What Would It Take to Change Your Mind? Moral Belief Revision under Counterevidence

Amount awarded: $25,000

Abstract: When people encounter evidence that challenges morally important beliefs, they often resist changing their minds. This resistance is well documented in work on motivated reasoning, motivated skepticism, moral conviction, and the ethics of belief, but we still know too little about where in the evidence-processing sequence resistance occurs. This one-year proof-of-concept project tests a staged model of moral belief revision. We propose that revision depends on two evidence-processing stages — sampling and weighting — and two moderators: threat sensitivity, or whether counterevidence is experienced as threatening to one’s moral self-conception, and perspective sensitivity, or whether reframing the issue from an alternative standpoint increases openness to revision. In the first, desktop part of the project, participants will complete a controlled moral counterevidence task while we measure belief revision, evidence choice, gaze allocation, pupil-linked arousal, and self-reported evidence acceptance and threat. These data will be used to build an interpretable, task-specific individualized belief-revision model—what we call a narrow digital twin—that estimates each participant’s sampling, evidential-weighting, threat-sensitivity, and perspective-sensitivity profile and tests whether this profile predicts belief revision on held-out trials. In the second, situated part, we will test whether these profiles generalize to a structurally matched extended-reality version of the task, where participants are perceptually situated within the moral scenario. This will test whether situational presence and self-location alter how participants sample and weight moral counterevidence while preserving the same judgment-and-counterevidence structure as the desktop task. The project will deliver a normed scenario set, a behavioral and eye-tracking paradigm, a pilot dataset, an interpretable individualized model, and a feasibility package for future personalized interventions on moral belief revision. Philosophically, the project contributes to moral cognition by distinguishing psychological mechanisms that are often grouped together under “resistance,” and it informs debates on intellectual character and epistemic situationism by testing whether resistance to moral counterevidence reflects stable evidence-handling tendencies or varies with features of the situation.

Description: People often resist changing moral beliefs even when confronted with relevant counterevidence, but the processes underlying revision or resistance remain unclear. Here, moral belief refers to a normative judgment about what is right, wrong, permissible, or obligatory; factual beliefs that bear on such judgments will be treated as supporting or challenging evidence rather than as the primary target of revision. This project tests whether moral belief revision depends on two evidence-processing stages: whether people sample belief-challenging evidence and how strongly they weigh it once encountered. It further tests whether threat sensitivity weakens counterevidence sampling or weighting, and whether perspective sensitivity increases openness to revision when the issue is reframed. The long-term goal is to develop individualized models of moral belief revision in controlled moral-conflict contexts, while contributing to conceptual clarification in the philosophy of moral cognition.

Maxi Becker, PhD. Research Scholar, Center for Cognitive Neuroscience, Duke University

Carme Isern-Mas, PhD. Assistant Professor, Department of Philosophy and Social Work, University of the Balearic Islands

Elay Shech, PhD. Professor, Department of Philosophy, Auburn University

Mariana Fernandez-Espinosa, PhD Candidate, Department of Computer Science and Engineering, University of Notre Dame

Sanja Sreckovic, PhD. Research Associate, Faculty of Philosophy, University of Belgrade

Evidence for the Absence of Neural Representations

Amount awarded: $7,400

Abstract: Neuroscientific investigations of neural representations often proceed by identifying patterns of neural activity that correlate with an experimental parameter and, ideally, cause experimentally relevant behavior. Such desiderata as these provide evidence that the pattern carries information or is content bearing about the relevant cognitive or behavioral explanandum. Because neural representations are theoretically discrete units, a challenge neuroscientists face is in individuating those content-bearing patterns from other structures and processes.

When researchers present results that purportedly provide evidence for neural representations, the results show the presence of some property that is contrasted with conditions in which that property is absent. For example, researchers might claim that a pattern of activity in the temporal lobe represents a consciously perceived face because such a pattern is strongly activated while a face is seen, but not when it is unseen despite constant sensory stimulation (Tong et al., 1998). The natural response from opposing interlocutors is that the pattern in question is not necessarily absent when the face is unseen: with a different tool or theory of the brain, the relevant property could still have been detected throughout all the conditions (Hebart & Baker, 2018).

