SymCog: An open-source toolkit for assessing human symbolic cognition.

Symbol systems have a profound influence on human behavior, spanning countless modalities such as natural language, clothing styles, monetary systems, and gestural conventions (e.g., handshaking). Selective impairments in understanding and manipulating symbols are collectively known as asymbolia. Here we address open questions about the nature of asymbolia in the context of both historical and contemporary approaches to human symbolic cognition. We describe a tripartite perspective on symbolic cognition premised upon (1) mental representation of a concept, (2) a stored pool of symbols segregated from their respective referents, and (3) fast and accurate mapping between concepts and symbols. We present an open-source toolkit for assessing symbolic knowledge premised upon matching animated video depictions of abstract concepts to their corresponding verbal and nonverbal symbols. Animations include simple geometric shapes (e.g., filled circles, squares) moving in semantically meaningful ways. For example, a rectangle bending under the implied weight of a large square denotes "heaviness." We report normative data for matching words and images to these target animations. In a second norming study, participants rated target animations across a range of semantic dimensions (e.g., valence, dominance). In a third study, we normed a set of concepts familiar to American English speakers but lacking verbal labels (e.g., the feeling of a Sunday evening). We describe how these tools may be used to assess human symbolic processing and identify asymbolic deficits across the span of human development.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units.

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory-evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time-wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound-level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross-validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand-average auditory-evoked potential waveforms (r2 > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative 'safety' and 'danger' units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.

Eyetracking during picture naming predicts future vocabulary dropout in progressive anomia.

The dynamic and unpredictable nature of expressive vocabulary dropout in progressive anomia presents a challenge for language intervention. We evaluated whether eye gaze patterns during naming could predict anomia for the same items in the near future. We tracked naming accuracy and gaze patterns as patients with semantic (n = 7) or logopenic (n = 2) variants of Primary Progressive Aphasia or amnestic Alzheimer's Disease (n = 1), named photographs of people and objects. Patients were tested three or more times spaced roughly evenly over an average duration of 19.1 months. Target words named accurately at baseline were retrospectively coded as either known (i.e., consistently named) or vulnerable (i.e., inaccurately or inconsistently named) based on naming accuracy over the study interval. We extracted gaze data corresponding to successful naming attempts and implemented logistic mixed effects models to determine whether common gaze measures could predict each word's naming status as known or vulnerable. More visual fixations and greater visual fixation dispersion predicted later anomia. These findings suggest that eye tracking may yield a biomarker of the robustness of particular target words to future expressive vocabulary dropout. We discuss the potential utility of this finding for optimizing treatment for progressive anomia.

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks.

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.

Classical and controlled auditory mismatch responses to multiple physical deviances in anaesthetised and conscious mice.

Human mismatch negativity (MMN) is modelled in rodents and other non-human species to examine its underlying neurological mechanisms, primarily described in terms of deviance-detection and adaptation. Using the mouse model, we aim to elucidate subtle dependencies between the mismatch response (MMR) and different physical properties of sound. Epidural field potentials were recorded from urethane-anaesthetised and conscious mice during oddball and many-standards control paradigms with stimuli varying in duration, frequency, intensity and inter-stimulus interval. Resulting auditory evoked potentials, classical MMR (oddball - standard), and controlled MMR (oddball - control) waveforms were analysed. Stimulus duration correlated with stimulus-off response peak latency, whereas frequency, intensity and inter-stimulus interval correlated with stimulus-on N1 and P1 (conscious only) peak amplitudes. These relationships were instrumental in shaping classical MMR morphology in both anaesthetised and conscious animals, suggesting these waveforms reflect modification of normal auditory processing by different physical properties of sound. Controlled MMR waveforms appeared to exhibit habituation to auditory stimulation over time, which was equally observed in response to oddball and standard stimuli. These findings are inconsistent with the mechanisms thought to underlie human MMN, which currently do not address differences due to specific physical features of sound. Thus, no evidence was found to objectively support the deviance-detection or adaptation hypotheses of MMN in relation to anaesthetised or conscious mice. These findings highlight the potential risk of mischaracterising difference waveform components that are principally influenced by physical sensitivities and habituation of the auditory system.

