Early-emerging combinatorial thought: Human infants flexibly combine kind and quantity concepts.

Combinatorial thought, or the ability to combine a finite set of concepts into a myriad of complex ideas and knowledge structures, is the key to the productivity of the human mind and underlies communication, science, technology, and art. Despite the importance of combinatorial thought for human cognition and culture, its developmental origins remain unknown. To address this, we tested whether 12-mo-old infants (N = 60), who cannot yet speak and only understand a handful of words, can combine quantity and kind concepts activated by verbal input. We proceeded in two steps: first, we taught infants two novel labels denoting quantity (e.g., "mize" for 1 item; "padu" for 2 items, Experiment 1). Then, we assessed whether they could combine quantity and kind concepts upon hearing complex expressions comprising their labels (e.g., "padu duck", Experiments 2-3). At test, infants viewed four different sets of objects (e.g., 1 duck, 2 ducks, 1 ball, 2 balls) while being presented with the target phrase (e.g., "padu duck") naming one of them (e.g., 2 ducks). They successfully retrieved and combined on-line the labeled concepts, as evidenced by increased looking to the named sets but not to distractor sets. Our results suggest that combinatorial processes for building complex representations are available by the end of the first year of life. The infant mind seems geared to integrate concepts in novel productive ways. This ability may be a precondition for deciphering the ambient language(s) and building abstract models of experience that enable fast and flexible learning.

Macaques recognize features in synthetic images derived from ventral stream neurons.

Primates can recognize features in virtually all types of images, an ability that still requires a comprehensive computational explanation. One hypothesis is that visual cortex neurons learn patterns from scenes, objects, and textures, and use these patterns to interpolate incoming visual information. We have used machine learning algorithms to instantiate visual patterns stored by neurons-we call these highly activating images prototypes. Prototypes from inferotemporal (IT) neurons often resemble parts of real-world objects, such as monkey faces and body parts, a similarity established via pretrained neural networks [C. R. Ponce et al., Cell 177, 999-1009.e10 (2019)] and naïve human participants [A. Bardon, W. Xiao, C. R. Ponce, M. S. Livingstone, G. Kreiman, Proc. Natl. Acad. Sci. U.S.A. 119, e2118705119 (2022)]. However, it is not known whether monkeys themselves perceive similarities between neuronal prototypes and real-world objects. Here, we investigated whether monkeys reported similarities between prototypes and real-world objects using a two-alternative forced choice task. We trained the animals to saccade to synthetic images of monkeys, and subsequently tested how they classified prototypes synthesized from IT and primary visual cortex (V1). We found monkeys classified IT prototypes as conspecifics more often than they did random generator images and V1 prototypes, and their choices were partially predicted by convolutional neural networks. Further, we confirmed that monkeys could abstract general shape information from images of real-world objects. Finally, we verified these results with human participants. Our results provide further evidence that prototypes from cortical neurons represent interpretable abstractions from the visual world.

Advancing interdisciplinary science for disrupting wildlife trafficking networks.

Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains-straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

MEG Evidence That Modality-Independent Conceptual Representations Contain Semantic and Visual Features.

The semantic knowledge stored in our brains can be accessed from different stimulus modalities. For example, a picture of a cat and the word "cat" both engage similar conceptual representations. While existing research has found evidence for modality-independent representations, their content remains unknown. Modality-independent representations could be semantic, or they might also contain perceptual features. We developed a novel approach combining word/picture cross-condition decoding with neural network classifiers that learned latent modality-independent representations from MEG data (25 human participants, 15 females, 10 males). We then compared these representations to models representing semantic, sensory, and orthographic features. Results show that modality-independent representations correlate both with semantic and visual representations. There was no evidence that these results were due to picture-specific visual features or orthographic features automatically activated by the stimuli presented in the experiment. These findings support the notion that modality-independent concepts contain both perceptual and semantic representations.

The successor representation subserves hierarchical abstraction for goal-directed behavior.

Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful "subgoals" in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named "community structure". Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the "successor representation", which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in "wings" representing community structure in the museum. We find that participants' choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.

The neural representation of body part concepts.

Neuropsychological and neuroimaging studies provide evidence for a degree of category-related organization of conceptual knowledge in the brain. Some of this evidence indicates that body part concepts are distinctly represented from other categories; yet, the neural correlates and mechanisms underlying these dissociations are unclear. We expand on the limited prior data by measuring functional magnetic resonance imaging responses induced by body part words and performing a series of analyses investigating the cortical representation of this semantic category. Across voxel-level contrasts, pattern classification, representational similarity analysis, and vertex-wise encoding analyses, we find converging evidence that the posterior middle temporal gyrus, the supramarginal gyrus, and the ventral premotor cortex in the left hemisphere play important roles in the preferential representation of this category compared to other concrete objects.

