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Most printed Chinese words are compounds built from the combination of meaningful characters. Yet, there is a poor understanding of how individual characters contribute to the recognition of compounds. Using a megastudy of Chinese word recognition (Tse et al., 2017), we examined how the lexical decision of existing and novel Chinese compounds was influenced by two properties of individual characters: family size (the number of distinct words that embed a character) and family semantic consistency (the average semantic relatedness between a character and all words containing it). Results revealed that both variables influence word and nonword processing:Words are recognized more quickly and accurately when they contain characters that occur frequently across different words and that make consistent meaningful contributions to those words, while nonwords containing those types of characters are rejected more slowly. These findings suggest that the learning of individual characters is based not only on the quantity of experience with them but also on the reliability of the semantic information they communicate. In addition, readers are able to generalize character knowledge acquired from previous word experiences to their daily encounters with familiar and unfamiliar words. We close by discussing how word experience shapes character knowledge when different ways of calculating family properties are considered. [ABSTRACT FROM AUTHOR]

The formation of false memories is one of the most widely studied topics in cognitive psychology. The Deese–Roediger–McDermott (DRM) paradigm is a powerful tool for investigating false memories and revealing the cognitive mechanisms subserving their formation. In this task, participants first memorize a list of words (encoding phase) and next have to indicate whether words presented in a new list were part of the initially memorized one (recognition phase). By employing DRM lists optimized to investigate semantic effects, previous studies highlighted a crucial role of semantic processes in false memory generation, showing that new words semantically related to the studied ones tend to be more erroneously recognized (compared to new words less semantically related). Despite the strengths of the DRM task, this paradigm faces a major limitation in list construction due to its reliance on human-based association norms, posing both practical and theoretical concerns. To address these issues, we developed the False Memory Generator (FMG), an automated and data-driven tool for generating DRM lists, which exploits similarity relationships between items populating a vector space. Here, we present FMG and demonstrate the validity of the lists generated in successfully replicating well-known semantic effects on false memory production. FMG potentially has broad applications by allowing for testing false memory production in domains that go well beyond the current possibilities, as it can be in principle applied to any vector space encoding properties related to word referents (e.g., lexical, orthographic, phonological, sensory, affective, etc.) or other type of stimuli (e.g., images, sounds, etc.). [ABSTRACT FROM AUTHOR]

Objective Background Method Results Conclusions We aimed to develop a machine learning model to infer OCEAN traits from text.The psycholexical approach allows retrieving information about personality traits from human language. However, it has rarely been applied because of methodological and practical issues that current computational advancements could overcome.Classical taxonomies and a large Yelp corpus were leveraged to learn an embedding for each personality trait. These embeddings were used to train a feedforward neural network for predicting trait values. Their generalization performances have been evaluated through two external validation studies involving experts (N = 11) and laypeople (N = 100) in a discrimination task about the best markers of each trait and polarity.Intrinsic validation of the model yielded excellent results, with R2 values greater than 0.78. The validation studies showed a high proportion of matches between participants' choices and model predictions, confirming its efficacy in identifying new terms related to the OCEAN traits. The best performance was observed for agreeableness and extraversion, especially for their positive polarities. The model was less efficient in identifying the negative polarity of openness and conscientiousness.This innovative methodology can be considered a “psycholexical approach 2.0,” contributing to research in personality and its practical applications in many fields. [ABSTRACT FROM AUTHOR]

Although mouse‐tracking has been taken as a real‐time window on different aspects of human decision‐making processes, whether purely semantic information affects response conflict at the level of motor output as measured through mouse movements is still unknown. Here, across two experiments, we investigated the effects of semantic knowledge by predicting participants' performance in a standard keyboard task and in a mouse‐tracking task through distributional semantics, a usage‐based modeling approach to meaning. In Experiment 1, participants were shown word pairs and were required to perform a two‐alternative forced choice task selecting either the more abstract or the more concrete word, using standard keyboard presses. In Experiment 2, participants performed the same task, yet this time response selection was achieved by moving the computer mouse. Results showed that the involvement of semantic components in the task at hand is observable using both standard reaction times (Experiment 1) as well as using indexes extracted from mouse trajectories (Experiment 2). In particular, mouse trajectories reflected the response conflict and its temporal evolution, with a larger deviation for increasing word semantic relatedness. These findings support the validity of mouse‐tracking as a method to detect deep and implicit decision‐making features. Additionally, by demonstrating that a usage‐based model of meaning can account for the different degrees of cognitive conflict associated with task achievement, these findings testify the impact of the human semantic memory on decision‐making processes. [ABSTRACT FROM AUTHOR]

Quantitative, data-driven models for mental representations have long enjoyed popularity and success in psychology (e.g., distributional semantic models in the language domain), but have largely been missing for the visual domain. To overcome this, we present ViSpa (Vision Spaces), high-dimensional vector spaces that include vision-based representation for naturalistic images as well as concept prototypes. These vectors are derived directly from visual stimuli through a deep convolutional neural network trained to classify images and allow us to compute vision-based similarity scores between any pair of images and/or concept prototypes. We successfully evaluate these similarities against human behavioral data in a series of large-scale studies, including off-line judgments—visual similarity judgments for the referents of word pairs (Study 1) and for image pairs (Study 2), and typicality judgments for images given a label (Study 3)—as well as online processing times and error rates in a discrimination (Study 4) and priming task (Study 5) with naturalistic image material. ViSpa similarities predict behavioral data across all tasks, which renders ViSpa a theoretically appealing model for vision-based representations and a valuable research tool for data analysis and the construction of experimental material: ViSpa allows for precise control over experimental material consisting of images and/or words denoting imageable concepts and introduces a specifically vision-based similarity for word pairs. To make ViSpa available to a wide audience, this article (a) includes (video) tutorials on how to use ViSpa in R and (b) presents a user-friendly web interface at http://vispa.fritzguenther.de. [ABSTRACT FROM AUTHOR]

