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During speech perception, humans constantly sense multiple sources of information. As so, the McGurk effect has been typically conceived as a prototypical example of language multi-modal integration, specifically, it has been used to study audio-visual
integration during speech. The McGurk effect arises when in-congruent audio-visual stimuli are paired and perceived as a different syllable ( auditory /ba/ +
visual /ga/ = percept “da” ).

We developed a hierarchical computer model, based on self-organizing maps and Hebbian learning to study speech multi-modal integration. Our architecture allows studying the McGurk effect purely on bottom-up processing of audio-visual information.
We trained several versions of the model and measured the activation similarity between
McGurk alike stimuli and congruent ones by means of mutual information. Our results suggest that the illusory percept arises from the best congruent representation that reduces uncertainty. Furthermore, the reliability of each sensory modality determines the best congruent representation.


CogSci 2022

Multimodal integration as predictions. An explanation of the McGurk effect

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Correlation judgments are at the core of belief formation. In previous studies of correlation judgment from 2D scatterplots, observers underestimate correlations, and display stronger underestimation biases when the scatterplot is shown in a landscape view than in a portrait view. Yet, it is unclear how these biases arise. Here, we propose that observers are Bayesian learners who perform “mental regression” using the observed data points in graph. Accordingly, judgment errors can arise from biased visual information sampling. We test our model’s predictions with two eye-tracking experiments and find that the Bayesian learning model, applied to information obtained from visual fixation data, replicates classic behavioral findings. The model also predicts trial-level estimation biases at a high accuracy level. Our study shows how computational models trained on process-level data can shed light on the cognitive mechanisms underlying belief formation, and yield theory-driven practical implications for data visualization and statistical communication.

Information sampling explains Bayesian learners’ biases in correlation judgment

The traveling salesperson problem (TSP) is an NP-Hard problem that computers find difficult to solve. Humans are surprisingly good at solving the TSP, with solutions within 10% of optimal for problems with up to 100 points, constructed in time linear with the number of points. We propose that humans solve the TSP by initially clustering the points and then connecting them first within and then between clusters. In this study, 67 participants first clustered 40 stimuli and then solved them as TSPs. Strikingly, participants' TSP solutions perfectly followed their clusters for 52% of the stimuli. Further, participants' TSP solutions' were more congruent with their clusters for stimuli with statistically higher levels of clustered structure. This provides strong evidence for the clustering proposal. Random TSP solutions, however, showed no such congruence to cluster structure. These findings suggest that clustering might be a fundamental ability for reasoning about graph-theoretic algorithmic problems.

Use of clustering in human solutions of the traveling salesperson problem

Humans are uniquely adept at extracting structure from the world around them. It is well known that people often form hierarchical task representations during learning, even when a task does not explicitly necessitate a hierarchical representation. Still, how individuals capitalize on this structure to facilitate behavior is an open question. In the present study, we address this question by carefully examining patterns of response time switch costs in a hierarchically structured visuomotor association learning task, to adjudicate between multiple models of behavior. We find evidence that participants do appear to navigate through a hierarchical representation of stimulus-response associations as they prepare responses, rather than maintaining a non-hierarchical, flat model or being primarily affected by changes in stimulus features. These results establish the existence of hierarchical mental representations even for static visuomotor mappings, and imply that such representations are internally navigated in an orderly manner during action selection.

Climbing the Tree: Structured Hierarchical Representations in Visuomotor Maps

Three-dimensional objects pose a challenge for our visual system, since we can only view objects from a single limited perspective at a given moment. Previous work found that given a limited perspective, infants represent 3D objects as complete volumes. Our study replicated this finding in 4- to 7-year-olds and adults, using an explicit prediction measure rather than looking times. We also explored whether humans have a bias to represent visually limited 3D objects as symmetrical rather than asymmetrical across shape, size, texture, and color. Overall, there was an above-chance preference for full volumetric and symmetrical object completion that increased with age. Low-level perceptual similarity of choices did not predict participants’ choices. Moreover, we evaluated ResNet-50 neural networks on the same tasks: they represented objects as complete volumes, but did not show substantial preference for symmetrical 3D representations. This raises the possibility that incorporating human symmetry biases could improve computer vision.

Three-Dimensional Object Completion in Humans and Computational Models

Because there are many situations in our daily life in which the option space is not discrete but continuous, recently developed decision models have been able to examine the cognitive processes underlying choice in laboratory tasks with a continuous outcome space. One of the most important of these continuous models is the circular diffusion model (CDM) by Smith, which has been shown to account for continuous space data from a wide range of paradigms, including color identification, orientation, brightness, pricing. However, in addition to the inherent complexity of this model, it has become more complex in order to predict reliable data patterns, making it a tool only for experts. Here we propose a more easy version of the CDM, the EZ version, to fit the model on continuous scale data. The EZ-CDM for continuous choice space tasks can estimate the parameter values for the cognitive processes underlying without considering the response time distribution but only using traditionally favored summary statistics (i.e. the mean and variance of response time, and angular variance of accuracy.) by simple formulas that can be computed easily and needs neither theoretical knowledge of model fitting nor much programming skills. Here, we formulate the EZ method and show that, despite being easy and fast to calculate, it’s performance in recovering true parameters is acceptable.

