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Thought-probes in the ANTI- Vea task

In our past study (Manini et al, 2021), we tested two attentional capture theories, which are based on opposite results even if using similar tasks. With the hypothesis that these differences may be caused by different types of distractor (i.e., inside vs. outside the attentional focus), we conjointly tested the perceptual load theory (Forster & Lavie, 2008b) and the attentional dwelling hypothesis (Gaspelin et al., 2016). Results were partially in line with our hypothesis that larger/reduced interference would be observed under high than low perceptual load for inside/outside distractors. However, even if we always found substantial differences in the attentional capture depending on the type of distractor used and on the perceptual load, we replicated the typical pattern predicted by the perceptual load theory only when using the original paradigm used by Foster and Lavie (2008b) (like In our last registered experiment: https://osf.io/hztn5), but not when using a different paradigm as the one in Manini et al. (2021). For this reason, we are conducting an experimental series based on the abovementioned experiment, to understand which variables are critical for observing or not the pattern predicted by the perceptual load theory, i.e., reduced distraction effect under high perceptual load for outside distractors. We conducted a second experiment (https://osf.io/sqv8k) in which we used this same paradigm, but manipulating between participants the stimuli time exposure: 100ms like in the original paradigm (Forster & Lavie, 2008b) and 200ms as the one used in Manini et al. (2021). After ruling out the possibility that stimuli time exposure was a relevant variable involved in the results, we conducted a third experiment (https://osf.io/uektd) to understand whether the way by which perceptual load is manipulated - between vs within blocks – represents an important modulating factor. This variable was presented separately between different blocks (as in the original experiments by Forster and colleagues) or within the same blocks (as in Manini et al., 2021). However, we found similar results independently of this manipulation. Because it seems that both variables alone (stimuli time exposure and perceptual load order presentation) do not explain the discrepancies between the original Forster and Lavies’ and Manini and colleagues’ study, we decided to run another experiment in which we will use both variables like in Manini et al. (2021), but manipulating the distractor time exposure. That is, while we will maintain constant the stimuli time exposure (200 ms) and the load presentation (mixed within blocks) like in our previous study, we will also manipulate between blocks the time exposure of the distractor: it will disappear after 200 ms together with the search array (similar to Manini et al., 2021), or will remain displayed until response (like many experiments of Forster and colleagues). Regarding the relevant distractor, the only relevant modifications that were made in relation to the previous experiments are changing the valid/invalid proportion.

In our previous study (https://osf.io/cwrua), we wanted to know how attention to a salient item changes based on its relevance to our task, and how it affects our performance depending on its relevance. We conducted an experiment in which we manipulated the proportion of salient distractors and target (i.e., a red letter between all white letters), in both feature(i.e., target letter among similar heterogeneous letters) and singleton (i.e., target letter among Os) search mode. This critical variable was manipulated within participants, with five different groups, to investigate the possible attentional sets triggered by the informativeness of the singleton distractor. The main goal of this manipulation was to change the salient item relevance to our task, so that for one group this color was completely irrelevant (no salient target) and in the other groups we increased the proportion of the salient target (25%, 50% or 75% of salient items), until presenting the salient item fully relevant (always the target). This manipulation allowed us to observe more quantitative effects of the salient item on attention, for example, the condition in which we observe a smaller or larger effect, or when we observe a benefit or a cost of its presence. But also, it was possible to see how the salient item interacted with the search mode, and how this interaction changed depending on its relevance. We expected to see an interaction between the relevance of the salient item and the search mode, predicting a larger impact of relevance in the feature search mode than in singleton detection mode. Because the observed results were generally as predicted, we could conclude that the effect of a salient item on attention changes depending on its relevance to the task; however, the behavioral data do not allow us to make a further qualitative difference of these changes. More specifically, in the feature search when the salient stimulus was never the target we expected faster responses when it was presented than when it was absent (Gaspar & McDonald, 2014). However, we observed no differences, perhaps due to the salient distractor not being salient enough. Therefore, in the present study, we will increase the saliency of the salient item. Furthermore, we would like to explore how our attention to the target and to the distractor changes depending on how relevant the salient item is to our task, observing specific evoked potentials (ERP), therefore, seeking qualitative differences of the interaction. We will use two main components: N2pc and Pd. The first is a negative component that is observed in the region contralateral to where the target - or selected stimuli - is presented, and for this reason it is considered to reflect the place in space where attention is oriented for target selection (Eimer, 1996). The Pd component is characterized by positive activity in regions of the cortex contralateral to where the distractor - or ignored stimuli - is presented and is considered to reflect distractor inhibition/suppression (Gaspar & McDonald, 2014). References Eimer, M. (1996). The N2pc component as an indicator of attentional selectivity. Electroencephalography and Clinical Neurophysiology, 99(3), 225–234. https://doi.org/10.1016/0013-4694(96)95711-9 Gaspar, J. M., & McDonald, J. J. (2014). Suppression of Salient Objects Prevents Distraction in Visual Search. 34(16), 5658–5666. https://doi.org/10.1523/JNEUROSCI.4161-13.2014 Gaspelin, N., & Luck, S. J. (2018). Combined Electrophysiological and Behavioral Evidence for the Suppression of Salient Distractors. Journal of Cognitive Neuroscience, 30(9), 1265–1280. https://doi.org/10.1162/jocn_a_01279

This study aims to test whether the aperiodic exponent change (post- minus pre-stimulation EEG measurement) reflects the mechanistic effects of HD-tDCS, and whether the baseline aperiodic exponent can predict HD-tDCS efficacy in mitigating the executive vigilance decrement.

