Theoretical models of reaction times arising from simple-choice tasks

Tejo, Mauricio; Araya, Héctor; Niklitschek-Soto, Sebastián; Marmolejo‐Ramos, Fernando

doi:10.1007/s11571-019-09532-1

Cited by 12 publications

(7 citation statements)

References 37 publications

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“…Due to issues related to the complexity of RT measurement and explanations of the underlying neural mechanisms subserving decision making, significant effort has been expended to develop other approaches to model the skewed distribution of RTs to obtain a deeper understanding of experimental effects on the underlying neural and psychological processes supporting these data ( Ratcliff et al, 2016 ; Weindel et al, 2021 ). These approaches have been divided into two groups, measurement models and process models ( Anders et al, 2016 ; Tejo et al, 2019 ). The measurement models include Weibull and lognormal ( Anders et al, 2016 ) models.…”

Section: Introductionmentioning

confidence: 99%

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

Ashford

Clifford

Anand

et al. 2022

Front. Aging Neurosci.

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A critical issue in addressing medical conditions is measurement. Memory measurement is difficult, especially episodic memory, which is disrupted by many conditions. On-line computer testing can precisely measure and assess several memory functions. This study analyzed memory performances from a large group of anonymous, on-line participants using a continuous recognition task (CRT) implemented at https://memtrax.com. These analyses estimated ranges of acceptable performance and average response time (RT). For 344,165 presumed unique individuals completing the CRT a total of 602,272 times, data were stored on a server, including each correct response (HIT), Correct Rejection, and RT to the thousandth of a second. Responses were analyzed, distributions and relationships of these parameters were ascertained, and mean RTs were determined for each participant across the population. From 322,996 valid first tests, analysis of correctness showed that 63% of these tests achieved at least 45 correct (90%), 92% scored at or above 40 correct (80%), and 3% scored 35 correct (70%) or less. The distribution of RTs was skewed with 1% faster than 0.62 s, a median at 0.890 s, and 1% slower than 1.57 s. The RT distribution was best explained by a novel model, the reverse-exponential (RevEx) function. Increased RT speed was most closely associated with increased HIT accuracy. The MemTrax on-line memory test readily provides valid and reliable metrics for assessing individual episodic memory function that could have practical clinical utility for precise assessment of memory dysfunction in many conditions, including improvement or deterioration over time.

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Section: Introductionmentioning

confidence: 99%

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

Ashford

Clifford

Anand

et al. 2022

Front. Aging Neurosci.

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“…Apart from the aforementioned studies on seriation (Young & Piaget, 1976) and mental rotation (Shepard & Metzler, 1971), reaction times have also been frequently used to measure how long people ponder before making a decision (Ratcliff, 1978;Ratcliff & McKoon, 2008). In this line of research, the theoretical shape of reaction times (Tejo, Araya, Niklitschek-Soto, & Marmolejo-Ramos, 2019) as well as how they can be used to compare cognitive and neuroscientific models has been discussed (Steinkamp, Fink, Vossel, & Weidner, 2022).…”

Section: Related Workmentioning

confidence: 99%

The Scaling of Mental Computation in a Sorting Task

Haridi

Dasgupta³

et al. 2022

SSRN Journal

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“…For example, Tejo et al. ( 2018 , 2019 ), and Martínez-Flórez et al. ( 2020 ) have put forward the shifted Birnbaum–Saunders, shifted Gamma distributions, and Exponential-Centred Skew-Normal distributions, respectively, as good fits to RTs.…”

Section: Introductionmentioning

confidence: 99%

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis

et al. 2022

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Reaction times (RTs) are an essential metric used for understanding the link between brain and behaviour. As research is reaffirming the tight coupling between neuronal and behavioural RTs, thorough statistical modelling of RT data is thus essential to enrich current theories and motivate novel findings. A statistical distribution is proposed herein that is able to model the complete RT’s distribution, including location, scale and shape: the generalised-exponential-Gaussian (GEG) distribution. The GEG distribution enables shifting the attention from traditional means and standard deviations to the entire RT distribution. The mathematical properties of the GEG distribution are presented and investigated via simulations. Additionally, the GEG distribution is featured via four real-life data sets. Finally, we discuss how the proposed distribution can be used for regression analyses via generalised additive models for location, scale and shape (GAMLSS).

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Theoretical models of reaction times arising from simple-choice tasks

Cited by 12 publications

References 37 publications

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

The Scaling of Mental Computation in a Sorting Task

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis

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