Cognitive Psychology seminars

Our research investigates how processes of attention, visual working memory (VWM), and decision-making combine to translate perception into action. Within this framework, the role of VWM is to form stable representations of transient stimulus events that allow them to be identified by a decision process, which we model as a diffusion process. In psychophysical tasks, we find the capacity of VWM is well defined by a sample-size model, which attributes changes in VWM precision with set-size to differences in the number evidence samples recruited to represent stimuli. In the first part of the talk, I review evidence for the sample-size model and highlight the model's strengths: It provides a parameter-free characterization of the set-size effect; it has plausible neural and cognitive interpretations; an attention-weighted version of the model accounts for the power-law of VWM, and it accounts for the selective updating of VWM in multiple-look experiments. In the second part of the talk, I provide a characterization of the theoretical relationship between two-choice and continuous-outcome decision tasks using the circular diffusion model, highlighting their common features. I describe recent work characterizing the joint distributions of decision outcomes and response times in continuous-outcome tasks using the circular diffusion model and show that the model can clearly distinguish variable-precision and simple mixture models of the evidence entering the decision process. The ability to distinguish these kinds of processes is central to current VWM studies.

Several studies have showed that face stimuli elicit extremely fast and involuntary saccadic responses toward them, relative to other categories of visual stimuli. In the talk, I will mainly focus on a quite recent research done in our team that investigated to what extent face stimuli influence the programming and execution of saccades. In this research, two experiments were performed using a saccadic choice task: two images (one with a face, one with a vehicle) were simultaneously displayed in the left and right visual fields of participants who had to execute a saccade toward the image (Experiment 1) or toward a cross added in the center of the image (Experiment 2) containing a target stimulus (a face or a vehicle). As expected participants were faster to execute a saccade toward a face than toward a vehicle and did less errors. We also observed shorter saccades toward vehicle than face targets, even if participants were explicitly asked to perform their saccades toward a specific location (Experiment 2). Further analyses, that I will detailed in the talk, showed that error saccades might be interrupted in mid-fight to initiate a concurrently programmed corrective saccade.

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

I will begin by describing recent research taking a new, model-based approach to perceptual development. This approach uncovers fundamental changes in information processing underlying the protracted development of perception, action, and decision-making in childhood. For example, integration of multiple sensory estimates via reliability-weighted averaging – widely used by adults to improve perception – is often not seen until surprisingly late into childhood, as assessed by both behaviour and neural representations. This approach forms the basis for a newer question: the scope for the nervous system to deploy useful computations (e.g. reliability-weighted averaging) to optimise perception and action using newly-learned sensory signals provided by technology. Our initial model system is augmenting visual depth perception with devices translating distance into auditory or vibro-tactile signals. This problem has immediate applications to people with partial vision loss, but the broader question concerns our scope to use technology to tune in to any signal not available to our native biological receptors. I will describe initial progress on this problem, and our approach to operationalising what it might mean to adopt a new signal comparably to a native sense. This will include testing for its integration (weighted averaging) alongside the native senses, assessing the level at which this integration happens in the brain, and measuring the degree of ‘automaticity’ with which new signals are used, compared with native perception.

It seems we see the world in full detail. However, the eye is not a camera nor is the brain a computer. Incredible metabolic constraints render us unable to encode more than a fraction of information available in each glance. Instead, our illusion of stable and complete perception is accomplished by parsimonious representation relying on natural order inherent in the surrounding environment. I will begin by discussing previous behavioral work from our lab demonstrating one such strategy by which the visual system represents average properties of Gestalt-grouped sets of individual objects, warping individual object representations toward the Gestalt-defined mean. I will then discuss on-going work using a behavioral index of averaging Gestalt-grouped information established in our previous work in conjunction with an ERP-index of VSTM capacity (the CDA) to measure whether the Gestalt-grouping and perceptual averaging strategy acts to boost memory capacity above the classic “four-item” limit. Finally, I will outline our pre-registered study to determine whether this perceptual strategy is indeed engaged in a “smart” manner under normal circumstances, or compromises fidelity for capacity by perceptually-averaging in trials with only four items that could otherwise be individually represented.

A striking range of individual differences has been reported in a variety of visual search tasks, which naturally leads to the idea that some people are better at finding things than others. However, this conclusion appears to be premature. We carried out a replication of three recent visual search experiments and found that each task showed a wide range of individual differences as predicted, and observed good test-retest reliability in all three. However, performance on any one task was not correlated with the performance in the others: participants who naturally adopt efficient search strategies in one paradigm may perform at chance in another! Furthermore, we also show that behaviour in different versions of the same paradigm can be radically different: When simple line segments are used for search items, a large range of search strategies are found. If we instead use more complex search items, all our participants effortlessly adopt an optimal strategy. These results suggest search strategies are stable over time, but context-specific. To understand visual search we, therefore, need to account not only for differences between individuals but also how individuals interact with the search task and context.

