Xiangqian Li · 2018
It has been suggested that task-switching costs can be eliminated if participants memorise all stimulus-response mappings thereby avoiding task-switching altogether (Dreisbach, Goschke & Haider, 2006, 2007; Dreisbach & Haider, 2008). This has been labelled the “Look-Up Table” (LUT) approach. It has also been suggested that the LUT approach could potentially explain why animals such as monkeys (Stoet & Snyder, 2003; Avdagic et al., 2013) and pigeons (Castro & Wasserman, 2016; Meier, Lea & McLaren 2016) were able to perform task-switching without showing any task-switching costs (Dreisbach, et al., 2006, 2007; Dreisbach & Haider, 2008; Forrest, Monsell & Mclaren, 2014). In a series of eight experiments the following two questions were addressed: (1) Why do some participants show significant task-switching costs even when they do not switch between tasks (e.g., Forrest, Monsell & Mclaren, 2014)? (2) Can the LUT approach explain the absence of task-switching costs? In an attempt to answer both questions different sources of human task-switching costs are investigated in eight behavioural experiments.
Chapter 1 provides an overview of different task-switching paradigms and accounts to explain task-switching costs. Chapter 2 summarises previous attempts to remove human task-switching costs. Evidence for the absence of task-switching costs in animals is also introduced. Following up on previous studies that suggested the LUT approach can explain the absence of task-switching costs, I conducted two task-switching experiments using visual tasks (i.e., colour task and shape task) with bivalent stimuli in an attempt to re-examine the conclusions of previous LUT studies (i.e., Dreisbach, et al., 2006, 2007; Dreisbach & Haider, 2008; Forrest, Monsell & Mclaren, 2014). The results in Chapter 2 indicate that human participants cannot always eliminate task-switching costs and do not always apply the LUT approach when the task-switching strategy is controlled.
Therefore, the experiments in Chapter 3 and 4 sought to ascertain the requirements for eliminating task-switching costs when using the LUT approach. The experiments in Chapter 3 applied visual tasks where each task had a different stimulus-set. Experiments in Chapter 4 applied two classical mathematical tasks (i.e., big/small task, odd/even task) and used Chinese numbers as stimuli. The results of the experiments in Chapters 3 and 4 suggest that human participants must be able to give the correct answer without processing task-relevant features from the stimuli in order to eliminate task-switching costs. In the experiment of Chapter 5 the cue-stimulus-response mappings from Experiments 2.1 and 2.2 were rearranged so that switching between conventional tasks and rules became impossible. The results suggest that task-relevant features can trigger interferences thereby causing “task-switching costs” even when participants do not switch between tasks.
In Chapter 7, I compare a modified interference account, introduced in Chapter 5, with the compound retrieval account (e.g., Logan & Schneider, 2010) and associative learning account (Forrest et al., 2014; Meier et al., 2016) in order to explain why human participants show task-switching costs even when they do not switch between tasks. I conclude that the modified interference account provides an alternative explanation. It has been proposed that only humans are affected by strong and long-lasting interference from previous trials during task-switching. As a consequence, this interference may explain why human participants consistently show task-switching costs whereas monkeys and pigeons show no task-switching costs.
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Nareg Berberian (5991320), Matt Ross (9942267), Sylvain Chartier (476706) · Figshare · 2021
<p>(A) Duration of evoked and spontaneous batches. (B) Raster plot of network activity. Stimulus configuration shapes local persistent activity; brown curve, average synaptic efficacy. (C) Rotating speed of left and right wheel motors as a function of time. (D) Motor trajectory of the robot during subsequent pairs of evoked and spontaneous activity. (E) Spatial location of the highest average incoming synaptic weights; blue and black dot, evoked and spontaneous activity, respectively.</p>
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Paola Fuentes-Claramonte (721580), César Ávila (428454), Aina Rodríguez-Pujadas (458446) et al. · Figshare · 2015
<p>Each run started with a fixation cross followed by a written label indicating the response rule: <i>color</i> or <i>forma</i> (<i>shape</i> in Spanish). Subsequent trials started with a switch cue (vertical dollar sign), which indicated a change in the response rule, or a repeat cue (horizontal dollar sign), which indicated repetition of the response rule. The cue was followed by a variable number (8–12) of response stimuli (blue and red circles and squares). The cue always referred to the response rule of the previous trial, independently of the instructions presented at the beginning of the run.</p>
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Weinstein, Yana, Wilford, Miko M. · Open Science Framework · 2017
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Miko M. Wilford, Yana Weinstein · OSF Preprints (OSF Preprints) · 2017
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Hichem Slama (298991), Gaétane Deliens (431901), Rémy Schmitz (431902) et al. · Figshare · 2015
<p>For each trial, a fixation cross is followed by a cue, after which three figures are presented. Participants are asked to decide which of the 2 lower figures matches the top one, depending on the cue, and is required to press the corresponding key (left key for left figure and right key for right figure).</p>
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Miko M. Wilford, Yana Weinstein · OSF Preprints (OSF Preprints) · 2018
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Yana Weinstein, Miko M. Wilford · OSF Preprints (OSF Preprints) · 2017
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Stephen Monsell · Trends in Cognitive Sciences · 2003
