Attention to future actions: the influence of instructed S-R versus S-S mappings on attentional control

Automatic effects of instructions: a tale of two paradigms

When examining rapid instructed task learning behaviorally, one out of two paradigms is usually used, the Inducer-Diagnostic (I-D) and the NEXT paradigm. Even though both paradigms are supposed to examine the same phenomenon of Automatic Effect of Instructions (AEI), there are some meaningful differences between them, notably in the size of the AEI. In the current work, we examined, in two pre-registered studies, the potential reasons for these differences in AEI size. Study 1 examined the influence of the data-analytic approach by comparing two existing relatively large data-sets, one from each paradigm (Braem et al., in Mem Cogn 47:1582–1591, 2019; Meiran et al., in Neuropsychologia 90:180–189, 2016). Study 2 focused on the influence of instruction type (concrete, as in NEXT, and abstract, as in I-D) and choice complexity of the task in which AEI-interference is assessed. We did that while using variants of the NEXT paradigm, some with modifications that approximated it to the I-D paradigm. Results from Study 1 indicate that the data-analytic approach partially explains the differences between the paradigms in terms of AEI size. Still, the paradigms remained different with respect to individual differences and with respect to AEI size in the first step following the instructions. Results from Study 2 indicate that Instruction type and the choice complexity in the phase in which AEI is assessed do not influence AEI size, or at least not in the expected direction. Theoretical and study-design implications are discussed.

Attention for future reward

When stimuli are consistently paired with reward, attention toward these stimuli becomes biased (e.g., Abrahamse, Braem, Notebaert & Verguts, et al., Psychological Bulletin 142:693-728, 2016, https://doi.org/10.1037/bul0000047). An important premise is that participants need to repeatedly experience stimulus-reward pairings to obtain these effects (e.g., Awh, Belopolsky & Theeuwes, Trends in Cognitive Sciences 16:437-443, 2012, https://doi.org/10.1016/j.tics.2012.06.010). This idea is based on associative learning theories (e.g., Pearce & Bouton, Annual Review of Psychology 52:111-139, 2001) that suggest that exposure to stimulus-reward pairings leads to the formation of stimulus-reward associations, and a transfer of salience of the reward to the neutral stimulus. However, novel learning theories (e.g., De Houwer, Learning and Motivation 53:7-23, 2009, https://doi.org/10.1016/j.lmot.2015.11.001) suggest such effects are not necessarily the result of associative learning, but can be caused by complex knowledge and expectancies as well. In the current experiment, we first instructed participants that a correct response to one centrally presented stimulus would be followed by a high reward, whereas a correct response to another centrally presented stimulus would be paired with a low reward. Before participants executed this task, they performed a visual probe task in which these stimuli were presented as distractors. We found that attention was drawn automatically toward high-reward stimuli relative to low-reward stimuli. This implies that complex inferences and expectancies can cause automatic attentional bias, challenging associative learning models of attentional control (Abrahamse et al., 2016; Awh et al., 2012).

How Does the (Re)Presentation of Instructions Influence Their Implementation?

Instructions are so effective that they can sometimes affect performance beyond the instructed context. Such 'automatic' effects of instructions (AEI) have received much interest recently. It has been argued that AEI are restricted to relatively simple and specific S-R tasks or action plans. The present study put this idea further to the test. In a series of experiments based on the NEXT paradigm (Meiran, Pereg, Kessler, Cole, & Braver, 2015a) we investigated the specificity of AEI. In Experiment 1, we presented category-response instructions instead of S-R instructions. Nevertheless, we observed AEI for novel stimuli from the instructed category (Experiment 1a), and abstractness of the category did not modulate the size of the NEXT effect (Experiment 1b). However, Experiment 2 revealed specificity at the response level: AEI were much smaller in conditions where the instructed GO response is semantically related to, but procedurally different from the required NEXT response, compared to a condition where the NEXT and GO responses were the same. Combined, these findings indicate that AEI can occur when S(C)-R instructions are abstract at the stimulus level, arguing against previous proposals. However, AEI does seem to require specificity at the response level. We discuss implications for recent theories of instruction-based learning and AEI.

