Retrospective Attention Interacts with Stimulus Strength to Shape Working Memory Performance

Abstract

Orienting attention retrospectively to selective contents in working memory (WM) influences performance. A separate line of research has shown that stimulus strength shapes perceptual representations. There is little research on how stimulus strength during encoding shapes WM performance, and how effects of retrospective orienting might vary with changes in stimulus strength. We explore these questions in three experiments using a continuous-recall WM task. In Experiment 1 we show that benefits of cueing spatial attention retrospectively during WM maintenance (retrocueing) varies according to stimulus contrast during encoding. Retrocueing effects emerge for supraliminal but not sub-threshold stimuli. However, once stimuli are supraliminal, performance is no longer influenced by stimulus contrast. In Experiments 2 and 3 we used a mixture-model approach to examine how different sources of error in WM are affected by contrast and retrocueing. For high-contrast stimuli (Experiment 2), retrocues increased the precision of successfully remembered items. For low-contrast stimuli (Experiment 3), retrocues decreased the probability of mistaking a target with distracters. These results suggest that the processes by which retrospective attentional orienting shape WM performance are dependent on the quality of WM representations, which in turn depends on stimulus strength during encoding.

Ignoring versus updating in working memory reveal differential roles of attention and feature binding

“…Binding of information can be corrupted by the presentation of subsequent stimuli ( Allen, Baddeley, & Hitch, 2014 ). In addition, a recent study reported that retrocues – cues presented after encoding that indicate which items are going to be probed – decrease misbinding ( Wildegger, Humphreys, & Nobre, 2016 ). These results suggest that attention is necessary to maintain the correct bindings between features as the presentation of distracters – which presumably consumes attentional resources – dramatically increased misbinding rates.…”

The influence of attentional biases on multiple working memory precision parameters for children and adults

“…To prevent confusion (as κ can also be referred to as ‘precision’ in the literature), we use the term precision as a measure of overall recall performance and SD (here, the converted kappa) as a measure of the representational quality of the probed target item in WM at the time it is recalled (cf. Wildegger et al., 2016 for a similar distinction); the pT parameter indicates the probability that participants recalled the target colour; the pNT parameter indicates the probability that participants erroneously recalled the colour of another array item instead of the target colour (misbinding or misreporting error); the pU indicates the probability that participants reported a colour other than that of the target and the non‐target, that is, pU indicates the presence of a uniform distribution in response across all possible colour values, suggesting random guessing. The model is described by the following mathematical equation: $$\begin{equation}p{\rm{\;\;}}\left( {\hat \theta } \right) = {\rm{\;}}\alpha {\Phi _\kappa }{\rm{\;}}\left( {\hat \theta - \theta } \right) + \beta {\rm{\;}}\frac{1}{m}\mathop \sum \limits_i^m {\Phi _\kappa }{\rm{\;}}\left( {\hat \theta - {\varphi _i}} \right) + {\rm{\;}}\gamma \frac{1}{{2\pi }}\;\end{equation}$$ θ corresponds to the target colour value, $$\hat \theta $$ corresponds to the reported colour value, Φ κ corresponds to the Von Mises distribution (the circular analogue of the Gaussian distribution, with a mean of 0 and concentration parameter κ), α corresponds to the probability of reporting the target colour value, β corresponds to the probability of reporting the non‐target colour with m corresponding to the number of non‐target items, and γ = 1 – α – β corresponding to the probability of responding randomly.…”

“…This model, introduced byBays et al (2009), assumes that the recall error may originate from different sources and provides four parameters, which we computed for our analyses 1. the Kappa (κ) parameter indicates Gaussian variability in memory for the probed target colour, that is, κ is a concentration parameter and indicates the (von Mises) distribution of responses centred around the target feature/colour; κ was converted to the more familiar SD(Bays et al, 2011b;Peich et al, 2013). To prevent confusion (as κ can also be referred to as 'precision' in the literature), we use the term precision as a measure of overall recall performance and SD (here, the converted kappa) as a measure of the representational quality of the probed target item in WM at the time it is recalled (cf Wildegger et al, 2016. for a similar distinction); 2. the pT parameter indicates the probability that participants recalled the target colour; 3. the pNT parameter indicates the probability that participants erroneously recalled the colour of another array item instead of the target colour (misbinding or misreporting error); 4. the pU indicates the probability that participants reported a colour other than that of the target and the non-target, that is, pU indicates the presence of a uniform distribution in response across all possible colour values, suggesting random guessing.…”

A dynamic neural field model of continuous input integration

“…The representational quality of WM items is not only shaped by bottom-up input features as discussed above but may be also modulated by top-down signals representing task demands (Wildegger et al 2016). A behavioral paradigm that has attracted considerable interest over the last decade is retro-cueing (for an overview see Souza and Oberauer 2016).…”

“…A more sophisticated circuit-based model should regulate also the shape and rate of localized persistent activity depending on the qualitative and quantitative characteristics of the preceding inputs. It has been shown for instance that the level of persistent activity during the delay period of a spatial WM task correlates with stimulus contrast (Constantinidis et al 2001) and can be modulated by additional spatially informative cues (Kuo et al 2012;Wildegger et al 2016). Importantly, the discharge rate of neural populations is predictive of the psychophysical task performance.…”