Nicole Rust is an Associate Professor of Psychology at the University of Pennsylvania. Her research investigates how the brain gives rise to the mind and to behavior, with an emphasis on memory. Much of her work lies at the nexus of real and artificial intelligence, as well as experiment and theory.

Humans have a remarkable ability to figure out what happened and why. In this talk, I will shed light on this ability from multiple angles. I will present a computational framework for modeling causal explanations in terms of counterfactual simulations, and several lines of experiments testing this framework in the domain of intuitive physics. The model predicts people's causal judgments about a variety of physical scenes, including dynamic collision events, complex situations that involve multiple causes, omissions as causes, and causal responsibility for a system's stability. It also captures the cognitive processes underlying these judgments as revealed by spontaneous eye-movements. More recently, we have applied our computational framework to explain multisensory integration. I will show how people's inferences about what happened are well-accounted for by a model that integrates visual and auditory evidence through approximate physical simulations.

Visual attention refers to a set of processes allowing selection of relevant and filtering out of irrelevant information in the visual environment. A large amount of research on visual attention has involved visual search paradigms, where observers are asked to report whether a single target is present or absent. However, recent studies have revealed that these classic single-target visual search tasks only provide a snapshot of how attention is allocated in the visual environment, and that multitarget visual foraging tasks may capture the dynamics visual attention more accurately. In visual foraging, observers are asked to select multiple instances of multiple target types, as fast as they can. A critical question in foraging research concerns the factors driving the next target selection. Most likely, this would require two steps: (1) identifying a set of candidates for the next selection, and (2) selecting the best option among these candidates. After having briefly described the advantage of visual foraging over visual search, I will review recent visual foraging studies testing the influence of several manipulations (e.g., target crypticity, number of items, selection modality) on foraging behaviour. Overall, these studies revealed that the next target selection during visual foraging is determined by the competition between three factors: target value, target proximity, and priming of features. I will explain how the analysis of individual differences in foraging behaviour can provide important information, with the idea that individuals show by-default internal biases toward value, proximity and priming that determine their search strategy and behaviour.

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

Constraints on control-dependent processing have become a fundamental concept in general theories of cognition that explain human behavior in terms of rational adaptations to these constraints. However, theories miss a rationale for why such constraints would exist in the first place. Recent work suggests that constraints on the allocation of control facilitate flexible task switching at the expense of the stability needed to support goal-directed behavior in face of distraction. We formulate this problem in a dynamical system, in which control signals are represented as attractors and in which constraints on control allocation limit the depth of these attractors. We derive formal expressions of the stability-flexibility tradeoff, showing that constraints on control allocation improve cognitive flexibility but impair cognitive stability. We provide evidence that human participants adapt higher constraints on the allocation of control as the demand for flexibility increases but that participants deviate from optimal constraints. In continuing work, we are investigating how collaborative performance of a group of individuals can benefit from individual differences defined in terms of balance between cognitive stability and flexibility.

Deciding whether two different face photographs or voice samples are from the same person represent fundamental challenges within applied settings. To date, most research has focussed on average performance in these tests, failing to consider individual differences and within-person consistency in responses. In the current studies, participants completed the same face or voice matching test on two separate occasions, allowing comparison of overall accuracy across the two timepoints as well as consistency in trial-level responses. In both experiments, participants were highly consistent in their performances. In addition, we demonstrated a large association between consistency and accuracy, with the most accurate participants also tending to be the most consistent. This is an important result for applied settings in which organisational groups of super-matchers are deployed in real-world contexts. Being able to reliably identify these high performers based upon only a single test informs regarding recruitment for law enforcement agencies worldwide.

Initial social perceptions are often thought to reflect direct “read outs” of facial features. Instead, we outline a perspective whereby initial perceptions emerge from an automatic yet gradual process of negotiation between the perceptual cues inherent to a person (e.g., facial cues) and top-down social cognitive processes harbored within perceivers. This perspective argues that perceivers’ social-conceptual knowledge in particular can have a fundamental structuring role in perceptions, and thus how we think about social groups, emotions, or personality traits helps determine how we visually perceive them in other people. Integrative evidence from real-time behavioral paradigms (e.g., mouse-tracking), multivariate fMRI, and computational modeling will be discussed. Together, this work shows that the way we use facial cues to categorize other people into social groups (e.g., gender, race), perceive their emotion (e.g., anger), or infer their personality (e.g., trustworthiness) are all fundamentally shaped by prior social-conceptual knowledge and stereotypical assumptions. We find that these top-down impacts on initial perceptions are driven by the interplay of higher-order prefrontal regions involved in top-down predictions and lower-level fusiform regions involved in face processing. We argue that the perception of social categories, emotions, and traits from faces can all be conceived as resulting from an integrated system relying on domain-general cognitive properties. In this system, both visual and social cognitive processes are in a close exchange, and initial social perceptions emerge in part out of the structure of social-conceptual knowledge.

