Semantic feature production norms for a large set of living and nonliving things

The upside of cumulative conceptual interference on exemplar-level mnemonic discrimination

Although long-term visual memory (LTVM) has a remarkable capacity, the fidelity of its episodic representations can be influenced by at least two intertwined interference mechanisms during the encoding of objects belonging to the same category: the capacity to hold similar episodic traces (e.g., different birds) and the conceptual similarity of the encoded traces (e.g., a sparrow shares more features with a robin than with a penguin). The precision of episodic traces can be tested by having participants discriminate lures (unseen objects) from targets (seen objects) representing different exemplars of the same concept (e.g., two visually similar penguins), which generates interference at retrieval that can be solved if efficient pattern separation happened during encoding. The present study examines the impact of within-category encoding interference on the fidelity of mnemonic object representations, by manipulating an index of cumulative conceptual interference that represents the concurrent impact of capacity and similarity. The precision of mnemonic discrimination was further assessed by measuring the impact of visual similarity between targets and lures in a recognition task. Our results show a significant decrement in the correct identification of targets for increasing interference. Correct rejections of lures were also negatively impacted by cumulative interference as well as by the visual similarity with the target. Most interestingly though, mnemonic discrimination for targets presented with a visually similar lure was more difficult when objects were encoded under lower, not higher, interference. These findings counter a simply additive impact of interference on the fidelity of object representations providing a finer-grained, multi-factorial, understanding of interference in LTVM.

The development of lexical processing: Real-time phonological competition and semantic activation in school age children

Prior research suggests that the development of speech perception and word recognition stabilises in early childhood. However, recent work suggests that development of these processes continues throughout adolescence. This study aimed to investigate whether these developmental changes are based solely within the lexical system or are due to domain general changes, and to extend this investigation to lexical-semantic processing. We used two Visual World Paradigm tasks: one to examine phonological and semantic processing, one to capture non-linguistic domain-general skills. We tested 43 seven- to nine-year-olds, 42 ten- to thirteen-year-olds, and 30 sixteen- to seventeen-year-olds. Older children were quicker to fixate the target word and exhibited earlier onset and offset of fixations to both semantic and phonological competitors. Visual/cognitive skills explained significant, but not all, variance in the development of these effects. Developmental changes in semantic activation were largely attributable to changes in upstream phonological processing. These results suggest that the concurrent development of linguistic processes and broader visual/cognitive skills lead to developmental changes in real-time phonological competition, while semantic activation is more stable across these ages.

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data

In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising.

Feats: A database of semantic features for early produced noun concepts

Semantic feature production norms have several desirable characteristics that have supported models of representation and processing in adults. However, several key challenges have limited the use of semantic feature norms in studies of early language acquisition. First, existing norms provide uneven and inconsistent coverage of early-acquired concepts that are typically produced and assessed in children under the age of three, which is a time of tremendous growth of early vocabulary skills. Second, it is difficult to assess the degree to which young children may be familiar with normed features derived from these adult-generated datasets. Third, it has been difficult to adopt standard methods to generate semantic network models of early noun learning. Here, we introduce Feats-a tool that was designed to make headway on these challenges by providing a database, the Language Learning and Meaning Acquisition (LLaMA) lab Noun Norms that extends a widely used set of feature norms McRae et al. Behavior Research Methods 37, 547-559, (2005) to include full coverage of noun concepts on a commonly used early vocabulary assessment. Feats includes several tools to facilitate exploration of features comprising early-acquired nouns, assess the developmental appropriateness of individual features using toddler-accessibility norms, and extract semantic network statistics for individual vocabulary profiles. We provide a tutorial overview of Feats. We additionally validate our approach by presenting an analysis of an overlapping set of concepts collected across prior and new data collection methods. Furthermore, using network graph analyses, we show that the extended set of norms provides novel, reliable results given their enhanced coverage.

Malay Lexicon Project 3: The impact of orthographic-semantic consistency on lexical decision latencies

Theories of word processing propose that readers are sensitive to statistical co-occurrences between spelling and meaning. Orthographic-semantic consistency (OSC) measures provide a continuous estimate of the statistical regularities between spelling and meaning. Here we examined Malay, an Austronesian language that is agglutinative. In Malay, stems are often repeated in other words that share a related meaning (e.g., sunyi/quiet; ke-sunyi-an/silence; makan/eat; makan-an/foods). The first goal was to expand an existing large Malay database by computing OSC estimates for 2,287 monomorphemic words. The second goal was to explore the impact of root family size and OSC on lexical decision latencies for monomorphemic words. Decision latencies were collected for 1,280 Malay words of various morphological structures. Of these, data from 1,000 monomorphemic words were analysed in a series of generalised additive mixed models (GAMMs). Root family size and OSC were significant predictors of decision latencies, particularly for lower frequency words. We found a facilitative effect of root family size and OSC. Furthermore, we observed an interaction between root family size and OSC in that an effect of OSC was only apparent in words with larger root families. This interaction has not yet been explored in English but has the potential to be a new benchmark effect to test distributional models of word processing.

Performance evaluation of automated scoring for the descriptive similarity response task

We examined whether a machine-learning-based automated scoring system can mimic the human similarity task performance. We trained a bidirectional encoder representations from transformer-model based on the semantic similarity test (SST), which presented participants with a word pair and asked them to write about how the two concepts were similar. In Experiment 1, based on the fivefold cross validation, we showed the model trained on the combination of the responses (N = 1600) and classification criteria (which is the rubric of the SST; N = 616) scored the correct labels with 83% accuracy. In Experiment 2, using the test data obtained from different participants in different timing from Experiment 1, we showed the models trained on the responses alone and the combination of responses and classification criteria scored the correct labels in 80% accuracy. In addition, human-model scoring showed inter-rater reliability of 0.63, which was almost the same as that of human-human scoring (0.67 to 0.72). These results suggest that the machine learning model can reach human-level performance in scoring the Japanese version of the SST.

