United States

Despite the increase in studies investigating people’s explana- tory preferences in the domains of psychology and philoso- phy, little is known about their preferences in more applied do- mains, such as the criminal justice system. We show that when people evaluate competing legal accounts of the same evidence that vary in complexity, their explanatory preferences are af- fected by: i) whether they are required to draw causal mod- els of the evidence, and ii) the actual structure that is drawn. Although previous research has shown that people can reason correctly about causality, ours is one of the first studies that shows that generating and drawing causal models directly af- fects people’s evaluations of explanations.

CogSci 2021

The role of causal models in evaluating simple and complex legal explanations

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**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**

<iframe width="560" height="315" src="https://www.youtube.com/embed/464h1m3iU68" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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

Keeping track of time is essential for everyday behavior. Theoretical models have proposed a wide variety of neural processes that could tell time, but it is unclear which ones the brain actually uses. Low-level neural models are specific, but rarely explicate how cognitive processes, such as attention and memory, modulate prospective and retrospective timing. Here we develop a neurocomputational model of prospective and retrospective timing, using a spiking recurrent neural network. The model captures behavior of individual spiking neurons and population dynamics when producing and perceiving time intervals, thus bridging low- and high-level phenomena. When interrupting events are introduced, the model delays responding in a similar way to pigeons and rats. Crucially, the model also explains why attending incoming stimuli decreases prospective estimates and increases retrospective estimates of time. In sum, our model offers a neurocomputational account of prospective and retrospective timing, from low-level neural dynamics to high-level cognition.

A Neurocomputational Model of Prospective and Retrospective Timing

We propose a biologically constrained, large-scale neural network model that solves the Wisconsin Card Sorting Test (WCST). The WCST has been widely used in clinical and research settings to study cognitive flexibility and executive function. The model shows a good quantitative match with human responses across a number of WCST scoring indices, while consisting of neural networks that functionally and anatomically map to brain areas and structures implicated in the task, such as the prefrontal cortex and the cortico-basal ganglia-thalamus-cortical loop. We argue that the model provides a mechanistic account of WCST solving, and demonstrate its robustness by examining its performance across a range of biologically motivated parameter values.

Biologically Constrained Large-Scale Model of the Wisconsin Card Sorting Test

The associative theory of creativity proposes that creative ability relies on the organization of semantic memory, yet the relationships between semantic memory structure and brain connectivity, in relation to creativity, remain poorly understood. Here, we explored the relationships between the network properties of individual semantic memory, patterns of functional brain connectivity, and real-life creativity. To this end, we acquired functional magnetic resonance imaging data while participants underwent a semantic relatedness judgment task. Participants’ relatedness ratings between word-pairs were used to estimate their individual semantic networks, whose network properties were significantly related to their real-life creativity. Using a connectome-based predictive modeling approach, we identified patterns of on-task functional connectivity that predicted creativity-related semantic memory network properties in novel individuals. Furthermore, the predicted semantic network properties partially mediated the relationship between functional connectivity and real-life creativity. These results provide new insights on how brain connectivity supports the associative mechanisms of creativity.

Brain Connectivity-Based Prediction of Semantic Network Properties Related to Creativity

This study examined dynamic attractor states in skin conductance activity during resting baselines and media viewing in order to determine if there are qualitatively distinct dynamics during information processing and whether those dynamics vary based on features of task stimuli. The results indicate that media viewing shifts one from a resting non-chaotic attractor to a chaotic attractor. Content valence (positive or negative) and the emotional context in which videos were delivered (presentation order) had significant impact on the probability of exhibiting a chaotic attractor. Using the nonlinear dynamic systems approach, this study provides novel understandings of emotional information processing, the electrodermal system, and the relationship between physiological and emotional experiences.

A Nonlinear Dynamical Systems Approach to Emotional Attractor States during Media Viewing

We present a neural network model of referent identification in a preferential looking task. The inputs are visual representations of pairs of objects concurrent with unfolding sequences of phonemes identifying the target object. The model is trained to output the semantic representation of the target object and to suppress the semantic representation of the distractor object. Referent identification is achieved in the model based only on bottom-up processing. The training set uses a lexicon of 200 words and their visual and semantic referents, reported by parents as typically known by toddlers. The phonological, visual and semantic representations are derived from real corpora.