An EZ-circular diffusion model of continuous decision processes

Affective factors such as anxiety, confidence, and motivation can impair and enhance task performance. Here, we used drift diffusion modeling (DDM) to examine how these variables affect visualization, manipulation, and decision making on a mental rotation task (MRT). The effects of affective factors on visuospatial reasoning are largely unknown, perhaps in part because analyses are generally concerned with overall accuracy and reaction time (RT), without decomposing the stages of processing. With DDM, we decompose performance on a MRT into separate processing components, particularly the speed of information update (drift rate) and the amount of evidence accumulation (decision threshold). 106 adult participants performed a two-alternative forced-choice (2-AFC) MRT, and throughout, they rated their levels of anxiety, confidence, and motivation. We found that although anxiety, confidence, and motivation all impacted drift rate, only confidence affected the decision threshold. Moreover, we observed a unique role for confidence in mediating the links between gender and model parameters, as well as a unique moderating role of motivation in this mediation. Altogether, these findings shed light on the interrelations between affective factors in accounting for mental rotation performance in men and women, including the unique combination of confidence and motivation in explaining the gender difference in mental rotation performance.

Affective Factors Affect Visuospatial Decision-making: A Dri Diffusion Modeling Approach

To achieve goals, people leverage cognitive control to adjust how they process information. Here we show that frequent adjustments in information processing strategies (e.g., response threshold) within a single task give rise to reconfiguration costs. In two experiments we induced different performance goals in a Stroop task via explicit instruction or incentives, and these goals either varied or were fixed across different blocks. Across both experiments, we find smaller adjustments in control intensity when people frequently adjust the amount of control they exert, relative to blocks in which they don’t. We show that these results can be accounted for with a model that maximizes reward rate while minimizing reconfiguration costs (proportional to the Euclidean distances between the previous and current control signals). These findings suggest that cognitive control adjustments are regularized to constrain larger adjustments in control, which has important implications for computational modeling and measurement of motivated cognitive control.

Empirical and Computational Evidence for Reconfiguration Costs During Within-Task Adjustments in Cognitive Control

People are often reluctant to reconsider their choices, sticking with their goals even when it is clear that they would be better off abandoning them. Explanations for this abound, including loss aversion, sunk costs, social and reputational pressures, and resource rational consideration of the costs of replanning. Here we propose another hypothesis: In adopting a goal, you immediately reap the rewards of gaining information about what to do and how to act. Insofar as goals are rewarding in themselves, we predict that unless a goal is specifically devalued or the costs associated with it are very high, the default is not to engage in any reconsideration at all. We test this hypothesis by creating a stripped down scenario involving choices between two goals with transparently obvious cost differentials. The task is designed to minimize other factors that might contribute to goal persistence and indeed, we test both adults and very young children on virtually the same task to ensure that the cognitive load for adults is negligible. Both adults (Experiments 1-2) and 4-6-year-old children (Experiments 3-4) choose the less costly of two goals when shown the costs and goals together. However, when participants are shown the goals first, and only then shown that their chosen goal is more costly than the alternative, participants stick with higher cost goals, unless the goals are explicitly devalued.

"Because I want to": Valuing goals for their own sake

When deciding whether and how to punish, people consider not only the potential direct consequences, but also, how their choice will affect observers’ judgements about the values and motives underlying the choice. We formalize the decision to punish as a rational communicative social action (RCSA). The model generates rational decisions to punish, incorporating anticipated observers’ judgements obtained from a recursive model of inference using an intuitive theory of mind. Using this model, we synthesize patterns of human punishment from recently published papers. RCSA thus offers a formal model of the cognitive process that humans use to balance preferences for how they are perceived, with other goals for punishing.

Modeling punishment as a rational communicative social action

We explore whether people can recognise the epistemic goal of active learners. In a novel online experiment, 110 adults watched screen recordings of other adults (``players'') manipulating objects in a 2D simulated physical microworld. Players had the goal of either identifying the magnet-like force connecting two of the objects, or their relative masses. Observers were asked to identify the learning goal of the player. By drawing from a previously collected dataset of active physical learning interactions and an ideal observer analysis, we manipulated how informative the players' actions are about the target property, and observers' level of access to the players' micro-control actions. We found observers were better at identifying the goals of successful players and of players trying to identify force, while the micro-dynamic evidence improved accuracy on identifying the mass goal. We use mixed methods to explore what cues observers used to make these judgments.

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Multimodal integration as predictions. An explanation of the McGurk effect

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Information sampling explains Bayesian learners’ biases in correlation judgment

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