In the attention field, the study of attentional capture, considered as the processing of some specific stimuli even if they are not essential for our goals achievement, has historically been a matter of interest and debate (Burnham, 2007; Folk, 2015; Luck et al., 2021; Ruz & Lupiáñez, 2002; Theeuwes, 2010). Through experimental paradigms that used salient items (like a red stimulus) as a distractor, many studies showed that the presence of these salient items tended to slow participants responses, even if these stimuli were not relevant for task achievement. This increment in participants’ responses is considered to represent the capture of the attention by the distractor. However, different studies have shown that the same salient item caused different and sometimes opposite effects on participants performance (Gaspelin & Luck, 2019). For example the effect of relevant stimuli changes depending on the experimental context. If in the experimental paradigm the target is easy to detect (it pops out) because is a singleton (the only diamond between circles), participants will implement a singleton search mode. That means that attention will be guided by any singleton, even if it is in a different dimension than the target (the only red item between green stimuli) and reaction times will decrease when this distractor is present in the search array (Bacon & Egeth, 1994). However, we won’t observe this increment in the reaction time with simply changing non-target stimuli to different shapes instead of only circles. Doing so, the context of the experiment will encourage a feature search mode, so that participants need to search for the specific feature of the target (Leber & Egeth, 2006). We can observe a variation on attention through the same salient stimuli by simply changing the context in which the stimulus is presented. These discrepancies caused an increment in the number of studies that tried to analyze how we process these salient stimuli, generating a debate in the attentional capture literature (Luck et al., 2021) about whether this effect is mainly or exclusively guided by bottom-up (Theeuwes, 1991) or top-down processes (Folk et al., 1994). Although this debate has continued to develop, creating more flexible positions and adding other explanatory variables such as suppression theory (Gaspelin & Luck, 2019) and selection history (Awh et al., 2012), there are still several aspects that remain unexplained. One aspect that we believe is important but often not taken into account in this debate, is how attention to a salient item changes based on its relevance to our task (Carlisle & Woodman, 2011). In fact, when comparing results from different studies, this variable is rarely taken into account. For example, when comparing the two opposing views of the debate, the additional singleton paradigm is usually considered as evidence of bottom-up processes guiding attention (Theeuwes, 1994), while the contingent capture as evidence of top-down processes (Folk et al., 1992). However, distractors used in these experiments differ in their relevance to the task. In fact, the additional singleton paradigm uses a salient item which is nevertheless always a distractor; while in the contingent theory the salient item is the target in 25% of trials. Based on this evidence, and on results obtained in our past article (Manini et al., 2021), and in other registered studies (https://osf.io/jxusq/?view_only=b302f6537f7440cfb6f6e3d1e2154f24), we believe that the relevance to the task of a salient item is an important variable that must be considered when studying the effect of salient items. For this reason, in the present study, we want to know how attention to a salient item changes based on its relevance to our task, and how it affects our performance depending on its relevance. To do so, we manipulated the proportion in which the salient item in the display was a distractor or a target, in both feature and singleton search mode. This critical variable was manipulated between participants, with five different groups, in order to investigate the possible attentional sets triggered by the informativeness of the singleton distractor. This manipulation allows us to observe more quantitative effects of the salient item on attention, for example in which condition we observe a smaller or larger effect, or when we observe a benefit or a cost of its presence. But observing how the salient item interacts with the search mode, and how this interaction could change depending on its relevance, could inform us about the qualitative effects of the salient item.

In this project we will investigate how the expresion of Implicit Learning (IL) is modulated by Cognitive Control. We will use different approaches to investigate this issue: a) Looking at IL sequential effects (reduction in the expression of IL after a non-sequential trials; Jiménez, Vaquero & Lupiáñez, 2009), b) Transfering to a new context involving or not involving the activation of a control set, and c) Inhibiting control brain areas by means of TMS stimulation .

It has been proposed that attention to eye-gaze may represent a unique attentional process. As such, it is predicted that the neural architecture supporting attention is different for gaze than for non-social symbols such as arrows. To test this prediction, we investigated the electrophysiological activity during a task in which participants were required to identify the direction of laterally presented eye-gaze or arrow targets. Opposite behavioural effects were observed: while arrows produced the typical effect, with faster responses when they were congruent with their position, eye-gaze targets produced a reversed effect with faster responses when they were incongruent. Event-related potentials showed common and dissociable congruency modulation: whereas eye-gaze and arrows showed similar effects on earlier ERP components (P1 and N1), they led to opposite effects in later components such as N2 and P3. This represents the first electrophysiological demonstration of both early shared and later dissociable congruency effects for eye-gaze and arrow stimuli. Both material and data of the study are fully reported.

Data (see Files section) reported in the manuscript "Disentangling the analytical biases, attentional components, and cognitive loads of the vigilance decrement" by Fernando Gabriel Luna (Universidad Nacional de Córdoba, Argentina, y Universidad de Granada, España), Pablo Barttfeld (Universidad Nacional de Córdoba, Argentina), Elisa Martín-Arévalo (Universidad de Granada, España), and Juan Lupiáñez (Universidad de Granada, España).