The model successfully replicates experimental evidence that phonological, perceptual and categorical relationships between target and distractor modulate the temporal pattern of visual attention. In particular, the network captures early effects of phonological similarity, followed by later effects of semantic similarity on referent identification.

A neural network model of referent identification in the inter-modal preference looking task

Learning the contingencies of a complex experiment is hard, and animals likely revise their strategies multiple times during the process. Individuals learn in an idiosyncratic manner and may even end up with different asymptotic strategies. 
Modeling such long-run acquisition requires a flexible and extensible structure which can capture radically new behaviours as well as slow changes in existing ones. To this end, we suggest a dynamic input-output infinite hidden Markov model whose latent states capture behaviours. We fit this model to data collected from mice who learnt a contrast detection task over tens of sessions and thousands of trials. Different stages of learning are quantified via the number and psychometric nature of prevalent behavioural states. Our model indicates that initial learning proceeds via drastic changes in behavior (i.e. new states), whereas later learning consists of adaptations to existing states, even if the task structure changes notably at this time.

Exploring learning trajectories with dynamic infinite hidden Markov models

We explore the separation of decision confidence and familiarity components in EEG data from recognition memory experiments. We first develop and test a classifier designed to classify decision confidence on new trials. We then use this classifier to control for confidence in the selection of trials of familiarity and correct rejection. This allows us to reveal a familiarity component that is of similar magnitude for recollection and familiarity judgements. This familiarity component reveals more of a frontal extent than obtained without confidence matching. We believe that this preliminary result can serve as a guide for designing future electrophysiological experiments to better separate the different components of recognition memory and that the technique of using classifiers to control for response-related covariates can be used for early exploration of these components in existing data.

EEG Reveals Familiarity by Controlling Confidence in Memory Retrieval

When you see a glass fall off the table, you can predict it will break without seeing it hit the ground. Similarly, if you hear the glass shatter, you can infer what happened without seeing anything at all. Our knowledge of the causal mechanisms structuring our world allows us to make impressively accurate inferences based on incomplete information spread across multiple sensory modalities. In this work, we study the cognitive processing that supports this remarkable behavior. We utilize the Plinko domain, an intuitive physics setup where marbles are dropped into a box from one of three holes, colliding with obstacles as they fall to the ground. Participants judge where they think the ball fell from based on visual and auditory evidence. We track participants' eye-gaze to gain deeper insight into their mental processes, and develop models that characterize the computational processes underlying participant behavior.

Eye-Tracking Multi-Modal Inference

This study investigates whether the ERPs of 4-years-olds in response to verbal overextensions reflect the encoding of actions through abstract categories. Participants were presented with images of actions (e.g. peeling an orange) while hearing a sentence containing a conventional verb (e.g. peeling), an approximative verb (e.g. undressing), a superficially related verb (e.g. pressing) or a pseudoverb (e.g. rauging). The N400 for approximative verbs significantly differed from the pseudoverb condition, but not from the conventional verb condition. In contrast, the N400 for superficially related verbs was significantly greater than for conventional verbs, but no significant difference was found with the pseudoverb condition. These results confirm our hypothesis that encoding mainly focuses on general categories (e.g. taking of an envelope). The implications of the findings are discussed regarding the conceptual organization of preschoolers and their analogical abilities.

Early Analogical Extensions: An ERP Study on Preschoolers' Semantic Approximations

An ongoing debate about the structure of conceptual space is based on two competing mathematical theories of similarity that make distinct predictions about the structure of mental representations and how to model the representational space they are stored in. These are known as metric (Shepard, 1962) and ultrametric (Tversky, 1977) theories, modeled by multidimensional scaling and additive trees respectively. Turning to the brain to resolve this conflict, we propose a computational framework to assess behavioral and neural data’s underlying structure and investigate whether the behaviorally known spaces for colors (metric) and letters (ultrametric) can be reproduced from neural data. Our results show that the metric color wheel can be reproduced from brain area V4, but that neural activations of the letters from extrastriate cortex (V2-V5) are also metric instead of being ultrametric. Finally, we discuss three possibilities for the brain’s similarity structure, including a potential metric bias.

Downloads

Next from CogSci 2021

A Neurocomputational Model of Prospective and Retrospective Timing

Similar lecture

Belief Change Triggers Behavioral Change