United States

Active Inference and its accompanying Bayesian Mechanics
(BM) are important psychological and cognitive science theories.
While there is a strong interaction between the theories
and the philosophical realm, it needs to be clarified what its
metaphysical commitments are. We tease out these commitments
while looking from the perspective of the psychology
of goals. We find that Active Inference cannot account for
the dynamic growth of goals, primarily because of its closed
generative model. We trace the reason for this through the
fixed ‘ontological constraint’ of BM to the inherent ‘logic of
machines’ in mechanistic models, grounding finally in the incompatibility
between substance metaphysics, impredicativity,
and emergence. Thus we argue that Active Inference implicitly
presupposes a Substance metaphysics, yielding the theory no
resource to model novelty, growth, and development observed
in human psychology. We briefly sketch a powerful alternative
grounded in process metaphysics to model biological and
cognitive systems.


CogSci 2023

Active Inference and Psychology of Goals: A study in Substance and Process Metaphysics

### 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/).


CogSci 2021

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

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference.<br>
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

<br>[**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
<br>[**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
<br><br>

#### **CogSci 2023 Program Book**
<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/tzgk/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>
<br>

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality. To seize this opportunity, we apply the principle of resource rationality to formulate a mathematically precise normative theory of (self-)management by goal-setting. We leverage this theory to computationally derive optimal subgoals from a resource-rational model of human goal pursuit. Finally, we show that the resulting subgoals improve the problem-solving performance of bounded agents and human participants. This constitutes a first step towards grounding prescriptive theories of management and practical recommendations for goal-setting in computational models of the relevant psychological processes and cognitive limitations.


Toward a normative theory of (self-)management by goal-setting

Cumulative culture requires efficient learning mechanisms that can withstand environmental change across generations. We unify two competing theories of the learning mechanism supporting cumulative culture in a common computational framework, distinguishing model-based from model-free social learning. We describe and analyze evolutionary models that explain when and why model-based and model-free social learning are each optimal, and in particular how environmental determines which strategy succeeds. Strikingly, we find that model-based social learning can succeed even in high-volatility environments. These results yield novel predictions concerning cultural variation in social learning mechanisms.


Computational principles underlying the evolution of cultural learning mechanisms

The expressive power of natural languages depends on their regular compositional structure, which allows us to express and understand an infinite set of messages. However, a complete model of language evolution should also account for irregular exceptions to regular rules, common in natural languages. Historical linguistics has established a correlation between irregularity and frequency in language use, which has been attributed to preferential irregularisation of frequent items, or preferential regularisation of infrequent items. In an iterated learning experiment where participants learn and reproduce a miniature language across multiple generations, we show that this correlation can be explained by the relationship between frequency, regularity and learnability, without needing to appeal to frequency-dependent irregularisation. We find that systems of plural marking regularise across generations of transmission, but that high-frequency items remain irregular. Our results further show that the persistence of irregularity is due to high frequency overriding pressures which normally reduce learnability.


The Relationship Between Frequency and Irregularity in the Evolution of Linguistic Structure: An Experimental Study

Many people turn to social media for public health information, 
but such platforms often contain conflicting and inaccurate 
medical advice. To assess complex health claims online, 
people may consider the prevailing consensus; however, 
previous work suggests that people may not be very sensitive 
to important cues to consensus “quality”. To explore further, 
across two experiments we tested people’s sensitivity to the 
consensus-quality cues of source diversity and source 
expertise. Via a mock Twitter platform, participants rated their 
belief in a series of health claims both before and after reading 
various kinds of tweets about the claims. Experiment 1 showed 
that experts (both individual medical experts and health 
organisations) were more persuasive than non-experts. 
Additionally, stances that were supported by a diverse set of 
sources were more persuasive. Experiment 2 showed that 
participants continue to favour experts even when 
outnumbered in tweet quantity by non-experts. When experts 
were not present, however, participants favoured high tweet 
quantity. Both experiments suggest that cues to consensus 
quality (namely, expertise and source diversity) and consensus 
quantity (tweet quantity) are salient cues in belief revision. 
These findings are important in understanding how socially 
acquired health information (and misinformation) shifts 
opinion, and the role that experts can play.