Introduction Previous researches have shown opposite effects of task difficulty modulation on vigilance (see e.g., Helton & Warm, 2008; Seli et al., 2018). This lack of consistency contributes to the ongoing debate concerning the theoretical explanations (i.e., resources depletion, mind-wandering, resources control, and arousal) about the vigilance decrement phenomenon (Esterman & Rothlein, 2019; Neigel et al., 2020; Thomson, Besner, & Smilek, 2015). Objectives The present study aims at testing the predictions of vigilance theories (i.e., resources depletion, mind-wandering, resources control, and arousal) on the vigilance decrement phenomenon as a function of task difficulty (easy/standard/high). Sample size In a recent study (Luna, Barttfeld, Martín-Arévalo, & Lupiáñez, n/d), an effect size of partial eta squared = .06 was observed for the between-within participants interaction (between-participant factor: task load, simple/dual/triple; within-participant factor: time-on-task, 6 blocks) with hits as dependent variable, thus showing that the decrement of hits across blocks was modulated by the cognitive load of the task. Using G*Power 3.1.9.7 (Faul, Erdfelder, Lang, & Buchner, 2007), we estimated the minimum sample size required to observe a similar effect size to the above-mentioned for a between-within interaction with task difficulty as a between-participant factor with three levels (easy/standard/high) and time-on-task as a within-participant factor with 6 levels (i.e., experimental blocks). Considering a power of 1 - Beta = .95 and an alpha error = .05, the minimum sample size estimated was of 84 participants. Anticipating the (potential) exclusion of outliers participants (which will be considered following the standard analysis of the ANTI-Vea, see Luna, Barttfeld, Martín-Arévalo, and Lupiáñez, 2020), we plan to gather data from 90 participants (30 participants by experimental group). Procedure and Design Participants will complete the online ANTI-Vea (https://www.ugr.es/~neurocog/ANTI/). The ANTI-Vea comprises three embedded subtasks (ANTI/EV/AV) in 6 experimental blocks of 80 randomized trials: 48 ANTI (suitable for measuring the independence and interactions of alertness -no tone/tone conditions-, orienting -invalid/no cue/valid conditions-, and executive control -congruent/incongruent conditions- as in the ANTI task), 16 EV (suitable for measuring the executive component of vigilance as in a signal-detection paradigm), and 16 AV (similar to a PVT task, suitable for measuring the arousal component of vigilance). Note that, importantly, cognitive control will be measured in the ANTI trials as the incongruent-congruent conditions performance. Further details of the experimental design of the online ANTI-Vea can be reviewed in Luna et al. (2020). Importantly, task difficulty will be manipulated across groups by the size of the vertical displacement of the target in EV trials: a) easy (difficulty level = 3 in the website, i.e., 14 pixels of displacement) b) standard, i.e., the typical setting of stimuli in the ANTI-Vea (difficulty level = 2 in the website, i.e., 8 pixels of displacement) c) difficult (difficulty level = 1 in the website, i.e., 5 pixels of displacement) In addition, all participants will complete: a) Prior to the task: the Intentional and Spontneous Mind-Wandering questionnaire (translation used by Cásedas, Cebollas, and Lupiáñez, 2021), the Self Assessment Manikin, and the Discrete Emotional Scale. b) After the task: the NASA-TLX method (for measuring mental workload, using the method suggested by Nygren, 1991), a thought-identification-question (for measuring task related/unrelated thoughts during the task; Goncalves et al., 2017), the Self Assessment Manikin, and the Discrete Emotional Scale. Hypotheses According to the resources depletion hypothesis, the difficulty of the task would modulate the vigilance decrement as well as the subjective workload perceived on the task: in particular, the larger the difficulty of the task, the larger the decrement and the workload perceived. Following the mind-wandering theory, reducing task difficulty could facilitate task-disengagement. Therefore, probably task-unrelated-thoughts would be more likely when completing the easy task rather than the standard/high difficulty tasks. Following Seli et al. (2018), whereas high scores of intentional mind-wandering might be linked with a large vigilance decrement when completing an easy vigilance task, high scores of spontaneous mind-wandering could be associated with a large decrement when performing a difficult vigilance task. Note that, however, in Seli et al. (2018) task difficulty was manipulated by the cognitive tasks completed (i.e., simple task or adding a working memory task). In the present study, instead, task difficulty will be manipulated at the perceptual level, by the salience of the infrequent critical signal. According to the resources control model, the vigilance decrement should be accompanied by a decrement in cognitive control across time-on-task. Probably the presence of task-unrelated-thoughts during the vigilance task would be more likely in those participants with a large cognitive control decrement across time-on-task. Importantly, given that resources control theory has not been empirically tested manipulating different experimental conditions as task load or difficulty, analysis of the modulation of task difficulty on the decrement of cognitive control will be presented as exploratory, with none a-priori hypothesis. Lastly, following the theoretical proposal that vigilance is better understood as two dissociated components (i.e., executive vigilance as a cognitive component measured in signal-detection tasks and arousal vigilance as an automatic component measured in reaction time tasks), we expect that task difficulty conditions in the embedded signal-detection task would not modulate the arousal vigilance decrement in the embedded reaction time task. Data analysis plan 1) The standard analysis of the ANTI-Vea will be conducted (Luna, Barttfeld, Martín-Arévalo, and Lupiáñez, 2021), including the group (easy/standard/high) as a between-participant factor in all analysis. Importantly, note that cognitive control will be analyzed across block by three dependent variables: RT and errors interference score (incongruent - congruent conditions), and interference as the Inversed Efficiency score. 2) Mental workload measured with the NASA-TLX method and predominant self-reported thoughts during the task measured with the though-identification-question will be compared between experimental groups. 3) Aiming at analyzing the associations between performance scores across time-on-task and self-reported measures, linear slopes of the executive vigilance decrement, arousal vigilance decrement, and cognitive control decrement will be computed for each participant and correlated with the mental workload score and though-identification-question data by each experimental group. 4) Finally, data from the Self Assessment Manikin and the Discrete Emotional Scale will be analyzed as exploratory analysis, aiming at exploring potential changes in emotional states after performing a vigilance task under different difficulty conditions. Note that these data might contribute to account some of the predictions stated by vigilance theories. References Cásedas, L., Cebollas, A., & Lupiáñez, J. (2021). To Be Attentive, Do Not React: Linking Dispositional Mindfulness to Attentional Networks and Vigilance Performance. Manuscript Submitted for Publication. Esterman, M., & Rothlein, D. (2019). Models of sustained attention. Current Opinion in Psychology, 29, 174–180. https://doi.org/10.1016/j.copsyc.2019.03.005 Gonçalves, Ó. F., Rêgo, G., Oliveira-Silva, P., Leite, J., Carvalho, S., Fregni, F., Amaro, E., & Boggio, P. S. (2017). Mind wandering and the attention network system. Acta Psychologica, 172, 49–54. https://doi.org/10.1016/j.actpsy.2016.11.008 Helton, W. S., & Warm, J. S. (2008). Signal salience and the mindlessness theory of vigilance. Acta Psychologica, 129(1), 18–25. https://doi.org/10.1016/j.actpsy.2008.04.002 Luna, F. G., Barttfeld, P., Martín-Arévalo, E., & Lupiáñez, J. (2021). The ANTI-Vea task: analyzing the executive and arousal vigilance decrements while measuring the three attentional networks. Psicológica, 42(1), 1–26. https://doi.org/10.2478/psicolj-2021-0001 Luna, F. G., Roca, J., Martín-Arévalo, E., & Lupiáñez, J. (2021). Measuring attention and vigilance in the laboratory vs. online: The split-half reliability of the ANTI-Vea. Behavior Research Methods, 53(3), 1124–1147. https://doi.org/10.3758/s13428-020-01483-4 Luna, F. G., Roca, J., Martín-Arévalo, E., & Lupiáñez, J. (n/d). Cognitive load mitigates the executive but not the arousal vigilance decrement. Manuscripted submitted for publication. Neigel, A. R., Claypoole, V. L., Smith, S. L., Waldfogle, G. E., Fraulini, N. W., Hancock, G. M., Helton, W. S., & Szalma, J. L. (2020). Engaging the human operator: a review of the theoretical support for the vigilance decrement and a discussion of practical applications. Theoretical Issues in Ergonomics Science, 21(2), 239–258. https://doi.org/10.1080/1463922X.2019.1682712 Nygren, T. E. (1991). Psychometric Properties of Subjective Workload Measurement Techniques: Implications for Their Use in the Assessment of Perceived Mental Workload. Human Factors, 33(1), 17–33. https://doi.org/10.1177/001872089103300102 Seli, P., Konishi, M., Risko, E. F., & Smilek, D. (2018). The role of task difficulty in theoretical accounts of mind wandering. Consciousness and Cognition, 65(July), 255–262. https://doi.org/10.1016/j.concog.2018.08.005 Thomson, D. R., Besner, D., & Smilek, D. (2015). A resource-control account of sustained attention: Evidence from mind-wandering and vigilance paradigms. Perspectives on Psychological Science, 10(1), 82–96. https://doi.org/10.1177/1745691614556681