Standardizing norms for 1286 colored pictures in Cantonese

This study established psycholinguistic norms in Cantonese for a set of 1286 colored pictures sourced from several picture databases, including 750 colored line drawings from MultiPic (Duñabeitia et al., 2018) and 536 photographs selected for McRae et al. (2005) concepts. The pictures underwent rigorous normalization processes. We provided picture characteristics including name and concept agreement, familiarity, visual complexity, and frequency of modal responses. Through correlational analyses, we observed strong interrelationships among these variables. We also compared the current Cantonese norming to other languages and demonstrated similarity and variations among different languages. Additionally, we embraced the multilingual diversity within the current sample, and found that higher Cantonese proficiency but lower non-native language proficiency were associated with better spoken picture naming. Last but not least, we validated the predictive power of normed variables calculated from typed responses to spoken picture naming, and the consistency between typed and spoken responses. The present norming provides a timely and valuable alternative for researchers in the field of psycholinguistics, especially those studying Cantonese production and lexical retrieval. All raw data, analysis scripts, and final norming results are available online as psycholinguistic norms for Cantonese in the following link at the Open Science Framework: https://osf.io/dz9j6/?view_only=a452d8a56c92430b9dedf21ac26b1bc1 .

Why is a flamingo named as pelican and asparagus as celery? Understanding the relationship between targets and errors in a speeded picture naming task

Speakers sometimes make word production errors, such as mistakenly saying pelican instead of flamingo. This study explored which properties of an error influence the likelihood of its selection over the target word. Analysing real-word errors in speeded picture naming, we investigated whether, relative to the target, naming errors were more typical representatives of the semantic category, were associated with more semantic features, and/or were semantically more closely related to the target than its near semantic neighbours were on average. Results indicated that naming errors tended to be more typical category representatives and possess more semantic features than the targets. Moreover, while not being the closest semantic neighbours, errors were largely near semantic neighbours of the targets. These findings suggest that typicality, number of semantic features, and semantic similarity govern activation levels in the production system, and we discuss possible mechanisms underlying these effects in the context of word production theories.

Describing and understanding the time course of the property listing task

To study linguistically coded concepts, researchers often resort to the Property Listing Task (PLT). In a PLT, participants are asked to list properties that describe a concept (e.g., for DOG, subjects may list "is a pet", "has four legs", etc.). When PLT data is collected for many concepts, researchers obtain Conceptual Properties Norms (CPNs), which are used to study semantic content and as a source of control variables. Though the PLT and CPNs are widely used across psychology, only recently a model that describes the listing course of a PLT has been developed and validated. That original model describes the listing course using order of production of properties. Here we go a step beyond and validate the model using response times (RT), i.e., the time from cue onset to property listing. Our results show that RT data exhibits the same regularities observed in the previous model, but now we can also analyze the time course, i.e., dynamics of the PLT. As such, the RT validated model may be applied to study several similar memory retrieval tasks, such as the Free Listing Task, Verbal Fluidity Task, and to research related cognitive processes. To illustrate those kinds of analyses, we present a brief example of the difference in PLT's dynamics between listing properties for abstract versus concrete concepts, which shows that the model may be fruitfully applied to study concepts.

Taxonomic structure in a set of abstract concepts

A large portion of human knowledge comprises "abstract" concepts that lack readily perceivable properties (e.g., "love" and "justice"). Since abstract concepts lack such properties, they have historically been treated as an undifferentiated category of knowledge in the psychology and neuropsychology literatures. More recently, the categorical structure of abstract concepts is often explored using paradigms that ask participants to make explicit judgments about a set of concepts along dimensions that are predetermined by the experimenter. Such methods require the experimenter to select dimensions that are relevant to the concepts and further that people make explicit judgments that accurately reflect their mental representations. We bypassed these requirements by collecting two large sets of non-verbal and implicit judgments about which dimensions are relevant to the similarity between pairs of 50 abstract nouns to determine the representational space of the concepts. We then identified categories within the representational space using a clustering procedure that required categories to replicate across two independent data sets. In a separate experiment, we used automatic semantic priming to further validate the categories and to show that they are an improvement over categories that were defined within the same set of abstract concepts using explicit ratings along predetermined dimensions. These results demonstrate that abstract concepts can be characterized beyond their negative relation to concrete concepts and that categories of abstract concepts can be defined without using a priori dimensions for the concepts or explicit judgments from participants.

A Chinese verb semantic feature dataset (CVFD)

Language is an advanced cognitive function of humans, and verbs play a crucial role in language. To understand how the human brain represents verbs, it is critical to analyze what knowledge humans have about verbs. Thus, several verb feature datasets have been developed in different languages such as English, Spanish, and German. However, there is still a lack of a dataset of Chinese verbs. In this study, we developed a semantic feature dataset of 1140 Chinese Mandarin verbs (CVFD) with 11 dimensions including verb familiarity, agentive subject, patient, action effector, perceptual modality, instrumentality, emotional valence, action imageability, action complexity, action intensity, and the usage scenario of action. We calculated the semantic features of each verb and the correlation between dimensions. We also compared the difference between action, mental, and other verbs and gave some examples about how to use CVFD to classify verbs according to different dimensions. Finally, we discussed the potential applications of CVFD in the fields of neuroscience, psycholinguistics, cultural differences, and artificial intelligence. All the data can be found at https://osf.io/pv29z/ .

Touch to learn: Multisensory input supports word learning and processing

Infants experience language in rich multisensory environments. For example, they may first be exposed to the word applesauce while touching, tasting, smelling, and seeing applesauce. In three experiments using different methods we asked whether the number of distinct senses linked with the semantic features of objects would impact word recognition and learning. Specifically, in Experiment 1 we asked whether words linked with more multisensory experiences were learned earlier than words linked fewer multisensory experiences. In Experiment 2, we asked whether 2-year-olds' known words linked with more multisensory experiences were better recognized than those linked with fewer. Finally, in Experiment 3, we taught 2-year-olds labels for novel objects that were linked with either just visual or visual and tactile experiences and asked whether this impacted their ability to learn the new label-to-object mappings. Results converge to support an account in which richer multisensory experiences better support word learning. We discuss two pathways through which rich multisensory experiences might support word learning.

Stimulus-response compatibility effects during object semantic categorisation: Evocation of grasp affordances or abstract coding of object size?

