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It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort.


CogSci 2022

Selecting between visuomotor lotteries to measure mental effort in risky decisions

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. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

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Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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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. 





Simple perceptual decision-making tasks such as the Stroop and flanker tasks are popular as a method of measuring individual variation in the processing of conflicting visual stimuli--for instance, the difference in accuracy on stimuli with and without conflict. A major challenge in applying these tasks, for instance, to compare two different populations of subjects, is the low reliability of the nonparametric measures of performance in the tasks. Here, we model dynamic adjustments in decision policies often seen in human behavior, thereby capturing trial-by-trial variation in decision policies, in addition to the classically used average statistics. We propose a recurrent network model to capture behavioral strategies in the task in a model-agnostic manner, and to overcome small-sample learning challenges by pooling across subjects. We show that by splitting the learning into a complex, shared meta-model and simple subject-specific parameters, we learn significantly better predictive models, and also identify latent dimensions indexing the decision policy that may serve as a better measure of individual differences in the task.

Meta-Learning of Dynamic Policy Adjustments in Inhibitory Control Tasks

In many real-life situations, we make decisions between a defined set of options, which can be either discrete (as when deciding between going on driving and stopping the car) or continuous (as when stirring the wheel, the possible range of angles goes from $-30$ to $30$ degrees). However, most computational models for decision-making focus on decisions between a discrete set of options. While there are a few sequential sampling models that can explain behavioral patterns (i.e., choices and response times) of decisions in a continuous option space (i.e., the CDM and the SCDM), these models have a few limitations. For example, these models assume no leakage in the evidence accumulation process and no spatial inhibition (i.e., inhibition among different areas of the option space depending on their distance to each other). In this paper, we propose a novel sequential sampling model based on an existing computational model (i.e., the leaky competing accumulator model) for decisions in a continuous option space. Our proposed model includes leakage and spatial inhibition and is thus more biologically plausible.

Radial Basis Leaky Competing Accumulator Model: A Biologically Plausible Framework for Decision-Making in a Continuous Option Space

Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work has added structured rewards to study generalization. However, past studies have often focused on a single one of these aspects, making it hard to compare results. We propose a generative model for constructing correlated trees to provide a unified and scalable method for studying exploration, planning, and generalization in a single task. In an online experiment, we find that people use structure (when provided) to generalize and perform uncertainty-directed exploration, with structure helping more in larger environments. In environments without structure, exploration becomes more random and more planning is needed. All behavioral effects are captured in a single model with recoverable parameters. In conclusion, our results connect past research on human RL in one framework using correlated trees.

Connecting Exploration, Generalization, and Planning in Correlated Trees

Many studies have shown that people may have the ability to extract summary statistics over objects/events in a set. Most of the investigations have mainly used simultaneous presented visual features. This experiment tested whether and how people could represent the average value over tactile stimuli presented in a temporal sequence. A voice-coil type vibrator was used to present the stimuli. In the averaging task, a sequence of stimuli was presented first, followed by a single stimulus. The sequence was composed of 4, 6, or 8 vibro-tactile stimuli. Participants judged whether the vibration intensity of the second stimulus was stronger or weaker than the estimated average value of the first sequence. The result demonstrated that 1) people can extract the average tactile stimuli, and 2) averaged intensity tend to be overestimated than the single stimuli. We will discuss characteristics of the summary statistical representation in the tactile modality.

Summary statistics extracted in tactile feature

In a ground war, achieving peace is challenging even when both sides suffer. Here we demonstrate that when humans jointly viewed a battlefield as perceptually grouped by colors, a factor irrelevant to the rules of land-war games, they waged fewer wars and accumulated more wealth due to the perceptual border’s constraint on their aggression. This facilitation of peace arose from perceptual grouping that serves as a visual common sense shared by humans, as a mismatched perceptual grouping between players failed to limit war. Moreover, it relies on the geopolitical principles that make ground wars destructive and disappeared when these principles were violated. Together, we show that perceptual grouping limits human ground war as it is a mutually acknowledged “rallying point” where expectations of peace converge.