On this project we aim at exploring the involvement of different regions along the right parietal lobe on different processes ranging from object detection to eventual memory. We will use Transcranial Magnetic Stimulation (TMS) to online disrupt activity in the right Temporo-parietal junction and the right superior parietal lobe to separately affect contextual updating and attentional allocation. Vertex will be used as a control region. This experiment is a follow-up from this previous experiment: https://doi.org/10.1016/j.cortex.2017.08.035

Introduction Previous research has separately analyzed the stability of: (a) the classic attentional networks components (i.e., phasic alertness, orienting, and executive control) by measuring these functions in ten repeated sessions with the ANT and the ANTI task (Ishigami & Klein, 2010), (b) the executive vigilance component by measuring vigilance in two repeated sessions with an adapted CPT task, i.e., a signal detection paradigm (Shaked et al., 2020), and (c) the arousal vigilance component by measuring vigilance in ten repeated sessions with the PVT task, i.e., a single reaction time paradigm (Basner et al., 2018). The ANTI-Vea has demonstrated to be a suitable tool for measuring simultaneously the classic attentional networks functions and vigilance components both in the typical lab conditions as well as in an online session (https://www.ugr.es/~neurocog/ANTI/). Importantly, note that the online ANTI-Vea has shown similar split-half reliability scores to those observed with the standard version of the task (Luna, Roca, Martín-Arévalo, & Lupiáñez, 2020). Objectives The main goal of the present study is to analyze the stability of the attentional and vigilance scores of the online ANTI-Vea across ten repeated sessions. Hypotheses Based on previous research, there could be a small practice effect on the orienting and cognitive control scores (Ishigami & Klein, 2010), as well as in some overall measures of the arousal vigilance subtask (Basner et al., 2018). However, it is possible that these practice effects would be more likely observed between the first and second experimental session than between the second and the tenth session. Moreover, given the extended practice of the ANTI-Vea we expect the practice effect to be milder, even between the first and second sessions. Most importantly, note that previous researches have not examined the changes on the vigilance decrement (i.e., performance across time on task) across sessions. We anticipate that, although practice might enhance some of the overall scores of the online ANTI-Vea, the vigilance decrement would be more independent of practice and therefore observed in a similar size across the sessions. Sample size We plan to gather data from 20 participants. This sample size is similar (or even higher) than those of previous studies separately investigating the stability of attentional and vigilance scores. Procedure and Design Participants will complete ten repeated sessions of the online ANTI-Vea (https://www.ugr.es/~neurocog/ANTI/). Each session will comprises 6 experimental blocks of 80 randomized trials: 48 ANTI (suitable for measuring the independence and interactions of alertness -no tone/tone conditions-, orienting -invalid/no cue/valid conditions-, and executive control -congruent/incongruent conditions- as in the ANTI task), 16 EV (suitable for measuring the executive component of vigilance as in a signal-detection paradigm), and 16 AV (similar to a PVT task, suitable for measuring the arousal component of vigilance). Full practice blocks will be completed only in the first session. Since the second session, only instructions and 40 practice trials without visual feedback will be given prior to experimental blocks. Further details of the experimental design of the online ANTI-Vea can be reviewed in Luna et al. (2020). Data analysis plan 1) The standard analysis of the ANTI-Vea will be conducted (Luna, Barttfeld, Martín-Arévalo, and Lupiáñez, 2021), including the session (10 levels) as a within-participant factor in all analysis. 2) To determine the presence of practice effect across sessions, Bayesian analyses will be conducted excluding N first sessions from which the possible practice effect could be present/absent. 3) Split-half reliability scores will be analyzed in two different ways: (a) including data from all sessions and (b) by sessions, as in Ishigami and Klein (2010). Split-half reliability scores will be computed in the same way as in Luna et al. (2020). References Basner, M., Hermosillo, E., Nasrini, J., McGuire, S., Saxena, S., Moore, T. M., Gur, R. C., & Dinges, D. F. (2018). Repeated Administration Effects on Psychomotor Vigilance Test Performance. Sleep, 41(1). https://doi.org/10.1093/sleep/zsx187 Ishigami, Y., & Klein, R. M. (2010). Repeated measurement of the components of attention using two versions of the Attention Network Test (ANT): stability, isolability, robustness, and reliability. Journal of Neuroscience Methods, 190(1), 117–128. https://doi.org/10.1016/j.jneumeth.2010.04.019 Luna, F. G., Barttfeld, P., Martín-Arévalo, E., & Lupiáñez, J. (2021). The ANTI-Vea task: analyzing the executive and arousal vigilance decrements while measuring the three attentional networks. Psicológica, 42(1), 1–26. https://doi.org/10.2478/psicolj-2021-0001 Luna, F. G., Roca, J., Martín-Arévalo, E., & Lupiáñez, J. (2021). Measuring attention and vigilance in the laboratory vs. online: The split-half reliability of the ANTI-Vea. Behavior Research Methods, 53(3), 1124–1147. https://doi.org/10.3758/s13428-020-01483-4 Shaked, D., Faulkner, L. M. D., Tolle, K., Wendell, C. R., Waldstein, S. R., & Spencer, R. J. (2020). Reliability and validity of the Conners’ Continuous Performance Test. Applied Neuropsychology: Adult, 27(5), 478–487. https://doi.org/10.1080/23279095.2019.1570199

