Recent research has seen many behavioral comparisons between humans and deep neural networks (DNNs) in the domain of image classification. Often, comparison studies focus on the end-result of the learning process by measuring and comparing the similarities in the representations of object categories once they have been formed. However, the process of how these representations emerge—that is, the behavioral changes and intermediate stages observed during the acquisition—is less often directly and empirically compared. In this talk, I'm going to report a detailed investigation of the learning dynamics in human observers and various classic and state-of-the-art DNNs. We develop a constrained supervised learning environment to align learning-relevant conditions such as starting point, input modality, available input data and the feedback provided. Across the whole learning process we evaluate and compare how well learned representations can be generalized to previously unseen test data. Comparisons across the entire learning process indicate that DNNs demonstrate a level of data efficiency comparable to human learners, challenging some prevailing assumptions in the field. However, our results also reveal representational differences: while DNNs' learning is characterized by a pronounced generalisation lag, humans appear to immediately acquire generalizable representations without a preliminary phase of learning training set-specific information that is only later transferred to novel data.

Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as structured, and controllable, neural subspaces (termed activity slots). By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM

Non-human great apes inform one another in ways that can seem very humanlike. Especially in the gestural domain, their behavior exhibits many similarities with human communication, meeting widely used empirical criteria for intentionality. At the same time, there remain some manifest differences. How to account for these similarities and differences in a unified way remains a major challenge. This presentation will summarise the arguments developed in a recent paper with Christophe Heintz. We make a key distinction between the expression of intentions (Ladyginian) and the expression of specifically informative intentions (Gricean), and we situate this distinction within a ‘special case of’ framework for classifying different modes of attention manipulation. The paper also argues that the attested tendencies of great ape interaction—for instance, to be dyadic rather than triadic, to be about the here-and-now rather than ‘displaced’—are products of its Ladyginian but not Gricean character. I will reinterpret video footage of great ape gesture as Ladyginian but not Gricean, and distinguish several varieties of meaning that are continuous with one another. We conclude that the evolutionary origins of linguistic meaning lie in gradual changes in not communication systems as such, but rather in social cognition, and specifically in what modes of attention manipulation are enabled by a species’ cognitive phenotype: first Ladyginian and in turn Gricean. The second of these shifts rendered humans, and only humans, ‘language ready’.

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) share similarities in pathology and clinical presentation and come under the umbrella term of Lewy body dementias (LBD). Fluctuating cognition is a key symptom in LBD and manifests as altered levels of alertness and attention, with a marked difference between best and worst performance. Cognition and alertness can change over seconds or minutes to hours and days of obtundation. Cognitive fluctuations can have significant impacts on the quality of life of people with LBD as well as potentially contribute to the exacerbation of other transient symptoms including, for example, hallucinations and psychosis as well as making it difficult to measure cognitive effect size benefits in clinical trials of LBD. However, this significant symptom in LBD is poorly understood. In my presentation I will discuss the phenomenology of cognitive fluctuations, how we can measure it clinically and limitations of these approaches. I will then outline the work of our group and others which has been focussed on unpicking the aetiological basis of cognitive fluctuations in LBD using a variety of imaging approaches (e.g. SPECT, sMRI, fMRI and EEG). I will then briefly explore future research directions.

Understanding what makes human unique is a fundamental research drive for comparative psychologists. Cognitive abilities such as theory of mind, cooperation or mental time travel have been considered uniquely human. Despite empirical evidence showing that animals other than humans are able (to some extent) of these cognitive achievements, findings are still heavily contested. In this context, being able to abstract relations of similarity has also been considered one of the hallmarks of human cognition. While previous research has shown that other animals (e.g., primates) can attend to relational similarity, less is known about what invertebrates can do. In this talk, I will present a series of spatial matching tasks that previously were used with children and great apes and that I adapted for use with wild-caught bumblebees. The findings from these studies suggest striking similarities between vertebrates and invertebrates in their abilities to attend to relational similarity.

In the first part of this talk I will present work showing that recurrent neural networks can replicate broad behavioral patterns associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide range of analysis tools frequently applied to neural data on artificial neural networks. I will present the motivation for this approach as well as the form the results could take and how this would benefit neuroscience.

