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Representational Similarity Analysis (RSA) is a powerful tool for linking brain activity patterns to cognitive processes via similarity, allowing researchers to identify the neural substrates of different cognitive levels of representation. However, the ability to map between levels of representation and brain activity using similarity depends on underlying assumptions about the dynamics of cognitive processing. To demonstrate this point, we present three toy models that make different assumptions about the interactivity within the reading system, (1) discrete, feedforward, (2) cascading, feedforward and (3) fully interactive. With the temporal resolution of fMRI, only the discrete, feedforward model provides a straightforward mapping between activation similarity and level of representation. These simulations indicate the need for a cautious interpretation of RSA results, especially with processes that are highly interactive and with neuroimaging methods that have low temporal resolution. The study further suggests a role for fully-fleshed out computational models in RSA analyses.

CogSci 2021

Challenges for using Representational Similarity Analysis to Infer Cognitive Processes: A Demonstration from Interactive Activation Models of Word Reading

### 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.

technical paper

This is the first kinematic investigation of articulator motion in Austrian Sign Language, which connects kinesiology of sign production and linguistic markers of Aktionsart in the native language of the Deaf community in Austria. Our work used a 3D motion capture approach to sign language analysis to investigate the relationship between the semantics (event structure) of signed verbs, and kinematics of hand articulator movement. The data indicates that the underlying semantics of events in verb signs is reflected in sign duration and acceleration of the dominant hand during sign production. The finding that articulator dynamics (acceleration and deceleration of hand motion) characterizes the event structure in verb signs has significance for linguistic theory of visual communication, and understanding of the relationship between iconicity in sign language, and perceptual biases in meaning construction based on visual input.

Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

Visual and auditory channels have different affordances and this is mirrored in what information is available for linguistic encoding. The visual channel has high spatial acuity, whereas the auditory channel has better temporal acuity. These differences may lead to different conceptualizations of events and affect multimodal language production. Previous studies of motion events typically present visual input to elicit speech and gesture. The present study compared events presented as audio-only, visual-only, or multimodal (visual+audio) input and assessed speech and co-speech gesture for path and manner of motion in Turkish. Speakers with audio-only input mentioned path more and manner less in verbal descriptions, compared to speakers who had visual input. There was no difference in the type or frequency of gestures across conditions, and gestures were dominated by path-only gestures. This suggests that input modality influences speakers’ encoding of path and manner of motion events in speech, but not in co-speech gestures.

Sensory Modality of Input Influences the Encoding of Motion Events in Speech But Not Co-Speech Gestures

The claim that contextual knowledge exerts a top-down influence on sensory processing is supported by evidence for lexically-mediated compensation for coarticulation (LCfC) in spoken language processing. In this phenomenon, a lexically restored context phoneme (e.g., the final phoneme in Christma# or fooli#) influences perception of a subsequent target phoneme (e.g., a phoneme ambiguous between /t/ and /k/). A recent report shows that carefully vetted materials produce robust, replicable LCfC effects in younger adults (18-34 years old). Here, we asked whether we would observe LCfC in a sample of older adults (aged 60+). This is of interest because older adults must often contend with age-related declines in sensory processing, with previous research suggesting that older adults may compensate for age-related changes by relying more strongly on contextual knowledge. We observed robust LCfC effects in younger and older samples, with no significant difference in the effect size between age groups.

Lexically-Mediated Compensation for Coarticulation in Older Adults

In recent years, the brain and cognitive sciences have made great strides developing a mechanistic understanding of object recognition in mature brains. Despite this progress, fundamental questions remain about the origins and computational foundations of object recognition. What learning algorithms underlie object recognition in newborn brains? Since newborn animals learn largely through unsupervised learning, we explored whether unsupervised learning algorithms can be used to predict the view-invariant object recognition behavior of newborn chicks. Specifically, we used feature representations derived from unsupervised deep neural networks (DNNs) as inputs to cognitive models of categorization. We show that features derived from unsupervised DNNs make competitive predictions about chick behavior compared to supervised features. More generally, we argue that linking controlled-rearing studies to image-computable DNN models opens new experimental avenues for studying the origins and computational basis of object recognition in newborn animals.

Modeling Object Recognition in Newborn Chicks using Deep Neural Networks

After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., “higher likelihood of shock for bluer stimuli”). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both “similarity” and “relational” rules, and generalizes using a mixture of these strategies. Using a “mixture of experts” modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.