Raw data collected with the standard and the online ANTI-Vea

Data (see Files section) reported in the manuscript "A High-Definition tDCS and EEG study on attention and vigilance: Brain stimulation mitigates the executive but not the arousal vigilance decrement" by Fernando G. Luna (Universidad Nacional de Córdoba, Argentina, and Universidad de Granada, España), Rafael Román-Caballero (Universidad de Granada, España), Pablo Barttfeld (Universidad Nacional de Córdoba, Argentina), Juan Lupiáñez (Universidad de Granada, España), and Elisa Martín-Arévalo (Universidad de Granada, España).

In our past studies (Manini et al, 2021 and https://osf.io/hztn5), we tested two attentional capture theories, which observes opposite results even if using similar tasks. With the hypothesis that these differences may be caused by different type of distractors (i.e., inside vs. outside the attentional focus), we conjointly tested the perceptual load theory (Forster & Lavie, 2008b) and the attnetional dwelling hypothesis (Gaspelin et al., 2016). Results were partially in line with our hypothesis, however, even if we found substantial differences depending on the type of distractor used, we could replicate the typical perceptual load pattern only when using the original paradigm used by this theory (like In our last registered experiment: https://osf.io/hztn5. For this reason, in the present study, we will use the same paradigm used in our last experiment, manipulating between particiapnts the stimuli time exposure: 100ms like our past study and 200ms as the one used in Manini et al. (2021).

In our past studies (Manini et al, 2021, https://osf.io/sqv8k and https://osf.io/hztn5); we tested two attentional capture theories, which observe opposite results even if using similar tasks. With the hypothesis that these differences may be caused by different type of distractors (i.e., inside vs. outside the attentional focus), we conjointly tested the perceptual load theory (Forster & Lavie, 2008b) and the attentional dwelling hypothesis (Gaspelin et al., 2016). Results were partially in line with our hypothesis. However, even if we found substantial differences in the attentional capture depending on the type of distractor used and on the perceptual load, we could replicate the typical pattern predicted by the perceptual load theory only when using the original paradigm used by this theory (like In our last registered experiment: https://osf.io/hztn5). For this reason, we are conducting an experimental series based on the abovementioned experiment, to understand which variables are involved in results discrepancy. We conducted a second experiment (https://osf.io/sqv8k) in which we used this same paradigm, but manipulating between participants the stimuli time exposure: 100ms like our past study and 200ms as the one used in Manini et al. (2021). After ruling out the possibility of the stimuli time exposure being a relevant variable involved in the results, we will conduct a third experiment in which we will manipulate the perceptual load presentation. This variable will be presented separately between different blocks (as in the original experiments by Forster and colleagues) or within the same blocks (as in Manini et al., 2021).

