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Humans are often faced with an exploration-versus-exploitation trade-off. A commonly used paradigm, multi-armed bandit, has shown humans to exhibit an &quot;uncertainty bonus&quot;, which combines with estimated reward to drive exploration. However, previous studies often modeled belief updating using either a Bayesian model that assumed the reward contingency to remain stationary, or a reinforcement learning model. Separately, we previously showed that human learning in the bandit task is best captured by a dynamic-belief Bayesian model. We hypothesize that the estimated uncertainty bonus may depend on which learning model is employed. Here, we re-analyze a bandit dataset using all three learning models. We find that the dynamic-belief model captures human choice behavior best, while also uncovering a much larger uncertainty bonus than the other models. More broadly, our results also emphasize the importance of an appropriate learning model, as it is crucial for correctly characterizing the processes underlying human decision making.

CogSci 2021

Revisiting the Role of Uncertainty-Driven Exploration in a (Perceived) Non-Stationary World

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Humans are often faced with an exploration-versus-exploitation trade-off. A commonly used paradigm, multi-armed bandit, has shown humans to exhibit an "uncertainty bonus", which combines with estimated reward to drive exploration. However, previous studies often modeled belief updating using either a Bayesian model that assumed the reward contingency to remain stationary, or a reinforcement learning model. Separately, we previously showed that human learning in the bandit task is best captured by a dynamic-belief Bayesian model. We hypothesize that the estimated uncertainty bonus may depend on which learning model is employed. Here, we re-analyze a bandit dataset using all three learning models. We find that the dynamic-belief model captures human choice behavior best, while also uncovering a much larger uncertainty bonus than the other models. More broadly, our results also emphasize the importance of an appropriate learning model, as it is crucial for correctly characterizing the processes underlying human decision making.

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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,**
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**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.

Children with Developmental Language Disorder (DLD) have deficits in verbal and nonverbal processing relative to typically developing (TD) peers. We examined working memory in DLD relative to age-matched TD peers (9-13 years) under the serial-order-in-a-box – complex span model. This model posits a time-based mechanism, Free Time, that governs how interference affects processing performance. Results showed that Free Time was positively associated with accuracy when recall and interference stimuli had verbal features (b = 0.00; stat = 3.11; p < .01), and combined verbal and nonverbal features (b = 0.00; stat = 3.05; p < .01). Group differences in this relationship were evident when recall stimuli had verbal features regardless of interference stimuli features (b = -0.00; stat = -3.66; p < .001; b = 0.00; stat = 2.97; p < .01). Findings suggest a greater role of Free Time for verbal than nonverbal content, which varies depending on participant characteristics.

Testing an interference-based model of working memory in children with developmental language disorder and their typically developing peers

The mental representation of relation-based concepts is different from that of feature-based concepts. In the present experiment, participants learned to categorize two fictional diseases that were defined either by a feature (e.g., short cells) or an ordinal relation (e.g., diseased cells being shorter than healthy cells). After the participants learned the categorization task to criterion, their strategies were probed in transfer task in which features and relations were pitted against one another. Finally, participants engaged in a stimulus reconstruction task. The results supported the prediction that participants who had adopted a feature-based strategy on a stimulus dimension, as identified by transfer data, tended to reconstruct values close to the means presented during training. By contrast, participants who had adopted a relation-based strategy tended to exaggerate that dimension away from the mean of the training examples and in the direction of the category-defining comparative relation. These data add to the growing literature suggesting that, unlike featural categories, relational categories are not represented in terms of the category’s central tendency.

Probing the Mental Representation of Relation-Defined Categories

Reasoning by exclusion allows us to infer properties of unobserved objects from currently observed objects, formalized by P or Q, not P, therefore Q. Previous work suggested that, by age 3, children can use this kind of reasoning to infer the location of a hidden object after learning that another location is empty (e.g. Mody & Carey, 2016). In the current study, we asked whether children could use reasoning by exclusion to infer the identities of previously unobserved occluded objects in a task that required them to track the locations of multiple occluded objects. Forty-nine 4-7-year-olds viewed animated arrays of virtual “cards” depicting images which were then hidden by occluders. The occluders then swapped locations during the maintenance period. Children were asked to select which card was hidden in a probed location. During the encoding period, we manipulated whether children saw all the card faces (Face-up block) or all but one of the card faces (Exclusion block), for which children had to reason by exclusion to infer the target in half of the trials. We found that all children succeeded in the Face-up block, but only 6-year-olds succeed in the Exclusion block when they had to deploy logical reasoning to identify a previously-unseen hidden target. Our results suggest that children’s ability to reason by exclusion to infer the identity of a hidden target while tracking multiple objects and locations may undergo protracted development.

Children’s use of Reasoning by Exclusion to Track Identities of Occluded Objects

This study investigated processing/strategy complexity to activate magnitude representation for common fractions (1/4, 1/3, 1/2, 2/3, and 3/4), and the precision of their magnitude representations on the mental number line. We compared the magnitude comparison performance for pairs involving any one of common fractions and pairs of two uncommon fractions. We hypothesized the mean reaction time to be shorter for pairs involving a common fraction with a simple processing/strategy to activate the magnitude representation, and the magnitude distance effect to be smaller for pairs involving common fractions with precise magnitude representation. Bayesian mixed-effect regression analysis found a shorter mean reaction time for pairs involving common fractions except 1/4. Comparisons involving common fractions did not show a smaller magnitude distance effect. Thus, processing to activate magnitude representation is simpler for common (except 1/4) than for uncommon fractions, although common fractions do not have more precise magnitude representation than uncommon fractions.