Although mouse-tracking has been seen as a real-time window into different aspects of human decision-making processes, currently little is known about how the decision process unfolds in veridical and false memory retrieval. Here, we directly investigated decision-making processes by predicting participants' performance in a mouse-tracking version of a typical Deese–Roediger–McDermott (DRM) task through distributional semantic models, a usage-based approach to meaning. Participants were required to study lists of associated words and then to perform a recognition task with the mouse. Results showed that mouse trajectories were extensively affected by the semantic similarity between the words presented in the recognition phase and the ones previously studied. In particular, the higher the semantic similarity, the larger the conflict driving the choice and the higher the irregularity in the trajectory when correctly rejecting new words (i.e., the false memory items). Conversely, on the temporal evolution of the decision, our results showed that semantic similarity affects more complex temporal measures indexing the online decision processes subserving task performance. Together, these findings demonstrate that semantic similarity can affect human behavior at the level of motor control, testifying its influence on online decision-making processes. More generally, our findings complement previous seminal theories on false memory and provide insights into the impact of the semantic memory structure on different decision-making components. [ABSTRACT FROM AUTHOR]

Massively multilingual models such as mBERT and XLM-R are increasingly valued in Natural Language Processing research and applications, due to their ability to tackle the uneven distribution of resources available for different languages. The models' ability to process multiple languages relying on a shared set of parameters raises the question of whether the grammatical knowledge they extracted during pre-training can be considered as a data-driven cross-lingual grammar. The present work studies the inner workings of mBERT and XLM-R in order to test the cross-lingual consistency of the individual neural units that respond to a precise syntactic phenomenon, that is, number agreement, in five languages (English, German, French, Hebrew, Russian). We found that there is a significant overlap in the latent dimensions that encode agreement across the languages we considered. This overlap is larger (a) for long- vis-à-vis short-distance agreement and (b) when considering XLM-R as compared to mBERT, and peaks in the intermediate layers of the network. We further show that a small set of syntax-sensitive neurons can capture agreement violations across languages; however, their contribution is not decisive in agreement processing. [ABSTRACT FROM AUTHOR]

Nonarbitrary phenomena in language, such as systematic association in the form-meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we reanalyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants' reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a generalpurpose capacity of extracting knowledge from complex statistical patterns. [ABSTRACT FROM AUTHOR]

Cognitive maps are assumed to be fundamentally spatial and grounded only in perceptual processes, as supported by the discovery of functionally dedicated cell types in the human brain, which tile the environment in a maplike fashion. Challenging this view, we demonstrate that spatial representations—such as large-scale geographical maps—can be as well retrieved with high confidence from natural language through cognitively plausible artificial-intelligence models on the basis of nonspatial associative-learning mechanisms. More critically, we show that linguistic information accounts for the specific distortions observed in tasks when college-age adults have to judge the geographical positions of cities, even when these positions are estimated on real maps. These findings indicate that language experience can encode and reproduce cognitive maps without the need for a dedicated spatial-representation system, thus suggesting that the formation of these maps is the result of a strict interplay between spatial- and nonspatial-learning principles. [ABSTRACT FROM AUTHOR]

Speakers of languages with synchronically productive compounding systems, such as English, are likely to encounter new compounds on a daily basis. These can only be useful for communication if speakers are able to rapidly compose their meanings. However, while compositional meanings can be obtained for some novel compounds such as bridgemill, this is far harder for others such as radiosauce ; accordingly, processing speed should be affected by the ease of such a compositional process. To rigorously test this hypothesis, we employed a fully implemented computational model based on distributional semantics to quantitatively measure the degree of semantic compositionality of novel compounds. In two large-scale studies, we collected timed sensibility judgements and lexical decisions for hundreds of morphologically structured nonwords in English. Response times were predicted by the constituents' semantic contribution to the compositional process, with slower rejections for more compositional nonwords. We found no indication of a difference in these compositional effects between the tasks, suggesting that speakers automatically engage in a compositional process whenever they encounter morphologically structured stimuli, even when it is not required by the task at hand. Such compositional effects in the processing of novel compounds have important implications for studies that employ such stimuli as filler material or "nonwords," as response times for these items can differ greatly depending on their compositionality. [ABSTRACT FROM AUTHOR]

It has been suggested that the origins of number words can be traced back to an evolutionarily ancient approximate number system, which represents quantities on a compressed scale and complies with Weber's law. Here, we use a data-driven computational model, which learns to predict 1 event (a word in a text corpus) from associated events, to characterize verbal behavior relative to number words in natural language, without appeal to perception. We show that the way humans use number words in spontaneous language reliably depends on numerical ratio--a clear signature of Weber's law--thus, perfectly mirroring the human and nonhuman psychophysical performance in comparative judgments of numbers. Most notably, the adherence to Weber's law is robustly replicated in a wide range of different languages. Together, these findings suggest that the everyday use of number words in language rests upon a preverbal approximate number system, which would affect the handling of numerical information not only at the input level but also at the level of verbal production. [ABSTRACT FROM AUTHOR]

In the present study, we provide a comprehensive analysis and a multi-dimensional dataset of semantic transparency measures for 1810 German compound words. Compound words are considered semantically transparent when the contribution of the constituents' meaning to the compound meaning is clear (as in airport), but the degree of semantic transparency varies between compounds (compare strawberry or sandman). Our dataset includes both compositional and relatedness-based semantic transparency measures, also differentiated by constituents. The measures are obtained from a computational and fully implemented semantic model based on distributional semantics. We validate the measures using data from four behavioral experiments: Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study. We demonstrate that different semantic effects emerge in different behavioral tasks, which can only be captured using a multi-dimensional approach to semantic transparency. We further provide the semantic transparency measures derived from the model for a dataset of 40,475 additional German compounds, as well as for 2061 novel German compounds. [ABSTRACT FROM AUTHOR]

Effects of semantic transparency, reflected in processing differences between semantically transparent (teabag) and opaque (ladybird) compounds, have received considerable attention in the investigation of the role of constituents in compound processing. However, previous studies have yielded inconsistent results. In the present article, we argue that this is due to semantic transparency's often being conceptualized only as the semantic relatedness between the compound and constituent meanings as separate units. This neglects the fact that compounds are inherently productive constructions. We argue that compound processing is routinely impacted by a compositional process aimed at computing a compositional meaning, which would cause compositional semantic transparency effects to emerge in compound processing. We employ recent developments in compositional distributional semantics to quantify relatedness- as well as composition-based semantic transparency measures and use these to predict lexical decision times in a large-scale data set. We observed semantic transparency effects on compound processing that are not captured in relatedness terms but only by adopting a compositional perspective. [ABSTRACT FROM AUTHOR]