Influence of a rhythmic context on the foreperiod effect: A behavioral and eye-tracker approach Rafael Román-Caballero, Elisa Martín-Arévalo, and Juan Lupiáñez Mind, Brain, and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain; and Department of Experimental Psychology, University of Granada, Granada, Spain Introduction Attention fluctuates over time. In the long-term, it seems to wax and wane throughout the day concerning the sleep-wake cycle (Oken et al., 2006) and, when exercised in a sustained way, it is usual to observe a decrease in performance (i.e., vigilance decrement; Luna et al., 2018; Thomson et al., 2015). Several theories (Jones, 1976; Large & Jones, 1999; Fiebelkorn & Kastner, 2019) extend this idea and propose that attention acts as an oscillatory system that alternate, following an intrinsic period (1–4 Hz), between moments of enhanced perceptual sensitivity with instants of attentional disengagement. Moreover, fluctuations of attention can be synchronized with rhythmic environmental stimuli through entrainment (Dynamic Attending Theory, DAT; Jones, 1976). In particular, the regularity of the external rhythm is a key factor in the degree of attentional coupling. Periodic sequences of stimuli promote a tight phase coupling and allow the perceiver to anticipate the nature (e.g., modality, quality) and precise timing of a future event (Henry & Herrman, 2014). Therefore, the presence of a rhythmic context can benefit the perceptual discrimination and response preparation for stimuli occurring in phase with the rhythm (Henry & Herrman, 2014). On the other hand, the DAT proposes that the attentional oscillation is self-sustained, that is, the oscillator decays back to its intrinsic period when the external rhythm disappears. However, evidence about the lasting effect of entrainment is still scarce. A recent study (Trapp et al., 2020) suggests that the benefits of a rhythmic background can remain some minutes after it discontinues. One example of the impact of rhythmic contexts on perceptual performance can be found in Chang et al. (2019). They presented deviants tones (higher or lower in pitch) embedded in rhythmic (isochronous) or arrhythmic (non-isochronous) sequences of standard tones (500 Hz). Pitch deviations (500 ± Δ Hz) were near to the higher and lower discrimination thresholds. During the task, participants benefitted from rhythmic contexts and showed faster responses and superior pitch discrimination for target tones than in arrhythmic sequences. Moreover, electroencephalographic (EEG) signals from bilateral auditory cortices (filtered with a dipole source model) were entrained to rhythms. Delta (1–3 Hz) frequency bands showed power peaks at 2 Hz and harmonic frequencies (4 and 6 Hz) exclusively in rhythmic sequences, according to the isochronous presentation of stimuli each 500 ms. Rhythmic regularity also modulated delta phase, beta (15–25 Hz) power, and delta-beta coupling. More importantly, those EEG changes were correlated with changes in behavioral outcomes (i.e., pitch sensitivity and response speed). Rhythm is not the only type of temporal structure that can support attention (Nobre & van Ede, 2018). One of them is the passage of time itself when there is the certainty that the target will appear after an interval (i.e., when the target is presented in all trials). The probability that an event will occur given that it has not yet occurred increases over time and, therefore, the temporal expectation. As a consequence, reaction time (RT) decreases as the foreperiod (i.e., the interval between the cue and the target) becomes longer, which is usually known as the foreperiod effect (Niemi & Näätänen, 1981; Triviño et al., 2010). Previous studies have explored the interaction between rhythmic structures and the foreperiod effect. Martin et al. (2005) found clear effects of foreperiod and rhythm in a task in which the participants had to discriminate the color of circles. In half of the trials, a single white circle preceded the target (serving as a warning cue), leaving a silent interval of 1000, 1500, or 2000 ms. In the other half, that interval was filled with extra white circles forming an isochronous sequence (with an interonset interval, IOI, of 500 ms between each cue, and between the last of them and the target). Although in both conditions RTs decreased as the cue-target intervals became longer, in the rhythmic condition the reduction was more pronounced. The advantage of the isochronous context disappeared when its pace was drastically slowed down, with an IOI of 4000 ms (Martin et al., 2005; Experiment 2). However, one possibility is that the improved performance in the rhythmic condition could be driven by a higher alerting state due to the greater number of cues. In their third experiment, Martin et al. found that both isochronous sequences and complex rhythmic context lead to allocate attention to moments in time in contrast to random sequences. Similarly, Ellis and Jones (2010) observed a foreperiod effect with RTs in an auditory pitch discrimination task when a scrambled context preceded the target. That is, participants were faster responding to targets that appeared 1000 ms after than those that occurred 500 ms after (and even faster than those appearing after only 250 ms). Interestingly, the use of a complex but metrical rhythm (with a beat each 1000 ms) before the target reversed the foreperiod effect. It was interpreted as a consequence of the rhythm reducing the uncertainty in all the temporal moments, as it elicited increases in temporal expectation to 1000 ms and their subdivisions, 500 and 250 ms. Thus, the better precision to estimate short intervals than longer (250 vs. 1000 ms) would explain the increase of RTs over time. Therefore, simple and complex rhythmic contexts seem to influence the foreperiod effect, at least with response speed outcomes. Nevertheless, in the previous studies, the target always appeared in phase with the period of the entrained rhythm and just one period after the cueing sequence (e.g., 500 ms following the offset of the 500-ms isochronous sequence; Martin et al., 2005, Experiment 1). To probe the oscillatory nature of the rhythm entrainment and the extension of its effect over time, one possibility is to include targets out of phase (e.g., 250 or 750 ms in the last example) along with cue-target intervals of several periods (e.g., since 250 to 2250 ms). Another issue with the aforementioned studies is the presence of remarkably low perceptual demands (Ellis & Jones, 2010, Experiment 2; Martin et al., 2005, Experiment 1) or high (75% of accuracy; Ellis & Jones, 2010, Experiment 1). With those designs, a great percentage of participants might respectively have reached a ceiling effect or performed under the discrimination threshold. Thus, it seems relevant to adjust the demands to the perceptual skills of each participant to assess with enough sensitivity the influence of rhythm. Finally, along with behavioral measures, recent research has shown that temporal expectation can be observed through eye-tracker measures. In two visual tasks of sustained attention, Shalev and Nobre (2020) found that the discrimination and processing speed of the participants improved in the rhythmic blocks (isochronous sequence with an IOI of 3500 ms), compared to blocks where stimuli appeared after a variable interval (2500–4500 ms). In addition, rhythmic regularity reduced pupil size across the tasks, suggesting an arousal adjustment to the conditions of high temporal certainty and the achieving of a more efficient energetic state. Interestingly, pupil size also exhibited a foreperiod-like pattern in variable blocks, with transient increases