Psychology seminars

The question of free will has been topical for millennia, especially considering its links to moral responsibility and the ownership of that responsibility. Free will, or volition, is an incredibly complex phenomenon - and cannot easily be reduced to a single empirical paradigm. Roskies (2010) proposes that there are five cognitive aspects to be considered when developing a more complete understanding of volition. These are: intention, initiation, feeling, executive control and decision-making. Decision-making will be the focus of this talk, which steps through aspects of the philosophy of free will; highlights experimental paradigms stemming from the seminal work of Benjamin Libet et al., and proposes machine learning as a promising method in progressing the empirical studies of decision-making and free will.

Drug addiction is a chronically relapsing disorder characterized by compulsive drug use despite catastrophic personal consequences (e.g., loss of family, job) and even when the substance is no longer perceived as pleasurable. In this talk, I will present results of human neuroimaging studies, utilizing a multimodal approach (neuropsychology, functional magnetic resonance imaging, event-related potentials recordings), to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, bingeing, withdrawal). The focus of this talk is on understanding the role of the dopaminergic mesocorticolimbic circuit, and especially the prefrontal cortex, in higher-order executive dysfunction (e.g., disadvantageous decision-making such as trading a car for a couple of cocaine hits) in drug addicted individuals. The theoretical model that guides the presented research is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex, as related to dopaminergic dysfunction, contribute to the core clinical symptoms in drug addiction. Specifically, our multi-modality program of research is guided by the underlying working hypothesis that drug addicted individuals disproportionately attribute reward value to their drug of choice at the expense of other potentially but no-longer-rewarding stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. In this talk I will also explore whether treatment (as usual) and 6-month abstinence enhance recovery in these brain-behavior compromises in treatment seeking cocaine addicted individuals. Promising neuroimaging studies, which combine pharmacological (i.e., oral methylphenidate, or RitalinTM) and salient cognitive tasks or functional connectivity during resting-state, will be discussed as examples for using neuroimaging for empirically guiding the development of effective neurorehabilitation strategies (encompassing cognitive reappraisal and transcranial direct current stimulation) in drug addiction.

Cases with visuospatial abnormalities provide opportunities for understanding the underlying cognitive mechanisms. Three cases of visual mirror-reversal have been reported: AH (McCloskey, 2009), TM (McCloskey, Valtonen, & Sherman, 2006) and PR (Pflugshaupt et al., 2007). This research reports a fourth case, BS -- with focal occipital cortical dysgenesis -- who displays highly unusual visuospatial abnormalities. They initially produced mirror reversal errors similar to those of AH, who -- like the patient in question -- showed a selective developmental deficit. Extensive examination of BS revealed phenomena such as: mirror reversal errors (sometimes affecting only parts of the visual fields) in both horizontal and vertical planes; subjective representation of visual objects and words in distinct left and right visual fields; subjective duplication of objects of visual attention (not due to diplopia); uncertainty regarding the canonical upright orientation of everyday objects; mirror reversals during saccadic eye movements on oculomotor tasks; and failure to integrate visual with other sensory inputs (e.g., they feel themself moving backwards when visual information shows they are moving forward). Fewer errors are produced under conditions of certain visual variables. These and other findings have led the researchers to conclude that BS draws upon a subjective representation of visual space that is structured phenomenally much as it is anatomically in early visual cortex (i.e., rotated through 180 degrees, split into left and right fields, etc.). Despite this, BS functions remarkably well in their everyday life, apparently due to extensive compensatory mechanisms deployed at higher (executive) processing levels beyond the visual modality.

People frequently interpret the same information differently, based on their prior beliefs and views. This may occur in everyday settings, as when two friends are watching the same movie, but also in more consequential circumstances, such as when people interpret the same news differently based on their political views. The role of subjective knowledge in altering how the brain processes narratives has been explored mainly in controlled settings. I will present two projects that examines neural mechanisms underlying narrative interpretation “in the wild” -- how responses differ between two groups of people who interpret the same narrative in two coherent, but opposing ways. In the first project we manipulated participant’s prior knowledge to make them interpret the narrative differently, and found that responses in high-order areas, including the default mode network, language areas and subsets of the mirror neuron system, tend to be similar among people who share the same interpretation, but different from people with an opposing interpretation. In contrast to the active manipulation of participants’ interpretation in the first study, in the second (ongoing) project we examine these processes in a more ecological setting. Taking advantage of people’s natural tendencies to interpret the world through their own (political) filters, we examine these mechanisms while measuring their brain response to political movie clips. These studies are intended to deepen our understanding of the differences in subjective construal processes, by mapping their underlying brain mechanisms.

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.

