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The simple brain time theory of time perception states that the temporal properties of the neural processing of sensory features are isomorphic to the temporal properties represented in experience. If true, this theory provides a simple solution to the temporal binding problem. I argue that the theory cannot account for an adaptive trade-off between, the speed of perceptual availability and the accuracy of temporal integration. I hold, that a theory of time perception must get this trade-off right to account for temporal binding. This requires solving two timing problems: the problem of neural delay and the problem of desynchronization. The simple brain time theory is unable to give a unified solution to these problems. Either it fails to answer one of these problems, and veers too much on one side of the trade-off, or it violates its commitment to its central theses. I end by sketching a few possible solutions.

CogSci 2023

Neural Delay &amp; Desynchronization problems for the Simple Brain Time Theory

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Neural Delay & Desynchronization problems for the Simple Brain Time Theory

poster

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

Lighthouse beams are often perceived as bent rather than straight, and observers sometimes infer that a rotating lighthouse beam originates from a "phantom lighthouse" that lies in the opposite direction from the true source of the beam. We argue that both illusions arise as a result of Bayesian inference based on natural scene statistics and support our argument by implementing a formal computational model. In addition to capturing both illusions, our model makes the novel prediction that the phantom lighthouse illusion should only emerge at a critical point at which the observer is located around 75 metres in front of the true source of a rotating lighthouse beam. Our theory therefore motivates a future line of experimental work, and contributes to a broader body of research that explains perceptual phenomena (including visual illusions) in terms of Bayesian inference.

A Bayesian account of two visual illusions involving lighthouse beams

Approximate number perception is noisy, but it is unclear whether that noise reflects an error-prone attempt to report one's best guess of the value, or an inherently stochastic process such as sampling. Here we demonstrate that approximate number estimation should be thought of as a sampling procedure, as the average of two estimates of the same stimulus tends to outperform either estimate alone. Additionally, the average difference between the two estimates of a given number linearly increases as a function of number, consistent with Weber's law. Finally, we provide evidence that the classic psychophysical curves associated with number representation are represented within the mind after a single brief viewing of a stimulus, and not only useful for describing a summary of behavior over many trials.

Sampling in Approximate Number Perception

Increased left-handedness and atypical lateralization in individuals with neurodevelopmental and psychiatric disorders point to a deep yet unresolved connection between handedness, hemispheric asymmetry, and normal brain functioning. Most handedness-lateralization research has either excluded left-handers due to their higher variability or failed to control the degree of handedness. The discrete categorization based on arbitrary criteria or cut-off has made it challenging to address inter-individual variations in the lateralization of cognitive functions. In this study, capturing responses from across the handedness continuum in tasks of divided visual half-field paradigm, we explored the lateralization patterns in different stages of visual processing of spatial frequency, global-and-local, faces and words. We found that the degree of handedness significantly affects the lateralization in all tasks except in spatial frequency. Notably, even though the direction of hemispheric preference did reverse in left-handers for visuospatial attention like in global-local processing, the same was not the case for word and face processing. Our results substantially evidence handedness to differentially influence the lateralization of visual processes. The observed relationship between the dominant hand and global-local processing significantly points to the action-dependent modulation of visual attention. We conclude that the degree of left-handedness is potentially a critical factor in lateralization, and a continuum approach would be beneficial to control for the individual variations in laterality research

Handedness Modulates Spatial Attention – Insights From Individual Variations In Lateralization Of Cognitive Functions

Many models of intuitive physical reasoning posit some kind of mental simulation mechanism, yet everyday environments frequently contain far more objects than people could plausibly represent with their limited cognitive capacity. What determines which objects are actually included in our representations? We asked participants to predict how a ball will bounce through a complex field of obstacles, and probed working memory for objects in the scene that were more and less likely to be relevant to the ball’s trajectory. We evaluate different accounts of relevance and find that successful object memory is best predicted by how frequently a ball’s trajectory is expected to contact that object under a probabilistic simulation model. This suggests that people construct representations for
mental simulation efficiently and dynamically, on the fly, by adding objects “just in time”: only when they are expected to become relevant for the next stage of simulation.

