Neural correlates of concept typicality and category membership

Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Ciências, 2022A tipicidade é uma dimensão chave no processamento de conceitos e refere-se ao grau em que um item é representativo da sua categoria. Itens típicos são processados mais facilmente em tarefas de categorização e de nomeação do que itens atípicos. O presente estudo tem como objetivo investigar as bases neuronais da categorização dos objetos e as regiões cerebrais envolvidas no processamento da tipicidade dos objetos. Estudos prévios centraram-se sobretudo na região do lobo temporal anterior e têm fornecido resultados contraditórios entre si. No presente estudo de fMRI, 26 jovens adultos realizaram uma tarefa de categorização, tendo sido manipulada a pertença à categoria e a tipicidade dos conceitos. Os resultados comportamentais e neuronais revelaram um efeito de interação. Objetos típicos foram categorizados mais rápida e acertadamente do que objetos atípicos, mas apenas quando pertenciam à categoria apresentada. A nível neuronal, verificou-se que quando os itens pertenciam à categoria, objetos típicos recrutaram o precuneus esquerdo associado à decisão com base na semelhança, enquanto os objetos atípicos elicitaram maior ativação no lobo frontal inferior esquerdo, que tem sido associado ao controlo semântico. Os resultados confirmam o papel central da tipicidade no processamento semântico e em particular na categorização e informam sobre as bases neuronais da variabilidade intra-categorial.Typicality is a key dimension in concept processing and refers to the extent to which an item is representative of its category. Typical items are processed more easily in categorization and naming tasks than atypical items. The present study aims to investigate the neural basis of object categorization and the brain regions involved in processing item typicality. Previous studies have mainly focused on the anterior temporal lobe region and have provided results that are contradictory to each other. In the present fMRI study, 26 young adults performed a categorization task, and category membership and concept typicality were manipulated. Behavioural and neuronal results revealed an interaction effect. Typical items were categorized more quickly and correctly than atypical objects, but only when they belonged to the shown category. At the neuronal level, it was found that when items belonged to the category, typical items recruited the left precuneus associated with similarity-based decision making, whereas atypical items elicited greater activation in the left inferior frontal lobe, which has been associated with semantic control. The results confirm the central role of typicality in semantic processing and in particular categorisation and inform about the neural basis of intra-category variability

A humanness dimension to visual object coding in the brain

Neuroimaging studies investigating human object recognition have primarily focused on a relatively small number of object categories, in particular, faces, bodies, scenes, and vehicles. More recent studies have taken a broader focus, investigating hypothesized dichotomies, for example, animate versus inanimate, and continuous feature dimensions, such as biologically similarity. These studies typically have used stimuli that are identified as animate or inanimate, neglecting objects that may not fit into this dichotomy. We generated a novel stimulus set including standard objects and objects that blur the animate-inanimate dichotomy, for example, robots and toy animals. We used MEG time-series decoding to study the brain's emerging representation of these objects. Our analysis examined contemporary models of object coding such as dichotomous animacy, as well as several new higher order models that take into account an object's capacity for agency (i.e. its ability to move voluntarily) and capacity to experience the world. We show that early (0–200 ​ms) responses are predicted by the stimulus shape, assessed using a retinotopic model and shape similarity computed from human judgments. Thereafter, higher order models of agency/experience provided a better explanation of the brain's representation of the stimuli. Strikingly, a model of human similarity provided the best account for the brain's representation after an initial perceptual processing phase. Our findings provide evidence for a new dimension of object coding in the human brain – one that has a “human-centric” focus

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Typicality in the brain during semantic and episodic memory decisions

Typicality is a key semantic dimension supporting the categorical organization of items based on their features. Typical items share more features with other members of their category than atypical items, which are more distinctive. Typicality influences episodic recollection. Yet, the neural substrates of this effect have never been studied. This fMRI study investigated the neural correlates of typicality during semantic and episodic memory decisions . 26 subjects performed a categorization task on typical and atypical word concept and completed a recognition memory task. During the correct recognition of old items, regions from the core recollection network were activated, and typical items were reinstated more than atypical ones in several regions including the anterior temporal lobe. Results suggest that the centrality of this region in the processing of typicality extends to memory retrieval, and that the correct retrieval of typical items requires finer-grained, item-specific, processing, possibly to resolve their greater confusability with other category members.</p

