Cognition

Title: Towards an integrative framework on visual attention and working memory: a few pointers

William X. Q. Ngiam, postdoc in the Awh/Vogel lab, University of Chicago

Abstract: What is visual working memory (VWM)? Our growing field has many varying definitions, measures and models – so much so that I think researchers may be talking past each other. In this talk, I will share how I am trying to get researchers to rethinking the poorly defined theoretical framework in our field. I start off with my own work examining a long-standing debate – whether the units of representation in VWM are objects or features. With this, I illustrate that this binarized debate may not actually be grounded on fundamental theoretical differences. Then to help rethink these debates, I offer a ’theory map’ – a common space to discuss phenomena, compare models and integrate theories. I will end with some recent work from our lab pursuing an exciting novel mechanism – content-independent pointers – that has the potential to link cognitive and neural models of working memory. My hope is that this talk will inspire steps in our department towards slower empirical research with a focus on theory development.

Title: Consequences of sustained attention’s floodlight for recognition memory

Anna Corriveau, doctoral student in the Rosenberg Lab, Department of Psychology, University of Chicago

Abstract: Attention is often described as a spotlight in that it selectively enhances processing of relevant or salient information. However, it is not clear whether the spotlight metaphor applies to sustained attention, which fluctuates over time. Specifically, when presented with both task-relevant and task-irrelevant information, do moments of high attention act as a spotlight, selectively increasing processing for task-relevant stimuli? Or, rather, does a high attentional state act more like to a floodlight, increasing processing for both task-relevant and task-irrelevant stimuli? To investigate this, we tested how changes in sustained attention state impact recognition memory for stimuli as a function of task-relevance. Across multiple studies, we find that high sustained attention predicts better memory for both task-relevant and task-irrelevant stimuli, lending support to a floodlight model of sustained attention. This work further characterizes the relationship between sustained attention and memory and highlights a key difference between sustained attention and other aspects of attention.

Title: ‘Eyes’ on the Street: How Computer Vision and Cognitive Psychology Can Help Us Get the Gist of Neighborhood Environmental Design and Explain Crime

Riley Tucker, postdoc in the Berman Lab and the Mansueto Institute of Urban Innovation, University of Chicago

Abstract: For half a century, scholars of crime have theorized that the spatial attributes and layouts of places shape how likely people are to take action against perceived crime threats. Specifically, people are expected to have heightened territoriality in areas with visually open, unobstructed spaces that are aesthetically pleasing. However, measures of these constructs have proved elusive, so studies testing this idea have generally been limited to data measuring the presence of objects such as vegetation or trash that impede sight-lines or degrade aesthetic value. This represents a substantial limitation, as research in cognitive psychology suggests that people make behavioral decisions by rapidly assessing the ‘gist’ of entire scenes rather than scanning specific objects. By training an image recognition AI to rate several forms of scene gist for 168k georeferenced Google Streetview images, this project introduces a strategy for measuring aesthetic value and natural surveillance quality across Chicago neighborhoods. Using data from Chicago’s 311 system to measure how likely neighborhood residents are to report man-made incivilities, this study explores the relationship between neighborhood-level visual characteristics, territoriality, and crime.

Title: Dynamic maps of a dynamic world

Alexandra Keinath, Assistant Professor, Department of Psychology, University of Illinois, Chicago

Abstract: Extensive research has revealed that the hippocampus and entorhinal cortex maintain a rich representation of space through the coordinated activity of place cells, grid cells, and other spatial cell types. Frequently described as a ‘cognitive map’ or a ‘hippocampal map’, these maps are thought to support episodic memory through their instantiation and retrieval. Though often a useful and intuitive metaphor, a map typically evokes a static representation of the external world. However, the world itself, and our experience of it, are intrinsically dynamic. Here I will present three projects where we address how hippocampal and entorhinal representations adapt to, incorporate, and overcome these dynamics. In the first project, I will describe how boundaries dynamically anchor entorhinal grid cells and human spatial memory alike when the shape of a familiar environment is changed. In the second project, I will describe how the hippocampus maintains a representation of the recent past even in the absence of disambiguating sensory and explicit task demands, a representation which causally depends on intrinsic hippocampal circuitry. In the third project, I will describe ongoing work from my lab leveraging environmental deformations to quantitatively compare spatial codes across regions, species, and levels of explanation, setting the stage for more exciting work to come.

