Psychology seminars

In this talk, I shall not argue for any single thesis or theory in the realm of the (un)consciousness and (in)attention. Instead I will discuss specific examples where philosophers and psychologists can have genuine collaborations in this area. Since issues concerning phenomenological overflow is already too familiar for this audience, I will briefly discuss it only, and focus on other issues that have not been overworked. The exact contents are to be determined, but I will perhaps focus on recent controversies over “sustained representation of perspectival shape” (Morales, Bax, and Firestone, 2020, 2021).

The talk will give a tour of Guided Search 6.0 (GS6), the latest evolution of the Guided Search model of visual search. Part 1 describes The Mechanics of Search. Because we cannot recognize more than a few items at a time, selective attention is used to prioritize items for processing. Selective attention to an item allows its features to be bound together into a representation that can be matched to a target template in memory or rejected as a distractor. The binding and recognition of an attended object is modeled as a diffusion process taking > 150 msec/item. Since selection occurs more frequently than that, it follows that multiple items are undergoing recognition at the same time, though asynchronously, making GS6 a hybrid serial and parallel model. If a target is not found, search terminates when an accumulating quitting signal reaches a threshold. Part 2 elaborates on the five sources of Guidance that are combined into a spatial “priority map” to guide the deployment of attention (hence “guided search”). These are (1) top-down and (2) bottom-up feature guidance, (3) prior history (e.g. priming), (4) reward, and (5) scene syntax and semantics. Finally, in Part 3, we will consider the internal representation of what we are searching for; what is often called “the search template”. That search template is really two templates: a guiding template (probably in working memory) and a target template (in long term memory). Put these pieces together and you have GS6.

In this talk he will present current work in progress on “irruption theory”, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.

Many tasks that are easy for humans are difficult for machines. In particular, while humans excel at tasks that require generalising across problems, machine systems notably struggle. One such task that has received a good amount of attention is the Synthetic Visual Reasoning Test (SVRT). The SVRT consists of a range of problems where simple visual stimuli must be categorised into one of two categories based on an unknown rule that must be induced. Conventional machine learning approaches perform well only when trained to categorise based on a single rule and are unable to generalise without extensive additional training to tasks with any additional rules. Multiple theories of higher-level cognition posit that humans solve such tasks using structured relational representations. Specifically, people learn rules based on structured representations that generalise to novel instances quickly and easily. We believe it is possible to model this approach in a single system which learns all the required relational representations from scratch and performs tasks such as SVRT in a single run. Here, we present a system which expands the DORA/LISA architecture and augments the existing model with principally novel components, namely a) visual reasoning based on the established theories of recognition by components; b) the process of learning complex relational representations by synthesis (in addition to learning by analysis). The proposed augmented model matches human behaviour on SVRT problems. Moreover, the proposed system stands as perhaps a more realistic account of human cognition, wherein rather than using tools that has been shown successful in the machine learning field to inform psychological theorising, we use established psychological theories to inform developing a machine system.

A common result is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. However still little is known of the solving strategies used by children to compare and generalize novel words. Understanding the temporal dynamics of children’s solving strategies may help assess which processes underlie generalization. We tested children in noun and relational noun generalization tasks and collected eye tracking data. To analyze and interpret the data we followed predictions made by existing models of analogical reasoning and generalization. The data reveals clear patterns of exploration in which participants compare learning items before searching for a solution. Analyses of the beginning of trials show that early comparisons favor generalization and that errors may be caused by a lake of early comparison. Children then pursue their search in different ways according to the task. In this presentation I will present the generalization strategies revealed by eye tracking, compare the strategies from both tasks and confront them to existing models.

The efficacy of images to create quantitative measures of urban perception has been explored in psychology, social science, urban planning and architecture over the last 50 years. The ability to scale these measurements has become possible only in the last decade, due to increased urban surveillance in the form of street view and satellite imagery, and the accessibility of such data. This talk will present a series of projects which make use of imagery and CNNs to predict, measure and interpret the social and physical environments of our cities.

Analogy offers a new interpretation of a common concern in ethics: whether decision making includes or excludes a consideration of moral issues. This is often discussed as the moral awareness of decision makers and considered a motivational concern. The possible new interpretation is that moral awareness is in part a matter of expertise. Some failures of moral awareness can then be understood as stemming from novicehood. Studies of analogical transfer are consistent with the possibility that moral awareness is in part a matter of expertise, that as a result motivation is less helpful than some prior theorizing would predict, and that many adults are not as expert in the domain of ethics as one might hope. The possibility of expert knowledge of ethical principles leads to new questions and opportunities.