NRXN1α+/- is associated with increased excitability in ASD iPSC-derived neurons.

BACKGROUND: NRXN1 deletions are identified as one of major rare risk factors for autism spectrum disorder (ASD) and other neurodevelopmental disorders. ASD has 30% co-morbidity with epilepsy, and the latter is associated with excessive neuronal firing. NRXN1 encodes hundreds of presynaptic neuro-adhesion proteins categorized as NRXN1α/ß/γ. Previous studies on cultured cells show that the short NRXN1ß primarily exerts excitation effect, whereas the long NRXN1α which is more commonly deleted in patients involves in both excitation and inhibition. However, patient-derived models are essential for understanding functional consequences of NRXN1α deletions in human neurons. We recently derived induced pluripotent stem cells (iPSCs) from five controls and three ASD patients carrying NRXN1α+/- and showed increased calcium transients in patient neurons. METHODS: In this study we investigated the electrophysiological properties of iPSC-derived cortical neurons in control and ASD patients carrying NRXN1α+/- using patch clamping. Whole genome RNA sequencing was carried out to further understand the potential underlying molecular mechanism. RESULTS: NRXN1α+/- cortical neurons were shown to display larger sodium currents, higher AP amplitude and accelerated depolarization time. RNASeq analyses revealed transcriptomic changes with significant upregulation glutamatergic synapse and ion channels/transporter activity including voltage-gated potassium channels (GRIN1, GRIN3B, SLC17A6, CACNG3, CACNA1A, SHANK1), which are likely to couple with the increased excitability in NRXN1α+/- cortical neurons. CONCLUSIONS: Together with recent evidence of increased calcium transients, our results showed that human NRXN1α+/- isoform deletions altered neuronal excitability and non-synaptic function, and NRXN1α+/- patient iPSCs may be used as an ASD model for therapeutic development with calcium transients and excitability as readouts.

Olfactory language and semantic processing in anosmia: a neuropsychological case control study.

A longstanding debate within philosophy and neuroscience involves the extent to which sensory information is a necessary condition for conceptual knowledge. Much of our understanding of this relationship has been informed by examining the impact of congenital blindness and deafness on language and cognitive development. Relatively little is known about the "lesser" senses of smell and taste. Here we report a neuropsychological case-control study contrasting a young adult male (P01) diagnosed with anosmia (i.e. no olfaction) during early childhood relative to an age- and sex-matched control group. A structural MRI of P01's brain revealed profoundly atrophic/aplastic olfactory bulbs, and standardized smell testing confirmed his prior pediatric diagnosis of anosmia. Participants completed three language experiments examining comprehension, production, and subjective experiential ratings of odor salient words (e.g. sewer) and scenarios (e.g. fish market). P01's ratings of odor salience of single words were lower than all control participants, whereas his ratings on five other perceptual and affective dimensions were similar to controls. P01 produced unusual associations when cued to generate words that smelled similar to odor-neutral target words (e.g. ink → plant). In narrative picture description for odor salient scenes (e.g. bakery), P01 was indistinguishable from controls. These results suggest that odor deprivation does not overtly impair functional language use. However, subtle lexical-semantic effects of anosmia may be revealed using sensitive linguistic measures.

The English Lexicon Mirrors Functional Brain Activation for a Sensory Hierarchy Dominated by Vision and Audition: Point-Counterpoint.

