Humans Optimize Decision-Making by Delaying Decision Onset

Teichert, Tobias; Ferrera, Vincent P.; Grinband, Jack

doi:10.1371/journal.pone.0089638

Cited by 19 publications

(26 citation statements)

References 39 publications

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“…It would be interesting to examine the PEP, for example, in near-threshold orientation discrimination tasks, which in humans lead to changes of non-decision time as a function of stimulus contrast (Gould et al 2007). Similarly, based on the findings of Laming (Laming 1979) and Teichert (Teichert et al 2014) we might have expected PEP and hence decision onset to vary as a function of speedaccuracy instructions. The lack of such findings in the study of Hanks et al may be related to task differences.…”

Section: Neural Correlatesmentioning

confidence: 95%

“…Recent findings by Teichert and colleagues (Teichert et al 2014) have provided a more detailed view of this issue. In particular, their findings suggest that even in interference tasks subjects have some control over decision onset.…”

Section: Reviewmentioning

confidence: 98%

“…Another suggestion to reduce computational complexity was made by Teichert et al (2014). They argued that there are two fundamentally different ways in which decision onset can be controlled that differ with respect to the amount of computational overhead they require: 1) online, on a case-by-case basis (interference-dependent gating), or 2) in a stimulus-independent way that can modify decision onset for certain task demands (task set-dependent gating).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…In addition to changes of non-decision time, a delay in decision onset may also have an effect on error rate, for example, if the available sensory information changes systematically during the decision process Smith and Ratcliff 2009;Teichert et al 2014). If the first piece of sensory information supports the wrong choice, then initiating the integration process earlier should result in higher error rates.…”

Section: Psychophysical Findingsmentioning

confidence: 99%

“…In this part, we outline potential neural correlates of decision onset in these presumed integrator neurons and whether or not these correlates exhibit a similar dependence on the factors that affect non-decision time and integration onset in psychophysical studies. We will not review several interesting findings in the EEG literature but refer the interested reader to the discussion section of our recent paper (Teichert et al 2014).…”

Section: Neural Correlatesmentioning

confidence: 99%

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The importance of decision onset

Teichert

Grinband

Ferrera

2016

Journal of Neurophysiology

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The neural mechanisms of decision making are thought to require the integration of evidence over time until a response threshold is reached. Much work suggests that response threshold can be adjusted via top-down control as a function of speed or accuracy requirements. In contrast, the time of integration onset has received less attention and is believed to be determined mostly by afferent or preprocessing delays. However, a number of influential studies over the past decade challenge this assumption and begin to paint a multifaceted view of the phenomenology of decision onset. This review highlights the challenges involved in initiating the integration of evidence at the optimal time and the potential benefits of adjusting integration onset to task demands. The review outlines behavioral and electrophysiolgical studies suggesting that the onset of the integration process may depend on properties of the stimulus, the task, attention, and response strategy. Most importantly, the aggregate findings in the literature suggest that integration onset may be amenable to top-down regulation, and may be adjusted much like response threshold to exert cognitive control and strategically optimize the decision process to fit immediate behavioral requirements. diffusion model; integration onset; non-decision time; sequential sampling model A GREAT DEAL OF WORK in perceptual decision making has focused on describing the "stopping rules" that terminate a decision process when integration of further evidence provides little added benefit. It is often neglected that understanding when and how the decision process is initiated may be equally important. In line with sequential sampling models of decision making reviewed below, the decision process can be defined as the integration of evidence over time. Within this framework, decision onset is the time at which evidence integration begins. If integration is initiated too early, i.e., before relevant information is available, it may be hampered by noise or irrelevant information (Laming 1979). Conversely, important information may be lost and/or the decision process may be prolonged unnecessarily if the integration is initiated too late. These issues are further complicated by the fact that the encoding of a stimulus and extraction of task-relevant features take variable amounts of time depending on task demands and stimulus properties. Hence, the optimal time for integration onset may be hard to predict and may vary on a trial-by-trial basis.This review argues that not only is a detailed understanding of the neural mechanisms governing decision onset necessary to provide a comprehensive description of evidence-based decision making but decision onset can have a profound impact on decision outcomes in everyday life. Over the past decade numerous articles have reviewed diverse aspects of decision making (Churchland and Ditterich 2012;Drugowitsch and Pouget 2012;Heekeren et al. 2008;Heitz and Schall 2013;Kepecs and Mainen 2012;Kurniawan et al. 2011;Mulder et al. 2014;Nienborg et al. 2...

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Section: Neural Correlatesmentioning

confidence: 95%

Section: Reviewmentioning

confidence: 98%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Psychophysical Findingsmentioning

confidence: 99%

Section: Neural Correlatesmentioning

confidence: 99%

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The importance of decision onset

Teichert

Grinband

Ferrera

2016

Journal of Neurophysiology

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Environmental impact and performance assessment of a new Zigbee‐based shotgun training system

Ali

Fraj

Oueslati

et al. 2020

Engineering Reports

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Only a few research works have studied the risks involving young athletes in shooting activities such as noise and risk of projectile impact. Besides, limited studies have explored the environmental concerns caused by remaining projectile fragments scattered into the environment. In recent years, there has been an increasing interest in integrating computing, modeling, and IoT‐based applications and used connected add‐ons (e.g., steams Virtual Reality VR, virtual guns, and game controllers) in sports activities displayed in virtual reality gaming environments. The aim of this paper is, first, to present a multi‐aspect Zigbee‐based protocol system used to assess and to improve reaction time and score prediction abilities of Shotgun sports practitioners indoor and outdoor. Second, B‐percept would be presented as a training solution to reduce environmental scattered wastes of used Clays. After 8 weeks of training, there was significant improvement (p < .001) of participants' reaction time by using the B‐percept simulator. In addition, improvement in real clay shotgun results (p < .0002) but it was difficult to correctly predict more than 60% of correct scores after the test. The results of this study encourage continuing to improve the B‐percept to use wireless moving targets for training purposes.

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Attention and Signal Detection: A Practical Guide

Ferrera

2016

From Human Attention to Computational Attention

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Humans Optimize Decision-Making by Delaying Decision Onset

Cited by 19 publications

References 39 publications

The importance of decision onset

The importance of decision onset

Environmental impact and performance assessment of a new Zigbee‐based shotgun training system

Attention and Signal Detection: A Practical Guide

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