Human cognitive abilities seem qualitatively different from the cognitive abilities of other primates, a difference Penn, Holyoak, and Povinelli (2008) attribute to role-based relational reasoning—inferences and generalizations based on the relational roles to which objects (and other relations) are bound, rather than just the features of the objects themselves. Role-based relational reasoning depends on the ability to dynamically bind arguments to relational roles. But dynamic binding cannot be sufficient for relational thinking: Some non-human animals solve the dynamic binding problem, at least in some domains; and many non-human species generalize affordances to completely novel objects and scenes, a kind of universal generalization that likely depends on dynamic binding. If they can solve the dynamic binding problem, then why can they not reason about relations? What are they missing? I will present simulations with the LISA model of analogical reasoning (Hummel & Holyoak, 1997, 2003) suggesting that the missing pieces are multi-role integration (the capacity to combine multiple role bindings into complete relations) and structure mapping (the capacity to map different systems of role bindings onto one another). When LISA is deprived of either of these capacities, it can still generalize affordances universally, but it cannot reason symbolically; granted both abilities, LISA enjoys the full power of relational (symbolic) thought. I speculate that one reason it may have taken relational reasoning so long to evolve is that it required evolution to solve both problems simultaneously, since neither multi-role integration nor structure mapping appears to confer any adaptive advantage over simple role binding on its own.

Analogical reasoning remains foundational to the human ability to forge meaningful patterns within the sea of information that continually inundates the senses. Yet, meaningful patterns rely not only on the recognition of attributional similarities but also dissimilarities. Just as the perception of images rests on the juxtaposition of lightness and darkness, reasoning relationally requires systematic attention to both similarities and dissimilarities. With that awareness, my colleagues and I have expanded the study of relational reasoning beyond analogous reasoning and attributional similarities to highlight forms based on the nature of core dissimilarities: anomalous, antinomous, and antithetical reasoning. In this presentation, I will delineate the character of these relational reasoning forms; summarize procedures and measures used to assess them; overview key research findings; and describe how the forms of relational reasoning work together in the performance of complex problem solving. Finally, I will share critical next steps for research which has implications for instructional practice.

As Socrates said that "I know that I know nothing," our mind's function to be aware of our ignorance is essential for abstract and conceptual reasoning. However, the biological mechanism to enable such a hierarchical thought, or meta-cognition, remained unknown. In the first part of the talk, I will demonstrate our studies on the neural mechanism for metacognition on memory in macaque monkeys. In reality, awareness of ignorance is essential not only for the retrospection of the past but also for the exploration of novel unfamiliar environments for the future. However, this proactive feature of metacognition has been understated in neuroscience. In the second part of the talk, I will demonstrate our studies on the neural mechanism for prospective metacognitive matching among uncertain options prior to perceptual decision making in humans and monkeys. These studies converge to suggest that higher-order processes to self-evaluate mental state either retrospectively or prospectively are implemented in the primate neural networks.

Computer AI thinks differently from us, which is why it's such a useful tool. Thanks to the ingenuity of human programmers, AI's different method of thinking has made humans redundant at certain human tasks, such as chess. Yet there are mechanical limits to how far AI can replicate the products of human thinking. In this talk, we'll trace one such limit by exploring how AI and humans create differently. Humans create by reverse-engineering tools or behaviors to accomplish new actions. AI creates by mix-and-matching pieces of preexisting structures and labeling which combos are associated with positive and negative results. This different procedure is why AI cannot (and will never) learn to innovate technology or tactics and why it also cannot (and will never) learn to generate narratives (including novels, business plans, and scientific hypotheses). It also serves as a case study in why there's no reason to believe in "general intelligence" and why computer AI would have to partner with other mechanical forms of AI (run on non-computer hardware that, as of yet, does not exist, and would require humans to invent) for AI to take over the globe.

In my talk I will discuss the concept of a cognitive ontology, which defines the parts of the mind that psychologists and neuroscientsts aim to study. I will discuss the way in which ontologies have traditionally been defined, and then discuss ways in which ontology might be reconsidered in the context of computational approaches to cognition.