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Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. 
However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim.
We present a model of social generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations.
Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.


CogSci 2023

Social learning with a grain of salt

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.

Many cultures share common constellations and common narratives about the stars in the night sky. Previous research has shown that this overlap in asterisms, minimal star groupings inside constellations, is clearly present across 22 distinct culture groups and can be explained in part by properties of individual stars (brightness) and properties of pairs of stars (proximity) (Kemp, Hamacher, Little and Cropper, 2022). The same work, however, found no evidence that properties of triples (angle) and quadruples (good continuation) predicted constellation formation. We developed a behavioural experiment to explore how individuals form constellations under conditions that reduce cultural learning. We found that participants independently selected and connected similar stars, and that their responses were predicted by two properties of triples (angle and even spacing) in addition to the properties of brightness and proximity supported by previous work. Our findings lend further evidence to the theory that commonality of constellations across cultures is not a result of shared human history but rather stems from shared human nature.


Visual perception principles in constellation creation in individuals

Computational models of multiple object tracking (MOT) presuppose the existence of non-conceptual indexes in visual perception, and as a result predict that identification performance on MOT tasks should be no worse than tracking performance for the same stimuli. However, empirical evidence suggests that identification performance is worse than tracking performance in MOT. We propose a computational model of MOT that is able to account for several empirical results related to tracking performance without the use of indexes and thus avoids yoking tracking performance to identification performance. We also test our model empirically and show that it accounts well for the variation in identification performance with increasing number of targets in MOT with visually identical objects. By employing an explicit (rather than an implicit) mechanism for the maintenance of correspondence between attended locations and object identities, our model accounts for the tracking vs ID performance disparity.


Tracking Multiple Objects without Indexes

The inverse base-rate effect is a robust irrational bias that arises when people face ambiguity. The most prominent theories of this irrational bias depend on prediction error.
In this study, we gradually removed elements of a predictive learning design to test the extent to which error-driven processes underly this bias. In our first experiment, we removed explicit feedback by implementing the inverse base-rate effect in an observational learning procedure. In our second study, we further removed any causal relationship between stimulus features and category labels by moving towards an unsupervised learning procedure. This removed any information participants could use to identify category labels. In both experiments, the inverse base-rate effect persisted and remained robust. This outcome suggests that this irrational bias is independent of supervised learning procedures. We propose that any theories and models of the inverse base-rate effect must manage information encoding and connection updates without explicit prediction error. We end by proposing two clear paths for future investigations.


Errorless irrationality: removing error-driven components from the inverse base-rate effect paradigm

Recent studies suggest that errors facilitate learning in certain conditions. Despite this, reinforcement paradigms dominate learning methods, subscribing to the narrative that errorless learning is the foundation of an ideal learning environment. If we continue to view learning from this restrictive perspective, we may fail to capture and apply the benefits of errors. In this paper, we investigate two potential mechanisms of post-error learning. Participants (N = 61) learned word pairs in either a study or error trial before taking a final test. Supporting past error learning literature, errors before a study opportunity led to better performance on a final test. Differences in reaction times between conditions support the theory that errors increase learning by acting as a mediator, or secondary cue, to the correct answer on subsequent tests.


Faulty Memories, Favored Outcomes: How Errors Impact Learning Processes

Efforts to correct misconceptions have produces multiple types of complex interventions. However, past research has consistently shown that simple, factual statements may be equally effective in activating the incorrect information and prompting an integration of the correct concept. The current study tests the effectiveness of simple statements on 76 different misconceptions. Findings indicate that for the majority of misconceptions, simple statements are an effective intervention to reduce misconceptions. Limitations and future directions are discussed.


Just Tell the Truth: Correcting Misconceptions with Simple, Factual Statements

Psycholinguistic analyses provide a means of evaluating large language model (LLM) output and making systematic comparisons to human-generated text. These methods can be used to characterize the psycholinguistic properties of LLM output and illustrate areas where LLMs fall short in comparison to human-generated text. In this work, we apply psycholinguistic methods to evaluate the psycholinguistic properties within sentences from student-generated long-form analogies about biochemical concepts. We compare analogies generated by human subjects enrolled in introductory biochemistry courses to analogies generated by chatGPT. We use 78 features extracted from Coh-metrix that analyze text cohesion, language, and readability (Graesser, McNamara, Louwerse, & Cai, 2004). We perform an unsupervised cluster analysis. Results illustrate a high level of performance for clustering student-generated and chatGPT-generated analogies. To evaluate which features contribute most to model performance, we use a hierarchical cluster analysis. Results from the hierarchical cluster analysis illustrate several linguistic differences between the two groups.


Long-form analogies generated by chatGPT lack human-like psycholinguistic properties

Regular polysemes are sets of ambiguous words that all
share the same relationship between their meanings, such
as CHICKEN and LOBSTER both referring to an an-
imal or its meat. To probe how a context embedding
model, here exemplified by BERT, represents regular pol-
ysemy, we analyzed whether its embeddings support an-
swering sense analogy questions similar to “is the map-
ping between CHICKEN (as an animal) to CHICKEN (as
a meat) the same as that which maps between LOBSTER
(as an animal) and LOBSTER (as a meat)?” We found
that (1) the model was sensitive to the shared structure
within a regularity type; (2) the shared structure varies
across regularity types, potentially reflective of a “reg-
ularity continuum;” (3) some high-order latent structure
may be shared across regularity types, suggestive of a
similar latent structure across types; and (4) the is equivo-
cal evidence that the aforementioned effects are explained
by meaning overlap.


Probing the Representational Structure of Regular Polysemy in a Contextual Word Embedding Model via Sense Analogy Questions

There is a growing interest in emotions in textual data. The studies on labeling dataset or detecting emotions from text are based on some psychological theories. However, there has been little research on the appropriateness of using these theories. In this study, we reviewed the commonly used basic emotion theories. We analyze 3 major theories: Ekman's Basic Emotions, Plutchik's Wheel of Emotions, and GoEmotions. We used an emotionally fine-tuned language model to analyze the emotional relationship between words that each theory designates as basic emotions and evaluated the adequacy of the emotional structure each theory presents. As a result of the fine-tuning, clear patterns of similarity emerged based on the emotional meaning of each word, and through this we analyzed the structure of basic emotions. Ekman's and GoEmotions were almost in line with our results, while Plutchik's had some differences. We thoroughly discussed these matches and mismatches.


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Visual perception principles in constellation creation in individuals

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Visual perception principles in constellation creation in individuals

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