United States

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.


CogSci 2023

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

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

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.


The Goal Bias Emerges Early in Motion Event Inspection and Speech Planning: Evidence from Eye-Movements

Decisions under risk have been classically studied with tasks involving lotteries with explicit monetary rewards and uncertain gambles. More recently, sensorimotor decisions, specifically single movements to targets yielding rewards and losses, have been conceptualized as decisions under risk. While human choices between gambles have long been known not to maximize expected gains, sensorimotor decisions have been well described by statistical decision theory in many tasks. However, because many naturalistic scenarios of sensorimotor decisions are inescapably governed by the laws of physics, the question arises, how people act under such circumstances. Here, participants slid pucks to target areas, providing gains and losses in a Pre record environment so that the uncertainty inherent in motor control interacts with the physical relationships governing objects' motion. Using model comparison with several generative models of participants' sliding actions, we find evidence, that humans use Newtonian physics in their motor decisions for scenarios with prospective economic outcomes.


People use Newtonian physics in intuitive sensorimotor decisions under risk

When we exercise sequences of actions, their execution becomes more fluent and precise. Here, we consider the possibility that exercised action sequences can also be used to make planning faster and more accurate by focusing expansion of the search tree on paths that have been frequently used in the past, and by reducing deep planning problems to shallow ones via multi-step jumps in the tree. To capture such sequences, we use a flexible Bayesian action chunking mechanism which finds and exploits statistically reliable structure at different scales. This gives rise to shorter or longer routines that can be embedded into a Monte-Carlo tree search planner. We show the benefits of this scheme using a physical construction task patterned after tangrams.


Habits of Mind: Reusing Action Sequences for Efficient Planning

Compositionality is a central component of the human faculty for generalization and flexibility. 
However, the computations involved are poorly understood, especially in terms of their cognitive costs. 
On one hand, compositionality requires searching combinatorially large hypothesis spaces, raising issues of tractability.
On the other hand, compositional representations afford efficient and compact compression. 
To shed light on the cognitive resource required for compositionality, we used a within-subject time pressure manipulation to study how participants navigated a series of mazes, generated using recursive operations over spatial primitives. 
We find evidence that behavior is guided by the use of primitives and abstract operations over them, where the degree of compositional structure increases performance and speeds up decisions.
And while time pressure led to more random errors, it did not impair the capacity for compositionality. 
Rather, participants increased their reliance on reusing and recombining previous computations, suggesting a remarkable robustness of human compositional reasoning.


Compositionality under time pressure

Previous computational models of early language acquisition have shown how linguistic structure of speech can be acquired using auditory or audiovisual learning mechanisms. However, real infants have sustained access to both uni- and multimodal sensory experiences. Therefore, it is of interest how the uni- and multimodal learning mechanisms could operate in concert, and how their interplay might affect the acquisition dynamics of different linguistic representations. This paper explores these questions with a computational model capable of simultaneous auditory and audiovisual learning from speech and images. We study how the model’s latent representations reflect phonemic, lexical, and semantic knowledge as a function of language experience. We also test how the findings vary with differential emphasis on the two learning mechanisms. As a result, we find phonemic learning always preceding lexical learning, followed by semantics. This pattern also emerges when the learner is all the time prioritizing audiovisual associative learning instead of lexical or sublexical representations. The result illustrates how the acquisition order of linguistic representations is decoupled from the primary learning objectives (mechanisms) of the learner, and how the emergence of phonemes and words can be facilitated by both auditory and audiovisual learning in a synergetic manner.


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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

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Next from CogSci 2023

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

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