No evidence for cross-paradigm transfer of abstract task knowledge in adults and school-aged children.

Cognitive control is a hallmark of human cognition. A large number of studies have focused on the plasticity of cognitive control and examined how repeated task experience leads to the improvement of cognitive control in novel task environments. However, it has been demonstrated that training-induced changes are very selective and that transfer occurs within one task paradigm but not across different task paradigms. The current study tested the possibility that cross-paradigm transfer would occur if a common cognitive control strategy is employed across different task paradigms. Specifically, we examined whether prior experience of using reactive control in one task paradigm (i.e., either the cued task-switching paradigm or the AX-CPT) makes adults (N = 137) and 9- to 10-year-olds (N = 126) respond in a reactive way in a subsequent condition of another task paradigm in which proactive control could have been engaged. Bayesian generalized mixed-effects models revealed clear evidence of an absence of cross-paradigm transfer of reactive control in both adults and school-aged children. Based on these findings, we discuss to what extent learned control could be transferred across different task contexts and the task-specificity of proactive/reactive control strategies.

When stimulus variability accelerates the learning of task knowledge in adults and school-aged children.

Experience with instances that vary in their surface features helps individuals to form abstract task knowledge, leading to transfer of that knowledge to novel contexts. The current study sought to examine the role of this variability effect in how adults and school-aged children learn to engage cognitive control. We focused on the engagement of cognitive control in advance (proactive control) and in response to conflicts (reactive control) in a cued task-switching paradigm, and conducted four preregistered online experiments with adults (Experiment 1A: N = 100, Experiment 1B: N = 105) and 9- to 10-year-olds (Experiment 2A: N = 98, Experiment 2B: N = 97). It was shown that prior task experience of engaging reactive control makes both adults and 9- to 10-year-olds respond more slowly in a subsequent similar-structured condition with different stimuli in which proactive control could have been engaged. 9- to 10-year-olds (Experiment 2B) exhibited more negative transfer of a reactive control mode when uninformative cue and pre-target stimuli, which do not convey task-relevant information, were changed in each block, compared with when they were fixed. Furthermore, adults showed suggestive evidence of the variability effect both when cue and target stimuli were varied (Experiment 1A) and when uninformative cue and pre-target stimuli were varied (Experiment 1B). The collective findings of these experiments provide important insights into the contribution of stimulus variability to the engagement of cognitive control.

Evidence for positive and negative transfer of abstract task knowledge in adults and school-aged children.

Engaging cognitive control is essential to flexibly adapt to constantly changing environments. However, relatively little is known about how prior task experience impacts on the engagement of cognitive control in novel task environments. We aimed to clarify how individuals learn and transfer the engagement of cognitive control with a focus on the hierarchical and temporal aspects of task knowledge. Highlighting two distinct cognitive control processes, the engagement of cognitive control in advance (proactive control) and in response to conflicts (reactive control), we conducted six preregistered online experiments with both adults (Experiment 1, 3, and 5: N = 71, N = 108, and N = 70) and 9- to 10-year-olds (Experiment 2, 4, 6: N = 69, N = 108, and N = 70). Using two different experimental paradigms, we demonstrated that prior task experience of engaging reactive control makes adults and 9-to 10-year-olds respond in a reactive way in a subsequent similar-structured condition with different stimuli in which proactive control could have been engaged. This indicates that individuals do learn knowledge about the temporal structure of task goal activation and, on occasion, negatively transfer this knowledge. Furthermore, individuals exhibited these negative transfer effects in a similar-structured condition with different task goals and stimuli, indicating that they learn hierarchically-structured task knowledge. The collective findings suggest a new way of understanding how hierarchical and temporal task knowledge influences the engagement of cognitive control and highlight potential mechanisms underlying the near transfer effects observed in cognitive control training.

To What Extent is the Contribution of Language to Learning via Instructions Modulated by the Expression of Autism Traits?

Language plays a fundamental role in enabling flexible, goal-directed behaviour. This study investigated whether the contribution of language to instruction encoding is modulated by the expression of autism traits, as measured by the Autism Spectrum Quotient (ASQ) questionnaire. Participants (N = 108) completed six choice reaction time tasks, with each task consisting of six stimulus-response mappings. During an instruction phase preceding each task, participants performed either a verbal, non-verbal or no distractor task. Participants made more errors in the verbal distractor task condition, but this detrimental effect did not differ significantly between the high (top 33%) and low (bottom 33%) ASQ groups. Hence, the contribution of language to instruction encoding does not appear to be modulated by the expression of autism traits.