When the size of visual objects matches the size of the response required to perform the task, a potentiation effect has been reported, with faster responses in compatible than incompatible situations. Size compatibility effects have been taken as evidence of close perception-action interrelations. However, it is still unclear whether the effect arises from abstract coding of the size of stimulus and response or from the evocation of grasp affordances from visual objects. We aimed to disentangle the two interpretations. Two groups of 40 young adults categorised small and large objects presented in standardised size as natural or artefact objects. One group categorised manipulable objects that may be associated with small or large size properties and evoke power or precision grasp affordances. The other group categorised non-manipulable objects that may only be associated with small or large size properties. Categorisation responses were made by reaching and grasping a monotonic cylindric device with a power or precision grip in a grasping condition and with large or small touch responses in a control condition. Compatibility effects were found in both grasping and control conditions, independently of the manipulability or category of objects. Participants were faster when the size of the expected response matched the size of the object than when they mismatched, especially for power grasps or whole-hand touch responses. Overall findings support the abstract coding hypothesis and suggest that compatibility between the conceptual size of the object and the size of the hand response is sufficient to facilitate semantic categorisation judgements.

Tomatoes Are Red: The Perception of Achromatic Objects Elicits Retrieval of Associated Color Knowledge

When preparing to name an object, semantic knowledge about the object and its attributes is activated, including perceptual properties. It is unclear, however, whether semantic attribute activation contributes to lexical access or is a consequence of activating a concept irrespective of whether that concept is to be named or not. In this study, we measured neural responses using fMRI while participants named objects that are typically green or red, presented in black line drawings. Furthermore, participants underwent two other tasks with the same objects, color naming and semantic judgment, to see if the activation pattern we observe during picture naming is (a) similar to that of a task that requires accessing the color attribute and (b) distinct from that of a task that requires accessing the concept but not its name or color. We used representational similarity analysis to detect brain areas that show similar patterns within the same color category, but show different patterns across the two color categories. In all three tasks, activation in the bilateral fusiform gyri ("Human V4") correlated with a representational model encoding the red-green distinction weighted by the importance of color feature for the different objects. This result suggests that when seeing objects whose color attribute is highly diagnostic, color knowledge about the objects is retrieved irrespective of whether the color or the object itself have to be named.

Using agreement probability to study differences in types of concepts and conceptualizers

Agreement probability p(a) is a homogeneity measure of lists of properties produced by participants in a Property Listing Task (PLT) for a concept. Agreement probability's mathematical properties allow a rich analysis of property-based descriptions. To illustrate, we use p(a) to delve into the differences between concrete and abstract concepts in sighted and blind populations. Results show that concrete concepts are more homogeneous within sighted and blind groups than abstract ones (i.e., exhibit a higher p(a) than abstract ones) and that concrete concepts in the blind group are less homogeneous than in the sighted sample. This supports the idea that listed properties for concrete concepts should be more similar across subjects due to the influence of visual/perceptual information on the learning process. In contrast, abstract concepts are learned based mainly on social and linguistic information, which exhibit more variability among people, thus, making the listed properties more dissimilar across subjects. Relative to abstract concepts, the difference in p(a) between sighted and blind is not statistically significant. Though this is a null result, and should be considered with care, it is expected because abstract concepts should be learned by paying attention to the same social and linguistic input in both, blind and sighted, and thus, there is no reason to expect that the respective lists of properties should differ. Finally, we used p(a) to classify concrete and abstract concepts with a good level of certainty. All these analyses suggest that p(a) can be fruitfully used to study data obtained in a PLT.

Semantic feature norms: a cross-method and cross-language comparison

The ability to assign meaning to perceptual stimuli forms the basis of human behavior and the ability to use language. The meanings of things have primarily been probed using behavioral production norms and corpus-derived statistical methods. However, it is not known to what extent the collection method and the language being probed influence the resulting semantic feature vectors. In this study, we compare behavioral with corpus-based norms, across Finnish and English, using an all-to-all approach. To complete the set of norms required for this study, we present a new set of Finnish behavioral production norms, containing both abstract and concrete concepts. We found that all the norms provide largely similar information about the relationships of concrete objects and allow item-level mapping across norms sets. This validates the use of the corpus-derived norms which are easier to obtain than behavioral norms, which are labor-intensive to collect, for studies that do not depend on subtle differences in meaning between close semantic neighbors.

Language with vision: A study on grounded word and sentence embeddings

Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioral datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT (Devlin et al, 2018), but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at ( https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2 ).

No need to integrate action information during coarse semantic processing of man-made tools

Action representation of man-made tools consists of two subtypes: structural action representation concerning how to grasp an object, and functional action representation concerning the skilled use of an object. Compared to structural action representation, functional action representation plays the dominant role in fine-grained (i.e., basic level) object recognition. However, it remains unclear whether the two types of action representation are involved differently in the coarse semantic processing in which the object is recognized at a superordinate level (i.e., living/non-living). Here we conducted three experiments using the priming paradigm, in which video clips displaying structural and functional action hand gestures were used as prime stimuli and grayscale photos of man-made tools were used as target stimuli. Participants recognized the target objects at the basic level in Experiment 1 (i.e., naming task) and at the superordinate level in Experiments 2 and 3 (i.e., categorization task). We observed a significant priming effect for functional action prime-target pairs only in the naming task. In contrast, no priming effect was found in either the naming or the categorization task for the structural action prime-target pairs (Experiment 2), even when the categorization task was preceded by a preliminary action imitation of the prime gestures (Experiment 3). Our results suggest that only functional action information is retrieved during fine-grained object processing. In contrast, coarse semantic processing does not require the integration of either structural or functional action information.

The effects of sensorimotor and linguistic information on the basic-level advantage

The basic-level advantage is one of the best-known effects in human categorisation. Traditional accounts argue that basic-level categories present a maximally informative or entry level into a taxonomic organisation of concepts in semantic memory. However, these explanations are not fully compatible with most recent views on the structure of the conceptual system such as linguistic-simulation accounts, which emphasise the dual role of sensorimotor (i.e., perception-action experience of the world) and linguistic distributional information (i.e., statistical distribution of words in language) in conceptual processing. In four preregistered word→picture categorisation studies, we examined whether novel measures of sensorimotor and linguistic distance contribute to the basic level-advantage in categorical decision-making. Results showed that overlap in sensorimotor experience between category concept and member concept (e.g., animal→dog) predicted RT and accuracy at least as well as a traditional division into discrete subordinate, basic, and superordinate taxonomic levels. Furthermore, linguistic distributional information contributed to capturing effects of graded category structure where typicality ratings did not. Finally, when image label production frequency was taken into account (i.e., how often people actually produced specific labels for images), linguistic distributional information predicted RT and accuracy above and beyond sensorimotor information. These findings add to our understanding of how sensorimotor-linguistic theories of the conceptual system can explain categorisation behaviour.

