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This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like &#39;good&#39; or &#39;bad&#39; and thick concepts such as &#39;honest&#39; or &#39;disgusting&#39;, based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. &quot;generous customer&quot;) generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g.&quot;dry soil&quot;).

CogSci 2022

Why are reckless socks not (more of) a thing? Towards an empirical classification of evaluative concepts.

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AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like 'good' or 'bad' and thick concepts such as 'honest' or 'disgusting', based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. "generous customer") generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g."dry soil").

poster

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Models of word meaning, like the Topics model (Griffiths et al., 2007) and word2vec (Mikolov et al., 2013), condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy etc.). However, their reliance on latent representations leaves them vulnerable to interference and makes them slow learners. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. 
We implement our spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains such as natural language text. After fixing the corpus across models, we show that spreading activation using a Dynamic-Eigen-Net outperforms the Topics model and word2vec in several cases when predicting human free associations and word similarity ratings. We argue in favour of the Dynamic-Eigen-Net as a fast learner that is not subject to catastrophic interference, and present it as an example of delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

Constructing Word Meaning without Latent Representations using Spreading Activation

Skilled readers are typically most accurate at identifying words when fixating them slightly left of the central character, the so-called optimal viewing position. There are two main explanations for this effect, which are not mutually exclusive. The first claims that the optimal viewing position lies left-of-center due to the particular constraints of the human perceptual system. The second explains the effect in terms of the beginnings of words generally being more informative about word identity. The complexities of natural languages make it difficult to tease apart the relative contribution of each explanation. We explore this issue through the lens of a Bayesian cognitive model and two experiments using artificial lexicons in which we can carefully control how information is distributed across wordforms. Our results replicate previous findings and further suggest that readers may use implicit knowledge about information distribution to minimize uncertainty when targeting words.

Minimizing Expected Uncertainty in Visual Word Recognition: Are Readers Sensitive to the Distribution of Information across Word Forms?

Is the visual word recognition system sensitive to compound (e.g., SNOWBALL) and pseudocompound (e.g., CARPET) constituents? To answer this question, we employed red-blue anaglyphs glasses to parse red-blue colored (pseudo)compounds at the fovea—by hypothesis split along the vertical meridian—and allowing for the independent processing of word segments in the visual word form area. Seventy-one participants performed a visual masked lexical decision task with 133 millisecond stimuli presentations. Compounds and pseudocompounds were either split at the (pseudo)morpheme boundary (legally-split) or within a (pseudo)morpheme (illegally-split). We found a statistically significant interaction effect between word type and legality. Planned comparisons revealed an advantage in accuracy for legally-split compounds and pseudocompounds over their respective illegal splits. Furthermore, legally-split compounds elicited more accurate responses than legally-split pseudocompounds. In line with previous findings supporting a purely morpho-orthographic prelexical process with faster presentation times, these findings suggest a temporally distinct mechanism during visual word recognition.

Parsing Compounds and Pseudocompounds at the Fovea

Learning to read is a critical skill; yet only a small portion of children in the United States are reading at or above grade level. Attention is one crucial process that affects the
acquisition of reading skills. The process involves selectively choosing task relevant information and requires monitoring competing demands. Many books for beginning readers include illustrations, but this design choice may require learners to split their attention between multiple sources of information. This study employed eye tracking to examine whether embedding text within illustrations in children’s e-books inadvertently induces attentional competition. The results showed that spatially separating illustrations from the text in beginning reader books reduces attentional competition and improves children’s reading comprehension. This study shows that changes to the design of books for beginning readers can help promote literacy development in children.

Low Spatial Proximity Between Text and Illustrations Improves Children’s Comprehension and Attention: An Eye Tracking Study

Readers rely on sentence context to generate predictions about the upcoming words so that processing of their visual forms is less necessary. Consequently, processing of an orthographic neighbor of a strongly predicted word is facilitated by that context (as indicated by a reduced N400 ERP amplitude), regardless of the perceived item’s lexicality (i.e., whether it is a real word or a pseudoword). The current study investigated whether lexicality becomes important when the sentence context is less helpful in generating predictions. Our findings indicate that in weakly constraining sentences, the lexical status of a word impacts word recognition processes as indicated by a left anterior negativity, suggesting that readers rely on sublexical properties of words in the absence of strong expectations.

