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Expectations play a critical role in human perception, cognition, and decision-making. There has been a recent surge in modelling such expectations and the resulting behaviour when they are violated. One prominent recent proposal is the ViolEx model. To move the model &amp; the literature forward, we identified three areas of concern and addressed two in this study - Lack of formalization and implementation. Specifically, we aim to provide the first implementation of ViolEx using the Active Inference formalism by leveraging the rich overlap between them. As a result, we successfully simulated all expectation violation coping strategies of ViolEx and analyzed their potential mediators. Through this analysis, we identified novel predictors of immunization &amp; assimilation and documented the computational mechanism of their mediation. We see this as the first step in developing a formal research framework to study expectation violations and hope to serve as a base for future ViolEx studies.

CogSci 2022

Formalization and Implementation of ViolEx: An Active Inference perspective

Expectations play a critical role in human perception, cognition, and decision-making. There has been a recent surge in modelling such expectations and the resulting behaviour when they are violated. One prominent recent proposal is the ViolEx model. To move the model & the literature forward, we identified three areas of concern and addressed two in this study - Lack of formalization and implementation. Specifically, we aim to provide the first implementation of ViolEx using the Active Inference formalism by leveraging the rich overlap between them. As a result, we successfully simulated all expectation violation coping strategies of ViolEx and analyzed their potential mediators. Through this analysis, we identified novel predictors of immunization & assimilation and documented the computational mechanism of their mediation. We see this as the first step in developing a formal research framework to study expectation violations and hope to serve as a base for future ViolEx studies.

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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





The debate of whether bilingualism provides a cognitive and or linguistic advantage is a lasting one. Underlying this debate is the idea that an additional language shapes cognition and linguistic processing. The current research analyzes a behavioral dataset containing individuals’ performance in different general cognitive and linguistic tests using a machine learning approach to classify individuals as bilinguals or multilinguals based on their performance. Using an extreme gradient boosting model, we were able to achieve a balanced accuracy of 77%. High scores on a prescriptive grammar test, a verbal fluency test, and a picture naming test were predictive for multilingualism. The implications of the reported results for the field and future research are discussed.

Can you tell them apart? Using machine learning to classify bilinguals’ and multilinguals’ cognitive and linguistic performance

In this experiment we set out to investigate how predictive processing may modulate alignment between interlocutors in a dyadic setting (Pickering & Garrod, 2013). The experiment presents a novel interactional task where participants are involved in a partially controlled association game where speaker A names a picture and speaker B responds with a semantically related word. Importantly, the predictability for the upcoming object is manipulated. Data has been collected from 20 dyads, and the results show a prediction effect with a mean difference of 400 ms between predictable and non-predictable conditions. Crucially, this prediction effect was not only present in speaker A who had to name the predictable or unpredictable object, but also for the interlocutor. To our knowledge, this is the first study to demonstrate speaker-listener prediction effects in a dyadic interaction. This will be further tested in a dual-EEG setting to explore this question at the neural level.

The effects of prediction in conversational alignment.

Does the human mind contain a task-general ‘randomization machine’? Stable biases of randomization have been identified that span multiple domains and modalities, in both lower-level perceptual tasks and in higher-level cognitive tasks. The stability of such biases indicates that the mind may rely on a stable set of properties to create and perceive randomness. But what computational principles support randomization? Here, we approach this question by building a computational model of human randomization that generalizes across spatial and numerical tasks. We show that simple computational heuristics capture higher-order properties of human-generated random sequences, in both numerical and spatial randomization tasks each with many possible options. Furthermore, we show that human behavior in both types of tasks can be approximated by the same low-dimensional model, implying that a domain-general set of computational principles may underlie randomization behavior in general.


A task-general model of human randomization

Decision-making is commonly impaired in those with cognitive decline. I compared individuals with Mild Cognitive Impairment (MCI; n = 45) and cognitively healthy older adults (n = 45) in their ability to improve their decision-making in the Iowa Gambling Task over time. On each trial, participants chose from one of four decks of cards and received a net gain or loss. The four decks differed in their expected value (EV). They continued choosing among the decks for one hundred trials, trying to maximize their winnings. Results indicate that both groups learned to choose from the higher EV decks over time, but those with MCI learned more slowly. The data were fit to a reinforcement learning model that indicated those with MCI performed worse in part because of task strategy: those with MCI were less likely to perseverate on wins and updated their beliefs more slowly to losses.