Title: The neural mechanisms underlying generalization

Sameera Shridhar, Doctoral Student, Department of Psychology, University of Chicago

Abstract: Unique experiences and memories are formed from events occurring in our daily lives. Our ability to apply knowledge from these experiences in new situations is termed as generalization. Research has implicated the roles of the hippocampus, prefrontal cortex, and the connections between them in generalization through abstraction of specifics of a single experience. But the nature of this abstraction isn’t well understood. How does one apply complex behavioural patterns learnt in one situation or environment to another? Through my experiments, I aim to look at how this occurs using two groups of rats. One group was trained and required to perform the same trained pattern on a novel maze, while the other was required to learn and perform a new pattern on the new maze. We have currently quantified the behaviour of these groups and found that rats across as well as within groups had different performance levels, indicating that prior experience can influence learning in different ways. Future analyses will include looking at the strategies chosen during learning and examining the neural activity in the hippocampus and prefrontal cortex.

 

 

Title: Revealing hidden biases in face representation via deceptively simple tasks

Stefan Uddenberg, Principal Researcher, Booth School of Business, University of Chicago
Future Assistant Professor in the Department of Psychology at University of Illinois Urbana Champaign

Abstract: When we look at someone’s face, we can’t help but ‘read’ it. Within the blink of an eye, we can extract someone’s demographic characteristics (e.g., age) and transient psychological states (e.g., emotion). Not only that, but we form robust impressions of what we think someone is like as a person (e.g., how trustworthy they seem) — regardless of how (in)accurate these impressions may be. My work explores the roles of perception and memory in such feats in two complementary ways. First, I have discovered the existence of default face representations within our minds, and I am actively investigating their nature. These defaults are essentially unconscious and generic assumptions about facial features that bias or “pull” the representation of (and memory for) individual faces towards them. Second, I have applied deep learning methods to develop hyper-realistic generative models of human faces that vary along trait dimensions of psychological interest, such as perceived trustworthiness and dominance. Unlike previous computer models used in the field, these faces are nearly indistinguishable from actual photos; but unlike actual photos, they can be systematically and easily manipulated. I am currently using them to investigate a variety of research questions, such as how impressions of leadership differ with political affiliation. In this way, my work serves to bridge many different parts of our field and its history — drawing inspiration (and contributing to) both cognitive and social psychology, as well as bleeding edge revelations in the realm of machine learning. Collectively, these projects demonstrate (and leverage the fact) that our perceptual systems are geared toward extracting social properties from the stimuli in our environment.

Title: The History of our Minds: Evidence for Co-Evolution of Cultural and Psychological Processes

Joshua Conrad Jackson, Kellogg School of Management, Northwestern University
Booth School of Business, University of Chicago

Abstract: Biologically modern humans are more than 100,000 years old. Many scientists have devoted their lives to understanding how architecture, social structure, and language has changed over this history. Yet we know almost nothing about the history of human minds. Behavioral science research has instead focused nearly exclusively on contemporary people, and psychological theories often draw from taxonomies which assume a culturally and historically stable structure to emotion, personality, morality, and other psychological processes. In this talk, I survey new insights into how psychological processes may have changed over human history in ways that challenge these taxonomical models. Psychological change is often patterned and predictable based on cultural change, and general evolutionary principles may explain psychological changes in multiple domains. We now have the methodological and theoretical tools to build a more historically enriched science of human cognition and behavior, with a basic capacity to make foundational discoveries and an applied capacity to predict human futures.

Title: Exploring Dimensions of Partisan Bias in Large Real-life Texts
Nak Won Rim, Doctoral Student, Department of Psychology, University of Chicago
People evaluate topics and objects differently based on their political beliefs. Several political and social psychology theories have suggested different dimensions where the evaluations based on political belief differ. For example, moral foundations theory proposes that liberals and conservatives have different moral frameworks – something a conservative evaluates as immoral might be moral to liberals, and vice versa. This work tests these theories in a large-scale, out-of-lab context by utilizing language models (word2vec) trained on large corpora (Reddit comments and news articles). Specifically, we use a combination of the dimension-based approach and a classifier to test (1) if these dimensions can distinguish the models trained on liberal corpora and models trained on conservative corpora above chance, (2) whether these dimensions achieve significantly better accuracies than random dimensions. We show that partisan (democrat vs republican) and morality (moral vs immoral) dimensions have significant classification accuracy in both tests and both corpora. Overall, our results show that morality is a dimension where people with different political beliefs differ to the point that we can detect the division in real-life texts people produce outside labs.

