Explaining an asymmetry in similarity and difference judgments
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.
Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation
Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributing to model fidelity, we identified key principles underlying the organization of mouse visual cortex. Structurally, we find that comparatively low-resolution and shallow structure were both important for model correctness. Functionally, we find that models trained with task-agnostic, unsupervised objective functions, based on the concept of contrastive embeddings were substantially better than models trained with supervised objectives. Finally, the unsupervised objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual, scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse’s limited resources to create a light-weight, general-purpose visual system – in contrast to the deep, high-resolution, and more task-specific visual system of primates.
Trial by trial predictions of subjective time from human brain activity
Our perception of time isn’t like a clock; it varies depending on other aspects of experience, such as what we see and hear in that moment. However, in everyday life, the properties of these simple features can change frequently, presenting a challenge to understanding real-world time perception based on simple lab experiments. We developed a computational model of human time perception based on tracking changes in neural activity across brain regions involved in sensory processing, using fMRI. By measuring changes in brain activity patterns across these regions, our approach accommodates the different and changing feature combinations present in natural scenarios, such as walking on a busy street. Our model reproduces people’s duration reports for natural videos (up to almost half a minute long) and, most importantly, predicts whether a person reports a scene as relatively shorter or longer–the biases in time perception that reflect how natural experience of time deviates from clock time
Learning-to-read and dyslexia: a cross-language computational perspective
How do children learn to read in different countries? How do deficits in various components of the reading network affect learning outcomes? What are the consequences of such deficits in different languages? In this talk, I will present a full-blown developmentally plausible computational model of reading acquisition that has been implemented in English, French, Italian and German. The model can simulate individual learning trajectories and intervention outcomes on the basis of three component skills: orthography, phonology, and vocabulary. I will use the model to show how cross-language differences affect the learning-to-read process in different languages and to investigate to what extent similar deficits will produce similar or different manifestations of dyslexia in different languages.
Hearing in an acoustically varied world
In order for animals to thrive in their complex environments, their sensory systems must form representations of objects that are invariant to changes in some dimensions of their physical cues. For example, we can recognize a friend’s speech in a forest, a small office, and a cathedral, even though the sound reaching our ears will be very different in these three environments. I will discuss our recent experiments into how neurons in auditory cortex can form stable representations of sounds in this acoustically varied world. We began by using a normative computational model of hearing to examine how the brain may recognize a sound source across rooms with different levels of reverberation. The model predicted that reverberations can be removed from the original sound by delaying the inhibitory component of spectrotemporal receptive fields in the presence of stronger reverberation. Our electrophysiological recordings then confirmed that neurons in ferret auditory cortex apply this algorithm to adapt to different room sizes. Our results demonstrate that this neural process is dynamic and adaptive. These studies provide new insights into how we can recognize auditory objects even in highly reverberant environments, and direct further research questions about how reverb adaptation is implemented in the cortical circuit.
The dynamics of temporal attention
Selection is the hallmark of attention: processing improves for attended items but is relatively impaired for unattended items. It is well known that visual spatial attention changes sensory signals and perception in this selective fashion. In the work I will present, we asked whether and how attentional selection happens across time. First, our experiments revealed that voluntary temporal attention (attention to specific points in time) is selective, resulting in perceptual tradeoffs across time. Second, we measured small eye movements called microsaccades and found that directing voluntary temporal attention increases the stability of the eyes in anticipation of an attended stimulus. Third, we developed a computational model of dynamic attention, which proposes specific mechanisms underlying temporal attention and its selectivity. Lastly, I will mention how we are testing predictions of the model with MEG. Altogether, this research shows how precisely timed voluntary attention helps manage inherent limits in visual processing across short time intervals, advancing our understanding of attention as a dynamic process.
Global visual salience of competing stimuli
Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the first saccade of participants who freely observed pairs of images. The pairs balanced the combinations of new and already seen images, as well as task and task-free trials. The coefficients of the model provided a reliable measure of the likelihood of each image to attract the first fixation when seen next to another image, that is their global salience. For example, images of close-up faces and images containing humans were consistently looked first and were assigned higher global salience. Interestingly, we found that global salience cannot be explained by the feature-driven local salience of images, the influence of task and familiarity was rather small and we reproduced the previously reported left-sided bias. This computational model of global salience allows to analyse multiple other aspects of human visual perception of competing stimuli. In the talk, I will also present our latest results from analysing the saccadic reaction time as a function of the global salience of the pair of images.