Representational shifts: increasing motivation for bottled water through simulation-enhancing advertisements

BACKGROUND

Despite its numerous health benefits, consumers' daily water consumption is below recommend levels while soft drink consumption remains high. Previous research has shown that the degree to which drinks are cognitively represented in terms of consumption and enjoyment (i.e., through simulations of consumption and reward) predicts desire and intake. Here, we examined whether simulation-enhancing advertisements that frame water in terms of consumption and reward change cognitive representations and increase motivation for a fictitious bottled water.

METHODS

In three pre-registered online experiments (Nexp1 = 984; Nexp2 = 786; Nexp3 = 907), UK participants viewed three advertisements that either highlighted the rewarding consumption experience of water (e.g., "Refresh all your senses with this smooth, cool water"; simulation-enhancing ads), the health consequences of drinking water (e.g., "This water takes care of your health"; health-focused ads), or control ads. We assessed cognitive representations of the bottled water with a semantic feature production task, and we coded the words used as consumption and reward features or positive long-term health consequences features. We assessed motivation through ratings of the attractiveness of the water (Exp. 1 only), desire to drink it, and willingness to pay for it (WTP).

RESULTS

In line with our hypotheses, participants represented the bottled water more in terms of consumption and reward after viewing simulation-enhancing advertisements, and more in terms of positive long-term health consequences after viewing health-focused advertisements. There was no direct effect of advertisement condition on motivation ratings. However, significant indirect effects showed that simulation-enhancing advertisements increased desire and WTP through the proportion of consumption and reward features, whereas health-focused advertisements increased motivation through an increase in the proportion of positive long-term health consequences features. The effects through consumption and reward were stronger.

CONCLUSIONS

These findings are consistent with research suggesting that the experience of immediate reward from drinking water underlies intake. Public health interventions should emphasize the enjoyment of drinking water, rather than the long-term health benefits.

Locating what comes to mind in empirically derived representational spaces

Real-world judgements and decisions often require choosing from an open-ended set of options which cannot be exhaustively considered before a choice is made. Recent work has found that the options people do consider tend to have particular features, such as high historical value. Here, we pursue the idea that option generation during decision making may reflect a more general mechanism for calling things to mind, by which relevant features in a context-appropriate representational space guide what comes to mind. In this paper, we evaluate this proposal primarily based on what comes to mind in different familiar categories. We first introduce an empirical approach for deriving the space of features that people use to represent items in a particular category and for locating the category members that come to mind within that space. We show that in both familiar and ad hoc categories, a category member's location along certain dimensions of the derived feature space predicts its likelihood of coming to mind. Next, we show that category members from these feature space locations come to mind by default in a way that is somewhat impervious to conscious control. We then demonstrate that the extent to which a given dimension is a predictor of what comes to mind within a category is related to how relevant that feature is for representing the category in question, using a novel measure of general feature relevance. Finally, we illustrate the usefulness of this framework in the context of a decision making task. We close with the proposal that people call category members to mind according to their location in representational space, specifically based on the predicted usefulness of considering category members with particular features.

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .

The unseen, the seen, and the spoken: Latent and overt priming in cyclic picture naming

Language production entails selecting words in the face of internally, and sometimes externally, driven competition that influences the long-term accessibility of both selected words and competitors. Because both endogenous and externally presented competitors usually result in semantic interference, it is often assumed that they engage the same underlying processes. We question this assumption. Specifically, we propose that latent primes may more naturally commingle with endogenous lexical activation whereas overt primes elicit strong control processes. Two experiments examined the effects of latent (masked) and overt (picture-word) priming in combination with cyclic picture naming of small sets of taxonomically or thematically related or unrelated pictures. A subsequent continuous picture naming phase was designed to assess enduring effects of service as a prime or target. Only taxonomic relations showed substantial interference in cyclic naming. Latent priming tended to increase interference for taxonomic relations, but it produced facilitation for thematic relations. In contrast, overt priming induced interference for both types of relation, indicating exertion of cognitive control. In the continuous picture naming phases, accessibility was hindered for previously presented latent primes (Experiment 1) but enhanced for previously overt primes (Experiment 2). Surprisingly, interference in cyclic naming did not carry forward to the continuous phase. These findings suggest that masked priming may be a viable intervention in the internal dynamics of lexical selection. In contrast, they also add to the body of evidence questioning the validity of using picture-word procedures to study inherent semantic interference.

Perirhinal cortex is associated with fine-grained discrimination of conceptually confusable objects in Alzheimer's disease

The perirhinal cortex (PrC) stands among the first brain areas to deteriorate in Alzheimer's disease (AD). This study tests to what extent the PrC is involved in representing and discriminating confusable objects based on the conjunction of their perceptual and conceptual features. To this aim, AD patients and control counterparts performed 3 tasks: a naming, a recognition memory, and a conceptual matching task, where we manipulated conceptual and perceptual confusability. A structural MRI of the antero-lateral parahippocampal subregions was obtained for each participant. We found that the sensitivity to conceptual confusability was associated with the left PrC volume in both AD patients and control participants for the recognition memory task, while it was specifically associated with the volume of the left PrC in AD patients for the conceptual matching task. This suggests that a decreased volume of the PrC is related to the ability to disambiguate conceptually confusable items. Therefore, testing recognition memory or conceptual matching of easily conceptually confusable items can provide a potential cognitive marker of PrC atrophy.

Multidimensional object properties are dynamically represented in the human brain

Our visual world consists of an immense number of unique objects and yet, we are easily able to identify, distinguish, interact, and reason about the things we see within several hundred milliseconds. This requires that we flexibly integrate and focus on different object properties to support specific behavioral goals. In the current study, we examined how these rich object representations unfold in the human brain by modelling time-resolved MEG signals evoked by viewing thousands of objects. Using millions of behavioral judgments to guide our understanding of the neural representation of the object space, we find distinct temporal profiles across the object dimensions. These profiles fell into two broad types with either a distinct and early peak (~150 ms) or a slow rise to a late peak (~300 ms). Further, the early effects are stable across participants in contrast to later effects which show more variability across people. This highlights that early peaks may carry stimulus-specific and later peaks subject-specific information. Given that the dimensions with early peaks seem to be primarily visual dimensions and those with later peaks more conceptual, our results suggest that conceptual processing is more variable across people. Together, these data provide a comprehensive account of how a variety of object properties unfold in the human brain and contribute to the rich nature of object vision.

