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Perceptual training leads to improvements in a wide range of simple visual tasks. However, it is still unclear how effective it can be for more difficult visual tasks in real-world domains such as radiology. Is it possible to train people to the level of experts? If so, what method is best, and how much training is necessary? Over four training sessions, we trained medically naive participants to identify the degree of fatty liver tissue present in ultrasound images. We found that both COMPARISON and SINGLE-CASE perceptual training techniques resulted in significant post-training improvement, but that the SINGLE-CASE training was more effective. Whilst people showed rapid learning with less than one hour of training, they did not improve to the level of experts, and additional training sessions did not provide significant benefits beyond the initial session. This suggests that perceptual training could usefully augment, but not replace, the traditional rule-based training that medical students currently receive.

CogSci 2021

How effective is perceptual training? Evaluating two perceptual training methods on a difficult visual categorisation task

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**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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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.

poster

Folk-biological concepts are sensitive to both statistical information about feature prevalence (Hampton, 1995; Kim & Murphy, 2011; Rosch & Mervis, 1975) and teleological beliefs about function (Atran, 1995; Keil, 1994; Kelemen, Rottman, & Seston, 2013; Lombrozo & Rehder, 2012), but it is unknown how these two types of information interact to shape concepts. In three studies (N = 438) using novel animal kinds, we found that information about prevalence and teleology inform each other: People assume that common features are functional, and they assume that functional features are common. However, people use teleological information to predict the future distribution of features across the category, despite conflicting information about current prevalence. Thus, both information about prevalence and teleological beliefs serve important conceptual functions: Prevalence information encodes the current state of the category, while teleological functions provides a means of predicting future category change.

The function of function: People use teleological information to predict prevalence

Many events that humans and other organisms experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co-occurrences between temporarily adjacent stimuli, others involve tracking the co-occurrences between temporarily distant stimuli (i.e., non-adjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here we report on four experiments that examined NAD learning from various types of visual stimuli. The results suggest that continuous movements aid the acquisition of NADs. We also found that human motion leads to more robust NAD learning compared to object motions, perhaps because of a richer representation. This richer representation could result in better memory and recall, and provide a stronger signal for NAD learning.

Dynamic Action Facilitates Learning of Non-Adjacent Dependencies in Visual Sequences

Process data recording students’ interactions with digital assessment items are available in digital educational assessments and have become a focus of cognitive scientists to analyze inquiry skills during problem solving. This study examines the inquiry behaviors of using tools (i.e., resource tabs) in short response construction items from the 2019 National Assessment of Educational Progress science assessment. We visualized the occurrence times and durations of response construction behaviors and tool use behaviors and conducted correlation analyses and mixed-effects regressions between the count (and duration) of tool use behaviors and item scores. The results reveal that tool use behaviors are significantly associated with item scores and probabilities of finishing the whole block the problem; increasing tool use durations or counts increases the chances of getting higher scores, but it also increases the chances of not finishing the block. This study exemplifies how to use process data to investigate scientific inquiry skills.

Investigating Scientific Inquiry Skills from Process Data

An open problem in Libet task literature regards the relationship between the moment in which awareness of motor intention arises and inhibition efficiency in response to an external stimulus (taking into account both the reactive and proactive mechanisms). In this study, 112 volunteers performed the Libet’s clock task to evaluate motor intention awareness, a Stop Signal Task (SST) to evaluate the inhibitory efficiency in its mainly reactive component, and a Cued Go/No-Go to evaluate the inhibitory efficiency in its mainly proactive dimension. We observed that a delayed insurgence of the awareness of motor intent is related to a better reactive inhibitory efficiency. No relationship was observed with the proactive component.

Awareness motor intention and inhibitory control: the role of reactive and proactive components

Misconceptions in science are ubiquitous and difficult to revise, but refutation texts are one effective tool for prompting conceptual change (Tippett, 2010). However, refutation texts are effective only to the extent that students are forming coherent representations. Self-explanation is a well-studied method for increasing coherence of readers’ mental representations (Allen, McNamara & McCrudden, 2015). The current study examines the unexplored question of whether these common interventions enhance each other’s positive learning outcomes. Two-hundred fifteen UCSD undergraduates were randomly assigned to read either a refutation or an expository text about the phases of the moon, and were prompted to either self-explain or think aloud while reading. Students then took a post-test assessing knowledge of moon phases and related misconceptions. We measured both accuracy and explanatory qualities, such as causality and circularity, in order to assess the relative efficacy of refutation and self-explanation and their combination.