The effect of orthographic relationships, lexical status and contextual constraint on visual word recognition: Evidence from event-related potentials

It was recently found that letter-shapes have a non-arbitrary relation with their canonical pronunciations, in multiple orthographies and quantified across the whole of each orthography: letters that look similar tend to have similar pronunciations. Similarly, there is phonosemantic systematicity at the word level: words that sound similar tend to have similar meanings. We investigated for the first time whether a similar systematicity exists in Chinese characters. We measured all the pairwise phonological distances and all the pairwise orthographic distances of the 58 Chinese pictographic characters that are taught to year 1 and 2 in Chinese primary schools. The correlation was tested between the two lists of distances and verified by a Mantel test. We found a significant negative correlation between characters and their segmental pronunciations: characters that look similar tend to have dissimilar segmental pronunciations. This contrasts with the positive correlations found in previous similar research with alphabetic writing. We conclude, first, that questions of systematicity in the Chinese writing system are tractable in the same terms and by the same methodology as that applied to alphabetic writing systems. Second, segment-based processing requires to be augmented by tones for there to be systematicity that is comparable to that found in alphabetic writing systems. Any non-arbitrary relation between letter shapes and sounds may help bootstrap the acquisition of literacy.

Grapho-Syllabic Systematicity in Chinese: Chinese Pictographs Have a Non-Arbitrary relation with their Pronunciations

Some sentences are hard to read, and we don’t fully understand why. Memory-based and expectation-based constraints both attempt to explain sentence processing difficulties, and decades of sentence processing literature have found evidence in support of both theories. We further investigate theories of sentence processing by exploring subject- and object-extracted relative clause processing in Standard Arabic. We conducted a self-paced reading task and found that SRCs are easier to process than ORCs in Arabic, in line with expectation-based theories. A follow-up analysis of comprehension question answers revealed that when suggested with the possibility of a noisy interpretation, readers preferentially accept an SRC interpretation over an ORC interpretation. Our future research will explore these findings and test the threshold for acceptance of noisy interpretations.

Expectations and Noisy-Channel Processing of Relative Clauses in Arabic

The English use of "they" to refer to a singular referent that is non-specific or of unknown
gender dates back to the 1300s. Recently, "they" has emerged as the dominant pronoun
for individuals who identify as gender nonbinary, and a coherent subset of English
speakers (innovators) explicitly accept "they" when referring to a specific individual of
known gender ((i) “Sarah slept because they were tired”) in off-line judgments. The
present work examines whether on-line comprehension difficulty patterns with off-line
judgments. Using a maze-task, participants' read sentences like (i) containing “they” with
plural or singular subjects, or “he/she” with named subjects that matched or mismatched
in their typical gender. Gender mismatched evoked reading difficulty. Plural “they” also
caused difficulty, but significantly less. Innovators experienced less difficulty with singular
“they” than non-innovators, but still processed plural “they” faster than singular “they”.
This work suggests that the grammar of “they” may have yet to stabilize even for
innovative speakers.

Processing singular "they" is harder than plural "they", but does not cause referential failure

This visual world eye-tracking pilot study investigates the comprehension of register variants (Stelzen_colloquial vs. Beine_standard transl:.‘’legs’) in a German target sentence when this target sentence (mis)matches the formality of a preceding context sentence, given the object argument either matches or mismatches verb meaning constraints (e.g. Ich rasiere bald meine Beine/Stelzen/#Autos/#Karren, transt.: ‘I shave my legs_standard/legs_coll/#cars_standard/#cars_coll’). The aim of this study is to examine whether register congruence rapidly interacts with verb-argument semantic relations. LME results (n=9) show a main effect of verb-argument congruence but no main effect of formality-register congruence at the region between the verb onset and object-argument onset, indicating that verb-argument relations are computed and used rapidly in online language comprehension. These pilot results suggest that situation formality may indeed modulate verb-argument congruency processing, possibly indicating that standard language processing mechanisms interact closely with register representations.

Investigating the real-time effect of register-situation formality congruence versus verb-argument semantic fit during spoken language comprehension

Little research to date has examined how listeners integrate cues to non-native speaker identity in real time sentence processing. Here, we examine listeners’ interpretation of the semantic and socio-pragmatic content of utterances produced by either a foreign accented speaker or a native speaker. Overall, our findings suggest that processing speed was slower in the presence of foreign accents. However, the extra perceptual demands of processing unfamiliar accents did not translate into listeners’ accuracy rates, and in certain sentence contexts, non-native speakers were also more likely to elicit higher semantic or pragmatic interpretation accuracy. Our findings show that non-native speaker identity plays an important role in listeners’ sentence interpretations.

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Constructing Word Meaning without Latent Representations using Spreading Activation

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