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Human learning efficiency in reinforcement learning tasks decreases when the number of the presented stimuli increases, a finding known as the &quot;set size effect&quot;. From the computational rationality perspective, this effect can be interpreted as the brain’s balancing task performance against rising cognitive costs. Still, it remains unclear how best to quantify cognitive cost in learning tasks. One candidate is policy complexity, defined in terms of information theory as the mutual information between the sensory input and behavioral response. However, using a published data set (Collins &amp; Frank, 2012), we show that policy complexity alone cannot explain the set size effect because the optimal policy complexity does not necessarily increase with the set size. We therefore propose a computational model and conduct a model-based analysis to show the minimal constituents of cognitive cost are policy complexity and representation complexity---the information quantity conveyed from sensory inputs to internal representations.

CogSci 2021

A computationally rational model of human reinforcment learning

**About**
<br>The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

<br>PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

<br>The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7
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PETRA 2021

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

Human learning efficiency in reinforcement learning tasks decreases when the number of the presented stimuli increases, a finding known as the "set size effect". From the computational rationality perspective, this effect can be interpreted as the brain’s balancing task performance against rising cognitive costs. Still, it remains unclear how best to quantify cognitive cost in learning tasks. One candidate is policy complexity, defined in terms of information theory as the mutual information between the sensory input and behavioral response. However, using a published data set (Collins & Frank, 2012), we show that policy complexity alone cannot explain the set size effect because the optimal policy complexity does not necessarily increase with the set size. We therefore propose a computational model and conduct a model-based analysis to show the minimal constituents of cognitive cost are policy complexity and representation complexity---the information quantity conveyed from sensory inputs to internal representations.

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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.

Recent studies provide experimental evidence for joint rushing - the phenomenon that participants in rhythmic group activities unintentionally increase their tempo. 
We hypothesized that joint rushing arises from the combined workings of established sensorimotor synchronization mechanisms (e.g., phase and period correction) with a simple phase advance mechanism that has been studied in mass-synchronizing insects. 
We invited musicians and non-musicians to participate in three tapping experiments. Participants were asked to continue to produce a constant target tempo alone (solo) or together with a partner (joint). 
The results show that joint rushing induces a lasting period correction but stops when auditory feedback of the partner’s taps is removed. Musical training reduced the magnitude of joint rushing but did not eliminate it.
These results are consistent with our hypothesis that joint rushing occurs because adjustments induced by a simple phase advance mechanism alter the period of internal timekeeping.

The role of period correction and continuous input from a co-performer in joint rushing

We report a supervised category learning experiment in which the training phase contains both classification and observation learning blocks. To explain the use of different categorization strategies, we propose an account in which use of a stimuli dimension depends on how well the dimension is learned. Our results show that there is an overall preference for a unidimensional categorization based on the perfectly diagnostic dimension. The preference for unidimensional categorization is negatively correlated with how well participants learn the partially diagnostic dimensions. Preference for unidimensional categorization is also negatively correlated with the mean response time. Bayesian modeling results show that participants use a partially diagnostic dimension only when it is learned with a very high level of accuracy. Different strategies are used for categorization depending on how well the perfectly and partially diagnostic dimensions are learned.

Supervised category learning: When do participants use a partially diagnostic feature?

The aim of this study was to identify the effects of perceptual (visual and olfactory) and emotional imageries of food names to word recognition memories. First, we asked Japanese healthy participants to imagine visual (Experiment 1), olfactory (Experiment 2), emotional (Experiment 3), or preferential features (Experiment 4) of presented words associated with foods and drinks (imagining condition), while we also asked them to passively read presented words (reading condition). Second, they judged whether each word was previously presented (word recognition memory task). Results showed that accuracy rates of the imagining condition were significantly higher than those of the reading condition in word recognition memory tasks of all the experiments. Our findings suggest that perceptual and emotional imageries of food names facilitate word recognition memories.

Effects of perceptual and emotional imageries of food names to word recognition memories: four behavioral experiments

Empirical evidence demonstrates the superiority of interleaved over blocked learning schedules in inductive learning, yet many students believe the opposite. We investigated the effect of metacognitive activities on learners’ awareness of interleaved benefits. Participants experienced two learning schedules for different painting styles and took a transfer test. Then we manipulated the provision of post-test feedback for each schedule and metacognitive prompts to reflect on the learning schedules. Finally, participants were asked to choose a more effective schedule between the two. Although there were no significant effects of either the post-test feedback or metacognitive prompts, the performance difference between the two schedules significantly predicted participants’ schedule selection. The greater benefits from the interleaved schedule the participants experienced, the more likely they chose the interleaved schedule. Furthermore, participants’ responses to metacognitive prompts revealed that those who acknowledged spacing benefits from the interleaved schedule were more likely to choose the interleaved schedule.