of the pupil as a function of time (i.e., larger pupil size in intervals close to 4500 than 2500 ms). However, the existence of a single cue-target interval did not allow assessing that pupil-dilation index of the foreperiod effect under a rhythmic context. It has been proposed that pupil size changes can be a sensitive measure of temporal expectation as they vary according to the locus coeruleus-noradrenergic response (via parasympathetic and sympathetic pathways: pupil constriction and dilation, respectively; Wang & Munoz, 2015). A reduced uncertainty such as that in rhythmic contexts would promote reduced tonic arousal along with marked short-scale phasic increases contingent on relevant stimuli (Aston-Jones & Cohen, 2005; Shalev & Nobre, 2020). On the other hand, high uncertainty (i.e., arrhythmic contexts) favors a continuous state of high tonic arousal and slight transient increases. In another visual vigilance task, Dankner et al. (2017) found that saccades were inhibited prior to the target onset at predictable compared with less predictable moments, given by the use of fixed (2000 ms) and variable (1000, 1500, 2000, or 2500 ms) IOI, respectively. The prestimulus saccadic inhibition (PSSI) was absent in most participants with attention-deficit/hyperactivity disorder (ADHD) and it was reduced in those (both neurotypical and with ADHD) with poorer sustaining attention scores. Thus, the PSSI-predictability effect is a sensitive measure of temporal expectation that can be related to suppression mechanisms to prevent image motion on the retina. By reducing eye motion for a short interval of time prior to the target, it may prevent transient visual distortions and enhance acuity. Even more, it has been observed an increase in saccade rates 200 ms after the saccadic suppression, which correlated with PSSI: the higher the PSSI, the higher the post-stimulus saccade rate (Dankner et al., 2017). This post-stimulus rate increase was interpreted as a rebound-like effect that could have pervasive consequences if the target appeared in an unpredictable moment, a few hundred milliseconds later. The PSSI effect has not yet been investigated with non-visual paradigms, such as auditory foreperiod tasks, as in the present research project. The present study aims to investigate the modulating effect of rhythms over the foreperiod effect, as well as its extension over time, in behavioral outcomes (RTs and accuracy) and eye-tracker measures (pupil size and PPSI-predictability effect). We will use a pitch discrimination task with a sequence of standard tones before the target onset. In one block, the IOI will be fixed at 500 ms forming a rhythmic context, while in another block the IOI will be randomly varied between 250 and 750 ms (arrhythmic context). To adjust the perceptual demands in each participant, at the same time that we leave a large proportion of correct responses, a 1-up/4-down procedure will be used to estimate the 84.1% correct pitch discrimination threshold (Levitt, 1971). Moreover, we will use variable cue-target intervals up to 2250 ms and targets in phase with the rhythmic sequence (i.e., n 500 ms after the last tone of a 500-ms isochronous sequence) and targets out of phase (i.e., n 500 +/− 250). This manipulation will allow observing whether attention is devoted more to the moments predicted by the entrained rhythm than those out of phase. Hypotheses We expect a classic foreperiod effect will appear in response speed and accuracy. That is, the later the target onset, the faster the response and the better discrimination (higher accuracy) of the participants. Moreover, and crucially for this study, the presence of a rhythmic context before the target will enhance the perceptual sensitivity and the preparation to the critical moment, and will modulate the foreperiod effect. The presentation of several isochronous standard tones will lead to better performance at moments in phase with the entrained rhythm (500, 1000, 1500, and 2000 ms after the last standard tone). Moreover, we predict that although those effects will extend to the later in-phase moments (1000, 1500, and 2000 ms), it is probable that the alignment with the rhythm will decay over time. Regarding eye-tracker measures, we also expect a modulation of the rhythmic context over the foreperiod effect. Specifically, we hypothesize a decrease of the pupil size at the beginning of the sequence of standard tones along with an increase before the target onset, that will be more marked in the rhythmic block. Furthermore, whether the PSSI is a cross-modal effect, it would be possible to find a similar block modulation over the PSSI: a higher saccade rate just before (−100 ms) the appearance of in-phase targets during the variable-IOI block than in the fixed-IOI block. Finally, the impact of the rhythmic context might decay with the pass of time, being greater in the first periods than in the last ones. Method Participants We have selected the three-way interaction [block (2) × period (4) × phase (2); for details, see below] to estimate the sample size in the present experiment. In a previous pilot study with 11 participants, we found an effect size of f2 = 0.12. Using the Superpower R package (Caldwell et al., 2020), we have found that at least 43 participants will be necessary for a small-to-medium effect size of f2 = 0.09 (similar to that observed in the pilot) with an alpha of .05 and a power of .80. Accordingly, we have chosen a sample size of 45 participants due to the possibility of some missing participants as a consequence of technical issues or misunderstandings of the instructions. All the participants will be students from the University of Granada that will receive 15€ for participating. All will have a self-reported normal or corrected-to-normal vision and will be naïve as to the purpose of the experiment. The study will be conducted following the ethical guidelines laid down by the University of Granada (536/CEIH/2018), in accordance with the ethical standards of the 1964 Declaration of Helsinki (last update: Seoul, 2008). Outlier detection will be based on performance (i.e., mean reaction times and accuracy) identified as poor in terms of meeting all the following indices: standard deviation from the mean of the sample (> 2.5), studentized deleted residuals (> 3), and Cook’s Di (> 4/n). Apparatus and Stimuli The stimulus presentation, timing, and data collection will be controlled by a program written using E-Prime 2.0 (Schneider et al., 2002), run on a standard Pentium 4 PC. During the task, a light gray fixation cross will be presented on the center of a 17-inch widescreen monitor with a 1,280 × 1024-pixel resolution and 60 Hz of refresh rate. Auditory stimuli will be sine wave tones created with Audacity. Tones will last 60 ms with 10 ms rise and 10 ms fall times. Standard tones will have 500 Hz of frequency and will be presented at 70 dB. Whereas, comparison higher and lower tones will have 500 ± Δ Hz and 78 dB, where Δ represents the 84.1% correct pitch discrimination threshold estimated with a 1-up/4-down procedure (Levitt, 1971). We will use a video-based eye-tracker (EyeLink 1000 Plus, SR Research, Ontario, Canada) to measure pupil diameter monocularly as well as microsaccades and blinks at 1000 Hz. Procedure The study will be carried out after preregistration, during June of 2021. The experiment will take place in a dimly-light room and using glasses or contact lenses if needed. The session will last approximately 1 hour and 15 minutes. Prior to the experimental task, participants will complete a questionnaire (Appendix A) with items about demographic information, the fatigue and vigor scales of the Spanish abbreviated