One way to make progress on the question of which properties are relevant for serving as a neural representation is to develop tools with which to determine whether a neural representation is not present. Developing a novel means to provide evidence for the absence of a representation would push many debates forward as well as improve theorizing about neural representations. We propose to leverage machine learning to provide novel means for detecting the properties of interest and determining when they are absent.

Training machine learning algorithms on neural data has allowed researchers to decode activity and predict some cognitive or behavioral output from neural activity. Our project uses classifiers trained on neural data to provide an estimate of the absence of a particular pattern in that data. In particular, we train numerous classifiers systematically varying processing pipelines to create classifiers with varying sensitivity to the pattern. Mapping out this classifier-space will provide an estimate of the sensitivity at which a pattern or its absence is detected. This tool feasibly provides evidence for an interpretation of data suggesting that a pattern exists only at a particular time or place and not elsewhere in the data.

Description: Neuroscientists are interested not only in finding meaningful signals in the brain through analysis of data, but also in showing that those signals are absent in other parts of the data. Such absence can support interpretations that the signal does not occur in another place or at another time. We develop a tool to measure the strength of evidence that a signal is recoverable from the data by leveraging degrees of classifier sensitivity to the signal.

Lucas Jeay-Bizot, PhD. Postdoctoral Scientist, Cedars-Sinai Medical Center

Caitlin Mace, PhD Candidate, Department of History and Philosophy of Science, University of Pittsburgh

Investigating the effects of outsourcing moral deliberation to LLMs: an approach through behaviour and EEG

Amount awarded: $10,450

Abstract: Many users now turn to AI for support in navigating their personal lives (Anthropic, 2025). Yet when we outsource cognitive work to technology, our brain-bound capacities for it adapt, change, or diminish (Sparrow et al., 2011; Dahmani & Bohbot, 2020). This project combines behavioural and neural measures to investigate what change may occur when we outsource moral deliberation to an LLM.

This project investigates how outsourcing moral deliberation (MD) to large language models (LLMs) might impact our capacity for MD. The primary aim is to ask whether outsourcing the processing of MD to an LLM changes that processing, measured through behaviour and neural activity. The project aims specifically at moral deliberation as opposed to moral decision making. The motivation for this is that moral deliberation is an integral part of moral cognition, and yet, manifests as a theoretically and neurally distinct phenomenon: deliberation elicits activation of distinct brain regions, for example (Schaich Borg et al., 2011), and Arendt (1971) identifies deliberation as a separate but complementary element in moral competence. However, deliberation has proven difficult to operationalise for experimental purposes (and difficult to conceptualise robustly (e.g., Quilty-Dunn & Krakauer, 2026)). Conceptually, we take moral deliberation to be the conscious, effortful process of identifying and weighing relevant moral considerations over time. To be clear, this need not result in any decision, judgment, or action. This may involve an array of cognitive capacities, articulated through Arendt (1971) who describes moral deliberation as a form of metacognitive, self-reflexive dialogue. However, there's no agreed mechanistic account of deliberation (Quilty-Dunn and Krakauer, 2026; Senghor and Racine, 2022). We therefore target plausible proxies of deliberation. While we utilize a multi-methodological approach, we put behaviour first, and use neuroimaging to complement and constrain our interpretation of the behavioural data (Krakauer et al., 2017). Behaviourally, we test subjects’ ability to reproduce and reformulate their deliberations, and probe them regarding their felt sense of ownership and accountability. We also use NLP to measure the convergence toward homogenization of their written deliberations (taking homogenization to be a further proxy for reduced deliberation, via increased reliance on LLM outputs). The study will involve an EEG element though this will be used not to draw any strong inferences about internal processing per se, but to support us in interpreting behavioural data. Strategically, the project is intended to serve as a pilot, a foundational and rigorous point of departure for further work undertaken by a multi-site international consortium, utilizing diverse methods including expanded behaviour measures, and functional magnetic resonance imaging (fMRI). In service of this, our primary goal with the pilot is to establish stable conceptual, empirical, and infrastructural foundations by leveraging the pilot’s deliverables (e.g., a linguistically standardized stimulus set, a containerised analysis pipeline, and effect-size estimates), in order to seed a public open call via a dedicated website at year-end and secure further public funding.