The Influence of Cues to Consensus Quantity and Quality on Belief in Health Claims

Consensus-based social learning strategies often outcompete other strategies in evolutionary models. But while formal proofs suggest that consensus’ reliability is compromised when individual judgments are not independent, this makes for a notoriously implausible assumption in the biological world: the people we learn from are constantly learning from each other as well. How do we avoid being misled by consensus? We present two experiments and a computational model examining commonsense reasoning about how people’s public and private judgments are influenced by the consensus and social status of those around them. Results suggest that while people realize that these two factors can cause others’ public and private judgments to diverge, their own trust in public consensus depends on how accurately they believe it reflects their informants’ true beliefs.


Agenda setting and The Emperor’s New Clothes: people infer that letting powerful agents make their opinion known early can trigger information cascades and pluralistic ignorance

Current theory suggests that the heterogeneity of metapopulation structure can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in populations with such minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for a novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations, in the absence of out-group aversion.

Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations

Much of our knowledge come from other people. In considering how argument quality and source reliability influences message persuasiveness, we conduct a comparison of the Elaboration Likelihood Model of Persuasion and the Bayesian Model of Argumentation, which are based on different assumptions. Participants were asked to judge a fictitious character’s degree of belief in a claim given evidence. To test competing predictions, we manipulate the character’s elaboration level, the argument’s quality, and the source’s reliability. The elaboration did not moderate the main effects of argument quality and source reliability, as they both were integral to the overall message strength in both high and low elaboration conditions. Bayesian predictions have better fit with the observed data, whilst ELM predictions did not align well. Overall, the BM is supported, but we discuss how this model could be further improvised while the ELM is contested


Comparing predictions from the Elaboration Likelihood Model and a Bayesian model of argumentation

Human memory does not simply function like an information
storage disk; instead, it flexibly reorganizes information. This
flexibility can sometimes produce false memories of items related
to those actually encountered–a possible byproduct of an
adaptive memory system that enables generalization across related
items or experiences. In the Deese-Roediger-McDermott
(DRM) task, participants often falsely remember seeing words
that are semantically related to presented words. Here, we propose
and test a new model of lexical (false) memory that predicts
these errors, made possible by integrating (i) theories of
memory that posit encoding of verbatim and gist-level information
with (ii) a computational framework adapted from the
perceptual false memory literature, and (iii) semantic relatedness
measures from word embeddings analysis of large-scale
text corpora. This Lexical Target Confusability Competition
(Lexical TCC) model successfully predicts false recognition
in the DRM task, with implications for understanding how and
when the mind produces false memories.


A Computational Model of Lexical False Memory based on Semantic Distance from Word Embeddings

In two experiments we examined the influence of ‘history-driven’ attentional biases on choice behavior. In Experiment 1 we used a value-modulated attentional capture procedure to induce an automatic reward-related attentional bias, and found that this bias shaped choice in a subsequent task in which participants were required to pick the highest number from a briefly displayed choice array. In Experiment 2 we investigated the influence of a working memory manipulation, and found that choice in the number-selection task was influenced by the current (and prior) contents of memory, consistent with an influence of memory-driven attentional bias on information encoding. Our findings indicate that history-driven attentional biases can translate to an influence on overt, downstream processes of behavioural choice, and should be incorporated into models of the interaction between attention and choice.


Reward-driven and memory-driven attentional biases automatically modulate rapid choice

After viewing motion events with a starting-point (Source) and end-point (Goal), people mention the Goal more often and remember it more accurately than the Source. This Goal privilege has been hypothesized to arise from an online attentional bias during event apprehension itself, yet no data exists that: (a) documents this online attentional bias and (b) correlates any online bias with offline memory and linguistic measures. Here we do just that: we recorded participants’ eye movements as they viewed or prepared to describe motion events and later tested their memory of Goals or Sources. We find an online attentional bias for Goals over Sources during initial encoding of events. Moreover, the extent of the attentional Goal bias is systematically related to both language production and memory: the bias is greatest when the Source is not mentioned in production or not remembered later at test.


Premium content

Downloads

Next from CogSci 2023

Toward a normative theory of (self-)management by goal-setting

Similar lecture

Passive Versus Active: Frameworks of Active Learning for Linking Humans to Machines