Cognitive Psychology seminars

Explicit similarity judgments tend to emphasize relational information more than do difference judgments. In this talk, I propose and test the hypothesis that this asymmetry arises because human reasoners represent the relation different as the negation of the relation same (i.e., as not-same). This proposal implies that processing difference is more cognitively demanding than processing similarity. Both for verbal comparisons between word pairs, and for visual comparisons between sets of geometric shapes, participants completed a triad task in which they selected which of two options was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard, either by virtue of featural similarity or by virtue of relational similarity. On ambiguous trials, one option was more featurally similar (but less relationally similar) to the standard, whereas the other was more relationally similar (but less featurally similar). Given the higher cognitive complexity of assessing relational similarity, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty accurately detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments. These results provide convergent evidence for a representational asymmetry between the relations same and different.

Vector-space models are used frequently to compare similarity and dimensionality among entity concepts. What happens when we apply these models to relational concepts? What is the evidence that such models do apply to relational concepts? If we use such a model, then one implication is that maximizing surface feature variation should improve relational concept learning. For example, in STEM instruction, the effectiveness of teaching by analogy is often limited by students’ focus on superficial features of the source and target exemplars. However, in contrast to the prediction of the vector-space computational model, the strategy of progressive alignment (moving from perceptually similar to different targets) has been suggested to address this issue (Gentner & Hoyos, 2017), and human behavioral evidence has shown benefits from progressive alignment. Here I will present some preliminary data that supports the computational approach. Participants were explicitly instructed to match stimuli based on relations while perceptual similarity of stimuli varied parametrically. We found that lower perceptual similarity reduced accurate relational matching. This finding demonstrates that perceptual similarity may interfere with relational judgements, but also hints at why progressive alignment maybe effective. These are preliminary, exploratory data and I to hope receive feedback on the framework and to start a discussion in a group on the utility of vector-space models for relational concepts in general.

Visual perception is traditionally studied in isolation from other sensory systems, and while this approach has been exceptionally successful, in the real world, visual objects are often accompanied by sounds, smells, tactile information, or taste. How is visual processing influenced by these other sensory inputs? In this talk, I will review studies from our lab showing that a sound can influence the perception of a visual object in multiple ways. In the first part, I will focus on spatial interactions between sound and sight, demonstrating that co-localized sounds enhance visual perception. Then, I will show that these cross-modal interactions also occur at a higher contextual and semantic level, where naturalistic sounds facilitate the processing of real-world objects that match these sounds. Throughout my talk I will explore to what extent sounds not only improve visual processing but also alter perceptual representations of the objects we see. Most broadly, I will argue for the importance of considering multisensory influences on visual perception for a more complete understanding of our visual experience.

Face detection is an initial step of many social interactions involving a comparison between a visual input and a mental representation of faces, built from previous experience. Whilst emotional state was found to affect the way humans attend to faces, little research has explored the effects of emotions on the mental representation of faces. Here, we examined the specific perceptual modulation of geometric properties of the mental representations associated with state anxiety and state depression on face detection, and to compare their emotional expression. To this end, we used an adaptation of the reverse correlation technique inspired by Gosselin and Schyns’, (2003) ‘Superstitious Approach’, to construct visual representations of observers’ mental representations of faces and to relate these to their mental states. In two sessions, on separate days, participants were presented with ‘colourful’ noise stimuli and asked to detect faces, which they were told were present. Based on the noise fragments that were identified as faces, we reconstructed the pictorial mental representation utilised by each participant in each session. We found a significant correlation between the size of the mental representation of faces and participants’ level of depression. Our findings provide a preliminary insight about the way emotions affect appearance expectation of faces. To further understand whether the facial expressions of participants’ mental representations reflect their emotional state, we are conducting a validation study with a group of naïve observers who are asked to classify the reconstructed face images by emotion. Thus, we assess whether the faces communicate participants’ emotional states to others.

A high-resolution central fovea is a prominent design feature of human vision. But how important is the fovea for information processing and gaze guidance in everyday visual-cognitive tasks? Following on from classic findings for sentence reading, I will present key results from a series of eye-tracking experiments in which observers had to search for a target object within static or dynamic images of real-world scenes. Gaze-contingent scotomas were used to selectively deny information processing in the fovea, parafovea, or periphery. Overall, the results suggest that foveal vision is less important and peripheral vision is more important for scene perception and search than previously thought. The importance of foveal vision was found to depend on the specific requirements of the task. Moreover, the data support a central-peripheral dichotomy in which peripheral vision selects and central vision recognizes.

