Cognitive Psychology seminars

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.

Interval timing is a fundamental component action, and is susceptible to motor-related temporal distortions. Previous studies have shown that movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes. https://www.biorxiv.org/content/10.1101/2020.10.26.355396v1

How do people learn in real-world environments where the space of possible actions can be vast or even infinite? The study of human learning has made rapid progress in past decades, from discovering the neural substrate of reward prediction errors, to building AI capable of mastering the game of Go. Yet this line of research has primarily focused on learning through repeated interactions with the same stimuli. How are humans able to rapidly adapt to novel situations and learn from such sparse examples? I propose a theory of how generalization guides human learning, by making predictions about which unobserved options are most promising to explore. Inspired by Roger Shepard’s law of generalization, I show how a Bayesian function learning model provides a mechanism for generalizing limited experiences to a wide set of novel possibilities, based on the simple principle that similar actions produce similar outcomes. This model of generalization generates predictions about the expected reward and underlying uncertainty of unexplored options, where both are vital components in how people actively explore the world. This model allows us to explain developmental differences in the explorative behavior of children, and suggests a general principle of learning across spatial, conceptual, and structured domains.

Reliable and validated measures of emotion in animals are of great import; they are crucial to better understanding and developing treatments for human mood disorders, and they are necessary for ensuring good animal welfare. We have developed a novel measure of emotion in animals that is grounded in theory and psychological research – decision-making under ambiguity. Specifically, we consider that more ‘optimistic’ decisions about ambiguous stimuli reflect more positive emotional states, while the opposite is true for more ‘pessimistic’ decisions. In this talk, we will outline the background behind and implementation of this measure, meta-analyses that have been conducted to validate the measure, and discuss how computational modelling has been used to further understand the cognitive processes underlying ‘optimistic’ and ‘pessimistic’ decision-making as an indicator of animal emotion and welfare.

Framing effects in individual decision-making have puzzled economists for decades because they are hard, if at all, to explain with rational choice theories. Why should mere changes in the description of a choice problem affect decision-making? Here, we examine the hypothesis that changes in framing cause changes in the allocation of attention to the different options – measured via eye-tracking – and give rise to changes in decision-making. We document that the framing of a sure alternative as a gain – as opposed to a loss – in a risk-taking task increases the attentional advantage of the sure option and induces a higher choice frequency of that option – a finding that is predicted by the attentional drift-diffusion model (aDDM). The model also correctly predicts other key findings such as that the increased attentional advantage of the sure option in the gain frame should also lead quicker decisions in this frame. In addition, the data reveal that increasing risk aversion at higher stake sizes may also be driven by attentional processes because the sure option receives significantly more attention – regardless of frame – at higher stakes. We also corroborate the causal impact of framing-induced changes of attention on choice with an additional experiment that manipulates attention exogenously. Finally, to study the precise mechanisms underlying the framing effect we structurally estimate an aDDM that allows for frame and option-dependent parameters. The estimation results indicate that – in addition to the direct effects of framing-induced changes in attention on choice – the gain frame also causes (i) an increase in the attentional discount of the gamble and (ii) an increased concavity of utility. Our findings suggest that the traditional explanation of framing effects in risky choice in terms of a more concave value function in the gain domain is seriously incomplete and that attentional mechanisms as hypothesized in the aDDM play a key role.

Emotion processing is thought to be impaired in autism and linked to atypical visual exploration and arousal modulation to others faces and gaze, yet evidence is equivocal. We propose that, where observed, atypical socioemotional processing is due to alexithymia, a distinct but frequently co-occurring condition which affects emotional self-awareness and Interoception. In study 1 (N = 80), we tested this hypothesis by studying the spatio-temporal dynamics and entropy of eye-gaze during emotion processing tasks. Evidence from traditional and novel methods revealed that atypical eye-gaze and emotion recognition is best predicted by alexithymia in both autistic and non-autistic individuals. In Study 2 (N = 70), we assessed interoceptive and autonomic signals implicated in socioemotional processing, and found evidence for alexithymia (not autism) driven effects on gaze and arousal modulation to emotions. We also conducted two large-scale studies (N = 1300), using confirmatory factor-analytic and network modelling and found evidence that Alexithymia and Autism are distinct at both a latent level and their intercorrelations. We argue that: 1) models of socioemotional processing in autism should conceptualise difficulties as intrinsic to alexithymia, and 2) assessment of alexithymia is crucial for diagnosis and personalised interventions in autism.

It has long been known that when viewing a set of images, we misjudge individual elements as being closer to the mean than they are (Hollingworth, 1910) and recall seeing the (absent) set mean (Deese, 1959; Roediger & McDermott (1995). Recent studies found that viewing sets of images, simultaneously or sequentially, leads to perception of set statistics (mean, range) with poor memory for individual elements. Ensemble perception was found for sets of simple images (e.g. circles varying in size or brightness; lines of varying orientation), complex objects (e.g. faces of varying emotion), as well as for objects belonging to the same category. When the viewed set does not include its mean or prototype, nevertheless, observers report and act as if they have seen this central image or object – a form of false memory. Physiologically, detailed sensory information at cortical input levels is processed hierarchically to form an integrated scene gist at higher levels. However, we are aware of the gist before the details. We propose that images and objects belonging to a set or category are represented as their gist, mean or prototype, plus individual differences from that gist. Under constrained viewing conditions, only the gist is perceived and remembered. This theory also provides a basis for compressed neural representation. Extending this theory to scenes and episodes supplies a generalized basis for false memories. They seem right, match generalized expectations, so are believable without challenging examination. This theory could be tested by analyzing the typicality of false memories, compared to rejected alternatives.

