On the assimilation of instructions : stimulus-response associations are implemented but not stimulus-task associations

The assimilation of instructions consists of two stages. First, a task model is formed on the basis of instructions. Second, this model is implemented, resulting in highly accessible representations, which enable reflexive behavior that guides the application of instructions. Research frequently demonstrated that instructions can lead to automatic response activation, which indicates that stimulus-response associations can be implemented on the basis of a task model. However, instructions not only indicate how to respond (stimulus-response mappings) but also when (i.e., the conditions under which mappings apply). Accordingly, we tested whether instruction implementation leads both to the activation of stimulus-response associations and of associations between stimuli and the context or task in which the instructed stimulus-response mappings are relevant (i.e., stimulus-task associations). In four experiments, we measured if implementing newly instructed stimulus-response mappings also leads to bivalence costs (i.e., shorter latencies when a stimulus can only occur in one task compared to when it can occur in two tasks), which indicate the presence of stimulus-task associations. We consistently observed automatic response activation on the basis of instructions, but no bivalence costs. A discrepancy thus exists between information conveyed in an instructed task model and the elements of that task model that are implemented. We propose that future research on automatic effects of instructions should broaden its scope and focus both on the formation of an instructed task model and its subsequent implementation

Evidence for capacity sharing when stopping

ArticleCopyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.Research on multitasking indicates that central processing capacity is limited, resulting in a performance decrement when central processes overlap in time. A notable exception seems to be stopping responses. The main theoretical and computational accounts of stop performance assume that going and stopping do not share processing capacity. This independence assumption has been supported by many behavioral studies and by studies modeling the processes underlying going and stopping. However, almost all previous investigations of capacity sharing between stopping and going have manipulated the difficulty of the go task while keeping the stop task simple. In the present study, we held the difficulty of the go task constant and manipulated the difficulty of the stop task. We report the results of four experiments in which subjects performed a selective stop-change task, which required them to stop and change a go response if a valid signal occurred, but to execute the go response if invalid signals occurred. In the consistent-mapping condition, the valid signal stayed the same throughout the whole experiment; in the varied-mapping condition, the valid signal changed regularly, so the demands on the rule-based system remained high. We found strong dependence between stopping and going, especially in the varied-mapping condition. We propose that in selective stop tasks, the decision to stop or not will share processing capacity with the go task. This idea can account for performance differences between groups, subjects, and conditions. We discuss implications for the wider stop-signal and dual-task literature.ESRCUniversity of ExeterERCNational Eye Institut

Association, inhibition, and action

This chapter reviews some of the basic properties of conditioned inhibition as studied in animals, and considers the extent to which these phenomena also apply to humans. The response-interference account of negative priming is akin to the interference account of conditioned inhibition that assumes US-US interference. The chapter focuses then switches to top-down cognitive and motor inhibition and an evaluation of the extent to which it can be associatively mediated. The idea that responses or motor actions can be inhibited in a top-down fashion receives the strongest support from paradigms such as the go/no-go paradigm and the stop-signal paradigm. The chapter reviews the evidence for this phenomenon and again seeks to establish some of its basic characteristics. It ends by taking an overtly computational perspective on both sets of phenomena as one can look for similarities and differences between them

Does learning influence the detection of signals in a response-inhibition task?

Learning can modulate various forms of action control, including response inhibition. People may learn associations between specific stimuli and the acts of going or stopping, influencing task performance. The present study tested whether people also learn associations between specific stimuli and features of the stop or no-go signal used in the task. Across two experiments, participants performed a response-inhibition task in which the contingencies between specific stimuli and the spatial locations of the ‘go’ and ‘withhold’ signals were manipulated. The contingencies between specific stimuli and either going or withholding were also manipulated, such that a subset of stimuli were associated with responding and another subset with withholding a response. Although there was clear evidence that participants learned to associate specific stimuli with the acts of going or withholding, there was no evidence that participants acquired the spatial signal-location associations. The absence of signal learning was supported by Bayesian analyses. These findings challenge our previous proposals that learning always influences signal-detection processes in response-inhibition tasks where features of the signal remain the same throughout the task

Automaticity of cognitive control: goal priming in response-inhibition paradigms.

This is a postprint of an article published in Journal of Experimental Psychology: Learning, Memory, and Cognition © 2009 copyright American Psychological Association. 'This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.' The Journal of Experimental Psychology: Learning, Memory, and Cognition is available online at: http://www.apa.org/pubs/journals/xlm/index.aspxResponse inhibition is a hallmark of cognitive control. An executive system inhibits responses by activating a stop goal when a stop signal is presented. The authors asked whether the stop goal could be primed by task-irrelevant information in stop-signal and go/no-go paradigms. In Experiment 1, the task-irrelevant primes GO, ###, or STOP were presented in the go stimulus. Go performance was slower for STOP than for ### or GO. This suggests that the stop goal was primed by task-irrelevant information. In Experiment 2, STOP primed the stop goal only in conditions in which the goal was relevant to the task context. In Experiment 3, GO, ###, or STOP were presented as stop signals. Stop performance was slower for GO than for ### or STOP. These findings suggest that task goals can be primed and that response inhibition and executive control can be influenced by automatic processing

Proactive adjustments of response strategies in the stop-signal paradigm.

This is a postprint of an article published in Journal of Experimental Psychology: Human Perception and Performance © 2009 copyright American Psychological Association. 'This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.' Journal of Experimental Psychology: Human Perception and Performance is available online at: http://www.apa.org/pubs/journals/xhp/index.aspxIn the stop-signal paradigm, fast responses are harder to inhibit than slow responses, so subjects must balance speed in the go task with successful stopping in the stop task. In theory, subjects achieve this balance by adjusting response thresholds for the go task, making proactive adjustments in response to instructions that indicate that relevant stop signals are likely to occur. The 5 experiments reported here tested this theoretical claim, presenting cues that indicated whether or not stop signals were relevant for the next few trials. Subjects made proactive response-strategy adjustments in each experiment: Diffusion-model fits showed that response threshold increased when participants expected stop signals to occur, slowing go responses and increasing accuracy. Furthermore, the results show that subjects can make proactive response-strategy adjustments on a trial-by-trial basis, suggesting a flexible cognitive system that can proactively adjust itself in changing environments