In the health domain, well‐being is primarily assessed as autonomy and mental distress, whereas the quality of daily experience is rarely investigated. In this study, the relationship between autonomy levels and daily experience was explored. Thirty‐five Italian adults with Hereditary Spastic Paraplegia provided for one week real‐time descriptions of daily activities and associated experiences through the Experience Sampling Method procedure. Participants were grouped based on autonomy levels assessed through Barthel Index. The relationships between activity typologies, the experiential dimensions, perceived challenges and skills, and autonomy level were analysed. Participants' predominant activities were personal care, associated with global disengagement, and leisure, associated with high control and desirability, but low perceived relevance. During social interactions participants reported engagement and emotional well‐being, and during productive activities high activation but negative affect. Multi‐level analysis highlighted that this association between activity type and experiential patterns recurred across autonomy levels. In addition, perceived challenges in the activity were lower that perceived personal skills across activities and autonomy levels. Findings suggest that persons with motor disabilities, regardless of their autonomy level, would benefit from more challenging opportunities for action in daily life, in order to attain well‐being through active skill mobilisation. [ABSTRACT FROM AUTHOR]

We release a database of cloze probability values, predictability ratings, and computational estimates for a sample of 205 English sentences (1726 words), aligned with previously released word-by-word reading time data (both self-paced reading and eye-movement records; Frank et al., Behavior Research Methods, 45(4), 1182–1190. 2013) and EEG responses (Frank et al., Brain and Language, 140, 1–11. 2015). Our analyses show that predictability ratings are the best predictors of the EEG signal (N400, P600, LAN) self-paced reading times, and eye movement patterns, when spillover effects are taken into account. The computational estimates are particularly effective at explaining variance in the eye-tracking data without spillover. Cloze probability estimates have decent overall psychometric accuracy and are the best predictors of early fixation patterns (first fixation duration). Our results indicate that the choice of the best measurement of word predictability in context critically depends on the processing index being considered. [ABSTRACT FROM AUTHOR]

Orthography-semantics consistency (OSC) is a measure that quantifies the degree of semantic relatedness between a word and its orthographic relatives. OSC is computed as the frequency-weighted average semantic similarity between the meaning of a given word and the meanings of all the words containing that very same orthographic string, as captured by distributional semantic models. We present a resource including optimized estimates of OSC for 15,017 English words. In a series of analyses, we provide a progressive optimization of the OSC variable. We show that computing OSC from word-embeddings models (in place of traditional count models), limiting preprocessing of the corpus used for inducing semantic vectors (in particular, avoiding part-of-speech tagging and lemmatization), and relying on a wider pool of orthographic relatives provide better performance for the measure in a lexical-processing task. We further show that OSC is an important and significant predictor of reaction times in visual word recognition and word naming, one that correlates only weakly with other psycholinguistic variables (e.g., family size, word frequency), indicating that it captures a novel source of variance in lexical access. Finally, some theoretical and methodological implications are discussed of adopting OSC as one of the predictors of reaction times in studies of visual word recognition. [ABSTRACT FROM AUTHOR]

Word frequency is one of the best predictors of language processing. Typically, word frequency norms are entirely based on natural-language text data, thus representing what the literature typically refers to as purely linguistic experience. This study presents Flickr frequency norms as a novel word frequency measure from a domain-specific corpus inherently tied to extra-linguistic information: words used as image tags on social media. To obtain Flickr frequency measures, we exploited the photo-sharing platform Flickr Image (containing billions of photos) and extracted the number of uploaded images tagged with each of the words considered in the lexicon. Here, we systematically examine the peculiarities of Flickr frequency norms and show that Flickr frequency is a hybrid metrics, lying at the intersection between language and visual experience and with specific biases induced by being based on image-focused social media. Moreover, regression analyses indicate that Flickr frequency captures additional information beyond what is already encoded in existing norms of linguistic, sensorimotor, and affective experience. Therefore, these new norms capture aspects of language usage that are missing from traditional frequency measures: a portion of language usage capturing the interplay between language and vision, which – this study demonstrates – has its own impact on word processing. The Flickr frequency norms are openly available on the Open Science Framework (https://osf.io/2zfs3/). [ABSTRACT FROM AUTHOR]

We present an eye-tracking study on the incremental derivation of the some-butnot- all scalar implicature (SI) associated to the scalar quantifier some. This question has been the matter of a vivid debate, both in linguistics and in psycholinguistics (Chemla & Singh 2014a,b). Experimentally, it was addressed by means of eyetracking and different results were obtained: while Huang & Snedeker (2009) found evidence for a delay of some with respect to all, Grodner et al. (2010) argued for a rapid integration of pragmatic some. More recently, Breheny et al. (2013a,b) raised some criticism on the paradigm employed in those studies and contributed with a looking-while-listening task showing incremental derivation of the scalar inference. We first raise some methodological questions, arguing that the paradigm used in previous studies was not apt to distinguish whether a scalar inference was derived or not, for different reasons. By means of a novel visual-world eye-tracking experiment in which we exploit the notion of focus in the activation of scalar alternatives, we show new evidence for the incremental derivation of the pragmatic some-butnot- all interpretation of some. We interpret these results within a grammatical approach to SIs (Chierchia et al. 2012; Chierchia 2013), according to which, when scalar alternatives are active, the SI is factored in locally and incrementally during the online processing of the scalar quantifier. [ABSTRACT FROM AUTHOR]

In this paper, we test the Cumulative Effect proposed by (Haegeman et al. 2014. Deconstructing the subject condition in terms of cumulative constraint violation. The Linguistic Review 31(1). 73-150). In particular, we focus on the role of factors of external syntax in the modulation of subject opacity to extraction, addressing different constraints presented in (Haegeman et al. 2014. Deconstructing the subject condition in terms of cumulative constraint violation. The Linguistic Review 31(1). 73-150). Two sets of formal acceptability judgments are presented. In the first experiment, we address the opacity of subjects from a broad point of view, in order to assess whether subject DPs are more resistant to extraction than other DPs. The results confirm that subject constituents are more opaque to extraction than object constituents. In the second experiment, we address the impact on the modulation of the Cumulative Effect of three different constraints individually: the Freezing Principle, the Inactivity Condition and the Edge Condition. We did that through the manipulation of two different factors: the position of the extraction site and the predicate type. The results do not confirm the predictions made by the Cumulative Effect, since the interaction between different factors does not appear to be additive and incremental. We discuss the implications of these results for the existing theories of subject islands and address an alternative perspective, recently proposed by (Bianchi, Valentina & Cristiano Chesi. 2014. Subject islands, reconstruction, and the flow of the computation. Linguistic Inquiry 45(4). 525-569), according to which the opacity of subjects is a function of the syntax semantics interface. [ABSTRACT FROM AUTHOR]