Talk 1:
Title: Loneliness intensifies acute feelings of social rejection
Anita Restrepo, Doctoral Student, Department of Psychology, University of Chicago
Loneliness, or perceived social isolation, is a psychological state where individuals feel unsafe since isolation from their social group leaves them vulnerable to external threats. As such, lonely individuals show hypervigilance to threat cues, particularly information about social threat. Because psychological states of perceived unsafety bias individuals to interpret ambiguous information as more threatening, we explored how the ambiguity of social cues moderated effects of loneliness on feelings of rejection after a social exclusion paradigm. An online sample of 142 adults completed a progressive, 5-round Cyberball ostracism paradigm where they were randomly assigned to either be equally included, excluded, or over-included. Importantly, ambiguity was highest in earlier rounds where exclusion/inclusion cues were unclear and systematically decreased from one round to the next. Participants completed the UCLA Loneliness Scale prior to beginning the task and responded to a brief questionnaire indexing feelings of rejection after each Cyberball round. Hierarchical linear models showed that higher loneliness predicted increased feelings of rejection across conditions (equal inclusion, exclusion, and over-inclusion). Notably, this positive relationship was strongest during earlier rounds when social cues were most ambiguous. These findings aid in integrating our understanding of loneliness within a larger stress response framework and how these systems modulate social perception to enable organisms to adequately adapt to changing circumstances.

Talk 2:
Title: A Metacognitive Framework for Modeling Authoritarian-led Reform via Dynamical Systems
Eugene Yu Ji, Doctoral Student, Department of Psychology, University of Chicago
Recent studies in comparative politics propose that one most frequent economic or political reform path in many authoritarian states in East and Southeast Asia is a top-down, state-led process (e.g. Riedl et al. 2020; Slater and Wong 2022). This influential theory of state-led reform strongly relies on a behavioral assumption: the authoritarian state leadership concedes to a major political reform (such as democratization) when a tradeoff, “bittersweet” signal is cognized, which suggests both high pressures encouraging the reform and a prospect of achieving stability or flourishment after the reform. But in this literature, cognitive and behavioral mechanisms from cognition to decision making are far from being sufficiently explored. Using the dynamical-systems approach, my work proposes a metacognitive model of decision-making of authoritarian-led reform (which is formally inspired by the FitzHugh-Nagumo model in computational neuroscience). The model includes two basic variables of cognitive uncertainty: uncertainty in risk assessment (URA) and uncertainty in expected utility (UEU), and the model investigates the decision-making dynamics generated from four types of metacognition together: metacognitive self-feedback of URA, metacognitive self-feedback of UEU, metacognitive feedback from URA to UEU, and metacognitive feedback from UEU to URA. Current modeling results show that the metacognitively-informed decision-making process can lead to different “prospect trajectories”, which further lead to different equilibria of reform-related decision-making outcomes. Current empirical testing applies this model to examine historical and contemporary cases of major, minor, or no reform in six countries or regions in East and Southeast Asia, and the results demonstrate the effectiveness and potential of this new framework.

Title: A Computational Theory of Flow
David Melnikoff, PhD. Postdoctoral Fellow, Northeastern University
Flow is a coveted psychological state characterized by immersion and engagement in an activity. The benefits of flow for productivity and health are well- documented, but a formal, mechanistic understanding of the flow-generating process remains elusive. I will be discussing recent work that addresses this problem by developing and empirically testing a theory of flow’s computational substrates—the informational theory of flow. The theory draws on the concept of mutual information, a fundamental quantity in information theory that quantifies the strength of association between two variables. The claim is that the mutual information between desired end states and means of attaining them—I(M;E)—gives rise to flow. I will support this claim across six experiments (five preregistered), which show, across multiple activities, that increasing I(M;E) increases flow and has important downstream benefits, including enhanced attention, enjoyment, and skilled performance.