Description: Many users now turn to AI for support in navigating their personal lives (Anthropic, 2025). Yet when we outsource cognitive work to technology, our brain-bound capacities for it adapt, change, or diminish (Sparrow et al., 2011; Dahmani & Bohbot, 2020). This project combines behavioural and neural measures to investigate what change may occur when we outsource moral deliberation to an LLM.

Ben White, PhD. Postdoctoral Fellow, Paris School of AI, PSL

Esme Stanford-Durkin, PhD. Researcher in Cognitive Science, School of Engineering & Informatics, University of Sussex

Krzysztof Dolega, PhD. Assistant Professor, Institute for Philosophy 2, Ruhr University Bochum

Roya Mohammadsadegh, PhD Candidate, Department of Psychological and Brain Sciences, University of Massachusetts-Amherst

Nicolás Hinrichs, PhD. Postdoctoral Researcher, Neural Data Science and Statistical Computing Group, Max Planck Institute for Human Cognitive and Brain Sciences

Rumination: Necessary or Disordered Computation?

Amount awarded: $20,000

When faced with a decision, we often contemplate whether to act immediately or reflect to find the best solution. To decide between immediate action and further reflection, we can sample memories to consider potential hypothetical outcomes. Memory theorists have argued that using recollection to conceive of hypotheticals in this way is how a healthy functioning memory system should work (De Brigard, 2014). However, in instances of psychopathology, agents may expend excessive time and cognitive resources to reflect on the plethora of action choices that may be available to them, resulting in a psychological standstill where individuals become “stuck” in the past. Reflection, in these instances, transforms into rumination. Rumination, traditionally conceived of as the repetitive and maladaptive rehearsal of past events, is a cognitive characteristic of dysphoria and Major Depressive Disorder (MDD) (Ingram, 1984; Martin & Tesser, 1989; Nolen-Hoeksema, 1987). While philosophers, neuroscientists, and psychologists agree that rumination impacts well-being, there is little agreement on what constitutes rumination (Ólafsson et al., 2020; Russel, 2021; Gilbar et al., 2010). The absence of definitional agreement amongst scholars, formal models capturing the dynamics of ruminative episodes, or of established frameworks for evaluating rumination hinder understanding of this clinical phenomenon. Of the varying definitions given of what rumination consists of, there are three common themes, rumination is: problem-oriented, repetitive, and evaluative. This project aims to develop a computational model of rumination that can be verified in general decision-making and planning paradigms. The purpose of this is threefold: (i) to gain theoretical clarity of rumination through the development of a computational model, (ii) to validate this model with human participants and (iii) to relate these measures to clinically relevant symptomatology.

We propose a computational model of rumination based on a Partially Observable Markov Decision Process (POMDP) framework. In a POMDP, the participant is required to persistently gauge the state of the environment and anticipate potential outcomes. This task appropriately models the decision-making process where the agent must decide whether to act on existing information or seek additional data, encapsulating the inherent uncertainty, cognitive load, and trade-offs typical of decision-making and reflective of rumination. The POMDP has been hypothesized to capture fundamental decision-making features in anxiety and depression (Paulus & Yu, 2012). Within this framework, our study aims are to: (1); formulate a computational analog of rumination, (2) validate this model of rumination with human participants using a behavioral task, (3) validate the connection task performance and rumination by collecting data on ruminative and depressive traits, expecting interactions between rumination, depression, and sampling behavior. This research could inform the development of therapeutic interventions targeting rumination, ultimately improving decision-making, clinical outcomes, and symptom alleviation in individuals experiencing depression, anxiety, or the effects of trauma.