On choosing the vehicles of metaphors 2.0: the interactive effects of semantic neighborhood density and body-object interaction on metaphor production

In a metaphor, such as language is a bridge, two distinct concepts known as the topic (i.e., language) and vehicle (i.e., bridge) are juxtaposed to produce figurative meaning. Previous work demonstrated that, when creating metaphors, participants choose vehicles that are concrete, rather than abstract, and are also a moderate semantic distance away from the topic. However, little is known about the semantic representations underlying metaphor production beyond topic-vehicle semantic distance and vehicle concreteness. Here, we studied the role of two semantic richness variables in metaphor production - semantic neighborhood density (SND), which measures the proximity of a word and its associations in semantic space, and body-object interaction (BOI), which reflects the ease with which a human body can motorically interact with a word's referent. In each trial, participants were presented with an abstract topic, such as miracle, and were instructed to make an apt and comprehensible metaphor by choosing a vehicle word (e.g., lighthouse). All of the topics were abstract but half were high-SND (from dense semantic neighborhoods) and half were low-SND (from sparse semantic neighborhoods). Similarly, half of the potential vehicle words were either high or low in SND and also differed on BOI such that half were high-BOI (e.g., bicycle), whereas half were low-BOI (e.g., rainbow). We observed a three-way interaction such that participants selected low-BOI, rather than high-BOI, vehicle words when topics or vehicles were high-SND. We interpret this finding to suggest that participants attempt to reduce the overall semantic richness of their created metaphors.

Items Outperform Adjectives in a Computational Model of Binary Semantic Classification

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human-range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory.

The Stroop legacy: A cautionary tale on methodological issues and a proposed spatial solution

The Stroop task is a seminal paradigm in experimental psychology, so much that various variants of the classical color-word version have been proposed. Here we offer a methodological review of them to emphasize the importance of designing methodologically rigorous Stroop tasks. This is not an end by itself, but it is fundamental to achieve adequate measurement validity, which is currently hindered by methodological heterogeneity and limitations. Among the several Stroop task variants in the literature, our methodological overview shows that the spatial Stroop task is not only a potentially methodologically adequate variant, which can thus assure measuring the Stroop effect with the required validity, but it might even allow researchers to overcome some of the methodological limitations of the classical paradigm due to its use of verbal stimuli. We thus focused on the spatial Stroop tasks in the literature to verify whether they really exploit such inherent potentiality. However, we show that this was generally not the case because only a few of them (1) are purely spatial, (2) ensure both all the three types of conflicts/facilitations (at the stimulus, response, and task levels) and the dimensional overlaps considered fundamental for yielding a complete Stroop effect according to the multiple loci account and Kornblum's theory, respectively, and (3) controlled for low-level binding and priming effects that could bias the estimated Stroop effect. Based on these methodological considerations, we present some examples of spatial Stroop tasks that, in our view, satisfy such requirements and, thus, ensure producing complete Stroop effects.

Effects of prior knowledge on brain activation and functional connectivity during memory retrieval

Previous studies have shown that the ventral medial prefrontal cortex (vmPFC) plays an important role in schema-related memory. However, there is an intensive debate to what extent the activation of subregions of the hippocampus is involved in retrieving schema-related memory. In addition, it is unclear how the functional connectivity (FC) between the vmPFC and the hippocampus, as well as the connectivity of the vmPFC with other regions, are modulated by prior knowledge (PK) during memory retrieval over time. To address these issues, participants learned paragraphs that described features of each unfamiliar word from familiar and unfamiliar categories (i.e., high and low PK conditions) 20 min, 1 day, and 1 week before the test. They then performed a recognition task to judge whether the sentences were old in the scanner. The results showed that the activation of the anterior-medial hippocampus (amHPC) cluster was stronger when the old sentences with high (vs. low) PK were correctly retrieved. The activation of the posterior hippocampus (pHPC) cluster, as well as the vmPFC, was stronger when the new sentences with high (vs. low) PK were correctly rejected (i.e., CR trials), whereas the cluster of anterior-lateral hippocampus (alHPC) showed the opposite. The FC of the vmPFC with the amHPC and perirhinal cortex/inferior temporal gyrus was stronger in the high (vs. low) PK condition, whereas the FC of the vmPFC with the alHPC, thalamus and frontal regions showed the opposite for the CR trials. This study highlighted that different brain networks, which were associated with the vmPFC, subregions of the hippocampus and cognitive control regions, were responsible for retrieving the information with high and low PK.

BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation

Spiking neural networks (SNNs) serve as a promising computational framework for integrating insights from the brain into artificial intelligence (AI). Existing software infrastructures based on SNNs exclusively support brain simulation or brain-inspired AI, but not both simultaneously. To decode the nature of biological intelligence and create AI, we present the brain-inspired cognitive intelligence engine (BrainCog). This SNN-based platform provides essential infrastructure support for developing brain-inspired AI and brain simulation. BrainCog integrates different biological neurons, encoding strategies, learning rules, brain areas, and hardware-software co-design as essential components. Leveraging these user-friendly components, BrainCog incorporates various cognitive functions, including perception and learning, decision-making, knowledge representation and reasoning, motor control, social cognition, and brain structure and function simulations across multiple scales. BORN is an AI engine developed by BrainCog, showcasing seamless integration of BrainCog's components and cognitive functions to build advanced AI models and applications.