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Weinstein, Yana, Wilford, Miko M. · Open Science Framework · 2018
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Hélie, Sébastien, Randez, Alyssa · Open Science Framework · 2024
Past research has indicated that people prefer a base level of stimulation and that boredom from under-stimulation can influence a person to exert cognitive effort on another available option (e.g., Agrawal et al., 2022). Experiment 1 measured how many times people chose to do a menial task against sitting and doing nothing for the same amount of time. Results demonstrated that the majority of participants preferred to do the menial task regardless of the level of difficulty or whether the task was a motor-perception task, a memory task, or a required combination of motor and memory components. Experiment 2 suggested that while participants overall still indicated a task preference, if a task was offered for more time than the no-task option, people would switch to the no-task option. Experiment 3 furthers this impact of time on decision making by attempting to find a point of indifference between a task and a no-task option. This experiment will use an adaptive staircase algorithm from Lu & Dosher (2013) in an attempt to switch participant’s preferences. This Experiment seeks to address two weaknesses in the algorithm used in Experiment 2: 1) as the participants moved through the sets, the total time spent in the experiment may have influenced choices and 2) the options offered in experiment 2 could only test a pre-determined range of time options. Experiment 3 instead will adjust the time offered for both options until the participants indicate a preference for the no-task option. This will allow us to examine individual differences in how time influences cognitive effort-based decision-making. Specifically, this study will allow us to investigate the opportunity cost of boredom and time.
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Francesca Foti (289572), Stefano Sdoia (141463), Deny Menghini (289574) et al. · Figshare · 2015
<p>Schematic representation of task cues and stimuli in the verbal task-switching paradigm (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142613#sec006" target="_blank">Experiment 1</a>). (Gallina: chicken; elefante: elephant; orso: bear).</p>
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Alva Appelgren (759151), Sara L Bengtsson (759152) · Figshare · 2015
<p>A) A univalent rule trial. A rule symbol is presented followed by one of two unique response symbols. B-C) Two bivalent rule trials. Two different rule symbols are used to determine the action to a subsequent response symbol. For example, when seeing the butterfly, either the left or the right key should be pressed depending on preceding rule symbol. A-C) The feedback presented was either task feedback ‘your choice was correct’ or trait feedback ‘you are clever’ for correct responses, and 'wrong' for incorrect responses.</p>
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Munakata, Yuko, Foster, Rachel · Open Science Framework · 2023
Successful implementation of cognitive control develops during childhood (Best & Miller, 2010; Diamond, 2013) and supports selection of and improved learning for relevant information through the inhibition of irrelevant or competing information. Young children struggle to effectively engage control and thus demonstrate increased learning of information outside of the task focus (Blanco et al., 2023; Blanco & Sloutsky, 2019; Deng & Sloutsky, 2016; Frank et al., 2021; Plebanek & Sloutsky, 2017). However, in children’s real-world, noisy environments, children cannot be processing all available task-irrelevant information. What other cues may be supporting children’s learning of information? Given that children are highly sensitive to environmental regularities from infancy (Saffran et al., 1996), we posit that children may utilize co-occurrence as a cue to drive attention and learning outside of the task focus. In this study, we utilize shape-color contingency learning to investigate developmental differences in learning of co-occurring information outside of task focus under varying levels of cognitive control engagement.
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Catherine M. Arrington, Kaitlin M. Reiman, Starla M. Weaver · Oxford University Press eBooks · 2014
Abstract This chapter discusses of one of the newest variants of task switching paradigms, the voluntary task switching (VTS) paradigm. The VTS procedure has garnered considerable interest among researchers, and a wealth of empirical investigation has already accumulated. The promise is that this paradigm captures nuances of cognitive control not measurable with other task switching paradigms, such as control over task choice. The chapter provides an overview of the empirical contributions, as well as detailed discussion of the theoretical accounts of VTS performance. It also highlights open questions in this important area of research, providing fruitful avenues for the next wave of research.
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Sio, Ut Na · Open Science Framework · 2021
This project examines the effect of tasking switching on creative problem solving
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Helen M. Genova (478828), Venkateswaran Rajagopalan (455933), John DeLuca (478829) et al. · Figshare · 2015
<p>Figure 1. shows an example of the task-switching paradigm performed in Experiment 1.</p>
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Sarah Schwöbel (18981093), Dimitrije Marković (817559), Michael N. Smolka (8031596) et al. · Figshare · 2024
<p>We show that the context is a fundamental part of the model to generate task switching effects.</p> <p>(PDF)</p>
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Miko M. Wilford, Meltem Karaca, Nayantara Kurpad · OSF Preprints (OSF Preprints) · 2019
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Beatriz Regina de Oliveira Trigo, Aristides I. Ferreira, Paulo César de Almeida · PsycTESTS Dataset · 2024
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