Joseph Austerweil, PhD. Associate Professor of Psychology and Computer Science, University of Madison-Wisconsin

Michael Payton, PhD Candidate, Department of Psychology, University of Madison-Wisconsin

Mary Vitello, PhD Candidate, Department of Psychology, University of California, Los Angeles

The Role of Mental Imagery: Investigating Cognitive Functions in Aphantasia Using Virtual Reality and EEG

Amount Awarded: $25,000

Mental imagery is often claimed to play a crucial role in cognitive functions such as episodic memory, decision-making, and planning for the future. Our project investigates aphantasia, a condition characterized by reduced or absent mental imagery (Zeman et al., 2015), and aims to uncover how the lack of mental imagery affects other cognitive functions. We aim to further our knowledge of the condition, firstly by using a novel paradigm to replicate findings about episodic memory in aphantasia, and secondly by investigating the temporal neural dynamics of individuals with aphantasia on a battery of well-established EEG tasks.

Previous findings from aphantasia research indicate that not only is imagery disrupted, but episodic memory is too (Bainbridge et al., 2021; Dawes et al., 2022). Specifically, aphantasics recall fewer perceptual details than controls, but a similar amount of spatial details, and exhibit fewer errors of commission. However, these findings come from studies with low ecological validity, and sometimes without the ability to test for the accuracy of recalled memory details. Experiment 1 tests these hypotheses in a new paradigm. Our first aim is to replicate these findings, using a virtual reality paradigm with eye tracking technology, which overcomes previous limitations. Assuming that results are replicated, our second aim is to test whether differences in metacognition could partially explain these results, using a confidence measure, and our third aim is to test whether differences in gaze pattern during encoding could partially explain these results, using eye tracking.

Experiment 2 turns to investigate the understudied area of temporal neural dynamics in aphantasia, using EEG, which has only been studied in a small sample of two subjects (Furman et al., 2023; Zhao et al. 2022). This does not allow for generalisation of results. Our fourth aim is to identify unique neurophysiological traits of aphantasia, using a battery of tests targeting various functions and a large sample of participants. Here, we will also investigate memory encoding and retrieval processes, which could contribute to an explanation of results in Experiment 1. Overall, this project will significantly forward our understanding of behavioural and neural characteristics of aphantasia, which in turn will shed light on the role of imagery in cognition.

Andrea Blomkvist, PhD. Postdoctoral Scholar, Department of Philosophy, Logic, and Scientific Method, London School of Economics and Political Science

Katherine Boere, PhD Candidate, Department of Neuroscience, University of Victoria

Em Walsh, PhD. Postdoctoral Scholar, Berman Institute of Bioethics, Johns Hopkins University

Raquel Krempel, PhD. Postdoctoral Scholar, Center for Logic, State University of Campinas, Brazil

Mary Vitello, PhD Candidate, Department of Psychology, University of California, Los Angeles

Is Sense of Agency Computed Differently Across the Hemispheres? A Comparative Study on Neurotypical Individuals and Split-Brain Patients

amount awarded: $25,750

The sense of agency (SoA) is the experience of controlling one’s own actions and intentionally influencing the external consequences in the world (Haggard, 2017). Understanding the mechanisms behind SoA formation is crucial due to its direct relevance to mental well-being and legal jurisdiction. From a computational perspective, disruption of SoA in psychiatric disorders is associated with imbalances in how different signals in time (referred to as prospective and retrospective cues) are weighted. For example, schizophrenic patients tend to overly rely on retrospective cues and under-weigh prospective cues (Voss, et al., 2010). Currently, a thorough investigation of how these distinct signals is computed and integrated is lacking. Given the implications in clinical as well as criminal cases relating to SoA, we aim to bridge this gap. We propose that there is a hemispheric difference in how distinct sources of information contribute to the overall SoA. 