Complexity of processing to activate magnitude representation for common fractions and precision of their magnitude representations in fraction magnitude comparison

From about six months of age onward, infants observing an action, such as a grasp, start to shift their gaze from the moving agent to the goal before the action is complete. A variety of factors that influence such goal-predictive gaze have been identified. However, the underlying cognitive processes are heavily debated. We propose that our minds structure sensorimotor dynamics into probabilistic, generative event-predictive models, and, choose actions with the objective to minimize predicted uncertainty. We implement this proposition by means of event-predictive learning and active inference. Trained on manual object-manipulations, the generation of goal-predictive gaze emerges: The model starts fixating the anticipated goal at the start of an observed event when a familiar agent (i.e., a hand) is involved. Meanwhile, the model keeps tracking unfamiliar agents (e.g., a claw) performing the same movement. We conclude that event-predictive learning combined with active inference may be critical for eliciting action-goal predictions.

I see where this is going: Modeling the development of infants' goal-predictive gaze

Visual comparison is used in education to convey important commonalties and differences. This process is more effective when the figures are spatially aligned so that the corresponding parts and relations are maximally clear (direct placement) (Matlen, Gentner, & Franconeri, 2020). Yet science textbooks often fail to follow this principle arranging figures meant to be compared (Jee et al., in prep)—perhaps in service of visual appeal. To explore whether this choice in fact maximizes visual appeal, we gave middle-school students illustrations characteristic of textbook figures, along with modified versions that followed direct placement principles. Students were significantly more likely to choose the direct placement version when given the goal of helping other students see differences among the figures (M=94%), than when given the goal to make the figure “look nice” (M=61%). These findings suggest that direct placements improve the educational value of a figure without sacrificing its aesthetic appeal.

Students Prefer to Learn from Figures that Include Spatial Supports for Comparison

Our decisions are informed by a variety of sources of information including prior knowledge and contextual cues. Previous research has shown that explicit and implicit cues influence our decisions differently. Using a choice reaction time task, we aimed to explore this differential influence through a Bayesian drift diffusion model of decision making. We contrasted the effect of two types of instructions — one presented in hypnosis and followed by a posthypnotic amnesia suggestion and the other in normal waking state — on drift rate (v), threshold (a), and non-decision time (t). And, we compared this effect between participants who reported involuntariness or amnesia and those who did not. Results suggest involuntary responses to implicit cues may require less evidence to be executed as it was characterized by a lower threshold and a higher drift rate, which should be tested by confirmatory research.

The differential effect of explicit and implicit instructions on response execution: a hypnosis study

Biologists, lay adults, and children alike value causal explanations of how biological entities work. Despite this, elementary school science education has historically lacked mechanistic content. In line with recent science education standards, we investigated the effects of mechanistic learning goals on understanding of an in-depth lesson about how the heart works. Children ages 6 to 11 who were given mechanistic learning goals performed better on knowledge assessments of the heart lesson and enjoyed their learning goal more than children who were given a relatively superficial learning goal—to focus on labels. Thus, learning goals orienting children towards mechanistic content during lessons enhance science learning and enjoyment.

Mechanistic Learning Goals Enhance Elementary Student Understanding and Enjoyment of Heart Lessons.

This study examined the influence of metacognition on declarative and reinforcement learning (RL) mechanisms. We collected data from 218 undergraduates using a within-subjects metacognitive manipulation of a stimulus-response (S-R) learning task created by Collins (2018). Contributions of declarative and RL mechanisms are assessed by differences in learning rate for blocks of 3 items versus 6 items, and by the rate of forgetting with an incidental post-test. If metacognition differentially affects declarative and RL, we expect a three-way interaction between the task phase (learning/post-test), block type (long/short), and metacognition (before/during). Our results showed significant main effects of phase (F(1,217) =143.18, p=9.18e-32), length (F(1,217)=541.11, p=2.06e-104) and metacognition (F(1,217) = 19.78, p = 9.22e-06), with better performance during the learning phase, short blocks, and metacognitive manipulation. A significant phase by metacognition interaction (F(1,217) =8.11, 4.45e-03) suggested that metacognition monitoring improved test performance while having little effect on learning performance.

Investigating the Impact of Metacognition on Working Memory and Procedural Learning Mechanisms

Research shows that there is a strong cultural bias against women: Many people believe that women are less intellectually talented than men (Kirkcaldy et al., 2007; Upson & Friedman, 2012), and these beliefs emerge in childhood. For example, girls as young as six were less motivated than boys to pursue gender-neutral activities that were described as being for children who are “really smart” and more inclined toward activities intended for “hardworking” children (Bian, Cimpian, & Leslie, 2015). 
This study investigates whether framing activities as stereotypically masculine or feminine alters the impact of messages about intellectual ability on 6- and 7-year-olds’ selection of preferred activities. Preliminary findings (N=75) indicate that making activities stereotypically feminine reduces girls’ preference for "hardworking" over "smart" games. Such results suggest that presenting young girls with toys that are targeted specifically to them might counteract the effects of negative gender stereotypes about intelligence.

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