Learning from others is good, with others is better: the role of social interaction in human acquisition of new knowledge.

Learning in humans is highly embedded in social interaction: since the very early stages of our lives, we form memories and acquire knowledge about the world from and with others. Yet, within cognitive science and neuroscience, human learning is mainly studied in isolation. The focus of past research in learning has been either exclusively on the learner or (less often) on the teacher, with the primary aim of determining developmental trajectories and/or effective teaching techniques. In fact, social interaction has rarely been explicitly taken as a variable of interest, despite being the medium through which learning occurs, especially in development, but also in adulthood. Here, we review behavioural and neuroimaging research on social human learning, specifically focusing on cognitive models of how we acquire semantic knowledge from and with others, and include both developmental as well as adult work. We then identify potential cognitive mechanisms that support social learning, and their neural correlates. The aim is to outline key new directions for experiments investigating how knowledge is acquired in its ecological niche, i.e. socially, within the framework of the two-person neuroscience approach. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Social interaction is a catalyst for adult human learning in online contexts.

Human learning is highly social.1-3 Advances in technology have increasingly moved learning online, and the recent coronavirus disease 2019 (COVID-19) pandemic has accelerated this trend. Online learning can vary in terms of how "socially" the material is presented (e.g., live or recorded), but there are limited data on which is most effective, with the majority of studies conducted on children4-8 and inconclusive results on adults.9,10 Here, we examine how young adults (aged 18-35) learn information about unknown objects, systematically varying the social contingency (live versus recorded lecture) and social richness (viewing the teacher's face, hands, or slides) of the learning episodes. Recall was tested immediately and after 1 week. Experiment 1 (n = 24) showed better learning for live presentation and a full view of the teacher (hands and face). Experiment 2 (n = 27; pre-registered) replicated the live-presentation advantage. Both experiments showed an interaction between social contingency and social richness: the presence of social cues affected learning differently depending on whether teaching was interactive or not. Live social interaction with a full view of the teacher's face provided the optimal setting for learning new factual information. However, during observational learning, social cues may be more cognitively demanding11 and/or distracting,12-14 resulting in less learning from rich social information if there is no interactivity. We suggest that being part of a genuine social interaction catalyzes learning, possibly via mechanisms of joint attention,15 common ground,16 or (inter-)active discussion, and as such, interactive learning benefits from rich social settings.17,18.

Dreaming with hippocampal damage.

The hippocampus is linked with both sleep and memory, but there is debate about whether a salient aspect of sleep - dreaming - requires its input. To address this question, we investigated if human patients with focal bilateral hippocampal damage and amnesia engaged in dreaming. We employed a provoked awakening protocol where participants were woken up at various points throughout the night, including during non-rapid eye movement and rapid eye movement sleep, to report their thoughts in that moment. Despite being roused a similar number of times, dream frequency was reduced in the patients compared to control participants, and the few dreams they reported were less episodic-like in nature and lacked content. These results suggest that hippocampal integrity may be necessary for typical dreaming to occur, and aligns dreaming with other hippocampal-dependent processes such as episodic memory that are central to supporting our mental life.

Dreaming has intrigued humans for thousands of years, but why we dream still remains somewhat of a mystery. Although dreams are not a precise replay of our memories, one idea is that dreaming helps people process past experiences as they sleep. If this is true, then part of the brain called the hippocampus that is important for memory should also be necessary for dreaming. Damage to the hippocampus can cause a condition called amnesia that prevents people from forming new memories and remembering past experiences. However, studies examining dreaming in people with amnesia have produced mixed results: some found that damage to the hippocampus had no effect on dreams, while others found it caused people to have repetitive dreams that lacked detail. One reason for these inconsistencies is that some studies asked participants about their dreams the next morning by which time most people, particularly those with amnesia, have forgotten if they dreamed. To overcome this limitation, Spanò et al. asked participants about their dreams immediately after being woken up at various points during the night. The experiment was carried out with four people who had damage to both the left and right hippocampus and ten healthy volunteers. Spanò et al. found that the people with hippocampal damage reported fewer dreams and the dreams they had were much less detailed. These findings suggest that a healthy hippocampus is necessary for both memory and dreaming, reinforcing the link between the two. Hippocampal damage is associated with a number of diseases, including dementia. If these diseases cause patients to dream less, this may worsen the memory difficulties associated with these conditions.

Cognitive functions underlying prospective memory deficits: A study on traumatic brain injury.

