Diffusion vs. linear ballistic accumulation: Different models, different conclusions about the slope of the zROC in recognition memory

Osth, Adam F; Bora, Beatrice; Dennis, Simon; Heathcote, Andrew

doi:10.1016/j.jml.2017.04.003

Cited by 52 publications

(61 citation statements)

References 81 publications

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“…Cross-trial variability in drift rate is analogous to variability in memory strength in SDT and enables the model to produce errors that are slower than correct responses, while variability in starting point enables the model to produce errors that are faster than correct responses. In recognition memory, errors are typically slower than correct responses and a fast error pattern has not usually been found (Ratcliff & Smith, 2004) and thus prior work suggests that starting point variability is unnecessary in recognition memory (Osth, Bora, et al, 2017). Thus, in our applications we fix s z = 0 in all cases.…”

Section: Relating Global Semantic Similarity To Recognition Performanmentioning

confidence: 99%

“…A summary of the datasets can be seen in Table 2. In addition to spanning a range of study list lengths, the experiments also contain manipulations such as the number of presentations (Criss, 2010), encoding tasks (Kiliç, Criss, Malmberg, & Shiffrin, 2017), and speed-accuracy emphasis (Rae et al, 2014;Osth, Bora, et al, 2017). Within-list manipulations (either within study or test lists) mix levels within a list, such as having high frequency (HF) and low frequency (LF) words randomly ordered within both the study and test lists.…”

Section: The Model Fitmentioning

confidence: 99%

“…For each datset, we used sensible exclusions to omit fast or slow guess responses. For the Rae et al (2014), Osth, Bora, et al (2017), and Criss (2010) datasets, responses faster than .2 seconds or slower than 2.5 seconds were excluded. For the Kiliç et al (2017) datasets, responses faster than .3 seconds or slower than 3.0 seconds were excluded.…”

Section: Data Exclusionsmentioning

confidence: 99%

“…A letter such as "E" indicates speed-accuracy emphasis and implies that a separate parameter was allocated for the speed and accuracy conditions. The Rae et al (2014) and Osth, Bora, et al (2017) datasets were combined into a single column as these datasets used extremely similar designs and identical parameterizations. The only difference between these datasets was that the Osth et al dataset used a two-stage confidence procedure where "high" and "low" confidence responses were collected after the initial yes/no response.…”

Section: Ddm Parameterizationsmentioning

confidence: 99%

“…While models of two-stage confidence have been developed with the DDM (Pleskac & Busemeyer, 2010;Moran, Teodorescu, & Usher, 2015), they result in intractable likelihoods when variability in model parameters is introduced. For each dataset, we follow prior precedent and allow drift rates, boundary separation, bias, and nondecision time to vary across the speed-accuracy emphasis conditions (Osth, Bora, et al, 2017;Rae et al, 2014).…”

Section: Ddm Parameterizationsmentioning

confidence: 99%

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Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

Osth

Shabahang

Mewhort

et al. 2020

Journal of Memory and Language

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We would like to thank Amy Criss and Asli Kiliç for generously sharing their data and Brendan Johns for providing the datasets of similarity and relatedness judgments. This work was supported by an ARC Discovery Early Career Research Award (DE170100106) awarded to Adam Osth. BEAGLE vectors, datasets, and model code can be found on https://osf.io/gtdqf/ GLOBAL SEMANTIC SIMILARITY EFFECTS 2 AbstractRecognition memory models posit that false alarm rates increase as the global similarity between the probe cue and the contents of memory is increased. Global similarity predictions have been commonly tested using category length designs where it has been found that false alarm rates increase as the number of studied items from a common category is increased. In this work, we explored global similarity predictions within unstructured lists of words using representations from the BEAGLE model (Jones & Mewhort, 2007). BEAGLE differs from traditional semantic space models in that it contains two types of representations: item vectors, which encode unordered co-occurrence, and order vectors, in which words are similar to the extent to which they are share neighboring words in the same relative positions. Global similarity among item and order vectors was regressed onto drift rates in the diffusion decision model (DDM: Ratcliff, 1978), which unifies both response times and accuracy. We implemented this model in a hierarchical Bayesian framework across seven datasets with lists composed of unrelated words. Results indicated clear deficits due to global similarity among item vectors, but only a minimal impact of global similarity among the order vectors.

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Section: Relating Global Semantic Similarity To Recognition Performanmentioning

confidence: 99%

Section: The Model Fitmentioning

confidence: 99%

Section: Data Exclusionsmentioning

confidence: 99%

Section: Ddm Parameterizationsmentioning

confidence: 99%

Section: Ddm Parameterizationsmentioning

confidence: 99%

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Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

Osth

Shabahang

Mewhort

et al. 2020

Journal of Memory and Language

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What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation?

2022

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It has traditionally been assumed that responding after an error is slowed because participants try to improve their accuracy by increasing the amount of evidence required for subsequent decisions. However, recent work suggests a more varied picture of post-error effects, with instances of post-error speeding, and decreases or no change in accuracy. Further, the causal role of errors in these effects has been questioned due to confounds from slow fluctuations in attention caused by factors such as fatigue and boredom. In recognition memory tasks, we investigated both post-error speeding associated with instructions emphasising fast responding and post-error slowing associated with instructions emphasising the accuracy of responding. In order to identify the causes of post-error effects, we fit this data with evidence accumulation models using a method of measuring post-error effects that is robust to confounds from slow fluctuations. When the response-to-stimulus interval between trials was short, there were no post-error effect on accuracy and speeding and slowing were caused by differences in non-decision time (i.e. the time to encode choice stimuli and generate responses). In contrast, when the interval was longer, due to participants providing a confidence rating for their choice, there were also effects on the rate of evidence accumulation and the amount of evidence required for a decision. We discuss the implications of our methods and results for post-error effect research.

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Measure-Theoretic Musings Cannot Salvage the Full Bayesian Significance Test as a Measure of Evidence

Wagenmakers

2022

Comput Brain Behav

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Ly and Wagenmakers (Computational Brain & Behavior:1–8, in press) critiqued the Full Bayesian Significance Test (FBST) and the associated statistic FBST ev: similar to the frequentist p-value, FBST ev cannot quantify evidence for the null hypothesis, allows sampling to a foregone conclusion, and suffers from the Jeffreys-Lindley paradox. In response, Kelter (Computational Brain & Behavior:1–11, 2022) suggested that the critique is based on a measure-theoretic premise that is often inappropriate in practice, namely the assignment of non-zero prior mass to a point-null hypothesis. Here we argue that the key aspects of our initial critique remain intact when the point-null hypothesis is replaced either by a peri-null hypothesis or by an interval-null hypothesis; hence, the discussion on the validity of a point-null hypothesis is a red herring. We suggest that it is tempting yet fallacious to test a hypothesis by estimating a parameter that is part of a different model. By rejecting any null hypothesis before it is tested, FBST is begging the question. Although FBST may be useful as a measure of surprise under a single model, we believe that the concept of evidence is inherently relative; consequently, evidence for competing hypotheses ought to be quantified by examining the relative adequacy of their predictions. This philosophy is fundamentally at odds with the FBST.

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Diffusion vs. linear ballistic accumulation: Different models, different conclusions about the slope of the zROC in recognition memory

Cited by 52 publications

References 81 publications

Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

Global semantic similarity effects in recognition memory: Insights from BEAGLE representations and the diffusion decision model

What Happens After a Fast Versus Slow Error, and How Does It Relate to Evidence Accumulation?

Measure-Theoretic Musings Cannot Salvage the Full Bayesian Significance Test as a Measure of Evidence

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