In many languages, compounding is a fundamental process for the generation of novel words. When this process is productive (as, e.g., in English), native speakers can juxtapose two words to create novel compounds that can be readily understood by other speakers. The present paper proposes a large-scale, data-driven computational system for compound semantic processing based on distributional semantics, the CAOSS model (Compounding as Abstract Operation in Semantic Space). In CAOSS, word meanings are represented as vectors encoding their lexical co-occurrences in a reference corpus. Given two constituent words, their composed representation (the compound) is computed by using matrices representing the abstract properties of constituent roles (modifier vs. head). The matrices are also induced through examples of language usage. The model is then validated against behavioral results concerning the processing of novel compounds, and in particular relational effects on response latencies. The effects of relational priming and relational dominance are considered. CAOSS predictions are shown to pattern with previous results, in terms of both the impact of relational information and the dissociations related to the different constituent roles. The simulations indicate that relational information is implicitly reflected in language usage, suggesting that human speakers can learn these aspects from language experience and automatically apply them to the processing of new word combinations. The present model is flexible enough to emulate this procedure, suggesting that relational effects might emerge as a by-product of nuanced operations across distributional patterns. [ABSTRACT FROM AUTHOR]

In this paper, we test the Cumulative Effect proposed by (Haegeman et al. 2014. Deconstructing the subject condition in terms of cumulative constraint violation. The Linguistic Review 31(1). 73-150). In particular, we focus on the role of factors of external syntax in the modulation of subject opacity to extraction, addressing different constraints presented in (Haegeman et al. 2014. Deconstructing the subject condition in terms of cumulative constraint violation. The Linguistic Review 31(1). 73-150). Two sets of formal acceptability judgments are presented. In the first experiment, we address the opacity of subjects from a broad point of view, in order to assess whether subject DPs are more resistant to extraction than other DPs. The results confirm that subject constituents are more opaque to extraction than object constituents. In the second experiment, we address the impact on the modulation of the Cumulative Effect of three different constraints individually: the Freezing Principle, the Inactivity Condition and the Edge Condition. We did that through the manipulation of two different factors: the position of the extraction site and the predicate type. The results do not confirm the predictions made by the Cumulative Effect, since the interaction between different factors does not appear to be additive and incremental. We discuss the implications of these results for the existing theories of subject islands and address an alternative perspective, recently proposed by (Bianchi, Valentina & Cristiano Chesi. 2014. Subject islands, reconstruction, and the flow of the computation. Linguistic Inquiry 45(4). 525-569), according to which the opacity of subjects is a function of the syntax semantics interface. [ABSTRACT FROM AUTHOR]

In the present study, the role of phonological information in visual word recognition is investigated by adopting a large-scale data-driven approach that exploits a new consistency measure based on distributional semantics methods. A recent study by Marelli, Amenta, and Crepaldi (2015) showed that the consistency between an orthographic string and the meanings to which it is associated in a large corpus is a relevant predictor in lexical decision experiments. Exploiting irregular mappings between orthography and phonology in English, we were able to compute a phonology-to-semantics consistency measure that dissociates from its orthographic counterpart and tested both measures on lexical decision data taken from the British Lexicon Project (Keuleers et al., 2012). Results showed that both orthography and phonology are activated during visual word recognition. However, their contribution is crucially determined by the extent to which they are informative of the word semantics, and phonology plays a crucial role in accessing word meaning. [ABSTRACT FROM AUTHOR]

Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora (such as movie subtitles) are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times (up to 3.6% increase in explained variance). The results are robust (observed for Twitter- and Facebook-based frequencies on American English and British English datasets) and are still substantial when we control for corpus size. [ABSTRACT FROM AUTHOR]

Abstract: By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models (DSMs) have demonstrated that it is possible to induce word meaning representations solely from word co‐occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set of experiments investigating whether minimal distributional evidence from very short passages suffices to trigger successful word learning in subjects, testing their linguistic and visual intuitions about the concepts associated with new words. After confirming that subjects are indeed very efficient distributional learners even from small amounts of evidence, we test a DSM on the same multimodal task, finding that it behaves in a remarkable human‐like way. We conclude that DSMs provide a convincing computational account of word learning even at the early stages in which a word is first encountered, and the way they build meaning representations can offer new insights into human language acquisition. [ABSTRACT FROM AUTHOR]

By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models (DSMs) have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set of experiments investigating whether minimal distributional evidence from very short passages suffices to trigger successful word learning in subjects, testing their linguistic and visual intuitions about the concepts associated with new words. After confirming that subjects are indeed very efficient distributional learners even from small amounts of evidence, we test a DSM on the same multimodal task, finding that it behaves in a remarkable human-like way. We conclude that DSMs provide a convincing computational account of word learning even at the early stages in which a word is first encountered, and the way they build meaning representations can offer new insights into human language acquisition. [ABSTRACT FROM AUTHOR]

Sophisticated senator and legislative onion. Whether or not you have ever heard of these things, we all have some intuition that one of them makes much less sense than the other. In this paper, we introduce a large dataset of human judgments about novel adjective-noun phrases. We use these data to test an approach to semantic deviance based on phrase representations derived with compositional distributional semantic methods, that is, methods that derive word meanings from contextual information, and approximate phrase meanings by combining word meanings. We present several simple measures extracted from distributional representations of words and phrases, and we show that they have a significant impact on predicting the acceptability of novel adjective-noun phrases even when a number of alternative measures classically employed in studies of compound processing and bigram plausibility are taken into account. Our results show that the extent to which an attributive adjective alters the distributional representation of the noun is the most significant factor in modeling the distinction between acceptable and deviant phrases. Our study extends current applications of compositional distributional semantic methods to linguistically and cognitively interesting problems, and it offers a new, quantitatively precise approach to the challenge of predicting when humans will find novel linguistic expressions acceptable and when they will not. [ABSTRACT FROM AUTHOR]