This study investigates prospective memory (PM) deficits as well as the interplay between performance in executive functions (EFs), speed of processing, episodic memory and PM in traumatic brain injury (TBI), differentiating between time based and event based tasks. The Memory for Intentions Screening Test was administrated to a sample of 19 participants with TBI and 50 healthy controls. Tasks probing different EFs (i.e., shifting, updating/working memory, inhibition, and access to long term memory), speed of processing, and episodic memory were also administrated to the TBI group. PM deficits were found in participants with TBI compared to controls. In the role of EFs in PM, only tasks probing updating/working memory and access to the long-term memory showed to play a specific role in PM performance in TBI. However, while updating/working memory was related to both time and event based PM, access to the long term memory was associated to performance on time based PM task only. Speed of processing and retrospective memory abilities do not seem to play a specific role in PM deficit in TBI. Our results provide a better understanding of the PM deficit in TBI, which may contribute to improve existing rehabilitation programs for individuals with TBI.

Intra-Individual Variability Across Fluid Cognition Can Reveal Qualitatively Different Cognitive Styles of the Aging Brain.

Dispersion is a measure of intra-individual variability reflecting how much performance across distinct cognitive functions varies within an individual. In cognitive aging studies, results are inconsistent: some studies report an increase in dispersion with increasing age and decline in performance, while others report an increasingly homogenous cognitive profile in older adults. We propose that inconsistencies may reflect qualitative differences in the cognitive functioning of the aging brain: age-groups may differ in how efficiently they engage resources, depending on both executive processing and resources available. This in turn would result in either greater or less dispersion. 21 young (mean 25.14 years, SD ± 2.85), 21 middle-old (65.05 ± 4.19), and 20 old-old (80.65 ± 4.38) healthy adults completed a series of neuropsychological tasks engaging executive processing, including switching, planning, updating, working memory and short-term memory. Individual dispersion profiles were obtained using a regression method which computes individual standard deviation across tasks from standardized test scores. Results revealed associations between performance, dispersion and cognitive reserve (measured as education level). Although differences across groups did not approach significance, there was a general pattern consistent with existing literature showing greater dispersion in the old-old group, and this was negatively associated with performance. In contrast, the middle-old group showed young-equivalent dispersion index, while performance was similar to the young group on some tasks and to the old-old group on others, possibly reflecting differences in cognitive demand. Educational level positively correlated with performance in the middle-old group only. Overall, a distinct pattern emerged for the middle-old adults: they showed young-equivalent performance on a number of measures and similar dispersion index, while uniquely benefitting from cognitive reserve. This may possibly reflect engagement in compensatory mechanisms. This study contributes to clarifying inconsistencies in previous studies and calls for more thoughtful selection of sample cohorts in aging research. The study of dispersion may provide a behavioral index of age-related changes in how cognition functions and recruits resources. Future work could examine whether this also reflects age-related changes in neural recruitment and aim at identifying factors contributing to cognitive reserve, in order to prolong good performance and improve cognition in aging.

Language processing and executive functions in early treated adults with phenylketonuria (PKU).

We provide an in-depth analysis of language functions in early-treated adults with phenylketonuria (AwPKUs, N = 15-33), as compared to age- and education-matched controls (N = 24-32; N varying across tasks), through: a. narrative production (the Cinderella story), b. language pragmatics comprehension (humour, metaphors, inferred meaning), c. prosody discrimination d. lexical inhibitory control and planning (Blocked Cyclic Naming; Hayling Sentence Completion Test, Burgess & Shallice, 1997). AwPKUs exhibited intact basic language processing (lexical retrieval, phonology/articulation, sentence construction). Instead, deficits emerged in planning and reasoning abilities. Compared to controls, AwPKUs were: less informative in narrative production (lower rate of Correct Information Units); slower in metaphorical understanding and inferred meaning; less accurate in focused lexical-search (Hayling test). These results suggest that i) executive deficits in PKU cannot be explained by an accumulation of lower-order deficits and/or general speed impairments, ii) executive functions engage dedicated neurophysiological resources, rather than simply being an emergent property of lower-level systems.

Speed of processing and executive functions in adults with phenylketonuria: Quick in finding the word, but not the ladybird.

A reduction in processing speed is widely reported in phenylketonuria (PKU), possibly due to white matter pathology. We investigated possible deficits and their relationships with executive functions in a sample of 37 early-treated adults with PKU (AwPKUs). AwPKUs were not characterized by a generalized speed deficit, but instead their performance could be explained by two more specific impairments: (a) a deficit in the allocation of visuo-spatial attention that reduced speed in visual search tasks, in some reading conditions and visuo-motor coordination tasks; and (b) a more conservative decision mechanism that slowed down returning an answer across domains. These results suggest that the impairments in executive functions seen in AwPKUs are not the consequence of a generalized speed deficit. They also suggest that processing speed is linked to the efficiency of a particular cognitive component and cannot be considered a general function spanning domains. Similarities with patterns in ageing are discussed.