David Chalmers’s (1995) hard problem famously states: “It is widely agreed that experience arises from a physical basis, but we have no good explanation of why and how it so arises.” Thomas Nagel (1974) wrote something similar: “If we acknowledge that a physical theory of mind must account for the subjective character of experience, we must admit that no presently available conception gives us a clue about how this could be done.” This presentation will point the way towards the long-sought “good explanation” -- or at least it will provide “a clue”. I will make three points: (1) It is unfortunate that cognitive science took vision as its model example when looking for a ‘neural correlate of consciousness’ because cortical vision (like most cognitive processes) is not intrinsically conscious. There is not necessarily ‘something it is like’ to see. (2) Affective feeling, by contrast, is conscious by definition. You cannot feel something without feeling it. Moreover, affective feeling, generated in the upper brainstem, is the foundational form of consciousness: prerequisite for all the higher cognitive forms. (3) The functional mechanism of feeling explains why and how it cannot go on ‘in the dark’, free of any inner feel. Affect enables the organism to monitor deviations from its expected self-states in uncertain situations and thereby frees homeostasis from the limitations of automatism. As Nagel says, “An organism has conscious mental states if and only if there is something that it is like to be that organism—something it is like for the organism.” Affect literally constitutes the sentient subject.

Humans’ ability to perceive and abstract relational structure is fundamental to our learning. It allows us to acquire knowledge all the way from linguistic grammar to spatial knowledge to social structures. How does a learner begin to perceive structure in the world? Why do we sometimes fail to see structural commonalities across events? To begin to answer these questions, I attempt to bridge two large, yet somewhat separate research traditions in understanding human’s structural abstraction: rule learning (Marcus et al., 1999) and analogical learning (Gentner, 1989). On the one hand, rule learning research has shown humans’ domain-general ability and ease—as early as 7-month-olds—to abstract structure from a limited experience. On the other hand, analogical learning works have shown robust constraints in structural abstraction: young learners prefer object similarity over relational similarity. To understand this seeming paradox between ease and difficulty, we conducted a series of studies using the classic rule learning paradigm (Marcus et al., 1999) but with an analogical (object vs. relation) twist. Adults were presented with 2-minute sentences or events (syllables or shapes) containing a rule. At test, they had to choose between rule abstraction and object matches—the same syllable or shape they saw before. Surprisingly, while in the absence of object matches adults were perfectly capable of abstracting the rule, their ability to do so declined sharply when object matches were present. Our initial results suggest that rule learning ability may be subject to the usual constraints and signatures of analogical learning: preference to object similarity can dampen rule generalization. Humans’ abstraction is also concrete at the same time.

From a developmental standpoint, it has been argued that two major complementary factors contribute to the development of analogy comprehension: world knowledge and executive functions. Here I will provide evidence in support of the second view. Beyond paradigms that manipulate task difficulty (e.g., number and types of distractors and semantic distance between domains) we will provide eye-tracking data that describes differences in the way children and adults compare the base and target domains in analogy problems. We will follow the same approach with ageing people. This latter population provides a unique opportunity to disentangle the contribution of knowledge and executive processes in analogy making since knowledge is (more than) preserved and executive control is decreasing. Using this paradigm, I will show the extent to which world knowledge (assessed through vocabulary) compensates for decreasing executive control in older populations. Our eye-tracking data suggests that, to a certain extent, differences between younger and older adults are analogous to the differences between younger adults and children in the way they compare the base and the target domains in analogy problems.

In this talk, I will present some data showing that people’s similarity judgments among word pairs reflect distinctions between abstract semantic relations like contrast, cause-effect, or part-whole. Further, the extent that individual participants’ similarity judgments discriminate between abstract semantic relations was linearly associated with both fluid and crystallized verbal intelligence, albeit more strongly with fluid intelligence. Finally, I will compare three models according to their ability to predict these similarity judgments. All models take as input vector representations of individual word meanings, but they differ in their representation of relations: one model does not represent relations at all, a second model represents relations implicitly, and a third model represents relations explicitly. Across the three models, the third model served as the best predictor of human similarity judgments suggesting the importance of explicit relation representation to fully account for human semantic cognition.

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.

The stream of consciousness refers to ideas, images, and memories that meander across the mind when we are otherwise unoccupied. The standard view is that these thoughts are associationistic in character and they arise from subpersonal processes—we are for the most part passive observers of them. Drawing on a series of laboratory studies we have conducted as well as Buddhist models of mind, I argue that these views are importantly incorrect. On the alternative view I put forward, these thoughts arise from minimal decision processes, which lie in a grey zone: They are both manifestations of agency as well as obstacles to it.