Frequency of prospective use modulates instructed task-set interference

Recent studies have demonstrated that keeping an instructed task set in working memory (WM) for prospective use can interfere with behavior on an intervening task that employs shared stimuli-the prospective task-set-interference effect. One open question is whether people have strategic control over prospective task-set interference based on their expectations of whether task instructions will have to be implemented or recalled. To answer this question, we conducted two experiments that varied the likelihood with which a set of prospective task instructions would have to be implemented or recalled. Based on the hypothesis that participants are able to strategically modulate the manner in which a prospective task set is encoded in WM, we predicted that, as the frequency of implementing task instructions increased, so too would the magnitude of the prospective task-set-interference effect. We found that task instructions held in WM caused significant task-set interference, even in mostly recall conditions, but-crucially-that this interference effect scaled positively with the likelihood of having to implement the prospective set. These data suggest that task instructions are obligatorily encoded as a procedural task set, but that the degree to which this set impinges on ongoing stimulus processing is subject to some strategic control, possibly via modulation of the associations between declarative and procedural WM contents. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The role of motor imagery in learning via instructions

Learning via instructions and learning through physical practice are complementary pathways to obtain skilled performance. Whereas an initial task representation can be formed on the basis of instructions, physically practicing novel instructions leads to a shift in processing mode from controlled processing toward more automatic processing. This shift in processing mode is supposedly caused by the formation of a pragmatic task representation, which includes task parameters needed to attain skilled task execution. In between learning via instructions and physical practice, a third type of learning can be situated, motor imagery. Two experiments are reported that studied the extent to which motor imagery can enhance the application of novel instructions. A procedure was developed in which performance improvement after motor imagery could be measured for behavioral markers of processes underlying response selection (i.e., initiation time of a response sequence) and for behavioral markers of processes underlying movement execution (i.e., completion time of the response sequence). Our results suggest that whereas physical practice improves response selection and movement execution, motor imagery only improves response selection. We propose that motor imagery also leads to a shift in processing mode and to the formation of a pragmatic task representation, albeit a less detailed one as compared to the representation that is formed on the basis of physical practice.

Instructed fear stimuli bias visual attention

We investigated whether stimuli merely instructed to be fear-relevant can bias visual attention, even when the fear relation was never experienced before. Participants performed a dot-probe task with pictures of naturally fear-relevant (snake or spider) or -irrelevant (bird or butterfly) stimuli. Instructions indicated that two pictures (one naturally fear-relevant and one fear-irrelevant) could be followed by an electrical stimulation (i.e., instructed fear). In reality, no stimulation was administered. During the task, two pictures were presented on each side of the screen, after which participants had to determine as fast as possible on which side a black dot appeared. After a first phase, fear was reinstated by instructing participants that the device was not connected but now was (reinstatement phase). Participants were faster when the dot appeared on a location where an instructed fear picture was presented. This effect seemed independent of whether picture content was naturally fear-relevant, but was only found in the first half of each phase, suggesting rapid extinction due to the absence of stimulation, and rapid re-evaluation after reinstatement. A second experiment similarly showed that instructed fear biases attention, even when participants were explicitly instructed that no stimulation would be given during the dot-probe task. Together, these findings demonstrate that attention can be biased towards instructed fear stimuli, even when these fear relations were never experienced. Future studies should test whether this is specific to fear, or can be observed for all instructions that change the relevance of a given stimulus.

Powerful Instructions: Automaticity Without Practice

Automaticity is widely assumed to reflect hardwired tendencies or the outcome of prior practice. Recent research on automatic effects of instruction (AEIs), however, indicates that newly instructed tasks can become immediately automatic without ever having been practiced. This research shows that the representations underlying AEIs need not always be directly linked to an overt response but must be highly accessible for future use and involve bidirectional links between stimuli and responses. AEIs were also found to decrease with increasing intellectual abilities among young adults and from childhood to young adulthood, possibly because of improved abstract cognitive control. We argue that AEIs are based on the unintentional retrieval of episodic memories that encode instructions.

The task novelty paradox: Flexible control of inflexible neural pathways during rapid instructed task learning

Rapid instructed task learning (RITL) is one of the most remarkable human abilities, when considered from both computational and evolutionary perspectives. A key feature of RITL is that it enables new goals to be immediately pursued (and shared) following formation of task representations. Although RITL is a form of cognitive control that engenders immense flexibility, it also seems to produce inflexible activation of action plans in inappropriate contexts. We argue that this "prepared reflex" effect arises because RITL is implemented in the brain via a "flexible hub" mechanism, in which top-down influences from the frontoparietal control network reroute pathways among procedure-implementing brain areas (e.g., perceptual and motor areas). Specifically, we suggest that RITL-based proactive control - the preparatory biasing of task-relevant functional network routes - results in inflexible associative processing, demanding compensation in the form of increased reactive (in-the-moment) control. Thus, RITL produces a computational trade-off, in which the top-down influences of flexible hubs increase overall cognitive flexibility, but at the cost of temporally localized inflexibility (the prepared reflex effect).