Canada

Although expectancy effects induced by placebo treatment are reported to attenuate depressive symptoms in the long run, mechanisms underlying situational dynamics are not well understood. Improved reward learning has been discussed as a candidate mediator for effects of positive expectancies on more positive mood. Here, we fitted a series of Q-learning models to measure the effect of sham antidepressant treatment vs. open-label placebo in a probabilistic reinforcement learning task. Treatment effects were observed mainly in those Q-learning models justified by the task structure. Additionally, interindividual variability remained the largest origin of unexplained variance in predictive match across models. These findings provide further support for the role of expectancies in reward learning. They also highlight the need for unraveling individual differences in cognitive mechanisms that account for differences in reward learning, and obtaining reliable estimates for them.

CogSci 2022

Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach

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Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





Sensory-related word association differences in Parkinson's Disease for verb processing

Free word association tasks are tools to study spontaneous lexical organization in children and adults. Lexical organization resembles a network of words grounded on relevant sensory information; for example, verbs engage the most with motor information. Studying lexical networks in populations with motor disorders, like Parkinson's Disease (PD), will insight into the effect of relevant information deprivation in lexical organization.

We applied a free word association task with early-acquisition Spanish nouns as stimuli. Participants consisted of 20 with PD -divided into two groups depending on their cognitive state- and ten healthy older adults for control. Then, we used an artificial neural network to determine the semantic distance between stimuli-response dyads.

We found differentiated semantic distances for verb responses in all groups in an inverse u-shaped curve depending on their cognitive and PD state. Our findings suggest that lexical organization and meaning are not crystallized, but adapt from sensory information.

Motor-related word association differences in Parkinson Disease for verb processing

The goal of language learning should be to fit in with the language community, and this often requires much more than linguistic knowledge. Although both social wellness in a second language (L2) society and L2 humor comprehension require sophisticated social and cultural knowledge beyond linguistic proficiency, their direct association has not previously been tested. Here we developed a novel method to assess different stages of humor comprehension (i.e., detection and appreciation) and conducted a series of experiments to explore its relationship with subjective social well-being in non-native English speakers. The results revealed significant correlations between language anxiety and social connectedness with both humor detection and humor appreciation in the L2. The findings suggest that the ability of L2 humor detection can be a hallmark of pragmatic proficiency and social wellness in an L2 community.

The Association between Humor Comprehension and Subjective Social Well-being in Non-native English Speakers

Humor comprehension is a great challenge for foreign/second language (L2) learners. Previous studies on humor comprehension in L2 speakers have relied only on descriptive approaches or subjective ratings on humor materials. However, no study has quantitatively investigated the behavior of L2 speakers in different stages of humor comprehension including humor detection and appreciation. This study first developed/validated a novel method to separately assess joke detection and appreciation, and conducted a series of experiments to explore the difference between native and non-native English speakers. The results showed that the non-native speakers achieved significantly lower accuracy in the joke detection than the native speakers, whereas the appreciation ratings were comparable between them. The results suggested that the non-native speakers are not necessarily disadvantageous in the joke appreciation, but detection, compared with native speakers

Non-native English Speakers Are Not Disadvantageous in Humor Appreciation, but Detection, Compared with Native Speakers

Racial and gender bias, from advertisement to political rhetoric, is ubiquitous in persuasion. However, the impact of bias on persuasive discourse is often muddied by intent and framing. Reasoners practicing anti-racism may be more likely to scrutinize racially-specific arguments, while arguments made by women may only be diminished when they are emotionally charged. We sought to study how humans evaluate interpretive arguments, what makes certain arguments persuasive, and the impact of bias and emotionality on persuasiveness. We found that shallow heuristics such as argument length and readability are poor indicators for persuasive impact, but reasoners are more likely to be persuaded by arguments made by White people, particularly White women. Further, no difference was observed based on a reasoner's ability to see the arguer's face, implying that judgments are made solely by name recognition. Our focus on written arguments has broad implications for information literacy and racial justice.

Of a Different Persuasion: Perception of Minority Status and Persuasive Impact

This work studies publications in cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic communities that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systematic framework to identify opportunities for scientific collaboration in a highly interdisciplinary and diverse field such as cognitive science.

