Semantic cognition brings meaning to our world – it allows us to make sense of what we see and hear, and to produce adaptive thoughts and behaviour. Since we have a wealth of information about any given concept, our store of knowledge is not sufficient for successful semantic cognition; we also need mechanisms that can steer the information that we retrieve so it suits the context or our current goals. This talk traces the neural networks that underpin this flexibility in semantic cognition. It draws on evidence from multiple methods (neuropsychology, neuroimaging, neural stimulation) to show that two interacting heteromodal networks underpin different aspects of flexibility. Regions including anterior temporal cortex and left angular gyrus respond more strongly when semantic retrieval follows highly-related concepts or multiple convergent cues; the multivariate responses in these regions correspond to context-dependent aspects of meaning. A second network centred on left inferior frontal gyrus and left posterior middle temporal gyrus is associated with controlled semantic retrieval, responding more strongly when weak associations are required or there is more competition between concepts. This semantic control network is linked to creativity and also captures context-dependent aspects of meaning; however, this network specifically shows more similar multivariate responses across trials when association strength is weak, reflecting a common controlled retrieval state when more unusual associations are the focus. Evidence from neuropsychology, fMRI and TMS suggests that this semantic control network is distinct from multiple-demand cortex which supports executive control across domains, although challenging semantic tasks recruit both networks. The semantic control network is juxtaposed between regions of default mode network that might be sufficient for the retrieval of strong semantic relationships and multiple-demand regions in the left hemisphere, suggesting that the large-scale organisation of flexible semantic cognition can be understood in terms of cortical gradients that capture systematic functional transitions that are repeated in temporal, parietal and frontal cortex.

Language production is a form of behavior and as such involves executive control and the prefrontal function. The cognitive architecture of prefrontal executive function thus certainly plays an important role in shaping language production. In this talk, I will review the main features of the prefrontal executive function we have uncovered during the last two decades and I will discuss how these features may help understanding language production.

Executive control processes and flexible behaviors rely on the integrity of, and dynamic interactions between, large-scale functional brain networks. The right insular cortex is a critical component of a salience/midcingulo-insular network that is thought to mediate interactions between brain networks involved in externally oriented (central executive/lateral frontoparietal network) and internally oriented (default mode/medial frontoparietal network) processes. How these brain systems reconfigure with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. I will describe studies examining how brain network dynamics support flexible behaviors in typical and atypical development, presenting evidence suggesting a unique role for the dorsal anterior insular from studies of meta-analytic connectivity modeling, dynamic functional connectivity, and structural connectivity. These findings from adults, typically developing children, and children with autism suggest that structural and functional maturation of insular pathways is a critical component of the process by which human brain networks mature to support complex, flexible cognitive processes throughout the lifespan.

Polaris Koi (Philosophy) and Jake Gavenas (Neuroscience) begin the seminar by arguing that agentive control is the key requirement for free will, drawing on folk-philosophy findings to support this claim (Gavenas et al., in prep). They explore how two executive control processes that functionally involve consciousness—inhibition and top-down control of attention—connect self-control and free will.

The question of free will has been topical for millennia, especially considering its links to moral responsibility and the ownership of that responsibility. Free will, or volition, is an incredibly complex phenomenon - and cannot easily be reduced to a single empirical paradigm. Roskies (2010) proposes that there are five cognitive aspects to be considered when developing a more complete understanding of volition. These are: intention, initiation, feeling, executive control and decision-making. Decision-making will be the focus of this talk, which steps through aspects of the philosophy of free will; highlights experimental paradigms stemming from the seminal work of Benjamin Libet et al., and proposes machine learning as a promising method in progressing the empirical studies of decision-making and free will.

From a developmental standpoint, it has been argued that two major complementary factors contribute to the development of analogy comprehension: world knowledge and executive functions. Here I will provide evidence in support of the second view. Beyond paradigms that manipulate task difficulty (e.g., number and types of distractors and semantic distance between domains) we will provide eye-tracking data that describes differences in the way children and adults compare the base and target domains in analogy problems. We will follow the same approach with ageing people. This latter population provides a unique opportunity to disentangle the contribution of knowledge and executive processes in analogy making since knowledge is (more than) preserved and executive control is decreasing. Using this paradigm, I will show the extent to which world knowledge (assessed through vocabulary) compensates for decreasing executive control in older populations. Our eye-tracking data suggests that, to a certain extent, differences between younger and older adults are analogous to the differences between younger adults and children in the way they compare the base and the target domains in analogy problems.