Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in (...) turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction. (shrink)

There is a growing interest in Evald Ilyenkov’s work and its significance for contemporary debates. This interest spans several disciplines. One key thread in Ilyenkov’s ideas concerns a perspective on the relation between biology and psychology. In rejecting crude reductionism and individualism, Ilyenkov put forward a view of mind and personhood as emerging from activity and social practice. In his rejection of brain-bound notions of the mind, Ilyenkov’s ideas bear interesting resonances with current work in 4E cognition. One particularly interesting (...) resonance that has occasionally been noticed are the connections with the enactive approach to life and mind. However, beyond some hints at interesting convergences, there is to date no detailed comparison between the two views. The present article attempts to address this gap, examining both complementarities and possible tensions between the two approaches. Rejecting cognitive views detached from environmental and social processes, the enactive approach, as exemplified by De Jaegher and Di Paolo’s concept of participatory sense-making, emphasises the dynamic constitution of cognition through embodied and situated activities. The article draws parallels between Ilyenkov’s emphasis on historically and culturally situated activity, notably labour, and the enactive understanding of human bodies as dynamically constituted in human activity. The article explores the Ilyenkovian and enactive perspectives on the dialectics of ideality, challenging traditional dualistic views and proposing that the ideal emerges as metastable patterns in the ongoing interactions between world, practices, norms, and bodies. The article concludes by suggesting future research directions for the enactive approach, particularly in areas emphasised by Ilyenkov, such as labour and the mediation of material/cultural artefacts. (shrink)

The paper adopts an inter-theoretical socio-cultural and -material perspective on the relationship between human + machine learning to propose a new way to investigate the human + machine assistive assemblages emerging in professional work (e.g. medicine, architecture, design and engineering). Its starting point is Hutchins’s (1995a) concept of ‘distributed cognition’ and his argument that his concept of ‘cultural ecosystems’ constitutes a unit of analysis to investigate collective human + machine working and learning (Hutchins, Philos Psychol 27:39–49, 2013). It argues that: (...) (i) the former offers a way to reveal the cultural constitution of and enactment of human + machine cognition and, in the process, the limitations of the computational and connectionist assumptions about learning that underpin, respectively, good old-fashioned AI and deep learning; and (2) the latter offers a way to identify, when amplified with insights from Socio-Materialism and Cultural-Historical Activity Theory, how ML is further rearranging and reorganising the distributed basis of cognition in assistive assemblages. The paper concludes by outlining a set of conjectures researchers that could use to guide their investigations into the ongoing design and deployment of HL + ML assemblages and challenges associated with the interaction between HL + ML. (shrink)