Noun compounds, consisting of two nouns (the head and the modifier) that are combined into a single concept, differ in terms of their plausibility: school bus is a more plausible compound than saddle olive. The present study investigates which factors influence the plausibility of attested and novel noun compounds. Distributional Semantic Models (DSMs) are used to obtain formal (vector) representations of word meanings, and compositional methods in DSMs are employed to obtain such representations for noun compounds. From these representations, different plausibility measures are computed. Three of those measures contribute in predicting the plausibility of noun compounds: The relatedness between the meaning of the head noun and the compound (Head Proximity), the relatedness between the meaning of modifier noun and the compound (Modifier Proximity), and the similarity between the head noun and the modifier noun (Constituent Similarity). We find non-linear interactions between Head Proximity and Modifier Proximity, as well as between Modifier Proximity and Constituent Similarity. Furthermore, Constituent Similarity interacts non-linearly with the familiarity with the compound. These results suggest that a compound is perceived as more plausible if it can be categorized as an instance of the category denoted by the head noun, if the contribution of the modifier to the compound meaning is clear but not redundant, and if the constituents are sufficiently similar in cases where this contribution is not clear. Furthermore, compounds are perceived to be more plausible if they are more familiar, but mostly for cases where the relation between the constituents is less clear. [ABSTRACT FROM AUTHOR]

Semantic transparency (ST) is a measure quantifying the strength of meaning association between a compound word (buttercup) and its constituents (butter, cup). Borrowing ideas from computational semantics, we characterize ST in terms of the degree to which a compound and its constituents tend to share the same contexts in everyday usage, and we collect separate measures for different orthographic realizations (solid vs. open) of the same compound. We can thus compare the effects of semantic association in cases in which direct semantic access is likely to take place (buttercup), vis-a-vis forms that encourage combinatorial procedures (butter cup). ST effects are investigated in an analysis of lexical decision latencies. The results indicate that distributionally based ST variables are most predictive of response times when extracted from contexts presenting the compounds as open forms, suggesting that compound processing involves a conceptual combination procedure focusing on the merger of the constituent meanings. [ABSTRACT FROM AUTHOR]

We present two eye-tracking experiments on the interpretation of sentences like 'The tall girl is (not) the only one that ...,' which are ambiguous between the anaphoric (the only girl that ...) and the exophoric interpretation (the only individual that ...). These interpretations differ in informativeness: in a positive context, the exophoric (strong) reading entails the anaphoric (weak), while in a negative context the entailment pattern is reversed and the anaphoric reading is the strongest one. We tested whether adults rely on considerations about informativeness in solving the ambiguity. The results show that participants interpreted one exophorically in both positive and negative contexts. Given these findings, we cast doubts on the idea that Informativeness plays a role in ambiguity resolution and proposes a Principle of Maximal Exploitation: When a context is provided, adults extend their domain of evaluation to include the whole scenario, independently from truth-conditional considerations about informativity and strength. [ABSTRACT FROM AUTHOR]

In this eye-tracking study, we investigated how semantics inform morphological analysis at the early stages of visual word identification in sentence reading. We exploited a feature of several derived Italian words, that is, that they can be read in a "morphologically transparent" way or in a "morphologically opaque" way according to the sentence context to which they belong. This way, each target word was embedded in a sentence eliciting either its transparent or opaque interpretation. We analyzed whether the effect of stem frequency changes according to whether the (very same) word is read as a genuine derivation (transparent context) versus as a pseudoderived word (opaque context). Analysis of the first fixation durations revealed a stem-word frequency effect in both opaque and transparent contexts, thus showing that stems were accessed whether or not they contributed to word meaning, that is, word decomposition is indeed blind to semantics. However, while the stem-word frequency effect was facilitatory in the transparent context, it was inhibitory in the opaque context, thus showing an early involvement of semantic representations. This pattern of data is revealed by words with short suffixes. These results indicate that derived and pseudoderived words are segmented into their constituent morphemes also in natural reading; however, this blind-to-semantics process activates morpheme representations that are semantically connoted. [ABSTRACT FROM AUTHOR]

A largely overlooked side effect in most studies of morphological priming is a consistent main effect of semantic transparency across priming conditions. That is, participants are faster at recognizing stems from transparent sets (e.g.,farm) in comparison to stems from opaque sets (e.g.,fruit), regardless of the preceding primes. This suggests that semantic transparency may also be consistently associated with some property of the stem word. We propose that this property might be traced back to the consistency, throughout the lexicon, between the orthographic form of a word and its meaning, here namedOrthography-Semantics Consistency(OSC), and that an imbalance in OSC scores might explain the “stem transparency” effect. We exploited distributional semantic models to quantitatively characterize OSC, and tested its effect on visual word identification relying on large-scale data taken from the British Lexicon Project (BLP). Results indicated that (a) the “stem transparency” effect is solid and reliable, insofar as it holds in BLP lexical decision times (Experiment 1); (b) an imbalance in terms of OSC can account for it (Experiment 2); and (c) more generally, OSC explains variance in a large item sample from the BLP, proving to be an effective predictor in visual word access (Experiment 3). [ABSTRACT FROM AUTHOR]

While distributional semantic models that represent word meanings as high-dimensional vectors induced from large text corpora have been shown to successfully predict human behavior across a wide range of tasks, they have also received criticism from different directions. These include concerns over their interpretability (how can numbers specifying abstract, latent dimensions represent meaning?) and their ability to capture variation in meaning (how can a single vector representation capture multiple different interpretations for the same expression?). Here, we demonstrate that semantic vectors can indeed rise up to these challenges, by training a mapping system (a simple linear regression) that predicts inter-individual variation in relational interpretations for compounds such as wood brush (for example brush FOR wood , or brush MADE OF wood) from (compositional) semantic vectors representing the meanings of these compounds. These predictions consistently beat different random baselines, both for familiar compounds (moon light , Experiment 1) as well as novel compounds (wood brush , Experiment 2), demonstrating that distributional semantic vectors encode variations in qualitative interpretations that can be decoded using techniques as simple as linear regression. [ABSTRACT FROM AUTHOR]