Novel participant-level meta-analytic evidence for AbSANT efficacy

The current study employed interrupted time series (ITS) models to analyze all available (published and unpublished) Abstract Semantic Associative Network Training (AbSANT) data. AbSANT is a semantically focused anomia treatment that targets not only concrete but abstract words, unique among existing anomia treatments. However, evidence to date for the positive effects of AbSANT comes only from small-sample, single-subject design studies, limiting the strength of this evidence and the inferences that can be drawn from it. The statistical power and greater representativeness afforded by this larger aggregated sample enabled us to look for group-level efficacy evidence for AbSANT, examine specific questions about AbSANT's direct training and generalization effects, and identify potential predictors and mechanisms of AbSANT treatment response. We found that across 33 participants from four different data sources, AbSANT appears to be a robust word retrieval therapy protocol, with overall direct training and generalization effects that were more meaningful than exposure effects. Similar to previous smaller-sample study conclusions, we found that in this large sample, training abstract words results not only in larger direct training effects than training concrete words, but also larger generalization effects, suggesting that while AbSANT successfully improves both abstract and concrete word retrieval, it may be better suited for training abstract words. In general, direct training effects were more persistent after treatment ended than generalization effects and effects for concrete words were more persistent than for abstract words. Additionally, the effects of generalization appear to be distinct from the effects of simple exposure to words, and generalization effects are consistent with AbSANT's hypothesized mechanism of action: spreading activation from directly trained concepts to related concepts. Also consistent with this mechanism, we found that milder aphasia and both conceptual and lexical semantic processing ability predicted both direct training and generalization gains, and that executive function was predictive of generalization effects. These factors are thus important to consider when evaluating the appropriateness of AbSANT for individual clients.

Sensitivity to Semantic Relationships in U.S. Monolingual English-Speaking Typical Talkers and Late Talkers

PURPOSE

Late talkers (LTs) are a group of children who exhibit delays in language development without a known cause. Although a hallmark of LTs is a reduced expressive vocabulary, little is known about LTs' processing of semantic relations among words in their emerging vocabularies. This study uses an eye-tracking task to compare 2-year-old LTs' and typical talkers' (TTs') sensitivity to semantic relationships among early acquired words.

METHOD

U.S. monolingual English-speaking LTs (n = 21) and TTs (n = 24) completed a looking-while-listening task in which they viewed two images on a screen (e.g., a shirt and a pizza), while they heard words that referred to one of the images (e.g., Look! Shirt!; target-present condition) or a semantically related item (e.g., Look! Hat!; target-absent condition). Children's eye movements (i.e., looks to the target) were monitored to assess their sensitivity to these semantic relationships.

RESULTS

Both LTs and TTs looked longer at the semantically related image than the unrelated image on target-absent trials, demonstrating sensitivity to the taxonomic relationships used in the experiment. There was no significant group difference between LTs and TTs. Both groups also looked more to the target in the target-present condition than in the target-absent condition.

CONCLUSIONS

These results reveal that, despite possessing smaller expressive vocabularies, LTs have encoded semantic relationships in their receptive vocabularies and activate these during real-time language comprehension. This study furthers our understanding of LTs' emerging linguistic systems and language processing skills.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.23303987.

The cognitive structure underlying the organization of observed actions

In daily life, we frequently encounter actions performed by other people. Here we aimed to examine the key categories and features underlying the organization of a wide range of actions in three behavioral experiments (N = 378 participants). In Experiment 1, we used a multi-arrangement task of 100 different actions. Inverse multidimensional scaling and hierarchical clustering revealed 11 action categories, including Locomotion, Communication, and Aggressive actions. In Experiment 2, we used a feature-listing paradigm to obtain a wide range of action features that were subsequently reduced to 59 key features and used in a rating study (Experiment 3). A direct comparison of the feature ratings obtained in Experiment 3 between actions belonging to the categories identified in Experiment 1 revealed a number of features that appear to be critical for the distinction between these categories, e.g., the features Harm and Noise for the category Aggressive actions, and the features Targeting a person and Contact with others for the category Interaction. Finally, we found that a part of the category-based organization is explained by a combination of weighted features, whereas a significant proportion of variability remained unexplained, suggesting that there are additional sources of information that contribute to the categorization of observed actions. The characterization of action categories and their associated features serves as an important extension of previous studies examining the cognitive structure of actions. Moreover, our results may serve as the basis for future behavioral, neuroimaging and computational modeling studies.

Offline dominance and zeugmatic similarity normings of variably ambiguous words assessed against a neural language model (BERT)

For any research program examining how ambiguous words are processed in broader linguistic contexts, a first step is to establish factors relating to the frequency balance or dominance of those words' multiple meanings, as well as the similarity of those meanings to one other. Homonyms-words with divergent meanings-are one ambiguous word type commonly utilized in psycholinguistic research. In contrast, although polysemes-words with multiple related senses-are far more common in English, they have been less frequently used as tools for understanding one-to-many word-to-meaning mappings. The current paper details two norming studies of a relatively large number of ambiguous English words. In the first, offline dominance norming is detailed for 547 homonyms and polysemes via a free association task suitable for words across the ambiguity continuum, with a goal of identifying words with more equibiased meanings. The second norming assesses offline meaning similarity for a partial subset of 318 ambiguous words (including homonyms, unambiguous words, and polysemes divided into regular and irregular types) using a novel, continuous rating method reliant on the linguistic phenomenon of zeugma. In addition, we conduct computational analyses on the human similarity norming data using the BERT pretrained neural language model (Devlin et al., 2018, BERT: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint. arXiv:1810.04805) to evaluate factors that may explain variance beyond that accounted for by dictionary-criteria ambiguity categories. Finally, we make available the summarized item dominance values and similarity ratings in resultant appendices (see supplementary material), as well as individual item and participant norming data, which can be accessed online ( https://osf.io/g7fmv/ ).

Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure

In adults, nouns and verbs have varied and multilevel semantic interrelationships. In children, evidence suggests that nouns and verbs also have semantic interrelationships, though the timing of the emergence of these relationships and their precise impact on later noun and verb learning are not clear. In this work, we ask whether noun and verb semantic knowledge in 16-30-month-old children tend to be semantically isolated from one another or semantically interacting from the onset of vocabulary development. Early word learning patterns were quantified using network science. We measured the semantic network structure for nouns and verbs in 3,804 16-30-month-old children at several levels of granularity using a large, open dataset of vocabulary checklist data. In a cross-sectional approach in Experiment 1, early nouns and verbs exhibited stronger network relationships with other nouns and verbs than expected across multiple network levels. Using a longitudinal approach in Experiment 2, we examined patterns of normative vocabulary development over time. Initial noun and verb learning was supported by strong semantic connections to other nouns, whereas later-learned words exhibited strong connections to verbs. Overall, these two experiments suggest that nouns and verbs demonstrate early semantic interactions and that these interactions impact later word learning. Early verb and noun learning is affected by the emergence of noun and verb semantic networks during early lexical development.

