A Limited Capacity, Wave Equality, Random Walk Model of Absolute Identification

Karpiuk, Peter; Lacouture, Yves; Marley, A. A. J.

doi:10.4324/9781315789408-17

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“…Modeling analysis revealed that spacing effects were best captured by allowing the discrimination constant ( c ) to vary with spacing (the rationale being that discriminability is better for more widely space stimuli) rather than response determinism (θ). In order to capture the full extent of set-size effects in accuracy (these are in part captured by the choice rule in Eq 4), Kent and Lamberts (2005) showed that it was necessary to allow both θ and c to vary across set-sizes (without the assumption of diminishing returns; a similar assumption was followed by Karpiuk, Lacouture, & Marley, 1997, in their accumulator model by allowing the response criterion to vary by set size). Here, allowing c to vary by set size, captured the set size effect (in accuracy) best, with c decreasing with larger set sizes (indicating lower discriminability).…”

Section: Resultsmentioning

confidence: 99%

The relative importance of perceptual and memory sampling processes in determining the time course of absolute identification.

Guest

Kent²,

Adelman

2018

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Perceptual and memory sampling in absolute identification 2 2 Abstract In absolute identification, the EGCM-RT (Kent & Lamberts, 2005, 2016 proposes that perceptual processing determines systematic response time (RT) variability; all other models of RT emphasise response selection processes. In the EGCM-RT the bow effect in RTs (longer responses for stimuli in the middle of the range) occurs because these middle stimuli are less isolated and so as perceptual information is accumulated, the evidence supporting a correct response grows more slowly than for stimuli at the ends of the range. More perceptual information is therefore accumulated in order to increase certainty in response for middle stimuli, lengthening RT.According to the model reducing perceptual sampling time should reduce the size of the bow effect in RT. We tested this hypothesis in two pitch identification experiments. Experiment 1 found no effect of stimulus duration on the size of the RT bow. Experiment 2 used multiple short stimulus durations as well as manipulating set size and stimulus spacing. Contrary to EGCM-RT predictions, the bow effect on RTs was large for even very short durations. A new version of the EGCM-RT could only capture this, alongside the effect of stimulus duration on accuracy, by including both a perceptual and a memory sampling process. A modified version of the SAMBA model (Brown, Marley, Donkin, & Heathcote, 2008) could also capture the data, by assuming psychophysical noise diminishes with increased exposure duration. This modelling suggests systematic variability in RT in absolute identification is largely determined by memory sampling and response selection processes.Keywords. Absolute identification, perceptual processing, memory sampling, response selection Perceptual and memory sampling in absolute identification 3 3The relative importance of perceptual and memory sampling processes in determining the time course of absolute identificationThe last 30 years has seen the development of several detailed mathematical models of the time course of cognitive processes. These models specify how, over the time course of a cognitive process, information about a stimulus is sampled and integrated into a decision mechanism. Such models have been applied successfully to a wide range of cognitive tasks and have been influential in helping understand the processes underlying such tasks (e.g., Smith & Ratcliff, 2009). An important distinction between these models is the emphasis placed on what is being sampled. In some models, there is a great emphasis on the importance of perceptual processing (Adelman, 2011; Guest & Lamberts, 2010, 2011Lamberts, 2000, Kent & Lamberts, 2005, 2006a, 2016. These models suggest that, over the time course of a trial, a representation of a stimulus is gradually built up through repeated sampling of the stimulus such that the information that is fed into the decision process changes over time. In such models (e.g., Lamberts, 2000) task performance at a given point in time is limited by the state of ...

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Section: Resultsmentioning

confidence: 99%