Richard P. Cooper

Previous studies have found that increased cognitive load during a task might result in the use of ‘easier’ motor strategies that nevertheless achieve task goals. Here, we investigated the influence of cognitive load on bimanual or unimanual strategy use in preschoolers, through a combination of secondary data analysis and new empirical data. Experiment 1 investigated block‐stacking strategies under high, medium and low cognitive load tasks in 3‐year‐olds and showed that 3‐year‐olds demonstrated significantly more unimanual strategy use in the high cognitive load task. Experiment 2 investigated (i) whether this effect persisted across preschool years and (ii) whether it was modulated by differences in executive function abilities. There was no age effect in motor strategy use under high cognitive load from 3 to 5 years of age. However, individual differences in inhibitory control and working memory use were significantly associated with differences in unimanual strategy use. These results are interpreted as evidence for a cognitive/action trade‐off in which higher cognitive demands result in the adjustment of motor strategies such as use of unimanual stacking instead of bimanual coordination in preschoolers.

In order to explore the effect of normal aging on executive function, we tested 25 younger adults and 25 neurologically healthy older adults on the Wisconsin Card Sorting Test (WCST) and the Brixton Spatial Anticipation Test (BRXT), two classic tests of executive function. We found that older participants were more likely than younger participants to err on both tasks, but the additional errors of older participants tended to be related to task set maintenance and rule inference rather than perseveration. We further found that the tendency to perseverate (across all participants) on the WCST was related to the tendency to produce stimulus or response perseverations on the BRXT, rather than any tendency to perseverate on BRXT rule application. Finally, on both tasks, older participants were also slower, particularly on trials following an error, than younger participants. To explore the neurocomputational basis for the observed behaviours we then extended an existing model of schema-modulated action selection on the WCST to the BRXT. We argue on the basis of the model that the performance of older participants on both tasks reflects a slower update of schema thresholds within the basal ganglia, coupled with a decrease in sensitivity to feedback.

A key strength of connectionist modelling is its ability to simulate both intact cognition and the behavioural effects of neural damage. We survey the literature, showing that models have been damaged in a variety of ways, e.g. by removing connections, by adding noise to connection weights, by scaling weights, by removing units and by adding noise to unit activations. While these different implementations of damage have often been assumed to be behaviourally equivalent, some theorists have made aetiological claims that rest on nonequivalence. They suggest that related deficits with different aetiologies might be accounted for by different forms of damage within a single model. We present two case studies that explore the effects of different forms of damage in two influential connectionist models, each of which has been applied to explain neuropsychological deficits. Our results indicate that the effect of simulated damage can indeed be sensitive to the way in which damage is implemented, particularly when the environment comprises subsets of items that differ in their statistical properties, but such effects are sensitive to relatively subtle aspects of the model’s training environment. We argue that, as a consequence, substantial methodological care is required if aetiological claims about simulated neural damage are to be justified, and conclude more generally that implementation assumptions, including those concerning simulated damage, must be fully explored when evaluating models of neurological deficits, both to avoid over-extending the explanatory power of specific implementations and to ensure that reported results are replicable.

It is argued that event perception and routine sequential action production share a range of characteristics (e.g., similar levels of automaticity, the involvement of sequentially and hierarchically organized schemata, and the coupled operation of predictive and monitoring processes). With this in mind, and in an effort to develop a mechanistic account of event perception, we consider how an existing model of routine sequential action production might be applied in the domain of event perception. We focus the discussion on the multiple roles of prediction in the two domains, and we consider the implications of the application of the model of action production to event perception and for sequential processing more generally.

Núñez et al. (2019) argue that cognitive science has failed either “to transition to a mature inter‐disciplinary coherent field” (p. 782) or “to generate a successful [Lakatosian] research program” (p. 789). We argue that the former was never the intention of many early researchers within the field, while the latter is an inappropriate criterion by which to judge an entire discipline. However, we concur with Núñez et al. (2019) that the individual disciplinary balance within cognitive science has changed over time. Of particular concern is the fact that the use of computational methods appears to be on the wane.

Relevance Theory (RT) argues that human language comprehension processes tend to maximize “relevance,” and postulates that there is a relevance-based procedure that a hearer follows when trying to understand an utterance. Despite being highly influential, RT has been criticized for its failure to explain how speaker-related information, either the speaker’s abilities or her/his preferences, is incorporated into the hearer’s inferential, pragmatic process. An alternative proposal is that speaker-related information gains prominence due to representation of the speaker within higher level goal-directed schemata. Yet the goal-based account is still unable to explain clearly how cross-domain information, for example linguistic meaning and speaker-related knowledge, is integrated within a modular system. On the basis of RT’s cognitive requirements, together with contemporary cognitive theory, we argue that this integration is realized by utilizing working memory and that there exist conversational constraints with which the constructed utterance interpretation should be consistent. We illustrate our arguments with a computational implementation of the proposed processes within a general cognitive architecture. Abbreviations: ACT-R Adaptive Control of Thought - RationalCOGENT Cognitive Objects within a Graphical ENvironmenTCS/SS Contention Scheduling/Supervisory SystemRBCP Relevance-Based Comprehension ProcedureRT Relevance Theory

Lake et al. underrate both the promise and the limitations of contemporary deep learning techniques. The promise lies in combining those techniques with broad multisensory training as experienced by infants and children. The limitations lie in the need for such systems to possess functional subsystems that generate, monitor, and switch goals and strategies in the absence of human intervention.