Perirhinal cortex automatically tracks multiple types of familiarity regardless of task-relevance

Perirhinal cortex (PrC) has long been implicated in familiarity assessment for objects and corresponding concepts. However, extant studies have focused mainly on changes in familiarity induced by recent exposure in laboratory settings. There is an increasing appreciation of other types of familiarity signals, in particular graded familiarity accumulated throughout one's lifetime. In prior work (Duke et al., 2017, Cortex, 89, 61-70), PrC has been shown to track lifetime familiarity ratings when participants make related judgements. A theoretically important characteristic of familiarity is its proposed automaticity. Support for automaticity comes from a documented impact of recent stimulus exposure on behavioral performance, and on PrC signals, under conditions in which this exposure is not task relevant. In the current fMRI study, we tested whether PrC also tracks lifetime familiarity of object concepts automatically, and whether this type of familiarity influences behavior even when it is not task relevant. During scanning, neurotypical participants (N = 30, age range 18-40, 7 males) provided animacy judgements about concrete object concepts presented at differing frequencies in an initial study phase. In a subsequent test phase, they made graded judgements of recent or lifetime familiarity. Behavioral performance showed sensitivity to lifetime familiarity even when it was not relevant for the task at hand. Across five sets of fMRI analyses, we found that PrC consistently tracked recent and lifetime familiarity of object concepts regardless of the task at hand. Critically, while several other temporal-lobe regions also showed isolated familiarity effects, none of them tracked familiarity with the same consistency. These findings demonstrate that PrC automatically tracks multiple types of familiarity. They support models that assign a broad role in the representation of information about object concepts to this structure.

The Affordance Directive: Affordance Priming Facilitates Object Detection Similar to Semantic Priming

The present study investigated differences in priming perceptions of target objects via affordance or semantic primes. Affordances denote possibilities for action in relation to objects (e.g., chair - sit), whereas semantic primes describe related concepts and features of objects (e.g., chair - legs). In Experiments 1A/1B the effects of affordance and semantic priming were compared via a semantic-categorization task using a normed word list of objects. In Experiments 2-4 we investigated affordance priming on object identification of pictures using a shoebox-classification task. In Experiment 1A participants were asked to respond by categorizing the presented word as concrete or abstract. Experiment 1B was similar to 1A, but with a 1000 ms response deadline. Experiment 2 presented target objects as words or photographs. Experiment 3 presented target objects as photographs degraded at three levels (clear, medium blur, extreme blur). Experiment 4 presented target objects as photographs that began degraded and slowly became clear. Experiment 1B found word priming for semantic primes, but not affordances. In contrast, Experiments 2-4 found object priming was facilitated by both affordances and semantic primes. Collectively, our results indicate that affordances facilitate object classification.

Dimensions underlying human understanding of the reachable world

Near-scale environments, like work desks, restaurant place settings or lab benches, are the interface of our hand-based interactions with the world. How are our conceptual representations of these environments organized? What properties distinguish among reachspaces, and why? We obtained 1.25 million similarity judgments on 990 reachspace images, and generated a 30-dimensional embedding which accurately predicts these judgments. Examination of the embedding dimensions revealed key properties underlying these judgments, such as reachspace layout, affordance, and visual appearance. Clustering performed over the embedding revealed four distinct interpretable classes of reachspaces, distinguishing among spaces related to food, electronics, analog activities, and storage or display. Finally, we found that reachspace similarity ratings were better predicted by the function of the spaces than their locations, suggesting that reachspaces are largely conceptualized in terms of the actions they support. Altogether, these results reveal the behaviorally-relevant principles that structure our internal representations of reach-relevant environments.

Semantic feature production norms for manipulable objects

Feature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification.

Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract-concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb-noun thematic role relations and noun-noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations.

Independency of Coding for Affective Similarities and for Word Co-occurrences in Temporal Perisylvian Neocortex

Word valence is one of the principal dimensions in the organization of word meaning. Co-occurrence-based similarities calculated by predictive natural language processing models are relatively poor at representing affective content, but very powerful in their own way. Here, we determined how these two canonical but distinct ways of representing word meaning relate to each other in the human brain both functionally and neuroanatomically. We re-analysed an fMRI study of word valence. A co-occurrence-based model was used and the correlation with the similarity of brain activity patterns was compared to that of affective similarities. The correlation between affective and co-occurrence-based similarities was low (r = 0.065), confirming that affect was captured poorly by co-occurrence modelling. In a whole-brain representational similarity analysis, word embedding similarities correlated significantly with the similarity between activity patterns in a region confined to the superior temporal sulcus to the left, and to a lesser degree to the right. Affective word similarities correlated with the similarity in activity patterns in this same region, confirming previous findings. The affective similarity effect extended more widely beyond the superior temporal cortex than the effect of co-occurrence-based similarities did. The effect of co-occurrence-based similarities remained unaltered after partialling out the effect of affective similarities (and vice versa). To conclude, different aspects of word meaning, derived from affective judgements or from word co-occurrences, are represented in superior temporal language cortex in a neuroanatomically overlapping but functionally independent manner.

Are they really stronger? Comparing effects of semantic variables in speeded deadline and standard picture naming

Investigations of effects of semantic variables on picture naming have often been inconclusive, with some studies reporting significant and others non-significant effects. One potential explanation may relate to the specific naming tasks used: While most previous studies have used standard picture naming, others have used speeded naming that requires participants to prioritise naming speed over accuracy. Speeded naming has been suggested to cause enhanced effects of item-inherent word characteristics due to disruptions of cognitive control and resulting modulations of responsiveness to input. Consequently, this study investigated whether effects are stronger in speeded compared to standard picture naming, focusing on six feature-based semantic variables: number of semantic features, intercorrelational density, number of near semantic neighbours, semantic similarity, typicality, and distinctiveness. The results showed few differences in the variables' effects between the two naming tasks: In the naming latency analysis, the inhibitory effect of distinctiveness was stronger in the speeded naming task, while in the accuracy analysis the effect of number of semantic features was stronger in the standard naming task. These findings cannot, therefore, be exclusively accounted for by increased responsiveness to input in speeded naming and we discuss possible underlying mechanisms. We conclude that, while some differences in effects of semantic variables between previous studies may have been caused by the specific naming task used, differences between studies more likely depend on statistical power and control of other influential variables in the experiment.

