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Persuasion is hard. Then why do some consumers think that marketers can easily manipulate them? Three studies and an internal meta-analysis suggest that consumers’ beliefs about marketing manipulation are rooted in humans’ deeper and older psychology—motivation to understand life events. Consumers higher in motivation to make sense of their environments not only detect persuasion where it exists but also where none (make false-positive errors). Whereas consumers higher in sense-making motives believe that manipulations are more effective, objective sense-making abilities negatively predict false manipulation detection. We also tested how manipulation beliefs are related to personality traits, gender, and age. These findings help 1) understand the origins of lay (false) beliefs about the marketplace and persuasion in general, 2) consider marketing segmentation strategies to reach out to particularly skeptical consumers, and 3) understand ways to attenuate false-positive beliefs and foster accurate persuasion detection, which is critical in the era of infodemic.

CogSci 2021

Lay Theories of Manipulation: Do Consumers Believe They are Susceptible to Marketers’ Trickery?

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

We hope that you enjoy the conference this year!<br><br>

**Your Program Chairs,**
![](https://assets.underline.io/uploads/markdown_image/1/image/99984908caba5e6453e5fc5ba34f3507.jpg)

**Learn more about the Vienna Cognitive Science Hub at the University of Vienna, hosts of CogSci 2021**

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To review the proceedings for the conference or review the searchable program, please visit the [website](https://cognitivesciencesociety.org/program/).


The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

poster

Previous studies have suggested testing and feature highlighting each facilitates category learning. We investigated whether the beneficial effects of interim testing change with the presence of feature highlighting in natural category learning. Participants learned various rock categories that were divided into two sections. They studied a series of rocks with or without feature descriptions highlighted on each image and either took an interim test or not between the two sections. On the final test, participants classified studied and new rock exemplars from both sections. The testing group outperformed the no-testing group for both sections, indicating both backward and forward effects of testing. Such beneficial effects of testing occurred regardless of whether the feature highlighting was provided or not. The feature highlighting, however, showed negative effects on learning of both sections, suggesting that provision of explicit instruction may impede learning when it is not appropriately embedded in the learning material.

Effects of interim testing and feature highlighting on natural category learning

A key element of conceptual understanding in physics education is the ability to make qualitative inferences. In this study, we investigated whether people use causal notions when solving simple, qualitative physics problems. We utilized the principles of directionality and holding variables constant to assess participant's accuracy in answering questions about inferences from cause to effect (CE), effect to cause (EC), and from one cause to the other cause (CC). Participants responded with greater accuracy for CE and EC questions when alternative causal information was held constant compared to when it is made explicitly unknown. For CC questions accuracy was lower across all alternative information types. Further, participants generally treated ambiguous alternative causes as if they were held explicitly constant. These results indicate that using notions of causality can potentially help identify difficult qualitative physics questions and be used as a tool in instructional design.

Using Causality to Map Difficulties in a Qualitative Physics Problem

Previous studies investigating the influence of a working memory load on category learning gained mixed findings. The current study investigated the role of increased working memory load in the processes of encoding two artificial categories with a rule-plus-similarity structure. During category learning, adults were trained with a dual-task that integrated the category learning task with a secondary working memory task. Participants were instructed to retain verbal (auditorily delivered) information while learning the categories in each trial. The learning phase was followed by recognition and categorization tasks evaluating their learning of the categories and memory for exemplar features. The results indicated that verbal working memory load affected category learning and representation as compared to the control condition where participants learned the categories without interference. The dual-task paradigm disrupted attention optimization and the formation of a rule-based category representation while increasing the probability of similarity-based encoding.

Verbal working memory capacity modulates category representation.

A set of fundamental beliefs governs our reasoning about objects and agents since infancy. Studies have shown that infants and children show enhanced exploration and learning when they observe apparent violations of these beliefs. However, little is known about whether these beliefs can be revised given counterevidence. In the present experiments, we demonstrate that 4- to 6-year-old children can revise their most fundamental beliefs in the physical domain (Experiment 1) and the psychological domain (Experiment 2) when they observe multiple pieces of belief-violating evidence.