The Cognitive Neuroscience of Stable and Flexible Semantic Typicality

Typicality effects are among the most well-studied phenomena in the study of concepts. The classical notion of typicality is that typical concepts share many features with category co-members and few features with members of contrast categories. However, this notion was challenged by evidence that typicality is highly context dependent and not always dependent on central tendency. Dieciuc and Folstein (2019) argued that there is strong evidence for both views and that the two types of typicality effects might depend on different mechanisms. A recent theoretical framework, the controlled semantic cognition framework (Lamdon Ralph et al., 2017) strongly emphasizes the classical view, but includes mechanisms that could potentially account for both kinds of typicality. In contrast, the situated cognition framework (Barsalou, 2009b) articulates the context-dependent view. Here, we review evidence from cognitive neuroscience supporting the two frameworks. We also briefly evaluate the ability of computational models associated with the CSC to account for phenomena supporting SitCog (Rogers and McClelland, 2004). Many predictions of both frameworks are borne out by recent cognitive neuroscience evidence. While the CSC framework can at least potentially account for many of the typicality phenomena reviewed, challenges remain, especially with regard to ad hoc categories

Representation of semantic typicality in brain activation in healthy adults and individuals with aphasia: a multi-voxel pattern analysis

Author manuscript; available in PMC 2022 Jul 30. Published in final edited form as: Neuropsychologia. 2021 Jul 30; 158: 107893. Published online 2021 May 19. doi: 10.1016/j.neuropsychologia.2021.107893This study aimed to investigate brain regions that show different activation patterns between semantically typical and atypical items in both healthy adults and individuals with aphasia (PWA). Eighteen neurologically healthy adults and twenty-one PWA participated in an fMRI semantic feature verification task that included typical and atypical stimuli from five different semantic categories. A whole-brain searchlight multi-voxel pattern analysis (MVPA) was conducted to classify brain activation patterns between typical and atypical conditions in each participant group separately. Behavioral responses were faster and more accurate for typical vs. atypical items across both groups. The searchlight MVPA identified two significant clusters in healthy adults: left middle occipital gyrus and right calcarine cortex, but no significant clusters were found in PWA. A follow-up analysis in PWA revealed a significant association between neural classification of semantic typicality in the left middle occipital gyrus and reaction times in the fMRI task. When the typicality effect was examined for each semantic category at the univariate level, significance was identified in the visual cortex for fruits in both groups of participants. These findings suggest that semantic typicality was modulated in the visual cortex in healthy individuals, but to a lesser extent in the same region in PWA.P50 DC012283 - NIDCD NIH HHS; R01 DC002852 - NIDCD NIH HHS; R01 DC007683 - NIDCD NIH HHS; U01 DC014922 - NIDCD NIH HHSAccepted manuscrip

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA

Now you see it, now you don’t: dynamism amplifies the typicality effect

Some safety events do not stabilise in a coherent state, presenting with transient or intermittent features. Such dynamism may pose problems for human performance, especially if combined with non-typical stimuli that are rarely encountered in everyday work. This may explain undesirable pilot behaviour and could be an important cognitive factor in recent aircraft accidents. Sixty-five airline pilots tested a real-world typicality gradient, composed of two cockpit events, a typical event, and a non-typical event, across two different forms of dynamism, a stable, single system transition, and an unstable, intermittent system transition. We found that non-typical event stimuli elicited a greater number of response errors and incurred an increased response latency when compared to typical event stimuli, replicating the typicality effect. These performance deteriorations were amplified when a form of unstable system dynamism was introduced. Typical stimuli were unaffected by dynamism. This indicates that dynamic, non-typical events are problematic for pilots and may lead to poor event recognition and response. Typical is advantageous, even if dynamic. Manufacturers and airlines should evolve pilot training and crew procedures to take account of variety in event dynamics