The meanings of most open class words are suffused with sensory and affective features. A word such as beach, for example, evokes polymodal associations ranging from gritty sand (tactile) and crashing waves (auditory) to the distinctive smell of sunscreen (olfactory). Aristotle argued for a hierarchy of the senses where vision and audition eclipse the lesser modalities of odor, taste, and touch. A direct test of Aristotle's premise was recently made possible with the establishment of the Lancaster Sensorimotor Norms (2019), a crowdsourced database cataloging sensorimotor salience for nearly 40,000 English words. Neurosynth, a metanalytic database of functional magnetic resonance imaging studies, can potentially confirm if Aristotle's sensory hierarchy is reflected in functional activation within the human brain. We correlated sensory salience of English words as assessed by subjective ratings of vision, audition, olfaction, touch, and gustation (Lancaster Ratings) with volumes of cortical activation for each of these respective sensory modalities (Neurosynth). English word ratings reflected the following sensory hierarchy: vision > audition > haptic > olfaction ≈ gustation. This linguistic hierarchy nearly perfectly correlated with voxel counts of functional activation maps by each sensory modality (Pearson r=.99). These findings are grossly consistent with Aristotle's hierarchy of the senses. We discuss implications and counterevidence from other natural languages.

Multiscale Entropy as a New Feature for EEG and fNIRS Analysis.

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; p < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach.

The human task-evoked pupillary response function is linear: Implications for baseline response scaling in pupillometry.

The human task-evoked pupillary response provides a sensitive physiological index of the intensity and online resource demands of numerous cognitive processes (e.g., memory retrieval, problem solving, or target detection). Cognitive pupillometry is a well-established technique that relies upon precise measurement of these subtle response functions. Baseline variability of pupil diameter is a complex artifact that typically necessitates mathematical correction. A methodological paradox within pupillometry is that linear and nonlinear forms of baseline scaling both remain accepted baseline correction techniques, despite yielding highly disparate results. The task-evoked pupillary response (TEPR) could potentially scale nonlinearly, similar to autonomic functions such as heart rate, in which the amplitude of an evoked response diminishes as the baseline rises. Alternatively, the TEPR could scale similarly to the cortical hemodynamic response, as a linear function that is independent of its baseline. However, the TEPR cannot scale both linearly and nonlinearly. Our aim was to adjudicate between linear and nonlinear scaling of human TEPR. We manipulated baseline pupil size by modulating the illuminance in the testing room as participants heard abrupt pure-tone transitions (Exp. 1) or visually monitored word lists (Exp. 2). Phasic pupillary responses scaled according to a linear function across all lighting (dark, mid, bright) and task (tones, words) conditions, demonstrating that the TEPR is independent of its baseline amplitude. We discuss methodological implications and identify a need to reevaluate past pupillometry studies.

A Neuropsychological Perspective on Abstract Word Representation: From Theory to Treatment of Acquired Language Disorders.

Natural languages are rife with words that describe feelings, introspective states, and social constructs (e.g., liberty, persuasion) that cannot be directly observed through the senses. Effective communication demands linguistic competence with such abstract words. In clinical neurological settings, abstract words are especially vulnerable to the effects of stroke and neurodegenerative conditions such as Alzheimer's disease. A parallel literature in cognitive neuroscience suggests that abstract and concrete words are at least partially neuroanatomically dissociable. Much remains to be learned about the nature of lexical-semantic deficits of abstract words and how best to promote their recovery. Here, we review contemporary theoretical approaches to abstract-concrete word representation with an aim toward contextualizing patient-based dissociations for abstract words. We then describe a burgeoning treatment approach for targeting abstract words and suggest a number of potential strategies for future interventions. We argue that a deeper understanding of is essential for informing language rehabilitation.

How to constrain and maintain a lexicon for the treatment of progressive semantic naming deficits: Principles of item selection for formal semantic therapy.

The progressive degradation of semantic memory is a common feature of many forms of dementia, including Alzheimer's disease and the semantic variant of primary progressive aphasia (svPPA). One of the most functionally debilitating effects of this semantic impairment is the inability to name common people and objects (i.e., anomia). Clinical management of a progressive, semantically based anomia presents extraordinary challenges for neurorehabilitation. Techniques such as errorless learning and spaced-retrieval training show promise for retraining forgotten words. However, we lack complementary detail about what to train (i.e., item selection) and how to flexibly adapt the training to a declining cognitive system. This position paper weighs the relative merits of several treatment rationales (e.g., restore vs. compensate) and advocates for maintenance of known words over reacquisition of forgotten knowledge in the context of semantic treatment paradigms. I propose a system for generating an item pool and outline a set of core principles for training and sustaining a micro-lexicon consisting of approximately 100 words. These principles are informed by lessons learned over the course of a Phase I treatment study targeting language maintenance over a 5-year span in Alzheimer's disease and SvPPA. Finally, I propose a semantic training approach that capitalises on lexical frequency and repeated training on conceptual structure to offset the loss of key vocabulary as disease severity worsens.