version of the Profile of Mood States questionnaire (Andrade et al., 2013; original version, Grove & Prapavessis, 1992), musical training background, and exclusion criteria: vision or hearing problems, neurological or psychiatric illness, diabetes, drug abuse, or chronic use of psychoactive medication. After that, participants will sit 64 cm from the monitor, and a chin rest will be used to keep their head still. First, the individual pitch discrimination threshold will be estimated using a 1-up/4-down procedure (84.1% correct pitch discrimination, following Levitt, 1971). In each trial, a standard tone will be presented followed by a comparison tone 500 ms after. The participants will be required to judge whether they perceive the second stimulus higher or lower in pitch than the standard (pressing “8” or “2” keys, respectively). The estimation of the discrimination threshold will comprise two blocks, one for the upper threshold and beginning with a 515-Hz tone and a lower threshold block beginning with a tone of 485 Hz of frequency (the order of the blocks will be counterbalanced). We will estimate the discrimination threshold by averaging the frequency of all the peaks and valleys in each type of block. Peaks and valleys are the frequency values at which a change in the perceptual trend occurs (for instance, when the individual perceives that the second tone has a lower pitch after a series of trials in which it was judged as higher). To reach a reliable discrimination threshold in each participant, the 1-up/4-down procedure will finish after the occurrence of ten peaks or valleys in each block. After the estimation of the discrimination threshold, participants will carry out the pitch discrimination task. The instructions will appear on the screen and will be explained to the participant by the experimenter, who will also answer any questions about the procedure. In each trial, the participants will hear five, six, or seven standard tones with an IOI of 500 ms or a random value between 250 and 750 ms, depending on the block (fixed vs. variable block; see Figure A in the OSF project https://osf.io/cw93r/). After the sequence of standard tones, a comparison tone will appear with an IOI randomly selected from 250, 500, 750, 1000, 1250, 1500, 1750, 2000, and 2250 ms. The participants will be required to discriminate as fast and accurately as possible whether the comparison tone has a higher or lower pitch than the standard tones, pressing “z” or “m” (the meaning of what indicates each key will be counterbalanced across participants). The discrimination task will begin with two practice blocks of eight trials each. Feedback for incorrect responses will be provided only during practice blocks. Then, participants will complete one block of 252 trials with fixed IOIs of 500 ms for the sequence of standard tones or with variable IOIs between 250 and 750 ms (the order will be counterbalanced across participants). As there are nine possible IOIs between the last standard tone and the target, and two pitches, there will be 14 trials for each type (with the same IOI and the same pitch). Subsequently, participants will be asked about the difficulty of the last task block (Appendix B). After 1 or 2 minutes of rest, they will carry out a second task block, with variable IOIs if the first block was with fixed IOIs or with fixed if the previous were variable. Finally, the participants will complete the question about the difficulty. The experimental task will compromise 28 trials for each experimental condition. Design and Statistical Analyses The experiment has a (2 × 4 × 2) three-factor repeated-measures design: block (fixed vs. variable), period (500–750/1000–1250/1500–1750/2000–2250 ms), and phase (in-phase: 500, 1000, 1500, and 2000 ms; vs. out-of-phase target: 750, 1250, 1750, and 2250 ms). For behavioral measures, we will conduct repeated-measures ANOVAs with mean RTs and accuracy as dependent variables. Moreover, paired t-tests will be used for comparison between conditions. We expect a higher number of misses in the cue-target interval of 250 ms, and, subsequently, a reduced proportion of trials to be analyzed. Regarding eye-tracker measures, blink intervals will be identified following Hershman et al.’s procedure (2018) and will be removed from the analyses. For analyses with trial time series, raw pupil size will be converted to z scores, by calculating the mean and standard deviation of each separate block for each participant. Then, the time series before the target onset of both blocks will be compared with permutation t-tests based on 10,000 samples. In addition, the slopes of the increase in the pupil size during foreperiod will be analyzed with a regression model, including block (fixed vs. variable) as a categorical factor. For saccade analyses, gaze-position data will be segmented into epochs from –500 to 500 ms relative to the target onset. For saccades detection, we will use a similar procedure to Dankner et al. (2017): saccades are identified by eye moments that exceed a threshold of 4 standard deviations from the median velocity during 7 ms (seven consecutive eye-position samples). The dependent variable will be the saccade rate at –100 to 0 ms relative to the onset of targets for PSSI and the saccade rate at 0 to 500 ms for the post-stimulus effect. We will exclude every trial in which a blink interval overlaps with that interval or in which a saccade larger than 3° occurs at that interval. 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In our past study (Manini et al, 2021), we tested two attentional capture theories, that even if using similar tasks, they observe opposite results. With the hypothesis that these differences may be caused by different type of distractors (i.e., inside vs. outside the attentional focus), we conjointly tested the perceptual load theory (Forster & Lavie, 2008b) and the attnetional dwelling hypothesis (Gaspelin et al., 2016). Results were partially in line with our hypothesis, however, even if we found sustancial differences depending on the type of distractor used, we could not replicate the typical perceptual load pattern. For this reason, In the present study, we will use the same paradigm used in Forster and Lavie (2016), in order to replicate their results, but adding the necessary changes in order to manipulate the distractor position (as in Manini et al. 2021).

The present study tests whether two different manipulations leading to an earlier appearance of Inhibition of Return operate by setting the system in different ways. Whereas the use of a range of very long SOAs has been proposed to set the system for the early reorienting of attention (Cheal & Chastain, 2002), introducing a distractor at the location opposite the target seems to induce a set to represent the cue and the target as separated events instead of the same event (Lupiáñez et al., 1999, 2001). The effects of these two manipulations were directly compared within the context of a spatial stroop paradigm. Although both manipulations altered the time course of cueing effects, we report here a pattern of interesting dissociations: the distractor manipulation was unique to introduce a shift towards more negative cueing effect affecting generally all levels of SOA, including the shortest level of 100 ms. Secondly, the distractor manipulation, but not the range of SOAs one, was able to prevent the expected interaction between spatial stroop effects and cueing at the short SOA, typically found in previous experiments in the absence of a distractor (Funes et al., 2003, 2005). This pattern of dissociations strongly favors the hypothesis that these two attentional sets are different in nature.