A Mixture of Experts in Associative Generalization

Processing differences between obligatory constituents like verbal complements and facultative ones like adjuncts are widely discussed in psycholinguistics. But the relative positional and semantic variability of adjuncts makes analyzing these differences a daunting challenge. Focusing on the intricate problem of the default position of manner adverbials in German, we present a re-analysis of a recent psycholinguistic study on their ordering preferences in which we explore the extent to which similarities between word embeddings can be used as stand-ins for shared semantic memory, representing the probability of seamless conceptual combination. In re-analyzing six experiments across different paradigms, the addition of the new predictors yields substantially better models which show that these factors considerably interact with established lexical and grammatical predictors.

Disentangling factors in the placement of manner adverbials in German: the effect of distributional similarity

Audio‐visual (AV) associative learning is central to many aspects of cognitive development and is key in reading acquisition. Most studies thus far have examined AV associative learning involving linguistic stimuli. Yet it is of importance to examine cross-modal learning free of familiarity confounds. We, therefore, designed an AV learning paradigm relying on novel, non-linguistic auditory and visual stimuli, which were both unfamiliar to participants. On top of AV learning, we collected performance in reading-related abilities, as well as in more domain-general skills, in a population of healthy Italian-speaking adults (N=57). By fitting trial-by-trial performance in our novel learning task, we demonstrate the expected variability in speed of learning (learning rate) across participants. We then show that speed of learning in our novel learning task is positively associated with working memory and replicate this result in a set of French-speaking participants (N=32), showing that it holds in another language.

A novel non-linguistic audio-visual learning paradigm to test the cognitive correlates of learning rate

Current models of cognitive control address selection among tasks in terms of a cost-benefit tradeoff. Importantly, they usually assume a fixed level of competence for each candidate task when estimating its value. However, performing a task can improve competence through learning, which should be factored into estimates of future value. Here, we consider an extension of the Expected Value of Control (EVC) theory that includes such estimates. We demonstrate that control allocation is a function of task learnability, and show the use of this model by generating novel predictions in cognitive effort discounting tasks. We argue that the value of learning in control allocation may account for the seemingly paradoxical finding that sometimes participants prefer more difficult (i.e., costly) tasks, and discuss how the model can be leveraged to further our understanding of human decision making and cognitive impairments.

The Value of Learning and Cognitive Control Allocation

This is a modern horror story about an innocently misbehaving
projector, and why we beseech everyone to report minimal col-
orimetric data about stimulus displays. We present anecdotal
experience of configuring a projector to display video stimuli
in a high-tesla MRI room, along with all the gotchas, (broken)
technical assumptions, and theoretical rehashings that should
be considered by every scientist who uses computers to dis-
play visual stimuli. The moral of our story is: (1) check that
your monitor/projector is actually showing the colors and lu-
minances that you think it is, (2) make explicit assumptions
regarding the physical/perceptual space of your stimuli and
how they relate to any model analysis you will perform. This
is especially important when modeling non-human animals,
since most equipment and data formats implicitly assume hu-
man perception. We show that innocent changes to display
settings such as brightness reduction can cause dangerously
unexpected results. Understanding and reporting colorimetric
data in scientific publications is important for two reasons: (1)
reproducibility, and (2) model fidelity.

Why We Should Report Colorimetric Data In Every Paper

The basal ganglia (BG) performs an important functional role in cognition, but models disagree about the nature of the relationship between BG activity and activity in other cortical areas. Previous computational models can be categorized as implementing the effects of the BG on prefrontal cortex as either local and direct, or involving other regions and, therefore, modulatory. To test which of these two effects best represents the role of the BG, a large fMRI dataset of 200 participants performing six, representative cognitive tasks was analyzed through Dynamic Causal Modeling (DCM). To ensure that DCM models were realistic and representative of a general brain architecture, the models were implemented within the putative neural underpinnings of the Common Model of Cognition, an abstract blueprint for cognition. The comparison showed that Mixed model, including both Direct and Modulatory connectivity, consistently outperformed models that included only direct or modulatory connections. It was also found that the relative rankings of the Direct and Modulatory models depended on the specific task, suggesting that the BG is a flexible system that adapts to task demands.

Challenges for using Representational Similarity Analysis to Infer Cognitive Processes: A Demonstration from Interactive Activation Models of Word Reading

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Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

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Challenges for using Representational Similarity Analysis to Infer Cognitive Processes: A Demonstration from Interactive Activation Models of Word Reading

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Next from CogSci 2021

Event visibility in sign language motion: Evidence from Austrian Sign Language (ÖGS)

Similar lecture

A model of timing in simple anticipatory decisions

Downloads