Learning strategy impacts medical diagnostic reasoning in early learners

Relating learned information to similar yet new scenarios, transfer of learning, is a key characteristic of expert reasoning in many fields including medicine. Psychological research indicates that transfer of learning is enhanced via active retrieval strategies. For diagnostic reasoning, this finding suggests that actively retrieving diagnostic information about patient cases could improve the ability to engage in transfer of learning to later diagnostic decisions. To test this hypothesis, we conducted an experiment in which two groups of undergraduate student participants learned symptom lists of simplified psychiatric diagnoses (e.g., Schizophrenia; Mania). Next, one group received written patient cases and actively retrieved the cases from memory and the other group read these written cases twice, engaging in a passive rehearsal learning strategy. Both groups then diagnosed test cases that had two equally valid diagnoses-one supported by "familiar" symptoms described in learned patient cases, and one by novel symptom descriptions. While all participants were more likely to assign higher diagnostic probability to those supported by the familiar symptoms, this effect was significantly larger for participants that engaged in active retrieval compared to passive rehearsal. There were also significant differences in performance across the given diagnoses, potentially due to differences in established knowledge of the disorders. To test this prediction, Experiment 2 compared performance on the described experiment between a participant group that received the standard diagnostic labels to a group that received fictional diagnostic labels, nonsense words designed to remove prior knowledge with each diagnosis. As predicted, there was no effect of diagnosis on task performance for the fictional label group. These results provide new insight on the impact of learning strategy and prior knowledge in fostering transfer of learning, potentially contributing to expert development in medicine.

Decoding semantic representations in mind and brain

A key goal for cognitive neuroscience is to understand the neurocognitive systems that support semantic memory. Recent multivariate analyses of neuroimaging data have contributed greatly to this effort, but the rapid development of these novel approaches has made it difficult to track the diversity of findings and to understand how and why they sometimes lead to contradictory conclusions. We address this challenge by reviewing cognitive theories of semantic representation and their neural instantiation. We then consider contemporary approaches to neural decoding and assess which types of representation each can possibly detect. The analysis suggests why the results are heterogeneous and identifies crucial links between cognitive theory, data collection, and analysis that can help to better connect neuroimaging to mechanistic theories of semantic cognition.

Delayed Lexical Access and Cascading Effects on Spreading Semantic Activation During Spoken Word Recognition in Children With Hearing Aids and Cochlear Implants: Evidence From Eye-Tracking

OBJECTIVE

The objective of this study was to characterize the dynamics of real-time lexical access, including lexical competition among phonologically similar words, and spreading semantic activation in school-age children with hearing aids (HAs) and children with cochlear implants (CIs). We hypothesized that developing spoken language via degraded auditory input would lead children with HAs or CIs to adapt their approach to spoken word recognition, especially by slowing down lexical access.

DESIGN

Participants were children ages 9- to 12-years old with normal hearing (NH), HAs, or CIs. Participants completed a Visual World Paradigm task in which they heard a spoken word and selected the matching picture from four options. Competitor items were either phonologically similar, semantically similar, or unrelated to the target word. As the target word unfolded, children's fixations to the target word, cohort competitor, rhyme competitor, semantically related item, and unrelated item were recorded as indices of ongoing lexical access and spreading semantic activation.

RESULTS

Children with HAs and children with CIs showed slower fixations to the target, reduced fixations to the cohort competitor, and increased fixations to the rhyme competitor, relative to children with NH. This wait-and-see profile was more pronounced in the children with CIs than the children with HAs. Children with HAs and children with CIs also showed delayed fixations to the semantically related item, although this delay was attributable to their delay in activating words in general, not to a distinct semantic source.

CONCLUSIONS

Children with HAs and children with CIs showed qualitatively similar patterns of real-time spoken word recognition. Findings suggest that developing spoken language via degraded auditory input causes long-term cognitive adaptations to how listeners recognize spoken words, regardless of the type of hearing device used. Delayed lexical access directly led to delays in spreading semantic activation in children with HAs and CIs. This delay in semantic processing may impact these children's ability to understand connected speech in everyday life.

Cross-domain semantic alignment: concrete concepts are more abstract than you think

We can easily evaluate similarities between concepts within semantic domains, e.g. doctor and nurse, or violin and piano. Here, we show that people are also able to evaluate similarities across domains, e.g. aligning doctors with pianos and nurses with violins. We argue that understanding how people do this is important for understanding conceptual organization and the ubiquity of metaphorical language. We asked people to answer questions of the form 'If a nurse were an animal, they would be a(n) …' (Experiments 1 and 2) and asked them to explain the basis for their response (Experiment 1). People converged to a surprising degree (e.g. 20% answered 'cat'). In Experiment 3, we presented people with cross-domain mappings of the form 'If a nurse were an animal, they would be a cat' and asked them to indicate how good each mapping was. The results showed that the targets people chose and their goodness ratings of a given response were predicted by similarity along abstract semantic dimensions such as valence, speed and genderedness. Reliance on such dimensions was also the most common explanation for their responses. Altogether, we show that people can evaluate similarity between very different domains in predictable ways, suggesting either that seemingly concrete concepts are represented along relatively abstract dimensions (e.g. weak-strong) or that they can be readily projected onto these dimensions. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies

In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.

Feature-rich multiplex lexical networks reveal mental strategies of early language learning

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.

Nonspecific Effects of Normal Aging on Taxonomic and Thematic Semantic Processing

OBJECTIVE

This study aimed to assess the effect of normal aging on the processing of taxonomic and thematic semantic relations.

METHOD

We used the Visual-World-Paradigm coupled with eye-movement recording. We compared performance of healthy younger and older adults on a word-to-picture matching task in which participants had to identify each target among semantically related (taxonomic or thematic) and unrelated distractors.

RESULTS

Younger and older participants exhibited similar patterns of gaze fixations in the two semantic conditions. The effect of aging took the form of an overall reduction in sensitivity to semantic competitors, with no difference between the taxonomic and thematic conditions. Moreover, comparison of the proportions of fixations between the younger and older participants indicated that targets were identified equally quickly in both age groups. This was not the case when mouse-click reaction times were analyzed.

CONCLUSIONS

Findings argue in favor of nonspecific effects of normal aging on semantic processing that similarly affect taxonomic and thematic processing. There are important clinical implications, as pathological aging has been repeatedly shown to selectively affect either taxonomic or thematic relations. Measuring eye-movements in a semantic task is also an interesting approach in the elderly, as these seem to be less impacted by aging than other motor responses.