Modeling cognitive load effects of conversation between a passenger and driver

Tillman, Gabriel; Strayer, David L.; Eidels, Ami; Heathcote, Andrew

doi:10.3758/s13414-017-1337-2

Cited by 52 publications

(72 citation statements)

References 43 publications

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“…Note that, for example, the model proposed by Ratcliff and Strayer [43] and the similar model by Tillmann et al [45] would not fit the meta-analysis in [39], since they don't account for the RT kinematics dependency in lead vehicle braking scenarios (although these models can be fitted to each study separately, as long as the kinematics is constant, which is typically what has been done).…”

Section: Discussionmentioning

confidence: 99%

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Engström

Markkula

Xue

et al. 2018

IET Intelligent Transport Systems

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Abstract:The recently proposed cognitive control hypothesis suggests that the performance of cognitively loading but nonvisual tasks such as cell phone conversation selectively impairs driving tasks that rely on top-down cognitive control while leaving automatized driving tasks unaffected. This idea is strongly supported by the existing experimental literature and we have previously outlined a conceptual model to account for the key underlying mechanisms. The present paper presents a mechanistically explicit account of the cognitive control hypothesis in terms of a computational simulation model. More specifically, it is shown how this model offers a straightforward explanation for why the effect of cognitive load on brake response time reported in experimental lead vehicle braking studies depends strongly on scenario kinematics, more specifically the initial time headway. It is demonstrated that this relatively simple model can be fitted to empirical data obtained from an existing meta-analysis on existing lead vehicle braking studies.

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Section: Discussionmentioning

confidence: 99%

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Engström

Markkula

Xue

et al. 2018

IET Intelligent Transport Systems

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“…If the model only has within-trial drift rate variability and no between-trial starting point variability, then the Wald distribution (Wald, 1947) describes the first-passage time distribution for a single accumulator with positive drift rate toward a positive response threshold. The Wald distribution (Wald, 1947) is often used to explain simple RT data, where participants only need to make one type of response (Heathcote, 2004;Ratcliff & Van Dongen, 2011;Ratcliff & Strayer, 2014;Ratcliff, 2015; W. Schwarz, 2001;Heathcote, 2004;Anders, Alario, & van Maanen, 2016;Tillman, Strayer, Eidels, & Heathcote, 2017). The Wald probability density function is:…”

Section: The Racing Diffusion Modelmentioning

confidence: 99%

Sequential Sampling Models Without Random Between-Trial Variability: The Racing Diffusion Model of Speeded Decision

Tillman¹

2017

Preprint

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Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models

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“…To understand the causes of dual-task interference, it is necessary to quantify the latent cognitive processes underlying responses in both primary and secondary tasks. Recent studies using dual-task paradigms have applied evidence accumulation models to quantify latent cognitive processes and understand dual-task performance (Tillman et al, 2017;Castro, Strayer, Matzke, & Heathcote, 2019). However, these studies only modeled the secondary task, and as a result, were unable to directly examine the processes underlying dual-task interference.…”

Section: Discussionmentioning

confidence: 99%

“…Using the capacity coefficient, individuals can be classified as having limited, unlimited, or super capacity if information processing rates decrease, remain unchanged, or increase with information load, respectively. For example, Tillman, Strayer, Eidels, and Heathcote (2017) found that although some individuals increased their rates of information processing when they had to process multiattribute stimuli compared to a single stimulus property, others' processing was constant, or decreased with added information load.…”

Section: Formally Modelling Adaptionmentioning

confidence: 99%

“…However, evidence to support this assumption is inconsistent. Using evidence accumulation models, including the linear ballistic accumulator (LBA; and the single-bound diffusion or Wald model (Heathcote, 2004), Tillman et al (2017) found that people were slower to respond to the DRT when having to hold a conversation while driving. However, this effect was due to increased response caution, rather than a decrease in the rate of information processing.…”

Section: The Detection Response Task (Drt)mentioning

confidence: 99%

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Adapting to workload in complex environments: Insights from evidence accumulation models of decision making

Palada¹

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The aim of this thesis is to examine how individuals manage workload in complex environments. I used formal models of decision making, known as evidence accumulation models, to quantify how individuals adapt in response to workload. The models assume that the individual samples evidence from the environment until a threshold amount of evidence is accumulated, triggering an overt response. The models can quantify latent cognitive processes that are relevant to workload, such as the rate of information processing, response caution, response biases, quality of information and encoding time. In Chapter 1, I provide an overview of the relevant workload literature and describe the architectures of evidence accumulation models. In Chapter 2, I test whether standard evidence accumulation models can account for performance in a relatively complex applied decision making task which varied time pressure and stimulus difficulty. In Chapter 3, I examine how individuals respond to distinct time pressure factors including stimulus complexity, the number of stimuli, as well as time available to classify stimuli. In Chapter 4, I extend the investigation by using a dual-task paradigm to understand the causes of dual-task performance interference and how people adapt to having to multitask in a time pressured environment. In Chapter 5, I discuss the findings as a whole, detailing the theoretical and practical implications of the thesis, as well as limitations and directions for future research.ii Declaration by authorThis thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

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Modeling cognitive load effects of conversation between a passenger and driver

Cited by 52 publications

References 43 publications

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios

Sequential Sampling Models Without Random Between-Trial Variability: The Racing Diffusion Model of Speeded Decision

Adapting to workload in complex environments: Insights from evidence accumulation models of decision making

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