Revising Core Beliefs in Young Children

Grapheme-color synesthetes experience linguistic symbols as having a consistent color (e.g., “The letter R is burgundy.”). Intriguingly, certain letters tend to be associated with certain colors, and these biases are not random: numerous properties of letters influence which letter is associated with which color. These influences, called “Regulatory Factors” (RFs), each explain some fraction of the variation in observed associations. No comprehensive model of the influences on grapheme-color associations exists: RFs have only been measured in isolation, are not always operationalized consistently, and often make competing predictions that cannot be accounted for in a univariate model. Here, we describe a statistical framework that integrates the predictions of all RFs into a single model, and thus yields a unified account of their influence on grapheme-color associations. Our model also links these predictions to measurable properties of language, offering a window into the multifactorial contributions to letter representation in the brain.

A Unifying Model of Grapheme-Color Associations in Synesthetes and Controls

We often organize objects around both visual and semantic boundaries in space. Across four experiments, we examined how semantically consistent partitions influenced memory for object locations. Participants learned the locations of items in a semantically partitioned display (where each partition contained objects from a single category), as well as a purely visually partitioned display (where each partition contained a random assortment of objects from different categories). While semantic partitions significantly improved location memory over the purely visually partitioned display, this advantage was significantly reduced when participants were cued to the correct partition during recall. Our results suggest that semantic category information benefits memory via strengthening the association between a given category and a spatial region delineated by a partition. Further, there was some indication that this benefit may come with the drawback of reducing memory precision for objects within a partitioned space.

The effect of semantic categorization on object location memory

The mind readily learns cue-outcome associations where an object predicts a specific outcome. Previous work suggested that when multiple objects associated with different outcomes were jointly presented , the mind made conjunctive predictions that represented the common property of the associated outcomes. Using attentional tracking measures, we provided more evidence for the weighted summation framework when the conjunctive predictions involved spatial locations (Experiment 1) or conceptual categories (Experiment 2). Then, we examined the reverse of conjunction, where participants were presented with a single object, which is a part of an object pair that was previously associated with an outcome (Experiment 3). Rather than making predictions based on mental operations such as subtraction, we found that participants’ predictions were purely based on previous associations. These results together demonstrated the robust tendency to make conjunctive predictions based on knowledge of cue-outcome associations.

Association knowledge guides conjunctive predictions in novel situations

In this study, we explicitly code and study the social, referential, and pragmatic features of gaze in human-human spontaneous dyadic interaction, providing novel observations that can be executed in a machine in order to improve multimodal human-agent dialogue. Gaze is an important non-verbal social signal that contains attentional cues about where to look and provides information about others' intentions and future actions. In this work, various types of gaze behaviour are annotated in detail along with speech to explore the meaning of temporal patterns in gaze cues and their co-relations. Considering that 80% of the total stimuli perceived by the brain is visual, gaze behaviour is complex and challenging; hence, implementing human-human gaze cues to an avatar/robots could improve human-agent interaction and make it more natural.

A Deep Gaze into Social and Referential Interaction

Humans are able to make sense of extraordinarily abstract concepts in mathematics (e.g., negative numbers). What is the underlying representation of these concepts? Integers extend natural numbers by including zero and negative numbers. To study the mental representation of integers, we employed a number comparison task in an online context. We replicated the previously-reported distance effect, in that far comparisons were faster than near comparisons. Namely, we observed reliable distance effects for positive and negative comparisons, and critically, an inverse distance effect for mixed comparisons. We conclude that the mental representation of integers may align with a hypothesis proposing the mental number line for negative numbers mirrors the natural number line. Moreover, we conclude that web-based data collection is a promising tool for future numerical cognition research.

The mental representation of integers: Further evidence for the negative number line as a reflection of the natural number line

In categorization tasks optimization of performance depends on attention to relevant stimulus features. However, studies show that adults and children seem to use different strategies when learning new categories. Deng & Sloutsky (2016) found that adults attend selectively, whereas children prefer to attend diffusely, even when the stimuli possess deterministic features (i.e. with 100% probability of belonging to a certain category). Most studies of attention in category learning rely on behavioral effects to infer attentional strategy, despite the availability of eye-tracking technology. In this study, we combine the use of category training and transfer paradigms (see Miles & Minda, 2009 and Deng & Sloutsky, 2016) and eye-tracking methods to investigate attentional strategies of children and adults during category learning. Preliminary results of our adult pilot (N = 14) confirm the prediction that adults optimize performance by attending increasingly to deterministic features of the stimulus once the categorical rule is found.

Lay Theories of Manipulation: Do Consumers Believe They are Susceptible to Marketers’ Trickery?

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Effects of interim testing and feature highlighting on natural category learning

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