Analogical reasoning –learning about new things by relating it to previous knowledge– lies at the heart of human intelligence and creativity and forms the core of educational practice. Children start creating and using analogies early on, making incredible progress moving from associative processes to successful analogical reasoning. For example, if we ask a four-year-old “Horse belongs to stable like chicken belongs to …?” they may use association and reply “egg”, whereas older children will likely give the intended relational response “chicken coop” (or other term to refer to a chicken’s home). Interestingly, despite state-of-the-art AI-language models having superhuman encyclopedic knowledge and superior memory and computational power, our pilot studies show that these large language models often make mistakes providing associative rather than relational responses to verbal analogies. For example, when we asked four- to eight-year-olds to solve the analogy “body is to feet as tree is to …?” they responded “roots” without hesitation, but large language models tend to provide more associative responses such as “leaves”. In this study we examine the similarities and differences between children's and six large language models' (Dutch/multilingual models: RobBERT, BERT-je, M-BERT, GPT-2, M-GPT, Word2Vec and Fasttext) responses to verbal analogies extracted from an online adaptive learning environment, where >14,000 7-12 year-olds from the Netherlands solved 20 or more items from a database of 900 Dutch language verbal analogies.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Individuals vary widely in how they categorize novel phenomena. This individual variation has led canonical theories in cognitive and social science to suggest that communication in large social networks leads populations to construct divergent category systems. Yet, anthropological data indicates that large, independent societies consistently arrive at similar categories across a range of topics. How is it possible for diverse populations, consisting of individuals with significant variation in how they view the world, to independently construct similar categories? Through a series of online experiments, I show how large communication networks within cultures can promote the formation of similar categories across cultures. For this investigation, I designed an online “Grouping Game” to observe how people construct categories in both small and large populations when tasked with grouping together the same novel and ambiguous images. I replicated this design for English-speaking subjects in the U.S. and Mandarin-speaking subjects in China. In both cultures, solitary individuals and small social groups produced highly divergent category systems. Yet, large social groups separately and consistently arrived at highly similar categories both within and across cultures. These findings are accurately predicted by a simple mathematical model of critical mass dynamics. Altogether, I show how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution. In particular, I discuss how participants in both cultures readily harnessed analogies when categorizing novel stimuli, and I examine the role of communication networks in promoting cross-cultural similarities in analogy-making as the key engine of category formation.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Perception of object motion is fundamentally multisensory, yet little is known about similarities and differences in the computations that give rise to our experience across senses. Insight can be provided by examining auditory motion processing in early blind individuals. In those who become blind early in life, the ‘visual’ motion area hMT+ responds to auditory motion. Meanwhile, the planum temporale, associated with auditory motion in sighted individuals, shows reduced selectivity for auditory motion, suggesting competition between cortical areas for functional role. According to the metamodal hypothesis of cross-modal plasticity developed by Pascual-Leone, the recruitment of hMT+ is driven by it being a metamodal structure containing “operators that execute a given function or computation regardless of sensory input modality”. Thus, the metamodal hypothesis predicts that the computations underlying auditory motion processing in early blind individuals should be analogous to visual motion processing in sighted individuals - relying on non-separable spatiotemporal filters. Inconsistent with the metamodal hypothesis, evidence suggests that the computational algorithms underlying auditory motion processing in early blind individuals fail to undergo a qualitative shift as a result of cross-modal plasticity. Auditory motion filters, in both blind and sighted subjects, are separable in space and time, suggesting that the recruitment of hMT+ to extract motion information from auditory input includes a significant modification of its normal computational operations.