Perceptual Grouping for War and Peace

As techniques for generating deepfake videos (in which a generative adversarial network is used to alter the human subject of a video) improve, so does the potential to use them for nefarious ends. It is therefore important that we identify those among us who are most susceptible to being fooled. Tidler and Catrambone (2021) presented results which suggest that one’s political orientation and affect detection ability are strong predictors of one’s ability to detect deepfakes. The current work is a re-analysis of their data, intended to reveal a more highly-resolved profile of a susceptible individual. The data (N = 169) were submitted to a k-means clustering algorithm which revealed a 2-cluster solution.

Younger, more progressive thinking individuals, with agnostic or atheistic religious beliefs, and greater adeptness at spotting deepfakes formed Cluster 1; and individuals with more conservative political leanings, greater religiosity, and less skill at detecting deepfakes formed Cluster 2.


Assessing Deepfake Video Detection Ability Using Unsupervised Machine Learning

Faces are an important source of perceptual and social information. Multiple cortical regions including the fusiform faces area (FFA), the superior temporal sulcus (STS), and medial prefrontal cortex (MPFC) respond more to dynamic faces than videos of toy objects, human bodies, and pastoral scenes. Do face-preferring regions in FFA, STS, and MPFC have different functions? To address this question, we re-analyzed functional magnetic resonance imaging (fMRI) data from seven different experiments that included a dynamic faces versus objects localizer. Each of the seven experiments tested different perceptual and social features using dynamic videos, point light displays, narratives, and animated cartoons. Using a functional region of interest approach, we observed a significant condition by region interaction in four of the seven experiments. Thus, although FFA, STS, and MPFC respond more to dynamic faces than objects, bodies, and scenes, these three regions differ from each other functionally.

Face-preferring regions in FFA, STS, and MPFC have different functions

The following studies investigated the perceptual processes that are the basis of the face inversion effect (FIE). We evaluated the effects of disrupting holistic information conveyed by the face contour/outline. In Experiment 1 (n=144) we blurred the contour of the faces and using an old/new recognition task we found that a robust inversion effect similar to that for normal faces remains for these new no-contour faces. However, a significant reduction in overall performance was found for no-contour vs normal faces. In Experiment 2 (n=74) instead of blurring we inserted a novel face contour to replace the normal one and found the same pattern of results as in Experiment 1. Our results suggest that the holistic information provided by the face contour does not on its own influence the FIE, however it plays a role in face recognition more generally.

Manipulating the face contour affects face recognition performance leaving the Face Inversion Effect unaltered

In virtually every activity we engage in — from analyzing economic trends, to predicting which of two football teams is more likely to win a game — our minds are tasked with separating signal from noise. Such computations benefit from the fact that our minds are highly attuned to the statistical structure of the world. But how quickly do we detect statistical structure — and to what extent is our sensitivity to structure rooted in perceptual processes? To address this, we asked observers to judge whether briefly presented visual stimuli were generated randomly or non-randomly. In as little as a tenth of a second, people exhibited the same stable biases of statistical perception that they exhibit in classic cognitive tasks (i.e., without time constraints). These results suggest that certain biases of subjective probability may arise not from how we think about randomness, but from how we perceive statistical information in the first place.

The speed of statistical perception

English is the dominant language in computer science. In addition to English-based academic papers, English is frequently the only language provided in introduction sections and manuals of command and software libraries, which are essential aspects of computer programming. Hence, English-as-a-second-language (ESL) learners may have difficulty studying computer science because they must learn this field while also learning English. Despite this problem, few studies have assessed the difficulty level of computer science texts for ESL learners.
Ideally, the difficulty levels of texts are assessed by having groups of ESL learners read them. However, owing to the excessive time and financial costs involved, such practices can be impractical.
Hence, using two highly accurate automatic readability assessors based on natural language processing (NLP) techniques, we assessed the readability of various computer-science-related texts for ESL learners. The first assessor is based on state-of-the-art deep transfer learning, and the second is based on classical machine learning and applied linguistics. For training the assessors, we used a standard corpus employed in NLP, which was annotated by professional English teachers to evaluate the readability of the texts for ESL learners. To conduct the experiments, we built a collection of computer science texts ranging from academic papers to software manuals (READMEs) crawled from a source-code hosting website, namely GitHub. The experimental results showed that intermediate ESL learners were able to read most of the computer science related texts.

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Meta-Learning of Dynamic Policy Adjustments in Inhibitory Control Tasks

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