Effects of Combining Refutation and Self-Explanation on Student Learning

Most models of statistical learning do not consider the perceptual properties of the units that make up a sequence. Here, we manipulate the similarity of units in a modified version of classic statistical learning paradigm (Aslin et al., 1998). After a 3-minute familiarization stream, we asked the participants to rate their familiarity with words and part-words, with the addition of non-words (trisyllabic words without any previous exposure). We developed a simple recurrent neural network that used distributed representations for the syllables and produced similar results to the human data. We then explored how perceptual similarity impacted learning of different familiarization streams. Specifically, greater similarity relations among units were predicted to lead to poorer discrimination between words and part-words. Based on the model’s results, we created a new familiarization sequence of trisyllabic words and tested for impacts on perceptual similarity on human performance.

Perceptual similarity and learning from sequential statistics.

Chinese texts are renowned for the lack of physical spaces between words in a sentence. Reading these sentences requires a stage of word segmentation, the mechanism of which may involve visual statistical learning. In three experiments employing the RSVP task along with the Saffran et al. (1997) paradigm, we provided evidence that foreign learners of Chinese could capture the statistical information embedded in a string of characters and use that information to tell apart a “word” from a “nonword”. The statistical learning effect (.57) was comparable to that observed previously in an auditory task using the same stimuli. The results of the experiments also suggested that significant visual statistical learning required a conscious level of processing that directed the participants’ attention at the characters as well as an unconscious level, at which the distributional information across the characters can be continuously computed and accumulated.

Visual Statistical Learning in the Reading of Unspaced Chinese Sentences

Simple, non-pharmaceutical health interventions (e.g., masks) could prevent avoidable COVID-19 deaths (Reiner et al., 2020). Why do some still refuse to wear masks? People may find it easier to rely on implicit, prior knowledge to avoid needing to inhibit new, competing knowledge (e.g., heuristics; Tversky & Kahneman, 1974). Not only does knowledge impact decision making, but from a contextualized deficit framework, knowledge should interact with context to promote differential health decisions (Allum et al., 2008). A computer mouse-tracking paradigm evaluated how context (e.g., trust-in-experts; incidence rate) and germ knowledge impacted cognitive conflict driving mask-usage decisions. Results indicated that increased trust-in-experts, higher positive COVID-19 incidence rates, as well as accurate germ theories promoted mask-usage endorsement, which also reduced cognitive conflict between knowledge about new and old public-health mask guidelines. Contextual factors may help remediate the cognitive stress associated with inhibiting prior inaccuracies in favor of updated, scientific mask recommendations.

Implicit and Explicit Cognitive Processes Associated with COVID-19 Mask-Usage Decisions

Can mental simulation of alternative research outcomes reduce bias? We attempted to extend Hirt et al.’s (2004) finding of debiasing when alternative basketball standings were easy to simulate and participants were low in need for structure (NFS). Amazon Mechanical Turk (AMT) participants explained why taking notes by hand might improve test scores, then three groups of them explained different outcomes: Consider-opposite participants explained benefits of laptop notetaking, and two transfer groups explained either a plausible or an implausible outcome for unrelated research. None of the three groups differed from a baseline group in test score estimates or likelihood that taking notes by hand leads to higher test scores. However, low-NFS participants estimated marginally lower likelihood that notetaking by hand is superior, suggesting less bias to their initial explanation. We consider whether variation in participants’ psychology backgrounds might have overwhelmed effects, and discuss a replication with students taking introductory psychology.

Does mental simulation of alternative research outcomes reduce bias in predicted results?

Certain colors are strongly associated with certain adjectives (e.g. red is hot, blue is cold). Some of these associations are grounded in visual experiences like seeing hot embers glow red. Surprisingly, many congenitally blind people show similar color associations, despite lacking all visual experience of color. Presumably, they learn these associations via language. Can we detect these associations in the statistics of language? And if so, what form do they take? We apply a projection method to word embeddings trained on corpora of spoken and written text to identify color-adjective associations as they are represented in language. We show that these projections are predictive of color-adjective ratings collected from blind and sighted people, and that the effect size depends on the training corpus. Finally, we examine how color-adjective associations might be represented in language by training word embeddings on corpora from which various sources of color-semantic information are removed.

How effective is perceptual training? Evaluating two perceptual training methods on a difficult visual categorisation task

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The function of function: People use teleological information to predict prevalence

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