Analogical reasoning remains foundational to the human ability to forge meaningful patterns within the sea of information that continually inundates the senses. Yet, meaningful patterns rely not only on the recognition of attributional similarities but also dissimilarities. Just as the perception of images rests on the juxtaposition of lightness and darkness, reasoning relationally requires systematic attention to both similarities and dissimilarities. With that awareness, my colleagues and I have expanded the study of relational reasoning beyond analogous reasoning and attributional similarities to highlight forms based on the nature of core dissimilarities: anomalous, antinomous, and antithetical reasoning. In this presentation, I will delineate the character of these relational reasoning forms; summarize procedures and measures used to assess them; overview key research findings; and describe how the forms of relational reasoning work together in the performance of complex problem solving. Finally, I will share critical next steps for research which has implications for instructional practice.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In this talk, I will present the results of two recent systematic reviews and meta-analyses related to analogical reasoning and metaphor processing in autism, together with the results of a study that investigated verbal analogical reasoning and metaphor processing in the same sample of participants. Both metaphors and analogies rely on exploiting similarities, and they necessitate contextual processing. Nevertheless, our findings relating to metaphor processing and analogical reasoning showed distinct patterns. Whereas analogical reasoning emerged as a relative strength in autism, metaphor processing was found to be a relative weakness. Additionally, both meta-analytic studies investigated the relations between the level of intelligence of participants included in the studies, and the effect size of group differences between the autistic and typically developing (TD) samples. These analyses suggested in the case of analogical reasoning that the relative advantage of ASD participants might only be present in the case of individuals with lower levels of intelligence. By contrast, impairments in metaphor processing appeared to be more pronounced in the case of individuals with relatively lower levels of (verbal) intelligence. In our experimental study, we administered both verbal analogies and metaphors to the same sample of high-functioning autistic participants and TD controls. The two groups were matched on age, verbal IQ, working memory and educational background. Our aim was to understand better the similarities and differences between processing analogies and metaphors, and to see whether the advantage in analogical reasoning and disadvantage in metaphor processing is universal in autism.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Previous work suggests that children’s ability to understand metaphors emerges late in development. Researchers argue that children’s initial failure to understand metaphors is due to an inability to reason about shared relational structures between concepts. However, recent work demonstrates that preschoolers, toddlers, and even infants are already capable of relational reasoning. Might preschoolers also be capable of understanding metaphors, given more sensitive experimental paradigms? I explore whether preschoolers (N = 200, ages 4-5) understand functional metaphors, namely metaphors based on functional similarities. In Experiment 1a, preschoolers rated functional metaphors (e.g. “Roofs are hats”; “Clouds are sponges”) as “smarter” than nonsense statements. In Experiment 1b, adults (N = 48) also rated functional metaphors as “smarter” than nonsense statements (e.g. “Dogs are scissors”; “Boats are skirts”). In Experiment 2, preschoolers preferred functional explanations (e.g. “Both hold water”) over perceptual explanations (e.g. “Both are fluffy”) when interpreting a functional metaphor (e.g. “Clouds are sponges”). In Experiment 3, preschoolers preferred functional metaphors over nonsense statements in a dichotomous-choice task. Overall, this work demonstrates preschoolers’ early-emerging ability to understand functional metaphors.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Humans are incredible learners, a talent supported by our ability to detect and transfer relational similarities between items and events. Spotting these common relations despite perceptual differences is challenging, yet there’s evidence that this ability begins early, with infants as young as 3 months discriminating same and different (Anderson et al., 2018; Ferry et al., 2015). How? To understand the underlying mechanisms, I examine how learning outcomes in the first year correspond with changes in input and in infant age. I discuss the commonalities in this process with that seen in older children and adults, as well as differences due to interactions with other maturing processes like language and visual attention.

Birdsong emerges when a set of highly interconnected brain areas manage to generate a complex output. The similarities between birdsong production and human speech have positioned songbirds as unique animal models for studying learning and production of this complex motor skill. In this work, we developed a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experiments to record neural activity from one of these nuclei and found that the low dimensional model could reproduce the neural dynamics observed during the experiments. Also, this model could reproduce the respiratory motor patterns used to generate song. We showed that sparse activity in one of the neural nuclei could drive a more complex activity downstream in the neural network. This interdisciplinary work shows how low dimensional neural models can be a valuable tool for studying the emergence of complex motor tasks

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

It is generally acknowledged that analogy is a core mechanism of human cognition, but paradoxically, analogies based on structural similarities would rarely be implemented spontaneously (e.g. without an explicit invitation to compare two representations). The scarcity of deep spontaneous analogies is at odds with the demonstration that familiar concepts from our daily-life are spontaneously used to encode the structure of our experiences. Based on this idea, we will present experimental works highlighting the predominant role of structural similarities in analogical retrieval. The educational stakes lurking behind the tendency to encode the problem’s structures through familiar concepts will also be addressed.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

After an injury in the adult mammalian central nervous system, lesioned axons fail to regenerate. This failure to regenerate contrasts with the remarkable potential of axons to grow during embryonic development and after an injury in the peripheral nervous system. Peripheral sensory neurons with cell soma in dorsal root ganglia (DRG) switch to a regenerative state after nerve injury to enable axon regeneration and functional recovery. Decades of research have focused on the signaling pathways elicited by injury in sensory neurons and in Schwann cells that insulate axons as central mechanisms regulating nerve repair. However, neuronal microenvironment is far more complex and is composed of multiple cell types including endothelial, immune and glial cells. Whether the microenvironment surrounding neuronal soma contribute to the poor regenerative outcomes following central injuries remains largely unexplored. To answer this question, we performed a single cell transcriptional profiling of the DRG neuronal microenvironment response to peripheral and central injuries. In dissecting the roles of the microenvironment contribution, we have focused on a poorly studied population of Satellite Glial Cells (SGC) surrounding the neuronal cell soma. This study has uncovered a previously unknown role for SGC in nerve regeneration and defined SGC as transcriptionally distinct from Schwann cells while sharing similarities with astrocytes. Upon a peripheral injury, SGC contribute to axon regeneration via Fatty acid synthase (Fasn)-PPARα signaling pathway. Through repurposing fenofibrate, an FDA- approved PPARα agonist used for dyslipidemia treatment, we were able to rescue the impaired regeneration in mice lacking Fasn in SGC. Our analysis reveals that in response to central injuries, SGC do not activate the PPAR signaling pathway. However, induction of this pathway with fenofibrate treatment, rescued axon regeneration following an injury to the central nerves. Collectively, our results uncovered a previously unappreciated role of the neuronal microenvironment differential response in central and peripheral injuries.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.