Lesion symptom mapping of manipulable object naming in nonfluent aphasia: can a brain be both embodied and disembodied?

Embodied cognition offers an approach to word meaning firmly grounded in action and perception. A strong prediction of embodied cognition is that sensorimotor simulation is a necessary component of lexical-semantic representation. One semantic distinction where motor imagery is likely to play a key role involves the representation of manufactured artefacts. Many questions remain with respect to the scope of embodied cognition. One dominant unresolved issue is the extent to which motor enactment is necessary for representing and generating words with high motor salience. We investigated lesion correlates of manipulable relative to nonmanipulable name generation (e.g., name a school supply; name a mountain range) in patients with nonfluent aphasia (N = 14). Lesion volumes within motor (BA4, where BA = Brodmann area) and premotor (BA6) cortices were not predictive of category discrepancies. Lesion symptom mapping linked impairment for manipulable objects to polymodal convergence zones and to projections of the left, primary visual cortex specialized for motion perception (MT/V5+). Lesions to motor and premotor cortex were not predictive of manipulability impairment. This lesion correlation is incompatible with an embodied perspective premised on necessity of motor cortex for the enactment and subsequent production of motor-related words. These findings instead support a graded or "soft" approach to embodied cognition premised on an ancillary role of modality-specific cortical regions in enriching modality-neutral representations. We discuss a dynamic, hybrid approach to the neurobiology of semantic memory integrating both embodied and disembodied components.

Neuropsychology and Politics Collide in the 2024 US Presdiential Election: Pitfalls of attacks on age, language, and memory.

Public perception of dementia has emerged as a key factor in the 2024 United States presidential election. The first televised presidential debate (27 June 2024) evoked a groundswell of concern about the neuropsychological health and political viability of President Joseph R. Biden, Jr. A rapid erosion of public support ensued, culminating in the collapse of the reelection campaign the following month. Political attacks on the cognitive fitness of world leaders create dissonance for clinical neuroscientists. We are ethically prohibited from remotely diagnosing public figures. Yet, we are also citizens with the right to feel and express personal concerns. In this commentary, I will address an often-uneasy relationship between politics and neuropsychology with a focus on the history and rationale for ethical guidelines such as the Goldwater Rule. I will also discuss lessons learned from recent events in the 2024 US election cycle about neurological health literacy (e.g. How is dementia diagnosed?) and broader impacts of age-based political attacks on global public health initiatives that target stigma reduction and improved early detection of dementia.

Conceptual structure of emotions.

Theories of semantic organization have historically prioritized investigation of concrete concepts pertaining to inanimate objects and natural kinds. As a result, accounts of the conceptual representation of emotions have almost exclusively focused on their juxtaposition with concrete concepts. The present study aims to fill this gap by deriving a large set of normative feature data for emotion concepts and assessing similarities and differences between the featural representation of emotion, nonemotion abstract, and concrete concepts. We hypothesized that differences between the experience of emotions (e.g., happiness and sadness) and the experience of other abstract concepts (e.g., equality and tyranny), specifically regarding the relative importance of interoceptive states, might drive distinctions in the dimensions along which emotion concepts are represented. We also predicted, based on constructionist views of emotion, that emotion concepts might demonstrate more variability in their representation than concrete and other abstract concepts. Participants listed features which we coded into discrete categories and contrasted the feature distributions across conceptual types. Analyses revealed statistically significant differences in the distribution of features among the category types by condition. We also examined variability in the features generated, finding that, contrary to expectation, emotion concepts were associated with less variability. Our results reflect subtle differences between the structure of emotion concepts and the structure of, not only concrete concepts, but also other abstract concepts. We interpret these findings in the context of our sample, which was restricted to native English speakers, and discuss the importance of validating these findings across speakers of different languages. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network.

