A chain-retrieval model for voluntary task switching

Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, B-9000 Gent, Belgium.
Cognitive Psychology (Impact Factor: 5.06). 05/2012; 65(2):241-83. DOI: 10.1016/j.cogpsych.2012.04.003
Source: PubMed

ABSTRACT To account for the findings obtained in voluntary task switching, this article describes and tests the chain-retrieval model. This model postulates that voluntary task selection involves retrieval of task information from long-term memory, which is then used to guide task selection and task execution. The model assumes that the retrieved information consists of acquired sequences (or chains) of tasks, that selection may be biased towards chains containing more task repetitions and that bottom-up triggered repetitions may overrule the intended task. To test this model, four experiments are reported. In Studies 1 and 2, sequences of task choices and the corresponding transition sequences (task repetitions or switches) were analyzed with the help of dependency statistics. The free parameters of the chain-retrieval model were estimated on the observed task sequences and these estimates were used to predict autocorrelations of tasks and transitions. In Studies 3 and 4, sequences of hand choices and their transitions were analyzed similarly. In all studies, the chain-retrieval model yielded better fits and predictions than statistical models of event choice. In applications to voluntary task switching (Studies 1 and 2), all three parameters of the model were needed to account for the data. When no task switching was required (Studies 3 and 4), the chain-retrieval model could account for the data with one or two parameters clamped to a neutral value. Implications for our understanding of voluntary task selection and broader theoretical implications are discussed.

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Available from: André Vandierendonck, Sep 29, 2015
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    • "An important part of research in recent years has indeed been concerned with aspects of self-control, volition, and the like. As shown by some studies on task switching, the WM system may provide an excellent substrate for self-control in the form of the phonological loop (Goschke, 2000; Baddeley et al., 2001; Emerson and Miyake, 2003; Miyake et al., 2004; Bryck and Mayr, 2005; Demanet et al., 2010; Vandierendonck et al., 2012). That there are links between executive control and self-control is beyond doubt, but should be the subject of a separate study. "
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    ABSTRACT: The notion of working memory (WM) was introduced to account for the usage of short-term memory resources by other cognitive tasks such as reasoning, mental arithmetic, language comprehension, and many others. This collaboration between memory and other cognitive tasks can only be achieved by a dedicated WM system that controls task coordination. To that end, WM models include executive control. Nevertheless, other attention control systems may be involved in coordination of memory and cognitive tasks calling on memory resources. The present paper briefly reviews the evidence concerning the role of selective attention in WM activities. A model is proposed in which selective attention control is directly linked to the executive control part of the WM system. The model assumes that apart from storage of declarative information, the system also includes an executive WM module that represents the current task set. Control processes are automatically triggered when particular conditions in these modules are met. As each task set represents the parameter settings and the actions needed to achieve the task goal, it will depend on the specific settings and actions whether selective attention control will have to be shared among the active tasks. Only when such sharing is required, task performance will be affected by the capacity limits of the control system involved.
    Frontiers in Human Neuroscience 08/2014; 08. DOI:10.3389/fnhum.2014.00588 · 2.99 Impact Factor
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    • "Converging evidence for such an interpretation of our results comes from voluntary task switching experiments, where subjects seem to choose sequences of actions, not individual actions in each trial (Vandierendonck et al. 2012). According to Holroyd and Yeung, the specific function of the ACC then is to select which of the available choice sequences to implement. "
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    ABSTRACT: When choosing actions, humans have to balance carefully between different task demands. On the one hand, they should perform tasks repeatedly to avoid frequent and effortful switching between different tasks. On the other hand, subjects have to retain their flexibility to adapt to changes in external task demands such as switching away from an increasingly difficult task. Here, we developed a difficulty-based choice task to investigate how subjects voluntarily select task-sets in predictably changing environments. Subjects were free to choose 1 of the 3 task-sets on a trial-by-trial basis, while the task difficulty changed dynamically over time. Subjects self-sequenced their behavior in this environment while we measured brain responses with functional magnetic resonance imaging (fMRI). Using multivariate decoding, we found that task choices were encoded in the medial prefrontal cortex (dorso-medial prefrontal cortex, dmPFC, and dorsal anterior cingulate cortex, dACC). The same regions were found to encode task difficulty, a major factor influencing choices. Importantly, the present paradigm allowed us to disentangle the neural code for task choices and task difficulty, ensuring that activation patterns in dmPFC/dACC independently encode these 2 factors. This finding provides new evidence for the importance of the dmPFC/dACC for task-selection and motivational functions in highly dynamic environments.
    Cerebral Cortex 07/2014; DOI:10.1093/cercor/bhu155 · 8.67 Impact Factor
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    • "On explicit trials, this mental model is not required, as the only current goal is to perform the instructed task. A recent model of task choice in the VTS paradigm suggests that participants generate short sequences which are maintained in working memory to guide task selection (Vandierendonck et al., 2012). Given that across a variety of tasks the LFP co-activates with the fronto-parietal control network and the other regions of the cingulo-opercular network (Gilbert et al., 2010), the LFP is in a position to represent overall task goals that can then be implemented by the more posterior control regions of the frontoparietal control network and the cinguloopercular network. "
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    ABSTRACT: While some prior work suggests that medial prefrontal cortex (MFC) regions mediate freely chosen actions, other work suggests that the lateral frontal pole (LFP) is responsible for control of abstract, internal goals. The present study uses fMRI to determine whether the voluntary selection of a task in pursuit of an overall goal relies on MFC regions or the LFP. To do so, we used a modified voluntary task switching (VTS) paradigm, in which participants choose an individual task to perform on each trial (i.e., a subgoal), under instructions to perform the tasks equally often and in a random order (i.e. the overall goal). In conjunction, we examined patterns of activation in the face of irrelevant, but task-related external stimuli that might nonetheless influence task selection. While there was some evidence that the MFC was involved in voluntary task selection, we found that the LFP and anterior insula (AI) were crucial to task selection in the pursuit of an overall goal. In addition, activation of the LFP and AI increased in the face of environmental stimuli that might serve as an interfering or conflicting external bias on voluntary task choice. These findings suggest that the LFP supports task selection according to abstract, internal goals, and leaves open the possibility that MFC may guide action selection in situations lacking in such top-down biases. As such, the current study represents a critical step towards understanding the neural underpinnings of how tasks are selected voluntarily to enable an overarching goal.
    NeuroImage 08/2013; 84. DOI:10.1016/j.neuroimage.2013.08.047 · 6.36 Impact Factor
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