Magnitude estimation and stimulus discrimination tasks are affected by perceptual biases that cause the stimulus parameter to be perceived as shifted toward the mean of its distribution. These biases have been extensively studied in psychophysics and, more recently and to a lesser extent, with neural activity recordings. New computational techniques allow us to train spiking recurrent neural networks on the tasks used in the experiments. This provides us with another valuable tool with which to investigate the network mechanisms responsible for the biases and how behavior could be modeled. As an example, in this talk I will consider networks trained to discriminate the durations of temporal intervals. The trained networks presented the contraction bias, even though they were trained with a stimulus sequence without temporal correlations. The neural activity during the delay period carried information about the stimuli of the current trial and previous trials, this being one of the mechanisms that originated the contraction bias. The population activity described trajectories in a low-dimensional space and their relative locations depended on the prior distribution. The results can be modeled as an ideal observer that during the delay period sees a combination of the current and the previous stimuli. Finally, I will describe how the neural trajectories in state space encode an estimate of the interval duration. The approach could be applied to other cognitive tasks.

Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In addition, we have shown that this bias is stable over periods of up to 8 s. At the end, I will show ongoing analysis of a recent experiment and argue that serial dependence may rely on distributed memory representations of stimuli and task relevant features.

Unfamiliar face processing (face cognition) ability varies considerably in the general population. However, the means of its assessment are not standardised, and selected laboratory tests vary between studies. It is also unclear whether 1) the most commonly employed tests are reliable, 2) participants show a degree of consistency in their performance, 3) and the face cognition tests broadly measure one underlying ability, akin to general intelligence. In this study, we asked participants to perform eight tests frequently employed in the individual differences literature. We examined the reliability of these tests, relationships between them, consistency in participants’ performance, and used data driven approaches to determine factors underpinning performance. Overall, our findings suggest that the reliability of these tests is poor to moderate, the correlations between them are weak, the consistency in participant performance across tasks is low and that performance can be broadly split into two factors: telling faces together, and telling faces apart. We recommend that future studies adjust analyses to account for stimuli (face images) and participants as random factors, routinely assess reliability, and that newly developed tests of face cognition are examined in the context of convergent validity with other commonly used measures of face cognition ability.

Our ability to perceive the world around us can be affected by a number of factors including the nature of the external information, prior experience of the environment, and the integrity of the underlying perceptual system. A particular challenge for the brain is to maintain a coherent perception from information encoded by the peripheral sensory organs whose function is affected by typical, developmental changes across the lifespan. Yet, how the brain adapts to the maturation of the senses, as well as experiential changes in the multisensory environment, is poorly understood. Over the past few years, we have used a range of multisensory tasks to investigate the role of ageing on the brain’s ability to merge sensory inputs. In particular, we have embedded an audio-visual task based on the sound-induced flash illusion (SIFI) into a large-scale, longitudinal study of ageing. Our findings support the idea that the temporal binding window (TBW) is modulated by age and reveal important individual differences in this TBW that may have clinical implications. However, our investigations also suggest the TWB is experience-dependent with evidence for both long and short term behavioural plasticity. An overview of these findings, including recent evidence on how multisensory integration may be associated with higher order functions, will be discussed.

I will present my recent work on mathematical modeling of human memory. I will argue that memory recall of random lists of items is governed by the universal algorithm resulting in the analytical relation between the number of items in memory and the number of items that can be successfully recalled. The retention of items in memory on the other hand is not universal and differs for different types of items being remembered, in particular retention curves for words and sketches is different even when sketches are made to only carry information about an object being drawn. I will discuss the putative reasons for these observations and introduce the phenomenological model predicting retention curves.

Visual perceptual learning (VPL) is defined as improved performance on a visual task due to visual experience. It was once argued that attention to a visual feature is necessary for VPL of the feature to occur. Contrary to this view, a phenomenon called task-irrelevant VPL demonstrated that VPL can occur due to exposure to a feature which is sub-threshold and task-irrelevant, and therefore, unattended. A series of findings based on task-irrelevant VPL has indicated the following two mechanisms. First, attention to a feature facilitates VPL of the feature while inhibiting VPL of unattended and supra-threshold features. Second, reward paired with a feature enables VPL of the feature irrespective of whether the feature is attended or not. However, we recently found an additional twist; VPL of a task-irrelevant and supra-threshold feature embedded in a natural scene is not subject to the inhibition of attention. This new finding suggests a need to revise the current view or add a new mechanism as to how VPL occurs.