We hypothesize that the left hemisphere (LH) is more sensitive to retrospective cues, such as observing the outcome, whereas the right hemisphere (RH) is more sensitive to prospective cues, such as premotor signals preceding the action. This hypothesis is based on extensive research in split-brain patients, in which the corpus callosum is severed to treat intractable epilepsy. Lateralized testing demonstrates that the LH, but not the RH, relies more on retrospective cues to make post-hoc inferences to make sense of the actions when prospective cues are absent (Gazzaniga, 2000). Additionally, when making moral judgments, the RH makes more intention-based judgments whereas the LH tends to make more outcome-based judgments (Miller et al., 2010). This evidence suggests that our LH-retrospective, RH-prospective hypothesis can be a plausible mechanism for SoA formation. Our project aims to investigate this hypothesis through a comparative study of healthy controls and split-brain patients. In Aim 1, we will manipulate (1) prospective cues, (2) retrospective cues, and (3) the acting-reporting hand, to establish whether hemispheres are differentially sensitive to prospective and retrospective cues in forming SoA. To establish the generalizability of our findings across different SoA paradigms, we will conduct two experiments: an implicit intentional binding task and an explicit SoA report task. In Aim 2, we will combine our dual-task design with recordings of whole-brain activity using fMRI, and structural properties using sMRI & dMRI, with the goal of identifying the functional and structural neural mechanisms involved in the integration of predictive and postdictive cues underlying SoA formation. In both Aims, we will compare the results from healthy controls to split-brain patients to establish the role of interhemispheric communication in forming SoA. This research will provide insights into the underlying mechanisms of SoA formation in both healthy individuals and split-brain patients. These findings have implications for understanding consciousness, self-awareness, and related neurological and psychiatric conditions, including schizophrenia and other disorders with altered self-awareness and agency.

Mike Miller, PhD. Professor, Department of Psychological and Brain Sciences, University of California, Santa Barbara

Selin Bekir, PhD Candidate, Department of Psychological and Brain Sciences, University of California, Santa Barbara

Jake Gavenas, PhD Candidate, Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University

Anna Ivanova, PhD. Postdoctoral Scholar, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

Akila Kadambi, PhD. Postdoctoral Scholar, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles

Eric Hochstein, PhD. Associate Professor, Department of Philosophy, University of Victoria

Corey Allen, PhD. Postdoctoral Scholar, The Mind Research Network

Laura Kaltwasser, PhD. Postdoctoral Scholar, Berlin School of Mind and Brain, Homboldt University of Berlin

Mapping the Conceptual Interface Between Philosophy and Neuroscience

amount awarded: $5,000

Concepts form the framework of philosophical and scientific discourse; they quite literally determine what we study and how we organize the results of our investigations. Concepts are not tied to the disciplinary context from which they emerged—they cross disciplinary boundaries. In this process, concepts can take on new meanings or find themselves used in dissimilar ways that deemphasize their initial complexity. In turn, this can introduce confusion, especially when experts across disciplinary boundaries communicate using seemingly shared concepts. This case is acute when we consider the interface between philosophy and neuroscience, where philosophically laden concepts, such as MECHANISM and REPRESENTATION find themselves at the heart of explanatory claims about how neural systems precipitate behavior (cf. Hochstein 2015, Hochstein 2016). Likewise, concepts originating from the cognitive sciences, and their experimental analogues in neuroscience, including WORKING MEMORY and ATTENTION, find themselves imported into contemporary philosophical debates about the structure and organization of the mind (Carruthers 2015; Prinz 2012; Mole 2011; Wu 2014).

