United States

Here we examined whether eye movement measures in global-local perceptual processing tasks, where abnormalities were typically found in individuals with schizophrenia, could be used to predict schizotypy through Eye Movement analysis with Hidden Markov Models (EMHMM). Using both multiple regression analysis and Gaussian process classifier to predict schizotypy, we found that in addition to longer response times in contour integration, a less consistent eye fixation to locate a stimulus and a more consistent subsequent fixation to start engaging local processing in the embedded figures task predicted high schizotypy. These effects may be related to reduced top-down attention control due to deficient global processing and enhanced local processing bias respectively. In addition, performance in embedded figures could further enhance classification accuracy when being used in conjunction with the above predictors, suggesting the multifactorial nature of the identification problem. These predictors may be important endophenotype markers for schizotypal personality.

CogSci 2021

Eye movement consistency in global-local perceptual processing predicts schizotypy

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

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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.

Frequency counters are computational mechanisms that track the frequency or probability of speech units. Such counters are idealizations which re-describe frequency effects in early word segmentation, not providing an underlying learning mechanism from which these effects arise. Previous work has shown that Implicit Chunking represents a plausible learning mechanism explaining infants’ sensitivity to statistical cues when segmenting small-scale artificial languages (French et al., 2011). However, no work has examined whether Implicit Chunking allows to segment naturalistic speech in a developmentally plausible way. Here, we show how a novel symbolic model of Implicit Chunking – CLASSIC-Utterance-Boundary - performs better or as well as previous frequency-based models (i.e., transitional probability, chunking) at predicting children’s word age of first production and a range of word-level characteristics of children’s vocabularies (word frequency, word length, neighborhood density, phonotactic probability). We suggest that explicit frequency counters are not necessary to explain infants’ speech segmentation in naturalistic settings.

Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

Signalling systems of various species (humans and non-human animals) as well as our world both exhibit discrete and continuous properties. However, continuous meanings are not always expressed using continuous forms but instead frequently categorised into discrete symbols. While discrete symbols are ubiquitous in communication, the emergence of discretisation from a continuous form space is not well understood. We investigate the emergence of discrete symbols by simulating the learning process of two agents that acquire a shared signalling system. The task is formalised as a reinforcement learning problem in continuous form and meaning space. We identify two central causes for the emergence of discretisation: 1) sub-optimal signalling conventions and 2) a topological mismatch between form and meaning space. A long version of this paper has been accepted for publication in Cognition (International Journal of Cognitive Science).

Discretisation and Continuity: Simulating the Emergence of Symbols in Communication Games

People have the fascinating ability to infer causality by observing other humans’ actions. We modelled this process using a Bayesian rational agent model and showed how people can reason about another agent’s beliefs and, by extension, infer the world’s causal structure. We compared the model’s predictions against humans’ causal judgements on a novel inference task. Participants (N = 171) were shown a dynamic scene depicting either a human agent, robot agent, or both agents acting on two objects sequentially before observing an outcome. After observing the human (vs the less intentional robot) agent, people were more likely to infer that both objects (in sequential order) caused the outcome. When two agents of different intentionality were shown, people favored the object that the intentional agent interacted with as the cause of the outcome. Our model captured these inference patterns well and revealed insights into reasoning about semi-intentional agents and multi-agent contexts.

Learning from Agentic Actions: Modelling Causal Inference from Intention

We test whether people are able to reason based on incidentally acquired probabilistic and context-specific magnitude information. We manipulated variance of values drawn from two normal distributions as participants perform an unrelated counting task. Our results show that people do learn category-specific information incidentally, and that the pattern of their judgments is broadly consistent with normative Bayesian reasoning at the cohort level, but with large individual-level variability. We find that this variability is explained well by a frugal memory sampling approximation; observer models making this assumption explain approximately 70% of the variation in participants' responses. We also find that behavior while judging easily discriminable categories is consistent with a model observer drawing fewer samples from memory, while behavior while judging less discriminable categories is better fit by models drawing more samples from memory. Thus, our model-based analysis additionally reveals resource-rationality in memory sampling.

One and known: Incidental probability judgments from very few samples

A fundamental goal in cognitive and historical linguistic research on semantic change is to characterize the regularity in how word meanings change over time. We examine a common belief that has not yet been evaluated comprehensively, which asserts that gender of a word influences its direction of semantic change. By this account, female terms like mistress should undergo pejorative change in meaning systematically more so than male terms like master. We evaluate this claim in gender-marked word pairs in English and French respectively as languages without and with grammatical gender. Our results provide supporting evidence for gender asymmetry in semantic change of English words but not French words. Our study raises questions about the generality of the claim about gender asymmetry in semantic change and provides a scalable computational framework for understanding the social roots of word meaning change.