Vision provides us with a holistic interpretation of the world that is, with very few exceptions, coherent and consistent across multiple levels of abstraction, from scene to objects to features. In this talk I will present results from past and ongoing work in my laboratory that investigates the role top-down signals play in establishing such coherent perceptual experience. Based on the results of several psychophysical experiments I will introduce a theory of “self-consistent inference” and show how it can account for human perceptual behavior. The talk will close with a discussion of how the theory can help us understand more cognitive processes.

Common factors are omnipresent in everyday life, e.g., it is widely held that there is a common factor g for intelligence. In vision, however, there seems to be a multitude of specific factors rather than a strong and unique common factor. In my thesis, I first examined the multidimensionality of the structure underlying visual illusions. To this aim, the susceptibility to various visual illusions was measured. In addition, subjects were tested with variants of the same illusion, which differed in spatial features, luminance, orientation, or contextual conditions. Only weak correlations were observed between the susceptibility to different visual illusions. An individual showing a strong susceptibility to one visual illusion does not necessarily show a strong susceptibility to other visual illusions, suggesting that the structure underlying visual illusions is multifactorial. In contrast, there were strong correlations between the susceptibility to variants of the same illusion. Hence, factors seem to be illusion-specific but not feature-specific. Second, I investigated whether a strong visual factor emerges in healthy elderly and patients with schizophrenia, which may be expected from the general decline in perceptual abilities usually reported in these two populations compared to healthy young adults. Similarly, a strong visual factor may emerge in action video gamers, who often show enhanced perceptual performance compared to non-video gamers. Hence, healthy elderly, patients with schizophrenia, and action video gamers were tested with a battery of visual tasks, such as a contrast detection and orientation discrimination task. As in control groups, between-task correlations were weak in general, which argues against the emergence of a strong common factor for vision in these populations. While similar tasks are usually assumed to rely on similar neural mechanisms, the performances in different visual tasks were only weakly related to each other, i.e., performance does not generalize across visual tasks. These results highlight the relevance of an individual differences approach to unravel the multidimensionality of the visual structure.

Visual comparisons are ubiquitous, and they can also be an important source for learning (e.g., Gentner et al., 2016; Kok et al., 2013). In science, technology, engineering, and math (STEM), key information is often conveyed through figures, graphs, and diagrams (Mayer, 1993). Comparing within and across visuals is critical for gleaning insight into the underlying concepts, structures, and processes that they represent. This talk addresses how people make visual comparisons and how visual comparisons can be best supported to improve learning. In particular, the talk will present a series of studies exploring the Spatial Alignment Principle (Matlen et al., 2020), derived from Structure-Mapping Theory (Gentner, 1983). Structure-mapping theory proposes that comparisons involve a process of finding correspondences between elements based on structured relationships. The Spatial Alignment Principle suggests that spatially arranging compared figures directly – to support correct correspondences and minimize interference from incorrect correspondences – will facilitate visual comparisons. We find that direct placement can facilitate visual comparison in educationally relevant stimuli, and that it may be especially important when figures are less familiar. We also present complementary evidence illustrating the preponderance of visual comparisons in 7th grade science textbooks.

Several lines of research have demonstrated that internally generated sensory experience - such as during memory, dreaming and mental imagery - activates similar neural representations as externally triggered perception. This overlap raises a fundamental challenge: how is the brain able to keep apart signals reflecting imagination and reality? In a series of online psychophysics experiments combined with computational modelling, we investigated to what extent imagination and perception are confused when the same content is simultaneously imagined and perceived. We found that simultaneous congruent mental imagery consistently led to an increase in perceptual presence responses, and that congruent perceptual presence responses were in turn associated with a more vivid imagery experience. Our findings can be best explained by a simple signal detection model in which imagined and perceived signals are added together. Perceptual reality monitoring can then easily be implemented by evaluating whether this intermixed signal is strong or vivid enough to pass a ‘reality threshold’. Our model suggests that, in contrast to self-generated sensory changes during movement, our brain does not discount self-generated sensory signals during mental imagery. This has profound implications for our understanding of reality monitoring and perception in general.