"Just In Time" Representations for Mental Simulation in Intuitive Physics

Computational models of perceptual decision-making depend heavily on empirical goodness-of-fit measures for model selection. However, it is not possible to improve models' fit to data indefinitely, particularly when the data in question are variable across multiple elicitations. The completeness of a model or a theory assesses the extent to which it can predict observations in comparison with an ideal model. We measure the completeness of contemporary race models on a random dot motion discrimination task and show that the simple drift diffusion model is already close to complete in describing random dot motion discrimination data, with more complex models being in fact over-fit to datasets. We also estimate the retest reliability of the random dot motion discrimination task as part of the exercise of measuring completeness, and describe some implications of our estimates for trial-level neural models of this task.

Measuring the completeness of race models for perceptual decision-making

Humans are surrounded by various typefaces, and the preliminary corpus analysis suggests that typefaces commonly used in picture books differ from others, which implies that familiar typefaces differ between children and adults. Thus, this study investigated whether children can easily recognize fonts considered familiar to them. Japanese-speaking children (aged 7–8 years) performed a hiragana-character discrimination task, which includes different (e.g., あ–お) and same (e.g.,あ–あ) character–pair conditions. It involves various typefaces familiar to children (i.e., picture book typefaces) or adults; the study indexed discriminability using averaged reaction times to the character–pair conditions. Accuracy was sufficiently high for all typefaces (>90%), and no significant difference existed in response times between any typeface. The results suggested that children can stably discriminate characters regardless of whether or not a font is commonly used in picture books.

Discriminability of picture book typeface in children

Reader’s ability to integrate words into the preceding linguistic context via inferences making is one fundamental component of reading comprehension. However, the underlying processes that contribute to effective word-to-text integration are not yet fully understood. In this study, we investigated the contribution of variables underlying lexical representations (e.g., vocabulary), and general reading skills (text comprehension) to the integration of words across sentences in a sample of adolescents. We implemented an ERP experiment, in which we contrasted two sentential context conditions: a repetition condition and an inference condition. The cognitive cost of inferential processing was indexed by the N400 component in both conditions. Participants’ individual differences in vocabulary and reading comprehension were included to examine the role of different components in this process. The results showed that participants with lower vocabulary exhibited a larger and extended cognitive cost of inference-making in the N400 component relative to participants with a higher vocabulary.

Integration of Words to Previous Linguistic Context is Modulated by Vocabulary: An ERP study

If we want to know whether a stack of blocks will fall, we might form our judgment by using mental simulation or simple heuristics. How do we know which strategy to use? Here, we test whether people’s strategy selection is “resource rational,” balancing their utilities and costs. We had participants judge how a balance beam will fall – a task that relies on both simulation and heuristics – and imposed visuospatial cognitive load to increase the cost of mental simulation, expecting the use of simulation to then decrease and the use of heuristics to increase. When cognitive load varied between participants, higher load led to less simulation. However, when load varied across trials for an individual participant, they instead used simulation at about the same rate as high-load-only participants. Thus strategy selection is sensitive to cognitive costs but it is also sticky, not accounting for rapidly fluctuating costs.

Strategy choice for physical reasoning is (partially) sensitive to cognitive costs

Robots must be able to communicate naturally and efficiently, e.g., using concise referring forms like it, that, and the ⟨N’⟩. Recently researchers have started working on Referring Form Selection (RFS) machine learning algorithms but only evaluating them offline using traditional metrics like accuracy. In this work, we investigated how a cognitive status-informed RFS computational model might fare in actual human-robot interactions in a human-subjects study (N=36). Results showed improvements over a random baseline in task performance, naturalness, understandability, and mental workload. However, the model was not perceived to outperform a simple, naive, non-random baseline (constant use of indefinite noun phrases). We contribute several key research directions for further development of cognitive status-informed RFS models, the inclusion of multi-modality, and further development of testbeds.


Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions

Androids that strongly but imperfectly resemble humans in shape can elicit negative emotions in people, a phenomenon known as the "uncanny valley," which has been replicated in laboratory experiments. Recently, the accuracy of face recognition utilizing machine learning has increased, raising the question of whether machine learning can replicate the uncanny valley. Using FaceNet as a representative face recognition algorithm, we examined the similarity of face recognition to human evaluation and its replication of the uncanny valley. The results revealed a strong correlation between machine learning and human evaluation of human-like shapes. However, it is evident that only certain aspects of the uncanny valley were replicated. Furthermore, visualization of the activation maps suggests that localized regions, such as the mouth and chin, acted as the basis for judgment. These findings support the idea that human and machine learning have distinct areas of attention.

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A Bayesian account of two visual illusions involving lighthouse beams

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