Neural representational geometry underlies few-shot concept learning.

Understanding the neural basis of the remarkable human cognitive capacity to learn novel concepts from just one or a few sensory experiences constitutes a fundamental problem. We propose a simple, biologically plausible, mathematically tractable, and computationally powerful neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. We further posit that a single plastic downstream readout neuron learns to discriminate new concepts based on few examples using a simple plasticity rule. We demonstrate the computational power of our proposal by showing that it can achieve high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations and can even learn novel visual concepts specified only through linguistic descriptors. Moreover, we develop a mathematical theory of few-shot learning that links neurophysiology to predictions about behavioral outcomes by delineating several fundamental and measurable geometric properties of neural representations that can accurately predict the few-shot learning performance of naturalistic concepts across all our numerical simulations. This theory reveals, for instance, that high-dimensional manifolds enhance the ability to learn new concepts from few examples. Intriguingly, we observe striking mismatches between the geometry of manifolds in the primate visual pathway and in trained DNNs. We discuss testable predictions of our theory for psychophysics and neurophysiological experiments.

Decoding the information structure underlying the neural representation of concepts.

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional "semantic hubs" contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

Conceptual Associations Generate Sensory Predictions.

A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. Although studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here, we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants of both sexes to arbitrary word-word pairs (e.g., car-dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared with unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning specific, selectively suppressing neural populations tuned toward the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate category-specific predictions, facilitating processing of expected visual input.SIGNIFICANCE STATEMENT Perceptual predictions play a crucial role in facilitating perception and the integration of sensory information. However, little is known about whether and how the brain uses more abstract, conceptual priors to form sensory predictions. In our preregistered study, we show that priors derived from recently acquired arbitrary conceptual associations result in category-specific predictions that modulate perceptual processing throughout the ventral visual hierarchy, including early visual cortex. These results suggest that the predictive brain uses prior knowledge across various domains to modulate perception, thereby extending our understanding of the extensive role predictions play in perception.

The Neural Corelates of Constructing Conceptual and Perceptual Representations of Autobiographical Memories.

Contemporary neurocognitive frameworks propose that conceptual and perceptual content of autobiographical memories-personal past experiences-are processed by dissociable neural systems. Other work has proposed a central role of the anterior hippocampus in initially constructing autobiographical memories, regardless of the content. Here, we report on an fMRI study that utilized a repeated retrieval paradigm to test these ideas. In an MRI scanner, participants retrieved autobiographical memories at three timepoints. During the third retrieval, participants either shifted their focus to the conceptual content of the memory, the perceptual content of the memory, or retrieved the memory as they had done so on previous trials. We observed stronger anterior hippocampal activity for the first retrieval compared with later retrievals, regardless of whether there was a shift in content in those later trials. We also found evidence for separate cortical systems when constructing autobiographical memories with a focus on conceptual or perceptual content. Finally, we found that there was common engagement between later retrievals that required a shift toward conceptual content and the initial retrieval of a memory. This final finding was explored further with a behavioral experiment that provided evidence that focusing on conceptual content of a memory guides memory construction, whereas perceptual content adds precision to a memory. Together, these findings suggest there are distinct content-oriented cortical systems that work with the anterior hippocampus to construct representations of autobiographical memories.

Experiential grounding of abstract concepts: Processing of abstract mental state concepts engages brain regions involved in mentalizing, automatic speech, and lip movements.

concepts like mental state concepts lack a physical referent, which can be directly perceived. Classical theories therefore claim that abstract concepts require amodal representations detached from experiential brain systems. However, grounded cognition approaches suggest an involvement of modal experiential brain regions in the processing of abstract concepts. In the present functional magnetic resonance imaging study, we investigated the relation of the processing of abstract mental state concepts to modal experiential brain systems in a fine-grained fashion. Participants performed lexical decisions on abstract mental state as well as on verbal association concepts as control category. Experiential brain systems related to the processing of mental states, generating verbal associations, automatic speech as well as hand and lip movements were determined by corresponding localizer tasks. Processing of abstract mental state concepts neuroanatomically overlapped with activity patterns associated with processing of mental states, generating verbal associations, automatic speech and lip movements. Hence, mental state concepts activate the mentalizing brain network, complemented by perceptual-motor brain regions involved in simulation of visual or action features associated with social interactions, linguistic brain regions as well as face-motor brain regions recruited for articulation. The present results provide compelling evidence for the rich grounding of abstract mental state concepts in experiential brain systems related to mentalizing, verbal communication and mouth action.