The present work proposes a computational model of morpheme combination at the meaning level. The model moves from the tenets of distributional semantics, and assumes that word meanings can be effectively represented by vectors recording their co-occurrence with other words in a large text corpus. Given this assumption, affixes are modeled as functions (matrices) mapping stems onto derived forms. Derived-form meanings can be thought of as the result of a combinatorial procedure that transforms the stem vector on the basis of the affix matrix (e.g., the meaning of nameless is obtained by multiplying the vector of name with the matrix of -less). We show that this architecture accounts for the remarkable human capacity of generating new words that denote novel meanings, correctly predicting semantic intuitions about novel derived forms. Moreover, the proposed compositional approach, once paired with a whole-word route, provides a new interpretative framework for semantic transparency, which is here partially explained in terms of ease of the combinatorial procedure and strength of the transformation brought about by the affix. Model-based predictions are in line with the modulation of semantic transparency on explicit intuitions about existing words, response times in lexical decision, and morphological priming. In conclusion, we introduce a computational model to account for morpheme combination at the meaning level. The model is data-driven, theoretically sound, and empirically supported, and it makes predictions that open new research avenues in the domain of semantic processing. [ABSTRACT FROM AUTHOR]

This paper draws a connection between statistical word association measures used in linguistics and confirmation measures from epistemology. Having theoretically established the connection, we replicate, in the new context of the judgments of word co-occurrence, an intriguing finding from the psychology of reasoning, namely that confirmation values affect intuitions about likelihood. We show that the effect, despite being based in this case on very subtle statistical insights about thousands of words, is stable across three different experimental settings. Our theoretical and empirical results suggest that factors affecting traditional reasoning tasks are also at play when linguistic knowledge is probed, and they provide further evidence for the importance of confirmation in a new domain. [ABSTRACT FROM AUTHOR]

Purpose/Objective: Motor rehabilitation programs in hospital aim at promoting patients’ highest attainable recovery of body functions and capabilities. Well-being is predominantly identified with physical autonomy, yet the psychological dimensions of rehabilitation are often overlooked. Research Method/ Design: To partially fill this gap, the quality of daily experience reported by 50 adult participants hospitalized in an Italian rehabilitation unit was investigated. Data were gathered through Experience Sampling Method, (ESM), providing repeated real-time assessments of the experience associated with daily activities. Before analysis, participants were divided into 3 groups, according to their low, moderate, or high levels of autonomy assessed through Barthel Index. Results: Participants predominantly associated rehabilitation activities with optimal experience, characterized by high concentration, engagement, control of the situation, and by the perception of high challenges matched with adequate personal skills. During personal care and leisure—the most frequent daily activities—participants reported instead low challenging experiences of apathy and boredom. During social interactions perceived high challenges prevailed. Multilevel analysis showed that the type of activity performed was a significant predictor of participants’ quality of experience, and the level of autonomy had a modest impact on it. Conclusions/lmplieations: Results highlighted the potential added value of rehabilitation tasks as opportunities to promote patients’ well-being. The predominantly negative experiences associated with the other daily activities point instead to the need for changes in hospital organization in order to more effectively promote patients’ autonomy and resource mobilization. [ABSTRACT FROM AUTHOR]

Evidence evaluation is a crucial process in many human activities, spanning from medical diagnosis to impression formation. The present experiments investigated which, if any, normative model best conforms to people's intuition about the value of the obtained evidence. Psychologists, epistemologists, and philosophers of science have proposed several models to account for people's intuition about the utility of the obtained evidence with respect either to a focal hypothesis or to a constellation of hypotheses. We pitted against each other the so-called optimal-experimental-design models (i.e., Bayes-ian diagnosticity, log10 diagnosticity, information gain, Kullback-Leibler distance, probability gain, and impact) and measures L and Z to compare their ability to describe humans' intuition about the value of the obtained evidence. Participants received words-and-numbers scenarios concerning 2 hypotheses and binary features. They were asked to evaluate the utility of "yes" and "no" answers to questions about some features possessed in different proportions (i.e., the likelihoods) by 2 types of extraterrestrial creatures (corresponding to 2 mutually exclusive and exhaustive hypotheses). Participants evaluated either how an answer was helpful or how an answer decreased/increased their beliefs with respect either to a single hypothesis or to both hypotheses. We fitted mixed-effects models and used the Akaike information criterion and the Bayesian information criterion values to compare the competing models of the value of the obtained evidence. Overall, the experiments showed that measure Z was the best fitting model of participants' judgments of the value of obtained answers. We discussed the implications for the human hypothesis-evaluation process. [ABSTRACT FROM AUTHOR]

Most compound words are constituted of a head constituent (e.g., light in moonlight) and a modifier constituent (e.g., moon in moonlight); the information transmitted by these head-modifier roles is fundamental for defining the grammatical and semantic properties of the compound and for identifying a correct combination of the constituents at the conceptual level. The objective of this study is to assess how lexical processing in aphasia is influenced by the head-modifier structure of nominal compounds. A picture-naming task of 35 compounds with head-initial (pescespada, swordfish, literally fishsword) and head-final (autostrada, highway, literally carroad) forms was administered to 45 Italian aphasic patients, and their accuracy in retrieving constituents was analysed with a mixed-effects logistic regression. The interaction between headedness and constituent position was significant: The modifier emerged as being more difficult to retrieve than the head, but only for head-final compounds. The results are consistent with previous data from priming experiments on healthy subjects and provide convincing evidence that compound headedness is represented at central processing levels. [ABSTRACT FROM PUBLISHER]

Compound words in Romance languages may have the head either in the initial or in the final position. In the present event-related potential (ERP) study, we address the hypothesis that Italian compounds are processed differently according to their head position and that this is mostly due to the perceived change in the canonical order of syntactic elements. Compound stimuli (head-initial, head-final, or exocentric) were visually displayed in two presentation modes, as whole words or separated into their constituents, in the context of a lexical decision task. Behavioural results showed an increased split cost in head-final and exocentric compounds as compared to head-initial compounds. ERP results showed an enhanced left anterior negativity (LAN) for head-final and exocentric compounds as compared to head-initial compounds, regardless of the presentation mode. Results suggest that the analogy with syntactic order may influence the internal structure of a compound and, as a consequence, its processing, but other characteristics (such as the grammatical properties of constituents) may affect the processing itself. [ABSTRACT FROM PUBLISHER]