Joint Content-Context Analysis of Scientific Publications: Identifying Opportunities for Collaboration in Cognitive Science

This study sought to replicate the effect of observing pitch gesture and clarify the effect of observing representational gesture on L2 lexical tone learning and to explore the influences of individual differences in lexical and non-lexical tone perception on these effects. The results revealed that observing representational gestures facilitates lexical tone discrimination, albeit to a lesser extent than observing pitch gestures, suggesting that task difficulty may influence its effect. Moreover, they revealed that individual differences in non-speech tone perception predict discrimination of lexical tones learned by observing pitch gesture and no gesture, but not representational gesture. Together, these findings suggest that task difficulty as well as individual differences in sensitivity to non-speech sounds influence the effects of observing gesture on novel L2 speech sound learning.

Task-Based and Individual Differences Influence the Effect of Gesture Observation on Novel L2 Speech Sound Learning

Second-language (L2) learning is characterized by both positive and negative transfer from the first language (L1). However, psycholinguistic studies focus on a few syntactic phenomena and L1-L2 pairs at a time, resulting in an incomplete picture. We apply machine learning to seven learner corpora in English and Spanish with 39 language pairs, showing that statistical models combined with simple $n$-grams of part-of-speech tags and syntactic dependency relations achieve good performance in recovering the L1, indicating structural transfer from L1 to L2. Further machine learning using a rich hand-curated linguistic feature set allowed us to identify aspects of L2 linguistic structure particularly influenced by L1 (verbal morphology, average dependency tree parse depth, and headedness of clausal structures) as well as those with minimal influence (distributions of dependency relations, basic word orders, or non-projective dependencies).

Data-driven Crosslinguistic Syntactic Transfer in Second Language Learning

While statistical learning is a well-established language learning mechanism, its usefulness in multiple language contexts is more unknown. A phenomenon known as entrenchment has been proposed, in which learning one language prevents the acquisition of a second language in the same speech stream. The observed L1 advantage or primacy effect has been previously mitigated with various cues to the presence of a second structure (L2). The present study manipulates the number of transitions between L1 and L2 to influence entrenchment. One condition was designed to replicate previous findings of entrenchment and the other was designed to overcome entrenchment. We find that adding more transitions between languages did not increase L2 learning, and second language learning is more dependent on the first learned language than on manipulations of the transitions between languages.

Deep in the Trenches: First language performance predicts primacy in statistical learning of two structures

When learners are exposed to multiple languages, semantic categories have been shown to undergo a process of convergence wherein concepts that overlap across natural languages come to be represented more similarly. Recently, we replicated this convergence effect using a simulated bilingual language learning paradigm in which participants learn one language (i.e., category boundary and associated labels) before then learning a second language with a shifted category boundary. This work, however, only assessed English-speaking monolinguals. In the present study, we extend this paradigm to bilinguals—asking whether extensive experience maintaining different label mapping systems modulates degree of semantic convergence when learners face two novel (artificial) languages. We first assessed the language experience of Polish-English bilinguals then measured representational change via the simulated bilingual language learning paradigm. We report on evidence that language history moderates the extent, and direction, of representational change, and we conclude with implications for theories of bilingual representation.

Language Experience Modulates L2-Related Representational Change when Learning Novel Categories

In discourse, entities that are discontinuous with the current storyline are seen as cues for an event boundary, as they are too irregular to be mapped to the existing scene. However, some instances of discontinuity can maintain coherency, as exemplified in Calvin & Hobbes comics, where visual discontinuities can be resolved by the understanding that they depict Calvin’s imagination rather than actual events. This requires the reconciliation of the original storyworld domain with a private, mental domain (the alternative). In our first experiment, we examined whether switching between domains and/or the nature of the presented domain(s) incurred processing costs. Our second experiment examined whether physical cues such as the contours between the discontinuous entities facilitated processing. The results indicate that switching domains is indeed more costly, despite still being understood as congruous sequences. Moreover, strong similarity in contours aided readers with greater comics proficiency. Overall, our results show that the processing of visual narratives extends beyond mere event understanding and is not universally transparent.

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Modeling Reward Learning Under Placebo Expectancies: A Q-Learning Approach

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