Conceptualization of utility in translational clinical genomics research.

Prior to integration into clinical care, a novel medical innovation is typically assessed in terms of its balance of benefits and risks, often referred to as utility. Members of multidisciplinary research teams may conceptualize and assess utility in different ways, which has implications within the translational genomics community and for the evidence base upon which clinical guidelines groups and healthcare payers make decisions. Ambiguity in the conceptualization of utility in translational genomics research can lead to communication challenges within research teams and to study designs that do not meet stakeholder needs. We seek to address the ambiguity challenge by describing the conceptual understanding of utility and use of the term by scholars in the fields of philosophy, medicine, and the social sciences of decision psychology and health economics. We illustrate applications of each field's orientation to translational genomics research by using examples from the Clinical Sequencing Evidence-Generating Research (CSER) consortium, and we provide recommendations for increasing clarity and cohesion in future research. Given that different understandings of utility will align to a greater or lesser degree with important stakeholders' views, more precise use of the term can help researchers to better integrate multidisciplinary investigations and communicate with stakeholders.

Emotional concepts shape the perceptual representation of body expressions.

Emotion perception interacts with how we think and speak, including our concept of emotions. Body expression is an important way of emotion communication, but it is unknown whether and how its perception is modulated by conceptual knowledge. In this study, we employed representational similarity analysis and conducted three experiments combining semantic similarity, mouse-tracking task, and one-back behavioral task with electroencephalography and functional magnetic resonance imaging techniques, the results of which show that conceptual knowledge predicted the perceptual representation of body expressions. Further, this prediction effect occurred at approximately 170 ms post-stimulus. The neural encoding of body expressions in the fusiform gyrus and lingual gyrus was impacted by emotion concept knowledge. Taken together, our results indicate that conceptual knowledge of emotion categories shapes the configural representation of body expressions in the ventral visual cortex, which offers compelling evidence for the constructed emotion theory.

Neural Representations of Emotions in Visual, Auditory, and Modality-Independent Regions Reflect Idiosyncratic Conceptual Knowledge.

Growing evidence suggests that conceptual knowledge influences emotion perception, yet the neural mechanisms underlying this effect are not fully understood. Recent studies have shown that brain representations of facial emotion categories in visual-perceptual areas are predicted by conceptual knowledge, but it remains to be seen if auditory regions are similarly affected. Moreover, it is not fully clear whether these conceptual influences operate at a modality-independent level. To address these questions, we conducted a functional magnetic resonance imaging study presenting participants with both facial and vocal emotional stimuli. This dual-modality approach allowed us to investigate effects on both modality-specific and modality-independent brain regions. Using univariate and representational similarity analyses, we found that brain representations in both visual (middle and lateral occipital cortices) and auditory (superior temporal gyrus) regions were predicted by conceptual understanding of emotions for faces and voices, respectively. Additionally, we discovered that conceptual knowledge also influenced supra-modal representations in the superior temporal sulcus. Dynamic causal modeling revealed a brain network showing both bottom-up and top-down flows, suggesting a complex interplay of modality-specific and modality-independent regions in emotional processing. These findings collectively indicate that the neural representations of emotions in both sensory-perceptual and modality-independent regions are likely shaped by each individual's conceptual knowledge.

Investigating the effects of perceptual complexity versus conceptual meaning on the object benefit in visual working memory.

Previous research has demonstrated greater visual working memory (VWM) performance for real-world objects compared with simple features. Greater amplitudes of the contralateral delay activity (CDA)-a sustained event-related potential measured during the delay period of a VWM task-have also been noted for meaningful stimuli, despite being thought of as a neural marker of a fixed working memory capacity. The current study aimed to elucidate the factors underlying improved memory performance for real-world objects by isolating the relative contributions of perceptual complexity (i.e., number of visual features) and conceptual meaning (i.e., availability of semantic, meaningful features). Participants (N = 22) performed a lateralized VWM task to test their memory of intact real-world objects, scrambled real-world objects and colours. The CDA was measured during both encoding and WM retention intervals (600-1000 ms and 1300-1700 ms poststimulus onset, respectively), and behavioural performance was estimated by using d' (memory strength in a two-alternative forced choice task). Behavioural results revealed significantly better performance within-subjects for real-world objects relative to scrambled objects and colours, with no difference between colours and scrambled objects. The amplitude of the CDA was also largest for intact real-world objects, with no difference in magnitude for scrambled objects and colours, during working memory maintenance. However, during memory encoding, both the colours and intact real-world objects had significantly greater amplitudes than scrambled objects and were comparable in magnitude. Overall, findings suggest that conceptual meaning (semantics) supports the memory benefit for real-world objects.