A substantial body of literature indicates that, at least at some level of processing, complex words are broken down into their morphemes solely on the basis of their orthographic form (e.g., Rastle, Davis, & New, Psychonomic Bulletin and Review 11:1090-1098, ). Recent evidence has shown that this process might not be obligatory, as indicated by the fact that morpho-orthographic effects were not found in a cross-case same-different task-that is, when lexical access was not necessarily required (Duñabeitia, Kinoshita, Carreiras, & Norris, Language and Cognitive Processes 26:509-529, ). In this study, we employed a task that required understanding a series of words and, thus, implied lexical access. Masked primes were shown very briefly right before the appearance of the target word; prime-target pairs entertained a morpho-semantic ( dealer- DEAL), a morpho-orthographic ( corner- CORN), or a purely orthographic ( brothel- BROTH) relationship. Eye fixation times clearly indicated facilitation for transparent pairs, but not for opaque pairs (or for orthographic pairs, which were used as a baseline). Conversely, the usual morpho-orthographic pattern was found in a control experiment, employing a lexical decision task. These results indicate that the access to a morpho-orthographic level of representation is not always necessary for lexical identification, which challenges models of visual word identification that cannot account for task-induced effects. [ABSTRACT FROM AUTHOR]

In two studies, we investigated how people use base rates and the presence versus the absence of new information to judge which of two hypotheses is more likely. Participants were given problems based on two decks of cards printed with 0-4 letters. A table showed the relative frequencies of the letters on the cards within each deck. Participants were told the letters that were printed on or absent from a card the experimenter had drawn. Base rates were conveyed by telling participants that the experimenter had chosen the deck by drawing from an urn containing, in different proportions, tickets marked either 'deck 1' or 'deck 2'. The task was to judge from which of the two decks the card was most likely drawn. Prior probabilities and the evidential strength of the subset of present clues (computed as 'weight of evidence') were the only significant predictors of participants' dichotomous (both studies) and continuous (Study 2) judgments. The evidential strength of all clues was not a significant predictor of participants' judgments in either study, and no significant interactions emerged. We discuss the results as evidence for additive integration of base rates and the new present information in hypothesis testing. [ABSTRACT FROM AUTHOR]

The present study employs neglect dyslexia (ND) as an experimental model to study compound-word processing; in particular, it investigates whether compound constituents are hierarchically organized at mental level and addresses the possibility of whole-word representation. Seven Italian-speaking patients suffering from ND participated in a word naming task. Both left-headed (pescespada, swordfish) and right-headed (astronave, spaceship) Italian compound nouns were used as stimuli. Non-existent compounds, which were generated by substituting the leftmost constituent of a compound with an orthographically similar word (e.g., *pestespada, *plaguesword), were also employed. A significant headedness effect emerged in the group analysis: patients read left-headed compounds better than right-headed compounds. A significant lexicality effect was also found: the participants read real compounds better than their non-existent compound pairs. Moreover, logit mixed-effects analyses indicated a left-hand constituent frequency effect. Results are discussed in terms of hierarchical representation of compounds and direct access to compound lemma nodes. [ABSTRACT FROM AUTHOR]

Abstract: There is a general debate as to whether constituent representations are accessed in compound processing. The present study addresses this issue, exploiting the properties of Italian compounds to test the role of headedness and semantic transparency in constituent access. In a first experiment, a lexical decision task was run on nominal compounds. Significant interactions between constituent-frequencies, headedness and semantic transparency emerged, indicating facilitatory frequency effects for transparent and head-final compounds, thus highlighting the importance of the semantic and structural properties of the compounds in lexical access. In a second experiment, converging evidence was sought in an eye-tracking study. The compounds were embedded into sentence contexts, and fixation durations were measured. The results did in fact confirm the effect observed in the first experiment. The results are consistent with a multi-route model of compound processing, but also indicate the importance of a semantic route dedicated to the conceptual combination of constituent meanings. [Copyright &y& Elsevier]

Abstract: It is not clear how compound words are represented within the influential framework of the lemma-lexeme theory. Theoretically, compounds could be structured through a multiple lemma architecture, in which the lemma nodes of both the compound and its constituents are involved in lexical processing. If this were the case, syntactic properties of both the compound and its constituents should play a role when performing tasks involving compound processing, e.g., compound-word reading. This issue is investigated in the present study through an assessment of the performance of a deep dyslexic patient (GR) in three compound-reading experiments. In the first experiment, verb–noun (VN) compound nouns (e.g., lavapiatti, “dishwasher”, lit. wash-dishes) were employed as stimuli, while in the second, VN compound stimuli were embedded in sentences, and were compared to paired verb phrases (e.g., lui lava piatti, “he washes dishes”). Position-specific effects were ruled out by means of a third experiment, which investigated the retrieval of noun-noun compounds (e.g., pescespada, “swordfish”, lit. fishsword). In experiment 1, GR made errors on the verb constituent more frequently than on the noun, an effect that did not emerge in Experiment 2: when embedded in sentences, VN compounds were read significantly better than verb-phrases and no grammatical-class effect emerged. In Experiment 3, the first and the second constituent were read with the same level of accuracy. The disproportionate impairment, which emerged in reading the verb component of nominal VN compounds, indicates that the grammatical properties of constituents are being retrieved, and thus confirms access to the constituent lemma-nodes. However, the results suggested a whole-word representation when compounds are embedded in sentences; since the sentence context affects the access to compounds through syntactic constraints, whole-word representation is arguably at the lemma level as well (multiple-lemma representation). Experiment 3 indicates that these effects cannot be accounted for by a position-specific impairment. [Copyright &y& Elsevier]

