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Our environment is surrounded by appetizing food stimuli that contribute to an increase in health problems such as obesity and overweight. Understanding the cognitive factors underlying the processing of food stimuli can play an important role in health interventions. Recent studies showed that high-calorie food stimuli impair working memory (WM) task performance, and some individuals, such as restrained eaters, are more susceptible to this WM performance decrement. The present study investigated the effect of low and high WM load on the processing of food stimuli in restrained and unrestrained eaters. Using an n-back task, identical food (low and high calorie) and non-food (object) stimuli were presented in colored (Experiment 1A) or in grayscale (Experiment 1B) versions. Performance was assessed by reaction time (RT), d-prime, and response bias C variables. Results revealed differences in the different WM load conditions. While no effects were observed in the low load, higher WM load impaired task performance. Processing the food stimuli, compared to objects, led to longer RTs and decreased task performance, indicated by d prime and response bias, only when the stimuli were presented in color but not in grayscale. Though no difference was observed in restrained and unrestrained eaters, the role of WM load on the visual processing of the food stimuli remains to be further examined. [ABSTRACT FROM AUTHOR]

In visual search tasks, repeating features or the position of the target results in faster response times. Such inter-trial 'priming' effects occur not just for repetitions from the immediately preceding trial but also from trials further back. A paradigm known to produce particularly long-lasting inter-trial effects–of the target-defining feature, target position, and response (feature)–is the 'priming of pop-out' (PoP) paradigm, which typically uses sparse search displays and random swapping across trials of target- and distractor-defining features. However, the mechanisms underlying these inter-trial effects are still not well understood. To address this, we applied a modeling framework combining an evidence accumulation (EA) model with different computational updating rules of the model parameters (i.e., the drift rate and starting point of EA) for different aspects of stimulus history, to data from a (previously published) PoP study that had revealed significant inter-trial effects from several trials back for repetitions of the target color, the target position, and (response-critical) target feature. By performing a systematic model comparison, we aimed to determine which EA model parameter and which updating rule for that parameter best accounts for each inter-trial effect and the associated n-back temporal profile. We found that, in general, our modeling framework could accurately predict the n-back temporal profiles. Further, target color- and position-based inter-trial effects were best understood as arising from redistribution of a limited-capacity weight resource which determines the EA rate. In contrast, response-based inter-trial effects were best explained by a bias of the starting point towards the response associated with a previous target; this bias appeared largely tied to the position of the target. These findings elucidate how our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance. Author summary: In many perceptual tasks, performance is faster and more accurate when critical stimulus attributes are repeated from trial to trial compared to when they change. Priming of pop-out (PoP), visual search with sparse search displays and random swapping of the target feature between trials, is a paradigm in which such inter-trial effects can be traced back over several recent trial episodes. While many studies have explored PoP paradigms, the mechanisms underlying priming of the search-critical target feature, the target position, and the response-critical information are not yet fully understood. Here, we addressed this question by applying evidence accumulation (EA) decision models to the data from a previously published PoP study. The modeling framework combines evidence accumulation with Bayesian updating of the model parameters. Comparison of (> 1000) different combinations of decision models and updating rules revealed that the featural and positional priming effects were best explained by assuming that attentional "weight" resources are dynamically redistributed based on the recent history of target color and position, whereas response decisions are biased based on the recent history of the response-critical property of targets occuring at a particular (and nearby) position(s). These findings confirm that our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance. [ABSTRACT FROM AUTHOR]