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dc.contributor.authorHarris, CMen
dc.contributor.authorWaddington, Jen
dc.contributor.authorBiscione, Ven
dc.contributor.authorManzi, Sen
dc.date.accessioned2016-06-02T12:05:04Z
dc.date.available2016-06-02T12:05:04Z
dc.date.issued2014en
dc.identifier.issn1662-5161en
dc.identifier.urihttp://hdl.handle.net/10026.1/4802
dc.description.abstract

Over the last 150 years, human manual reaction times (RTs) have been recorded countless times. Yet, our understanding of them remains remarkably poor. RTs are highly variable with positively skewed frequency distributions, often modeled as an inverse Gaussian distribution reflecting a stochastic rise to threshold (diffusion process). However, latency distributions of saccades are very close to the reciprocal Normal, suggesting that "rate" (reciprocal RT) may be the more fundamental variable. We explored whether this phenomenon extends to choice manual RTs. We recorded two-alternative choice RTs from 24 subjects, each with 4 blocks of 200 trials with two task difficulties (easy vs. difficult discrimination) and two instruction sets (urgent vs. accurate). We found that rate distributions were, indeed, very close to Normal, shifting to lower rates with increasing difficulty and accuracy, and for some blocks they appeared to become left-truncated, but still close to Normal. Using autoregressive techniques, we found temporal sequential dependencies for lags of at least 3. We identified a transient and steady-state component in each block. Because rates were Normal, we were able to estimate autoregressive weights using the Box-Jenkins technique, and convert to a moving average model using z-transforms to show explicit dependence on stimulus input. We also found a spatial sequential dependence for the previous 3 lags depending on whether the laterality of previous trials was repeated or alternated. This was partially dissociated from temporal dependency as it only occurred in the easy tasks. We conclude that 2-alternative choice manual RT distributions are close to reciprocal Normal and not the inverse Gaussian. This is not consistent with stochastic rise to threshold models, and we propose a simple optimality model in which reward is maximized to yield to an optimal rate, and hence an optimal time to respond. We discuss how it might be implemented.

en
dc.format.extent418 - ?en
dc.languageengen
dc.language.isoengen
dc.subjectPieron's lawen
dc.subjectautoregressive integrated moving average (ARIMA)en
dc.subjectlatencyen
dc.subjectoptimalityen
dc.subjectreaction timesen
dc.subjectreciprocal Normalen
dc.subjectspeed-accuracy trade-offen
dc.titleManual choice reaction times in the rate-domain.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/24959134en
plymouth.volume8en
plymouth.publication-statusPublished onlineen
plymouth.journalFront Hum Neuroscien
dc.identifier.doi10.3389/fnhum.2014.00418en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Psychology
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Centre for Brain, Cognition and Behaviour (CBCB)
plymouth.organisational-group/Plymouth/Research Groups/Centre for Brain, Cognition and Behaviour (CBCB)/Brain
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Professional Services staff
dc.publisher.placeSwitzerlanden
dcterms.dateAccepted2014-05-21en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.3389/fnhum.2014.00418en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014en
rioxxterms.typeJournal Article/Reviewen


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