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Please, see the workshop's website here: https://sites.google.com/view/cogconceptsThe goal of this workshop is to initiate an interdisciplinary conversation about reconceptualizing cognitive science disciplines. This workshop will bring together researchers proposing new conceptualizations in their disciplines, cognitive scientists investigating the mechanisms of concept learning and the role of concepts in human cognition, researchers building infrastructures to study and improve cognitive concepts, and philosophers analyzing scientific conceptualizations. The workshop will include activities which will prompt the audience to think about the conceptual foundations of their respective areas, and about ways to improve these foundations. These activities are designed to maximize audience participation and include panel discussions, as well as mind-matching sessions. One of the outcomes of the workshop is identifying the diversity of approaches for improving cognitive science concepts that could be relevant to both discipline-wide and more specific efforts.

This study explores how scientific conceptualizations, such as partitioning of the brain into distinct regions, shape investigation. One hundred fifty-six undergraduate psychology students (novices) completed a science learning task in which they explored the behavioral functions of a fictional brain segment by conducting simplified neuroimaging and lesioning experiments on it. We investigated how the partitioning of the segment into regions influenced participants' experimental choices and learning outcomes by randomly seeding the brain regions for each participant. The participants exhibited conceptual influences on their experimentation: they preferred to explore the boundaries and prototypical--or "skeletal"--locations of the delineated regions. These conceptual biases significantly shaped learning outcomes; for example, participants were more successful at identifying signals near region boundaries. Additionally, participants demonstrated conceptual expectations that led them to associate a discovered signal with locations within one region rather than locations that straddled region boundaries. This research contributes to our understanding of how the scientific concepts affect scientific investigation.

Many paradigms in cognitive science posit that human learning is characterized by a limited capacity to represent the information relevant for a given task. We argue that excess capacity -- using more representational resources than needed for a task at hand -- is a plausible alternative paradigm for the study of human learning. Leveraging recent results from machine learning, we show that excess capacity can be consistent with high predictive ability. We also review extant empirical findings from the cognitive science literature, demonstrating that excess capacity learning can account for a range of empirical phenomena, such as humans' simultaneous yet apparently contradictory tendency to both memorize observations and capture higher-level patterns in them. We conclude by discussing promising directions for future inquiry under the excess capacity learning paradigm.

In light of constraints inherent to empirical research, such as finite time and resources, there has been growing interest in using artificial intelligence to streamline the scientific process. However, despite advancements in automating scientific discovery, the implementation of strategies for sampling useful experiments remains a challenge. This metascientific study evaluates different experimental sampling strategies based on their effectiveness in advancing the discovery of linear models of human cognition based on synthetic data. We investigate the hypothesis put forth by Dubova et al. (2022) that random sampling of experiments is more effective than model-driven sampling. Indeed, the results of this study indicate that random sampling is more effective in a majority of cases, and that the underperformance of model-driven strategies can be attributed to a narrow sampling of the design space. Despite limitations in our approach, the work presented offers a novel framework for the metascientific study of autonomous empirical research.

There is broad empirical evidence suggesting that higher-level cognitive processes, such as language, categorization, and emotion, shape human visual perception. For example, categories that we acquire throughout lifetime have been found to alter our perceptual discriminations and distort perceptual processing. Here, we study categorical effects on perception by adapting the perceptual matching task to minimize the potential non-perceptual influences on the results. We found that the learned category-color associations bias human color matching judgments away from their category ideal on a color continuum. This effect, however, unequally biased two objects (probe and manipulator) that were simultaneously present on the screen, thus demonstrating a more nuanced picture of top-down influences on perception than has been assumed both by the theories of categorical perception and the El Greco methodological fallacy. We suggest that only the concurrent memory for visually present objects is subject to a contrast-from-caricature distortion due to category-association learning.

We explore different ways in which the human visual system can adapt for perceiving and categorizing the environment. There are various accounts of supervised (categorical) and unsupervised perceptual learning, and different perspectives on the functional relationship between perception and categorization. We suggest that common experimental designs are insufficient to differentiate between hypothesized perceptual learning mechanisms and reveal their possible interplay. We propose a relatively underutilized way of studying potential categorical effects on perception, and we test the predictions of different perceptual learning models using a two-dimensional, interleaved categorization-plus-reconstruction task. We find evidence that the human visual system adapts its encodings to the feature structure of the environment, uses categorical expectations for robust reconstruction, allocates encoding resources with respect to categorization utility, and adapts to prevent miscategorizations.

We propose a unified explanation of contrastive and assimila-tive adaptation aftereffects from the perspective of higher-levelcognitive processes: rational category learning and categoricalperception. We replicate (twice) previously reported assimila-tive and contrastive effects (Uznadze illusion in visual modal-ity), propose a rational computational model of the process,and evaluate our model performance against the Bayesian lo-gistic regression baseline. We conclude by discussing theo-retical implications of our study and directions for further re-search.

Many life-influencing social networks are characterized by considerable informational isolation. People within a community are far more likely to share beliefs than people who are part of different communities. The spread of useful information across communities is impeded by echo chambers (far greater connectivity within than between communities) and filter bubbles (more influence of beliefs by connected neighbors within than between communities). We apply the tools of network analysis to organize our understanding of the spread of beliefs across modularized communities and to predict the effect of individual and group parameters on the dynamics and distribution of beliefs. In our Spread of Beliefs in Modularized Communities (SBMC) framework, a stochastic block model generates social networks with variable degrees of modularity, beliefs have different observable utilities, individuals change their beliefs on the basis of summed or average evidence (or intermediate decision rules), and parameterized stochasticity introduces randomness into decisions. SBMC simulations show surprising patterns; for example, increasing out-group connectivity does not always improve group performance, adding randomness to decisions can promote performance, and decision rules that sum rather than average evidence can improve group performance, as measured by the average utility of beliefs that the agents adopt. Overall, the results suggest that intermediate degrees of belief exploration are beneficial for the spread of useful beliefs in a community, and so parameters that pull in opposite directions on an explore–exploit continuum are usefully paired. [ABSTRACT FROM AUTHOR]

In many areas of the social and behavioral sciences, the nature of the experiments and theories that best capture the underlying constructs are themselves areas of active inquiry. Integrative experiment design risks being prematurely exploitative, hindering exploration of experimental paradigms and of diverse theoretical accounts for target phenomena.