We propose to begin the process of mapping the conceptual interface between philosophy and neuroscience by drawing on our team’s expertise with interdisciplinary methods. In doing so we can determine whether and to what extent core concepts from each discipline have changed as they’ve crossed the disciplinary divide. We have two aims that correspond to the major studies we plan to run: First, we will use topic modeling on custom corpora comprised of 104 – 105 philosophy and neuroscience documents to extract latent topics corresponding to each concept of interest. These topics will help us visualize the extent and shape that each concept takes up within each disciplines’ literature, and in turn will help us winnow down the set of views that we will test in subsequent studies. Second, we will use these results to design xphi vignette and conjoint analysis studies that canvass participant’s judgments about the intuitiveness and explanatory scope of prominent views of each of our four target concepts (e.g., by presenting participants with quotes from real and sham papers corresponding to different theories for each concept) while simultaneously tracking individual’s level of familiarity and expertise with each concept. Analyses of these concepts will allow us to narrow our final stimuli and measures as we develop a brief five-minute survey aimed at 400 expert attendees at the 2024 APA Central and CNS meetings. Results from these surveys will allow us to test our hypotheses about whether multivalent concepts (e.g., REPRESENTATION and WORKING MEMORY) from one discipline are treated univocally as they move across disciplinary boundaries. Our proposal combines novel methods to determine how concepts change and will help facilitate clearer communication between experts across this key disciplinary divide.

Javier Gomez-Lavin, PhD. Assistant Professor, Department of Philosophy, Purdue University

Tim-Elmo Feiten, PhD Candidate, Department of Philosophy and the Life Sciences, University of Cincinnati

Eric Hochstein, PhD. Associate Professor, Department of Philosophy, University of Victoria

Mary Vitello, PhD Candidate, Department of Psychology, University of California, Los Angeles

Raquel Krempel, PhD. Postdoctoral Scholar, Center for Logic, State University of Campinas, Brazil

A Multilevel Neural Framework of Self-processing

amount awarded: $30,000

As we navigate our complex, dynamic environments, we need to keep track of the ever-changing boundaries between ourselves and our environments. To do this, we construct representations of ourselves at multiple levels: of our physical bodies as they interact with objects in the world, our goals as we navigate obstacles to bring about events in the world, our social identities as we simultaneously emulate and differentiate ourselves from our peers. Although self-representations can be loosely separated into levels, these levels are tightly interacting: for example, physical objects like clothing are often included in our self-representations because of their role in our social identities. A key challenge in the science of the self is how to integrate these levels within empirical frameworks in order to examine the complex interactions between them.

Our project is uniquely designed to test the first multileveled architecture of the self in the human brain. We will revise the traditional empirical segregation of levels of self-processing and integrate a large philosophical corpus of work that characterizes its multileveled architecture. Towards this aim, we introduce two paradigms that selectively elicit self-representations at progressively higher levels of spatiotemporal abstraction: (a) concrete, action-based, (b) intermediate, intention-based, and (c) abstract, trait-based self-representations. We will use functional brain imaging (fMRI) and neuromodulation (TMS) to identify neural circuitry implicated, and co-active, at these levels that vary in spatiotemporal abstraction. The first aim seeks to identify the neural bases for the different kinds of self-representations targeted by our paradigms. The second aim seeks to identify interactions between these levels using functional connectivity and behavioral measures in our respective paradigms. In the third aim, we will downregulate neural activity for abstract, trait-based self-representation (posterior cingulate cortex) and concrete self-processing (inferior parietal lobule) using TMS and assess behavioral changes during concrete, action-based processing of the self. Thus, we will assess potential causal interactions between levels of the self. These results could have broader implications for clinical research by providing experimental approaches to probe breakdowns between self-representations at different levels, implicated in a variety of psychopathologies.