Perceptual learning (PL) is defined as long-term performance improvement on a perceptual task as a result of perceptual experience (Sasaki, Nanez& Watanabe, 2011, Nat Rev Neurosci, 2011). We first found that PL occurs for task-irrelevant and subthreshold features and that pairing task-irrelevant features with rewards is the key to form task-irrelevant PL (TIPL) (Watanabe, Nanez & Sasaki, Nature, 2001; Watanabe et al, 2002, Nature Neuroscience; Seitz & Watanabe, Nature, 2003; Seitz, Kim & Watanabe, 2009, Neuron; Shibata et al, 2011, Science). These results suggest that PL occurs as a result of interactions between reinforcement and bottom-up stimulus signals (Seitz & Watanabe, 2005, TICS). On the other hand, fMRI study results indicate that lateral prefrontal cortex fails to detect and thus to suppress subthreshold task-irrelevant signals. This leads to the paradoxical effect that a signal that is below, but close to, one’s discrimination threshold ends up being stronger than suprathreshold signals (Tsushima, Sasaki & Watanabe, 2006, Science). We confirmed this mechanism with the following results: Task-irrelevant learning occurs only when a presented feature is under and close to the threshold with younger individuals (Tsushima et al, 2009, Current Biol), whereas with older individuals who tend to have less inhibitory control task-irrelevant learning occurs with a feature whose signal is much greater than the threshold (Chang et al, 2014, Current Biol). From all of these results, we conclude that attention and reward play important but different roles in PL. I will further discuss different stages and phases in mechanisms of PL (Seitz et al, 2005, PNAS; Yotsumoto, Watanabe & Sasaki, Neuron, 2008; Yotsumoto et al, Curr Biol, 2009; Watanabe & Sasaki, 2015, Ann Rev Psychol; Shibata et al, 2017, Nat Neurosci; Tamaki et al, 2020, Nat Neurosci).

In many ways, cognitive neuroscience is the attempt to use physiological observation to clarify the mechanisms that shape behavior. Over the past 25 years, fMRI has provided a system-wide and yet somewhat spatially precise view of the response in human cortex evoked by a wide variety of stimuli and task contexts. The current talk focuses on the other direction of inference; the implications of this observed functional organization for behavior. To begin, we must interrogate the methodological and empirical frameworks underlying our derivation of this organization, partially by exploring its relationship to and predictability from gross neuroanatomy. Next, across a series of studies, the implications of two properties of functional organization for behavior will be explored: 1) the co-localization of visual working memory and perceptual processing and 2) implicit learning in the context of distributed responses. In sum, these results highlight the limitations of our current approach and hint at a new general mechanism for explaining observed behavior in context with the neural substrate.

Over the past decade, the domain of face identity processing has seen a surging interest in inter-individual differences, with a focus on individuals with superior skills, so-called Super-Recognizers (SRs; Ramon et al., 2019; Russell et al., 2009). Their study can provide valuable insights into brain-behavior relationships and advance our understanding of neural functioning. Despite a decade of research, and similarly to the field of developmental prosopagnosia, a consensus on diagnostic criteria for SR identification is lacking. Consequently, SRs are currently identified either inconsistently, via suboptimal individual tests, or via undocumented collections of tests. This state of the field has two major implications. Firstly, our scientific understanding of SRs will remain at best limited. Secondly, the needs of government agencies interested in deploying SRs for real-life identity verification (e.g., policing) are unlikely to be met. To counteract these issues, I suggest the following action points. Firstly, based on our and others’ work suggesting novel and challenging tests of face cognition (Bobak et al., 2019; Fysh et al., in press; Stacchi et al., 2019), and my collaborations with international security agencies, I recommend novel diagnostic criteria for SR identification. These are currently being used to screen the Berlin State Police’s >25K employees before identifying SRs via bespoke testing procedures we have collaboratively developed over the past years. Secondly, I introduce a cohort of SRs identified using these criteria, which is being studied in-depth using behavioral methods, psychophysics, eye-tracking, and neuroimaging. Finally, I suggest data acquired for these individuals should be curated to develop and share best practices with researchers and practitioners, and to gain an accurate and transparent description of SR cases to exploit their informative value.

Learning depends on both the learning mechanism and the regularities in the training material, yet most research on human and machine learning focus on the discovering the mechanisms that underlie powerful learning. I will present evidence from our research focusing on the statistical structure of infant visual learning environments. The findings suggest that the statistical structure of those learning environments are not like those used in laboratory experiments on visual learning, in machine learning, or in our adult assumptions about how teach visual categories. The data derive from our use of head cameras and head-mounted eye trackers capturing FOV experiences in the home as well as in simulated home environments in the laboratory. The participants range from 1 month of age to 24 months. The observed statistical structure offers new insights into the developmental foundations of visual object recognition and suggest a computational rethinking of the problem of visual category formation. The observed environmental statistics also have direct implications for understanding the development of cortical visual systems.

Cognitive architectures are attempts to build larger-scale models of minds. This talk will explore how structure-mapping models of analogical matching, retrieval, and generalization are used in the Companion cognitive architecture. Examples will include modeling conceptual change, learning by reading, and analogical Q/A training.