Articulatory suppression during instruction encoding impedes performance in choice reaction time tasks.

Theories of instruction following assume that language contributes to our ability to understand and implement instructions. The two experiments reported here investigated that assumption. Participants (total N = 96) were required to learn a series of novel tasks, with each task consisting of six arbitrary stimulus-response rules. All tasks were preceded by an instruction phase (a visual depiction of the correct stimulus-response rules for each task), during which participants performed a verbal distractor task (articulatory suppression), a non-verbal distractor task (foot tapping) or no distractor task. Additionally, the duration of the instruction phase was varied so that it was either long (60 s) or short (30 s in Experiment 1, or 10 s in Experiment 2). In both experiments participants made more errors when they had performed articulatory suppression during the instruction interval, compared to the foot tapping and no distractor task conditions. Furthermore, Experiment 2 found that this detrimental effect of articulatory suppression was especially pronounced with a very short instruction duration. These findings demonstrate that language plays a crucial role in the encoding of novel task instructions, especially when instructions are encoded under time pressure.

How does language support the acquisition of novel cognitive tasks? Investigating the role of task complexity and task instructions.

Recent findings have shown that language plays an important role in the acquisition of novel cognitive tasks (van 't Wout & Jarrold, 2020). The current study sought to elucidate the factors that influence the contribution of language to novel task learning, focusing specifically on the role of task complexity (defined by the number of stimulus-response [S-R] rules per task) and the role of task instructions (by comparing trial-and-error learning to instruction-based learning). In each experiment participants were required to learn the correct response to novel sets of picture stimuli. When analyzed as a function of stimulus occurrence within a task, both experiments found that initial performance was worse under articulatory suppression (AS; verbal distractor task) than under foot tapping (i.e., nonverbal distractor task), but only if the task was more complex (consisting of 6S-R rules) and not if it was less complex (consisting of three or four S-R rules), suggesting that the acquisition of a simpler task by trial and error might not require verbal mediation. Experiment 2 furthermore found that the role of language was modulated by the manner of acquisition: For trial-and-error learning, the detrimental effect of AS increased and then decreased again as a function of stimulus occurrence. Conversely, for instruction-based learning, AS exclusively affected the first few stimulus occurrences, suggesting that participants can create a verbal representation of the task during the instruction phase. Together, these experiments demonstrate that the role of language in novel task learning is modulated by task complexity and task instructions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Prior task experience increases 5-year-old children's use of proactive control: Behavioral and pupillometric evidence.

Children engage cognitive control reactively when they encounter conflicts; however, they can also resolve conflicts proactively. Recent studies have begun to clarify the mechanisms that support the use of proactive control in children; nonetheless, sufficient knowledge has not been accumulated regarding these mechanisms. Using behavioral and pupillometric measures, we tested the novel possibility that 5-year-old children (N = 58) learn to use proactive control via the acquisition of abstract task knowledge that captures regularities of the task. Participants were assigned to either a proactive training group or a control training group. In the proactive training group, participants engaged in a training phase where using proactive control was encouraged, followed by a test phase using different stimuli where both proactive and reactive control could be used. In the control training group, participants engaged in a training phase where both cognitive control strategies could be used, followed by a similarly-structured test phase using different stimuli. We demonstrated children in the control training group responded more quickly and accurately and showed greater cue-related pupil dilation in the test phase than in the training phase. However, there were no differences in response times, accuracies, and pupil dilation between the proactive and control training groups in the training and test phases. These findings suggest that prior task experience, that goes beyond specific knowledge about the timing of task goal activation, can lead children to engage more proactive control endogenously, even if they are not directly encouraged to do so.

The role of language in novel task learning.

The ability to rapidly acquire novel cognitive skills is a hallmark of human cognition. Theories of skill acquisition assume that this process is reliant on language, but to date this assertion has not been conclusively supported by empirical evidence. In two experiments participants (total N = 68) were required to learn, by trial-and-error, the correct response to sets of five object stimuli. To investigate the contribution of language to this process, participants performed a verbal (articulatory suppression), a non-verbal (foot tapping), or no distractor task during the first or second half of each task. In both experiments, articulatory suppression resulted in increased error rates (compared to foot tapping), but only during the first (and not the second) half of each task. These results constitute the first convincing evidence for the diminishing role of language in novel task learning and are discussed in relation to theories of skill acquisition.

