United States

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


CogSci 2023

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

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>
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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.

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.


Computational Insights to Acquisition of Phonemes, Words, and Word Meanings in Early Language: Sequential or Parallel Acquisition?

Representational gestures are co-speech gestures that carry semantic content related to the content of speech. Previous studies focusing on adult-adult conversation have investigated the temporal alignment of gestures and speech finding that the overwhelming majority of representational gestures are produced right before the lexical content they refer to (their lexical affiliate, LA). However, nothing is yet known about whether caregivers would also time their gestures in the same way in naturalistic interactions. We annotated representational gestures from a large corpus (anonymized) of semi-naturalistic conversations between caregivers and their 3-4 year old children (n = 899 gestures from n=36 caregivers). We found that, just as in adult-directed language (ADL), representational gestures in child-directed language (CDL) were more tightly linked to the onset of LAs than the onset of the utterance in which Las were produced (hence planned when full events are encoded); with the overall majority of the representational gestures starting before their LAs. We further found that age of acquisition (AoA) rating of the LA had a significant effect on the speech-gesture latency. We found that for words acquired earlier, the gesture’s stroke (the meaningful part of a gesture) tended to be produced before the LA’s onset; for the later acquired word, the stroke tended to be produced at the same time or after the onset of the LA in speech. Our findings suggest that: (1) Regardless of their addressee, speakers always time the production of representational gestures to specific conceptual/linguistic units, rather than the full event/utterance. (2) In contrast to ADL, caregivers’ gestures may support addresses’ linguistic processing not only by supporting word prediction (of likely better known words), but also by supporting the learning of conceptual features (of likely less well-known words).


How Speech and Representational Gestures Align in Child-Directed Language: a Corpus-based Study

While naturalistic daylong audio recordings of children’s auditory environments have the potential to reveal key insights about the input children receive and inform our theories of language development, it also presents various methodological hurdles. In the present work, we used three fully transcribed daylong audio recordings to investigate the challenge of manually extrapolating aggregate statistics and quantify the kinds of sampling choices daylong researchers can make. Our findings highlight sampling choices that maximize sampling from the full distribution of the day and potential tradeoffs between human effort and obtaining accuracy.


Optimizing Random Sampling of Daylong Audio

Using evidential expressions to indicate one’s source of information for an utterance tends to convey uncertainty on the speaker’s part. Previous accounts of this uncertainty inference attribute it to either extralinguistic reasoning about evidence directness, or to pragmatic reasoning about alternative utterances. Here we present a novel hybrid account, and introduce a set of utterances which allows us to tease apart the three accounts’ predictions. We test these predictions in two studies by manipulating the directness of evidence indicated by an evidential expression. Exp. 1 shows that listeners infer more uncertainty with extreme values of directness. Exp. 2 shows that speakers are more likely to indicate evidence in contexts where the evidence is unreliable. We argue that these findings support an account which involves both extralinguistic and pragmatic reasoning, and develop a formal implementation of such an account within the Rational Speech Act framework.


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

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

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