Individuals with Mild Cognitive Impairment are impaired in a gambling task

Procrastination is universal but not monolithic; a second dimension, the Intention–Behavior Gap (IBG), has been suggested to be relevant. The IBG is the difference between one’s intentions and one’s behavior. So far, the IBG has not been directly measured within a behavioral experiment. We assigned subjects a week to work on a lengthy online reading task. Before the task started, subjects reported their plan by the number of reading units for each day. We found great variability in their plans, actual time course and the difference between the two (IBG). The time course of work ranged from completing earlier than planned to not finishing. We calculated correlations between the mean unit completion day, the IBG, the Causal Dimension Scale, indices of regret and satisfaction, and scales for procrastination (General, Active and Irrational). Besides the correlations expected based on definitions, we did not find any significant correlations.

Procrastination and the Intention-Behavior Gap

The propensity for people to avoid mentally demanding tasks in the absence of reward is well documented. As a result, humans are often described as cognitive misers. This characterisation, while consistent with the psychological literature, contradicts everyday instances of effort being sought: reading, board games, and brain-teasing puzzles. Such examples however are markedly different from the types of tasks typically used in the mental effort literature (e.g., working memory tasks, demand selection tasks). The current set of experiments assessed whether the type of task (i.e., N-Back, Number Sequence Problems [NSP], or Anagrams) affects people’s aversion to, or desire for, increased effort. On average, across 3 experiments, participants showed an aversion to effort regardless of whether the effort required was more attentional (N-Back) or cognitive (NSP and anagrams) in nature, and were willing to forgo financial reward in order to avoid more difficult tasks. A minority of participants, however, sought more effortful tasks for equal or lesser reward.

Does Mental Effort Avoidance Depend on the ‘Type of Effort’?

To maintain performance in perceptual-motor tasks involving interactions with the external environment, appropriate regulation of the arousal level is essential. Both internal (learning and saturation) and external (stimuli) factors can be assumed to affect such arousal levels. We investigated these factors by using a line-following task, in which participants had to follow a scrolling line with a circle. Participants recruited through crowdsourcing engaged in this task for 30 minutes. They responded to periodical probes during the task to indicate their level of concentration thereon. Experiment 1 tested the effect of external stimuli designed to decrease/increase arousal levels in this task. Based on the results of this basic experiment, Experiment 2 switched the stimulus pattern from high to low arousal / low to high arousal during the task. As a result, we found differences between task conditions, suggesting the effect of adaptive stimulus presentation to maintain the arousal level.

Analyzing the Effect of External Environments on Mind Wandering during a Perceptual-Motor Task

We test whether people flexibly shift their sampling strategy for learning a functional relationship, based on the strategy’s perceived effectiveness. While general-purpose heuristics such as gathering information evenly across the environment may often approximate optimal sampling policies when opportunities for learning are sparse, these strategies may systematically fail when much of the environment is uninformative. Across several different classes of arbitrary smooth functions, participants (N = 89) made sampling choices that were initially consistent with a simple heuristic, but shifted their sampling strategy when this heuristic failed to be informative. People were subsequently more accurate at approximating the true function for smoother functions that can be more easily predicted by sampling evenly, t(645.3) = 6.803, p < .001; nevertheless, while individuals vary considerably, aggregate judgments across all functions are very accurate, suggesting that people show inductive biases in active learning, but effectively learn when to adjust their policies.

Dynamic Strategy Selection in Active Function Learning

Sequential sampling models have provided accurate accounts of people’s choice, response time, and preference strength in value-based decision-making tasks. Conventionally, these models are developed as Markov-type processes (such as random walks or diffusion processes) following the Kolmogorov axioms. Quantum probability theory has been proposed as an alternative framework upon which to develop models of cognition, including quantum random walk models. When modeling people's behavior during decision-making tasks, previous work has demonstrated that both the Markov and quantum models have their respective strengths. Recently, the open system model, which is a hybrid version of the Markov and quantum models, has been shown to provide a more accurate account of preference strength compared to the Markov and quantum models in isolation. In this work, we extend the open system model to make predictions on pairwise choice and response time and compare it to the Markov and quantum random walk models.


Open System Model of Choice and Response Time

The direction of our attention can influence our performance on a variety of tasks. For example, reading from left to right relates to people associating small numbers on the left and large numbers on the right. In contrast, reading from right to left relates to people associating small numbers on the right and large numbers on the left. The current study tests if this type of “attentional momentum” can be induced by storytelling based on pictures and whether it affects college students’ reaction time on an arithmetic verification task with equations in a traditional (e.g., 2+2=4) or non-traditional (e.g., 4=2+2) direction. Our results show that students were faster at verifying simple traditional math problems after telling stories based on pictures arranged from left to right, but faster at verifying simple non-traditional math problems after telling stories based on pictures arranged from right to left.


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Formalization and Implementation of ViolEx: An Active Inference perspective

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