Select, then decide: Further evidence for separable selection and decision processes in short-term visual recognition

Shepherdson, Peter

doi:10.1080/13506285.2020.1733155

Cited by 5 publications

(18 citation statements)

References 53 publications

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“…In agreement with previous findings [19,20], retro-cues reduced nondecision time, Δ = -0.45 [-0.48, -0.43], BF10 = 3.54 x 10 16 (Figure 3B). Retro-cue benefits for nondecision time were larger in the delayed estimation than in the change detection paradigms, Δ = 0.41 [0.37, 0.46], BF10 = 7.55 x 10 19 , and they were larger for the tasks using color than orientation as memoranda, Δ = 0.28, [0.24, 0.33], BF10 = 6.07 x 10 22 .…”

Section: Nondecision Timesupporting

confidence: 93%

“…Consistent with previous findings [19,20], retro-cues increased drift rate, Δ = 0.54, HDI = [0.52, 0.57], BF10 = 1.96 x 10 15 (Figure 3A). There was moderate evidence for the benefit on drift rate being similar for the change detection and delayed estimation paradigms, Δ = -0.02 [-0.08, 0.03], BF10 = 0.11.…”

Section: Drift Ratesupporting

confidence: 92%

“…Our results are consistent with prior interpretations of the retro-cue effect on drift rate and nondecision time. The retro-cue benefit in drift rate was explained as reflecting the strengthening and protection of the cued item from subsequent visual interference [19,20]:…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, only two previous studies examined the retro-cue effect through the lens of the two-choice diffusion decision model. Shepherdson et al [19] modeled the data of four change-detection tasks with visual (colored squares) and verbal (letters and German words) materials, and Shepherdson [20] analyzed the data of two visual experiments with a two-alternative forced choice test. The presentation of the retro-cue increased drift rate compared to the no-cue condition, particularly in the visual tasks.…”

Section: Modeling Of the Retro-cue Effectmentioning

confidence: 99%

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A Diffusion Model Analysis of Age and Individual Differences in the Retro-Cue Benefit

Souza¹,

Frischkorn²

2023

Preprint

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The limited capacity of working memory constrains how well we can think and act. Focused attention alleviates this limitation by prioritizing the most relevant mental content at a given time. Retro-cues tap into this ability by guiding attention to one working memory content, thereby improving memory speed and accuracy. So far, few attempts have been made to understand the retro-cue effect through well-established computational models, nor how their parameters track age-related changes and individual differences in focusing efficiency. The present study aims to close these gaps. We applied the drift-diffusion model to the data from a large sample of younger and older adults (total N = 346) that completed four retro-cue tasks. Diffusion modeling showed that retro-cues increased the quality of evidence accumulation, reduced the time taken for retrieval, and changed response conservativeness. Younger and older adults benefited from retro-cues in all tasks and parameters. Yet, age-related decline was observed in the retro-cue boost for evidence quality. Likewise, evidence quality was the only parameter capturing individual differences in focusing efficiency. Our results suggest that people differ in how well they can strengthen and protect a focused representation to boost evidence-quality accumulation, and this ability declines with aging.

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Section: Nondecision Timesupporting

confidence: 93%

Section: Drift Ratesupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Modeling Of the Retro-cue Effectmentioning

confidence: 99%

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A Diffusion Model Analysis of Age and Individual Differences in the Retro-Cue Benefit

Souza¹,

Frischkorn²

2023

Preprint

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“…Non-decision time encompasses the necessary early, low level, preconscious perceptual encoding of the cue as well as the post-decision motor responses required to behaviourally indicate the decision. In cueing paradigms, this parameter is sensitive to attention orienting effects for cued versus miscued trials (Sewell & Smith, 2012;Smith & Ratcliff, 2009), and is primarily recognized as indexing the time course of attentional selection 1 (Lilburn & Smith, 2020;Sewell et al, 2016;Shepherdson, 2020;Smith et al, 2016). Specifically, a low-level, reflexive shift of attention to the gazed-at location that results in the target being processed later in miscued trials compared to cued trials (Sewell et al, 2016;Sewell & Smith, 2012;Smith & Ratcliff, 2009).…”

Section: Figurementioning

confidence: 99%

Uncovering the Cognitive Mechanisms Underlying the Gaze Cueing Effect

Alister¹,

McKay²,

Sewell³

et al. 2022

Preprint

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The gaze cueing effect is the tendency for people to respond faster to targets appearing at locations gazed-at by others compared to locations gazed away from by others. However, although the gaze cueing effect has been studied extensively and has implications for a variety of theoretical and clinical contexts, much is still unknown about the cognitive mechanisms underlying its emergence. Formal evidence accumulation models provide the dominant account of the cognitive processes underlying speeded decisions, however, they have never been applied to understand the gaze cueing effect. In this study we applied the Diffusion Decision Model (DDM) to gaze and arrow cueing data for the first time in order to 1) better understand the cognitive processes underlying the gaze cueing effect, and 2) see whether these processes could be considered the same as those that underlie arrow cueing effects. To achieve these aims, we applied variants of the DDM to three preexisting gaze cueing data sets and one preexisting arrow cueing data set (n = 171, 139,001 trials total), using a combination of individual-level and hierarchical computational modelling techniques. At the group level, people were best described by an attentional orienting mechanism rather than higher-order decision bias or information processing mechanisms. However, we found evidence for individual differences such that not everyone was best described by an attentional orienting mechanism. Further, the same people who were best described by an attentional orienting mechanism for gaze cues tended not to be best described by that same mechanism for arrow cues, suggesting these cueing effects may induce different responses within the same people – although we interpreted this finding cautiously.

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A diffusion model analysis of age and individual differences in the retro-cue benefit

Souza,

Frischkorn

2023

Sci Rep

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The limited capacity of working memory (WM) constrains how well we can think and act. WM capacity is reduced in old age, with one explanation for this decline being a deficit in using attention to control WM contents. The retro-cue paradigm has been used to examine the ability to focus attention in WM. So far, there are conflicting findings regarding an aging deficit in the retro-cue effect. The present study evaluated age-related changes and individual differences in the retro-cue effect through a well-established computational model that combines speed and accuracy to extract underlying psychological parameters. We applied the drift–diffusion model to the data from a large sample of younger and older adults (total N = 346) that completed four retro-cue tasks. Retro-cues increased the quality of the evidence entering the decision process, reduced the time taken for memory retrieval, and changed response conservativeness for younger and older adults. An age-related decline was observed only in the retro-cue boost for evidence quality, and this was the only parameter capturing individual differences in focusing efficiency. Our results suggest that people differ in how well they can strengthen and protect a focused representation to boost evidence-quality accumulation, and this ability declines with aging.

show abstract

Select, then decide: Further evidence for separable selection and decision processes in short-term visual recognition

Cited by 5 publications

References 53 publications

A Diffusion Model Analysis of Age and Individual Differences in the Retro-Cue Benefit

A Diffusion Model Analysis of Age and Individual Differences in the Retro-Cue Benefit

Uncovering the Cognitive Mechanisms Underlying the Gaze Cueing Effect

A diffusion model analysis of age and individual differences in the retro-cue benefit

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