Steve Fleming, PhD. Professor, Department of Experimental Psychology, University College London

Marco Iacoboni, PhD. Professor-in-Residence, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles

Akila Kadambi, PhD. Postdoctoral Scholar, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles

Aliya Rumana, PhD Candidate, Department of Philosophy, University of Texas

Eric Hochstein, PhD. Associate Professor, Department of Philosophy, University of Victoria

Urte Laukaityte, PhD Candidate, Department of Philosophy, University of California, Berkeley

Wes Skolits, PhD. Postdoctoral Scholar, Department of Philosophy, Rutgers University

Laura Kaltwasser, PhD. Postdoctoral Scholar, Berlin School of Mind and Brain, Homboldt University of Berlin

George Deane, PhD. Postdoctoral Scholar, Department of Philosophy, University of Montreal

Temporal Dynamics of Conflict Detection in Moral Decision-Making

Amount awarded: $20,000

From seemingly banal decisions regarding exercising and shopping habits, to high-risk questions as to whether one should drive following alcohol consumption, humans are constantly deciding between allocations of harm and reward to ourselves and others. Notably, these decisions are characterized by a presence of conflict (e.g., paying for a cab to get home versus putting others and yourself at risk). In non-moral decision-making, the detection of conflict has been argued to be associated with a potential way to overcome (or at least recognize) biases in decision-making. However, this lens of conflict detection has yet to be turned on ecologically valid types of moral decision-making: processes that involve making intra- and inter-personal decisions with positive or negative valence. To date, research on conflict detection in moral decision-making contexts has been limited to economic games where financial harm and reward are the sole metrics in which the decisions are defined as moral. Recent advances in moral psychology have improved the ecological validity of these paradigms by implementing physical rather than financial harm (i.e., the administration of painful electrical shocks), yet have left conflict detection in these scenarios largely uninvestigated. Nowhere is the investigation of the processes underlying decision-making more important than in the context of moral decision-making, where one has the capacity to allocate harm and rewards to themselves and others. The overall aim of this project is to characterize the behavioral and neurobiological temporal dynamics of conflict detection in inter- and intra-personal moral decision-making, with a specific focus on egoistic and altruistic decisions, in order to make direct comparisons to the temporal dynamics of conflict detection in non-moral decision making. This aim will be achieved by utilizing electroencephalogram (EEG) imaging techniques while subjects participate in a moral decision-making task involving the allocation of harms (i.e., painful electrical shocks) and rewards (i.e., monetary value) between themselves and others and non-moral decision-making tasks involving conflict detection (i.e., the Stroop Task and Eriksen Flanker task). We expect that cognitive conflict detection (rather than perceptual conflict detection) is a task-invariant process, such that the neural mechanisms underlying such a process will be consistent regardless of the moral valence of the task at hand.

This project will address significant gaps in the literature on moral decision-making by examining the (behavioral and neurobiological) temporal dynamics underlying self and other-oriented distributions of harm and benefit through the lens of conflict detection. Additionally, this is the first project to systematically investigate and compare the temporal dynamics of conflict detection in moral vs. non-moral decision-making tasks. Broader Impact: By identifying the temporal dynamics of conflict detection via an ecologically valid moral decision-making paradigm, this study will help to improve our understanding of how altruistic and egoistic decisions culminate in social contexts. As such, this project carries the potential to significantly impact society and increase transparency in self and other-oriented decisions regarding the distribution of harm and welfare.

Olave Krigolson, PhD. Associate Professor, Division of Medical Sciences/Exercise Science, Physical and Health Education, University of Victoria

Corey Allen, PhD. Postdoctoral Scholar, The Mind Research Network

Katherine Boere, PhD Candidate, Department of Neuroscience, University of Victoria

Aliya Rumana, PhD Candidate, Department of Philosophy, University of Texas

Varieties of Mental Imagery and Their Effects on Working Memory

Amount awarded: $25,050

According to the standard view, mental imagery plays an indispensable role in working memory. Recent work on aphantasia, a condition characterized as an impairment in mental imagery, appears to challenge this view. According to some studies (Pounder et al., 2022; Keogh et al., 2021), aphantasics perform as well as controls on working memory tasks that purportedly require conscious mental images. However, mental imagery is not a unitary phenomenon (Dance et al., 2021; Palermo et al., 2022). While aphantasics are typically defined as individuals who lack visual imagery for objects, they might be unimpaired in other forms of imagery (e.g., spatial imagery or verbal imagery/inner speech) which could be used to perform certain working memory tasks. We propose to clarify the role of mental imagery in working memory by developing a methodology to dissociate different forms of mental imagery and their specific contributions to working memory tasks. In particular, we will focus on a potential dissociation between visual object imagery and spatial imagery and a similar dissociation between phonetic imagery and verbal imagery.