When mentally exploring maps representing large-scale environments (e.g., countries or continents), humans are assumed to mainly rely on spatial information derived from direct perceptual experience (e.g., prior visual experience with the geographical map itself). In the present study, we rather tested whether also temporal and linguistic information could account for the way humans explore and ultimately represent this type of maps. We quantified temporal distance as the minimum time needed to travel by train across Italian cities, while linguistic distance was retrieved from natural language through cognitively plausible AI models based on non-spatial associative learning mechanisms (i.e., distributional semantic models). In a first experiment, we show that temporal and linguistic distances capture with high-confidence real geographical distances. Next, in a second behavioral experiment, we show that linguistic information can account for human performance over and above real spatial information (which plays the major role in explaining participants’ performance) in a task in which participants have to judge the distance between cities (while temporal information was found to be not relevant). These findings indicate that, when exploring maps representing large-scale environments, humans do take advantage of both perceptual and linguistic information, suggesting in turn that the formation of cognitive maps possibly relies on a strict interplay between spatial and non-spatial learning principles. [ABSTRACT FROM AUTHOR]

There is an ongoing, vibrant debate about whether numerical information in both nonsymbolic and symbolic notations would be supported by different neurocognitive systems or rather by a common preverbal approximate number system, which is ratio dependent and follows Weber's law. Here, we propose that the similarities between nonsymbolic and symbolic number processing can be explained based on the principle of efficient coding. To probe this hypothesis we employed a new empirical approach, by predicting the behavioural performance in number comparison tasks with symbolic (i.e., number words) and nonsymbolic (i.e., arrays of dots) information not only from numerical ratio, but for the first time also from natural language data. That is, we used data extracted from vector-space models that are informative about the distributional pattern of number-words usage in natural language. Results showed that linguistic estimates predicted the behavioural performance in both symbolic and nonsymbolic tasks. However, and critically, our results also showed a task-dependent dissociation: linguistic data better predicted the performance in the symbolic task, whereas real numerical ratio better predicted the performance in the nonsymbolic task. These findings indicate that efficient coding of environmental regularities is an explanatory principle of human behavior in tasks involving numerical information. They also suggest that the ability to discriminate a stimulus from similar ones varies as a function of the specific statistical structure of the considered learning environment. [ABSTRACT FROM AUTHOR]

The exact semantic processes subserving the formation of false memories are still poorly understood. Here, we directly probed the semantic origins of false memories in a typical Deese-Roediger-McDermott (DRM) task, by predicting participants' performance in this task through data-driven distributional semantic models. Participants were required to study lists of words and then to perform a recognition task. Our findings indicate that the participants' performance is better accounted for by a local rather than a global strategy on the task at hand: the single lists composing the task activate specific semantic clusters that are responsible for the occurrence of false memories. In particular, memory performance followed a continuous semantic gradient, with higher false recognitions occurring for higher sematic similarity between the lures (i.e., the false memory items) and the words in the relative lists. Crucially, our findings also show that semantic memory is differently involved in veridical and false memories, with this pattern being consistent across two reanalyses of data from previous studies and being replicated in an independent experiment. We thus outline an empirically-driven theoretical framework to account for the semantic processes supporting veridical and false memories formation. [ABSTRACT FROM AUTHOR]

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data‐driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition (AoA) while controlling for frequency, contextual diversity, concreteness, length, dominant part of speech, orthographic neighborhood density, and diachronic frequency variation. We analyze measures of language change tackling both the change in lexical representations and the change in the relation between lexical representations and the words with the most similar usage patterns, showing that they capture different aspects of language change. Our results show a unique relation between language change and AoA, which is stronger when considering neighborhood‐level measures of language change: Words with more coherent diachronic usage patterns tend to be acquired earlier. The results support theories positing a link between ontogenetic and ethnogenetic processes in language. [ABSTRACT FROM AUTHOR]

In human language the mapping between form and meaning is arbitrary, as there is no direct connection between words and the objects that they represent. However, within a given language, it is possible to recognise systematic associations that support productivity and comprehension. In this work, we focus on the consistency between orthographic forms and meaning, and we investigate how the cognitive system may exploit it to process words. We take morphology as our case study, since it arguably represents one of the most notable examples of systematicity in form–meaning mapping. In a series of three experiments, we investigate the impact of form–meaning mapping in word processing by testing new consistency metrics as predictors of priming magnitude in primed lexical decision. In Experiment 1, we re-analyse data from five masked morphological priming studies and show that orthography–semantics–consistency explains independent variance in priming magnitude, suggesting that word semantics is accessed already at early stages of word processing and that crucially semantic access is constrained by word orthography. In Experiments 2 and 3, we investigate whether this pattern is replicated when looking at semantic priming. In Experiment 2, we show that orthography–semantics–consistency is not a viable predictor of priming magnitude with longer stimulus onset asynchrony (SOA). However, in Experiment 3, we develop a new semantic consistency measure based on the semantic density of target neighbourhoods. This measure is shown to significantly predict independent variance in semantic priming effect. Overall, our results indicate that consistency measures provide crucial information for the understanding of word processing. Specifically, the dissociation between measures and priming paradigms shows that different priming conditions are associated with the activation of different semantic cohorts. [ABSTRACT FROM AUTHOR]

Normative measures of verbal material are fundamental in psycholinguistic and cognitive research for the control of confounding in experimental procedures and for achieving a better comprehension of our conceptual system. Traditionally, normative studies have focused on classical psycholinguistic variables, such as concreteness and imageability. Recent works have shifted researchers' focus to perceptual strength, in which items are rated separately for each of the five senses. We present a resource that includes perceptual norms for 1,121 Italian words extracted from the Italian version of ANEW. Norms were collected from 57 native speakers. For each word, the participants provided perceptual-strength ratings for each of the five perceptual modalities. The perceptual norms performance in predicting human behavior was tested in two novel experiments, a lexical decision task and a naming task. Concreteness, imageability, and different composite variables representing perceptual-strength scores were considered as competing predictors in a series of linear regressions, evaluating the goodness of fit of each model. For both tasks, the model with imageability as the only predictor was found to be the best-fitting model according to the Akaike information criterion, whereas the model with the separately considered five modalities better described data according to the explained variance. These results differ from the ones previously reported for English, in which maximum perceptual strength emerged as the best predictor of behavior. We investigated this discrepancy by comparing Italian and English data for the same set of translated items, thus confirming a genuine cross-linguistic effect. We thus confirmed that perceptual experience influences linguistic processing, even though evaluations from different languages are needed to generalize this claim. [ABSTRACT FROM AUTHOR]