The contribution of stimulus frequency and recency to set-size effects.

Hick's law describes the increase in choice reaction time (RT) with the number of stimulus-response (S-R) mappings. However, in choice RT experiments, set-size is typically confounded with stimulus recency and frequency: With a smaller set-size, each stimulus occurs on average more frequently and more recently than with a larger set-size. To determine to what extent stimulus recency and frequency contribute to the set-size effect, stimulus set-size was manipulated independently of stimulus recency and frequency, by keeping recency and frequency constant for a subset of the stimuli. Although this substantially reduced the set-size effect (by approximately two-thirds for these stimuli), it did not eliminate it. Thus, the time required to retrieve an S-R mapping from memory is (at least in part) determined by the number of alternatives. In contrast, a recent task switching study (Van 't Wout et al. in Journal of Experimental Psychology: Learning, Memory & Cognition., 41, 363-376, 2015) using the same manipulation found that the time required to retrieve a task-set from memory is not influenced by the number of alternatives per se. Hence, this experiment further supports a distinction between two levels of representation in task-set control: The level of task-sets, and the level of S-R mappings.

Is it harder to switch among a larger set of tasks?

When stimuli afford multiple tasks, switching among them involves promoting one of several task-sets in play into a most-active state. This process, often conceptualized as retrieving task parameters and stimulus-response (S-R) rules into procedural working memory, is a likely source of the reaction time (RT) cost of a task-switch, especially when no time is available for task preparation before the stimulus. We report 2 task-cuing experiments that asked whether the time consumed by task-set retrieval increases with the number of task-sets in play, while unconfounding the number of tasks with their frequency and recency of use. Participants were required to switch among 3 or 5 orthogonal classifications of perceptual attributes of an object (Experiment 1) or of phonological/semantic attributes of a word (Experiment 2), with a 100 or 1,300 ms cue-stimulus interval. For 2 tasks for which recency and frequency were matched in the 3- and 5-task conditions, there was no effect of number of tasks on the switch cost. For the other tasks, there was a greater switch cost in the 5-task condition with little time for preparation, attributable to effects of frequency/recency. Thus, retrieval time for active task-sets is not influenced by the number of alternatives per se (unlike several other kinds of memory retrieval) but is influenced by recency or frequency of use.

Are the effects of response inhibition on gambling long-lasting?

A recent study has shown that short-term training in response inhibition can make people more cautious for up to two hours when making decisions. However, the longevity of such training effects is unclear. In this study we tested whether training in the stop-signal paradigm reduces risky gambling when the training and gambling task are separated by 24 hours. Two independent experiments revealed that the aftereffects of stop-signal training are negligible after 24 hours. This was supported by Bayes factors that provided strong support for the null hypothesis. These findings indicate the need to better optimise the parameters of inhibition training to achieve clinical efficacy, potentially by strengthening automatic associations between specific stimuli and stopping.

Are stimulus-response rules represented phonologically for task-set preparation and maintenance?

Accounts of task-set control generally assume that the current task's stimulus-response (S-R) rules must be elevated to a privileged state of activation. How are they represented in this state? In 3 task-cuing experiments, we tested the hypothesis that phonological working memory is used to represent S-R rules for task-set control by getting participants to switch between 2 sets of arbitrary S-R rules and manipulating the articulatory duration (Experiment 1) or phonological similarity (Experiments 2 and 3) of the names of the stimulus terms. The task cue specified which of 2 objects (Experiment 1) or consonants (Experiment 2) in a display to identify with a key press. In Experiment 3, participants switched between identifying an object/consonant and its color/visual texture. After practice, neither the duration nor the similarity of the stimulus terms had detectable effects on overall performance, task-switch cost, or its reduction with preparation. Only in the initial single-task training blocks was phonological similarity a significant handicap. Hence, beyond a very transient role, there is no evidence that (declarative) phonological working memory makes a functional contribution to representing S-R rules for task-set control, arguably because once learned, they are represented in nonlinguistic procedural working memory.