Conceptualization of Breast Cancer and Attitudes toward Breast Cancer Screening: A Qualitative Study on Iranian and Australian Women.

OBJECTIVE: Regular screening can lead to early diagnosis of breast cancer. Australia is one of the countries with a high rate of participation in breast cancer screening, while in Iran, most patients are diagnosed in advanced stages. This qualitative study used a thematic analysis method to examine the attitudes and beliefs of Iranian and Australian women toward breast cancer and breast cancer screening. METHOD: In-depth interviews were conducted with eight Iranian and seven Australian women. Data were collected through semi-structured interviews and analyzed using thematic analysis with an inferential approach. RESULTS: According to the results, four main themes were extracted for the perceptions of breast cancer, including "the meaning of breast cancer", "causes of breast cancer", "prevention of breast cancer" and "effects of significant people in life". Considering the perceptions of breast cancer screening, three themes were identified, including "beliefs about breast cancer screening," "sources of information about breast cancer screening," and "barriers to breast cancer screening." CONCLUSION: Cultural and social factors influence women's perceptions of breast cancer and screening. Therefore, investigating the cultural meaning of breast cancer and breast cancer screening for women can be useful for screening and prevention purposes.

Exploration, Distributed Attention, and Development of Category Learning.

Category learning is a crucial aspect of cognition that involves organizing entities into equivalence classes. Whereas adults tend to focus on category-relevant features, young children often distribute attention between relevant and irrelevant ones. The reasons for children's distributed attention are not fully understood. In two category-learning experiments with adults and with children aged 4, 5, and 6 (N = 201), we examined potential drivers of distributed attention, including (a) immature filtering of distractors and (b) the general tendency for exploration or broad information sampling. By eliminating distractor competition, we reduced filtering demands. Despite identifying the features critical for accurate categorization, children, regardless of their categorization performance, continued sampling more information than was necessary. These results indicate that the tendency to sample information extensively contributes to distributed attention in young children. We identify candidate drivers of this tendency that need to be examined in future research.

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning.

The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building "task metamers" that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. We find that humans perform better at abstract tasks than metamer tasks whereas common neural network architectures typically perform worse on the abstract tasks than the matched metamers. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with more human-like behavior.

Miscategorized subset-knowers: Five- and six-knowers can compare only the numbers they know.

Initial acquisition of the first symbolic numbers is measured with the Give a Number (GaN) task. According to the classic method, it is assumed that children who know only 1, 2, 3, or 4 in the GaN task, (termed separately one-, two-, three-, and four-knowers, or collectively subset-knowers) have only a limited conceptual understanding of numbers. On the other hand, it is assumed that children who know larger numbers understand the fundamental properties of numbers (termed cardinality-principle-knowers), even if they do not know all the numbers as measured with the GaN task, that are in their counting list (e.g., five- or six-knowers). We argue that this practice may not be well-established. To validate this categorization method, here, the performances of groups with different GaN performances were measured separately in a symbolic comparison task. It was found that similar to one to four-knowers, five-, six-, and so forth, knowers can compare only the numbers that they know in the GaN task. We conclude that five-, six-, and so forth, knowers are subset-knowers because their conceptual understanding of numbers is fundamentally limited. We argue that knowledge of the cardinality principle should be identified with stricter criteria compared to the current practice in the literature. RESEARCH HIGHLIGHTS: Children who know numbers larger than 4 in the Give a Number (GaN) task are usually assumed to have a fundamental conceptual understanding of numbers. We tested children who know numbers larger than 4 but who do not know all the numbers in their counting list to see whether they compare numbers more similar to children who know only small numbers in the GaN task or to children who have more firm number knowledge. Five-, six-, and so forth, knowers can compare only the numbers they know in the GaN task, similar to the performance of the one, two, three, and four-knowers. We argue that these children have a limited conceptual understanding of numbers and that previous works may have miscategorized them.