diffusion models decompose overall IAT performance ( D-scores) into construct-specific (ease of decision-making) and nonconstruct-specific processes (speed–accuracy trade-offs, non-decision-related processes like motor execution). Interventions that effectively reduced D-scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed–accuracy trade-offs between compatible and incompatible trials. Non-decision-related processes were affected by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.

scholarly journals Speed versus accuracy in decision-making ants: expediting politics and policy implementation

2008 ◽  
Vol 364 (1518) ◽  
pp. 845-852 ◽  
Author(s):  
Nigel R Franks ◽  
François-Xavier Dechaume-Moncharmont ◽  
Emma Hanmore ◽  
Jocelyn K Reynolds
Keyword(s):  
Decision Making ◽  
Quorum Sensing ◽  
Policy Implementation ◽  
Positive Feedback ◽  
High Speed ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Tandem Running ◽  
Trade Offs ◽  
Speed Accuracy

Compromises between speed and accuracy are seemingly inevitable in decision-making when accuracy depends on time-consuming information gathering. In collective decision-making, such compromises are especially likely because information is shared to determine corporate policy. This political process will also take time. Speed–accuracy trade-offs occur among house-hunting rock ants, Temnothorax albipennis . A key aspect of their decision-making is quorum sensing in a potential new nest. Finding a sufficient number of nest-mates, i.e. a quorum threshold (QT), in a potential nest site indicates that many ants find it suitable. Quorum sensing collates information. However, the QT is also used as a switch, from recruitment of nest-mates to their new home by slow tandem running, to recruitment by carrying, which is three times faster. Although tandem running is slow, it effectively enables one successful ant to lead and teach another the route between the nests. Tandem running creates positive feedback; more and more ants are shown the way, as tandem followers become, in turn, tandem leaders. The resulting corps of trained ants can then quickly carry their nest-mates; but carried ants do not learn the route. Therefore, the QT seems to set both the amount of information gathered and the speed of the emigration. Low QTs might cause more errors and a slower emigration—the worst possible outcome. This possible paradox of quick decisions leading to slow implementation might be resolved if the ants could deploy another positive-feedback recruitment process when they have used a low QT. Reverse tandem runs occur after carrying has begun and lead ants back from the new nest to the old one. Here we show experimentally that reverse tandem runs can bring lost scouts into an active role in emigrations and can help to maintain high-speed emigrations. Thus, in rock ants, although quick decision-making and rapid implementation of choices are initially in opposition, a third recruitment method can restore rapid implementation after a snap decision. This work reveals a principle of widespread importance: the dynamics of collective decision-making (i.e. the politics) and the dynamics of policy implementation are sometimes intertwined, and only by analysing the mechanisms of both can we understand certain forms of adaptive organization.

scholarly journals A Dynamic Approach to Recognition Memory

2017 ◽  
Author(s):  
Gregory Edward Cox ◽  
Rich Shiffrin
Keyword(s):  
Decision Making ◽  
Recognition Memory ◽  
Response Time ◽  
Source Memory ◽  
Dynamic Approach ◽  
Short Term ◽  
Trade Off ◽  
The Core ◽  
Speed Accuracy ◽  
Over Time

We present a dynamic model of memory that integrates the processes of perception, retrieval from knowledge, retrieval of events, and decision making as these evolve from one moment to the next. The core of the model is that recognition depends on tracking changes in familiarity over time from an initial baseline generally determined by context, with these changes depending on the availability of different kinds of information at different times. A mathematical implementation of this model leads to precise, accurate predictions of accuracy, response time, and speed-accuracy trade-off in episodic recognition at the levels of both groups and individuals across a variety of paradigms. Our approach leads to novel insights regarding word frequency, speeded responding, context reinstatement, short-term priming, similarity, source memory, and associative recognition, revealing how the same set of core dynamic principles can help unify otherwise disparate phenomena in the study of memory.

scholarly journals Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Jan Drugowitsch ◽  
Gregory C DeAngelis ◽  
Dora E Angelaki ◽  
Alexandre Pouget
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Human Subjects ◽  
Neural Population ◽  
Trade Off ◽  
Reward Rate ◽  
Trade Offs ◽  
Sensory Modalities ◽  
Effective Decision Making ◽  
Speed Accuracy

For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (<xref ref-type="bibr" rid="bib4">Drugowitsch et al., 2014</xref>), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

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