Appreciating interleaved benefits: The effect of metacognitive activities on the selection of learning strategy

Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants' judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. However, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn't affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA.

Causal Learning With Interrupted Time Series

Humans can adapt their knowledge acquired for one problem to solve other problems of different problem domains. To seek for evidence of knowledge transfer, we investigated
the human navigation behavior on the network of Wikipedia. We showed that the performance of human players is between that of the best navigator with full knowledge
on the network structure of Wikipedia and of the poorest navigator with no knowledge on it. This suggests that human players transferred their knowledge on the nominal concepts onto the network structure of Wikipedia. Further, we conducted an analysis of the degree of confidence in decision making based on reinforcement learning. We found that human
players might be very certain even about their first choice, from which we suspect that human players can solve the new problem by transferring their everyday knowledge successfully from the beginning and this can be a true power of knowledge transfer across problem domains.

Transfer of Knowledge in a Semantic Navigation Task Without the Accurate Map: Model-based Analysis of Knowledge Transfer

In choice blindness (CB) experiments participants often accept a manipulated outcome as their actual choice. In a typical CB experiment the manual actions that participants perform are always correct (pointing, writing, etc.), while the outcome is mismatched. However, what would happen if an error was induced at the motor level, but the outcome nevertheless remained correct? We investigated this by having participants drag a mouse cursor across the screen to the face they found the most attractive, while we manipulated either the outcome (classic CB), or the cursor (forced motor deviation), or sometimes both. Interestingly, what we found was that when the cursor was manipulated but not the outcome, the motor ‘wrongness’ would override the goal ‘rightness’, and participants ended up rejecting the outcome they actually wanted. We will discuss the implications of this new reverse choice blindness effect for theories of self-monitoring, agency and preference change.

If you think your action was erroneous, you will reject the outcome you actually wanted: a case of reverse choice blindness

To what extent do human reward learning and decision-making rely on the ability to represent and generate richly structured relationships between options? We provide evidence that structure learning and the principle of compositionality play crucial roles in human reinforcement learning. In a new multi-armed bandit paradigm, we found evidence that participants are able to learn representations of different reward structures and combine them to make correct generalizations about options in novel contexts. Moreover, we found substantial evidence that participants transferred knowledge of simpler reward structures to make compositional generalizations about rewards in complex contexts. This allowed participants to accumulate more rewards earlier, and to explore less whenever such knowledge transfer was possible. We also provide a computational model which is able to generalize and compose knowledge for complex reward structures. This model describes participant behaviour in the compositional generalization task better than various other models of decision-making and transfer learning.

Compositional generalization in multi-armed bandits

Hindsight bias is exhibited when knowledge of an outcome (i.e., an anchor) affects subsequent recollections of previous predictions (i.e., an estimate). Hindsight bias usually leads to estimates being remembered as closer to the anchor than they actually were. The exact amount of hindsight bias exhibited depends on the anchor value and the anchor plausibility, with experimental results showing that hindsight bias is elicited only when the anchor is perceived to be plausible. In this paper we present a Bayesian model that captures the relationship between hindsight bias and anchor plausibility. This model provides a rational account of hindsight bias by considering memory recall as a statistical problem, where the goal is to reconstruct the original estimate using the anchor as new evidence. Simulations show that the modeled trends align closely with previously published human data.

A Rational Account of Anchor Effects in Hindsight Bias

Probability density estimation is widely-known as an ill-posed statistical problem whose solving depends on extra constraints. We investigated what prior beliefs people might have in their learning of an arbitrary probability distribution, especially whether the distribution is believed to be unimodal or multimodal. In each block of our experiments, participants repeatedly reconstructed a one-dimensional spatial distribution after observing every 60 new samples from the distribution. The probability distribution function (PDF) they reported on each trial was submitted to a spectral analysis, where the powers for 1-cycle, 2-cycle, …, n-cycle components respectively indicate participants’ tendency of reporting unimodal, bimodal, …, n-modal distributions. In two experiments, the reported PDFs showed significant bimodality—that the 2-cycle power was above chance and even larger than the 1-cycle power—not only for bimodal distributions, but also for uniform distribution. Such illusory bimodality for uniform distribution was first found when we used an adaptive procedure analogous to “human MCMC”, updating the generative distribution of samples from trial to trial to reinforce potential biases in PDFs (Experiment 1). However, even when we fixed the generative distribution across trials (Experiment 2), the illusory bimodality did not vanish. The illusory bimodality was even observed before participants experienced any bimodal distributions in the experiment . We considered a few kernel density models and discuss further computational explanations (e.g. prior beliefs following Chinese Restaurant Process) for this new phenomenon.

A computationally rational model of human reinforcment learning

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