Face matching tests have traditionally been developed to assess human face perception in the neurotypical range, but methods that underlie their development often make it difficult for these measures to be applied in atypical populations (developmental prosopagnosics, super recognizers) due to unadjusted difficulty. We have recently presented the development of the Oxford Face Matching Test, a measure that bases individual item-difficulty on algorithmically derived similarity of presented stimuli. The measure seems useful as it can be given online or in-laboratory, has good discriminability and high test-retest reliability in the neurotypical groups. In addition, it has good validity in separating atypical groups at either of the spectrum ends. In this talk, I examine the stability of the OFMT and other traditionally used measures in atypical groups. On top of the theoretical significance of determining whether reliability of tests is equivalent in atypical population, this is an important question because of the practical concerns of retesting the same participants across different lab groups. Theoretical and practical implications for further test development and data sharing are discussed.

The eyes send a continuous stream of about two million nerve fibers to the brain, but only a fraction of this information is stored as visual memories. This talk will detail three neurocomputational models that attempt an understanding how the visual system makes on-the-fly decisions about how to encode that information. First, the STST family of models (Bowman & Wyble 2007; Wyble, Potter, Bowman & Nieuwenstein 2011) proposes mechanisms for temporal segmentation of continuous input. The conclusion of this work is that the visual system has mechanisms for rapidly creating brief episodes of attention that highlight important moments in time, and also separates each episode from temporally adjacent neighbors to benefit learning. Next, the RAGNAROC model (Wyble et al. 2019) describes a decision process for determining the spatial focus (or foci) of attention in a spatiotopic field and the neural mechanisms that provide enhancement of targets and suppression of highly distracting information. This work highlights the importance of integrating behavioral and electrophysiological data to provide empirical constraints on a neurally plausible model of spatial attention. The model also highlights how a neural circuit can make decisions in a continuous space, rather than among discrete alternatives. Finally, the binding pool (Swan & Wyble 2014; Hedayati, O’Donnell, Wyble in Prep) provides a mechanism for selectively encoding specific attributes (i.e. color, shape, category) of a visual object to be stored in a consolidated memory representation. The binding pool is akin to a holographic memory system that layers representations of select latent representations corresponding to different attributes of a given object. Moreover, it can bind features into distinct objects by linking them to token placeholders. Future work looks toward combining these models into a coherent framework for understanding the full measure of on-the-fly attentional mechanisms and how they improve learning.

Perception can be strongly serially-dependent (i.e. biased toward previously seen stimuli). Recently, serial dependencies in perception were proposed as a mechanism for perceptual stability, increasing the apparent continuity of the complex environments we experience in everyday life. For example, stable scene perception can be actively achieved by the visual system through global serial dependencies, a special kind of serial dependence between summary statistical representations. Serial dependence occurs also between emotional expressions, but it is highly selective for the same identity. Overall, these results further support the notion of serial dependence as a global, highly specialized, and purposeful mechanism. However, serial dependence could also be a deleterious phenomenon in unnatural or unpredictable situations, such as visual search in radiological scans, biasing current judgments toward previous ones even when accurate and unbiased perception is needed. For example, observers make consistent perceptual errors when classifying a tumor- like shape on the current trial, seeing it as more similar to the shape presented on the previous trial. In a separate localization test, observers make consistent errors when reporting the perceived position of an objects on the current trial, mislocalizing it toward the position in the preceding trial. Taken together, these results show two opposite sides of serial dependence; it can be a beneficial mechanism which promotes perceptual stability, but at the same time a deleterious mechanism which impairs our percept when fine recognition is needed.

Many psychological theories assume that qualitatively different cognitive processes can result in identical responses. Multinomial processing tree (MPT) models allow researchers to disentangle latent cognitive processes based on observed response frequencies. Recently, MPT models have been extended to explicitly account for participant and item heterogeneity. These hierarchical Bayesian MPT models provide the opportunity to connect two traditionally isolated disciplines. Whereas cognitive psychology has often focused on the experimental validation of MPT model parameters on the group level, psychometrics provides the necessary concepts and tools for measuring differences in MPT parameters on the item or person level. Moreover, MPT parameters can be regressed on covariates to model latent processes as a function of personality traits or other person characteristics.