A computational evaluation of gender asymmetry in semantic change

Cognates – words which share form and meaning across two languages – have been extensively studied to understand the bilingual mental lexicon. One consistent finding is that bilingual speakers process cognates faster than non-cognates, an effect known as cognate facilitation. Yet, there is no agreement on the underlying factors driving this effect. In this paper, we use computational modeling to test whether the effect can be explained by the cumulative frequency hypothesis. We train a computational language model on two language pairs (Dutch–English, Norwegian–English) under different conditions of input presentation and test it on sentence stimuli from two existing studies with bilingual speakers of those languages. We find that our model can exhibit a cognate effect, lending support to the cumulative frequency hypothesis. Further analyses reveal that the size of the effect in the model depends on its linguistic accuracy. We interpret our results within the literature on cognate processing.

Cumulative frequency can explain cognate facilitation in language models

Moral disengagement is a mechanism whereby people distance or disconnect their actions from their moral evaluation. This work presents a novel knowledge graph schema, dataset, and transformer-based NLP model to identify and represent indicators of moral disengagement in text. Our graph schema is informed by Albert Bandura’s psychosocial mechanisms of moral disengagement, including dehumanization, victimization, moral condemnation and justification, and attribution (or displacement) of responsibility. Our preliminary dataset is comprised of online posts from five different communities. We present initial evidence that (1) our theory-based schema can represent moral disengagement indicators across these communities and (2) our transformer-based NLP model can identify indicators of moral disengagement in text. As it matures, this thread of computational social science research can help us understand the spread of morally-disengaged language and its effect on online communities.

Toward Transformer-Based NLP for Extracting Psychosocial Indicators of Moral Disengagement

A growing toolbox is emerging for linking neuroimaging data to computations supporting human cognition. In representational similarity analysis (RSA), for example, activity patterns over voxels are compared in response to similar vs. dissimilar stimuli. Resulting similarity matrices are compared to similarity matrices based on theoretical principles or computational models. Similarly, complex EEG or MEG time series can be compared to information-theoretic variables, such as stimulus entropy or surprisal, allowing inferences about the sensitivity of neural responses to aspects of signal information over time. A challenge is determining to what degree such analyses can identify hallmarks of specific computations rather than computationally non-specific resonance with inputs. Here, we apply RSA and information-theoretic analyses to one well-characterized model: TRACE. We consider whether these analyses identify known principles underlying TRACE, and whether TRACE exhibits sensitivity to information-theoretic variables similar to that observed in human brains.

Discovering computational principles in models and brains

Order effects occur when judgments about a hypothesis's probability given a sequence of information do not equal the probability of the same hypothesis when the information is reversed. Different experiments have been performed in the literature that supports evidence of order effects.

We proposed a Bayesian update model for order effects where each question can be thought of as a mini-experiment where the respondents reflect on their beliefs. We showed that order effects appear, and they have a simple cognitive explanation: the respondent's prior belief that two questions are correlated.

The proposed Bayesian model allows us to make several predictions: (1) we found certain conditions on the priors that limit the existence of order effects; (2) we show that, for our model, the QQ equality is not necessarily satisfied (due to symmetry assumptions); and (3) the proposed Bayesian model has the advantage of possessing fewer parameters than its quantum counterpart.

Order Effects in Bayesian Updates

What computations enable humans to leap from mere obser- vations to rich explanatory theories? Prior work has focused on stochastic algorithms that rely on random, local perturba- tions to model the search for satisfactory theories. Here we introduce a new approach inspired by the practice of ‘debug- ging’ from computer programming, whereby learners use past experience to constrain future proposals, and are thus able to consider large leaps in their current theory to fix specific de- ficiencies. We apply our ‘debugging’ algorithm to the mag- netism domain introduced by (Ullman, Goodman, & Tenen- baum, 2010) and compare its efficiency and accuracy to their stochastic-search algorithm. We find that our algorithm not only requires fewer iterations to find a solution, but that the solutions it finds more reliably recover the correct latent theo- ries, and are more robust to sparse data. Our findings suggest the promise of such constraint-based approaches to emulate the way humans efficiently navigate large, discrete hypothesis spaces.

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Are Explicit Frequency Counters Necessary in Computational Models of Early Word Segmentation?

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