Gliomas observed in medical images require expert neuro-radiologist evaluation for treatment planning and monitoring, motivating development of intelligent systems capable of automating aspects of tumour evaluation. Deep learning models for automatic image segmentation rely on the amount and quality of training data. In this study we developed a neuroimaging synthesis technique to augment data for training fully-convolutional networks (U-nets) to perform automatic glioma segmentation. We used StyleGAN2-ada to simultaneously generate fluid-attenuated inversion recovery (FLAIR) magnetic resonance images and corresponding glioma segmentation masks. Synthetic data were successively added to real training data (n = 2751) in fourteen rounds of 1000 and used to train U-nets that were evaluated on held-out validation (n = 590) and test sets (n = 588). U-nets were trained with and without geometric augmentation (translation, zoom and shear), and Dice coefficients were computed to evaluate segmentation performance. We also monitored the number of training iterations before stopping, total training time, and time per iteration to evaluate computational costs associated with training each U-net. Synthetic data augmentation yielded marginal improvements in Dice coefficients (validation set +0.0409, test set +0.0355), whereas geometric augmentation improved generalization (standard deviation between training, validation and test set performances of 0.01 with, and 0.04 without geometric augmentation). Based on the modest performance gains for automatic glioma segmentation we find it hard to justify the computational expense of developing a synthetic image generation pipeline. Future work may seek to optimize the efficiency of synthetic data generation for augmentation of neuroimaging data.

Language and the Cerebellum: Structural Connectivity to the Eloquent Brain.

Neurobiological models of receptive language have focused on the left-hemisphere perisylvian cortex with the assumption that the cerebellum supports peri-linguistic cognitive processes such as verbal working memory. The goal of this study was to identify language-sensitive regions of the cerebellum then map the structural connectivity profile of these regions. Functional imaging data and diffusion-weighted imaging data from the Human Connectome Project (HCP) were analyzed. We found that (a) working memory, motor activity, and language comprehension activated partially overlapping but mostly unique subregions of the cerebellum; (b) the linguistic portion of the cerebello-thalamo-cortical circuit was more extensive than the linguistic portion of the cortico-ponto-cerebellar tract; (c) there was a frontal-lobe bias in the connectivity from the cerebellum to the cerebrum; (d) there was some degree of specificity; and (e) for some cerebellar tracts, individual differences in picture identification ability covaried with fractional anisotropy metrics. These findings yield insights into the structural connectivity of the cerebellum as relates to the uniquely human process of language comprehension.

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network.

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes. This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method. The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates. To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.

Blind source separation of event-related potentials using a recurrent neural network.

Event-related potentials (ERPs) are a superposition of electric potential differences generated by neurophysiological activity associated with psychophysical events. Spatiotemporal dissociation of these signal sources can supplement conventional ERP analysis and improve source localization. However, results from established source separation methods applied to ERPs can be challenging to interpret. Hence, we have developed a recurrent neural network (RNN) method for blind source separation. The RNN transforms input step pulse signals representing events into corresponding ERP difference waveforms. Source waveforms are obtained from penultimate layer units and scalp maps are obtained from feed-forward output layer weights that project these source waveforms onto EEG electrode amplitudes. An interpretable, sparse source representation is achieved by incorporating L1 regularization of signals obtained from the penultimate layer of the network during training. This RNN method was applied to four ERP difference waveforms (MMN, N170, N400, P3) from the open-access ERP CORE database, and independent component analysis (ICA) was applied to the same data for comparison. The RNN decomposed these ERPs into eleven spatially and temporally separate sources that were less noisy, tended to be more ERP-specific, and were less similar to each other than ICA-derived sources. The RNN sources also had less ambiguity between source waveform amplitude, scalp potential polarity, and equivalent current dipole orientation than ICA sources. In conclusion, the proposed RNN blind source separation method can be effectively applied to grand-average ERP difference waves and holds promise for further development as a computational model of event-related neural signals.

What we mean when we say semantic: Toward a multidisciplinary semantic glossary.

Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y).