We will begin by developing and validating a questionnaire that improves upon prior mental imagery questionnaires’ (such as the VVIQ) ability to show the degree to which different imagery types correlate across individuals). We will then recruit a large sample of participants to complete our questionnaire, with the goal of estimating the prevalence and co-occurrence of different imagery types (Aim 1). From these respondents, we will recruit participants that report selective deficits in one imagery subdomain (object, spatial, phonetic, linguistic) to participate in a series of behavioral tasks that allow us to dissociate the contributions object and spatial working memory (Aim 2) or phonetic and linguistic working memory (Aim 3).We will then analyze the correlations between the self-report scores on our questionnaires and performance on our working memory tasks to probe the extent to which conscious mental imagery plays a role in working memory. This project will significantly advance the literature on mental imagery. In addition to improving upon the previous questionnaires probing individual differences in mental imagery experiences, it will show the extent to which varieties of mental imagery dissociate within individuals, and illuminate our understanding of the role of specific imagery types in working memory. Finally, it will provide the necessary groundwork for future phenomenological, behavioral, and neuroscientific research on mental imagery and working memory.

Evelina Fedorenko, PhD. Associate Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

Anna Ivanova, PhD. Postdoctoral Scholar, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

Raquel Krempel, PhD. Postdoctoral Scholar, Center for Logic, State University of Campinas, Brazil

Douglas Wadle, PhD. Visiting Assistant Professor, Department of Philosophy, University of Kansas

Michael Payton, PhD Candidate, Department of Psychology, University of Madison-Wisconsin

Wes Skolits, PhD. Postdoctoral Scholar, Department of Philosophy, Rutgers University

Pro-Social and Emotional Effects of Narratives: The Role of Perspective-Taking Brain Networks

Amount awarded: $26,684

Human beings intrinsically are tellers and receivers of stories - and stories can be a powerful and beneficial tool for change (Green, 2021). When engaging with another person’s story, we gain access to their motives, beliefs, and emotions, which enables us to have a better understanding of their actions than merely observing their behavior (Oatley, 2011). Work in the psychology of fiction suggests that engaging with stories can improve individuals' socio-cognitive abilities and increase empathy (Dodell-Feder & Tamir, 2018; Mumper & Gerrig, 2016). Additionally, research suggests that narratives of first-person experiences of critical life-events - such as those experiences of mental disorders, can generate awareness, impart knowledge, and promote understanding on topics such as mental illness (Boltanski & Burtchell, 1999; Baldwin, 2005). This project aims to shed light on the mechanisms and circumstances in which narratives can impact stigma towards those in stigmatized groups, specifically those who experience psychotic mental disorders (e.g. Schizophrenia). First, through an online behavioral study, subjects will be randomly assigned to read either a) non-narrative (informational) texts, b) non-fictional (personal) narrative stories or c) fictional (literary) narrative stories about psychotic disorders. The effect of this intervention will be assessed on measures of Theory of Mind (ToM), emotional engagement, and prosocial behavior towards people suffering from these disorders. In the second stage Transcranial Magnetic Stimulation (TMS) will be used to temporarily inhibit the right temporo-parietal junction (rTPJ) - a brain region associated with perspective taking processes (Saxe & Kanwisher, 2003) - while reading the narratives to assess the role of perspective-taking on these measures of socio-emotional processing. Through the proposed methodology, this project will: a) Provide experimental evidence of the effect of fictional and non-fictional narratives on perspective taking and socio-emotional processes (those involved in ToM, emotional engagement, and prosocial behavior). b) Enable a deeper understanding of the neurocognitive mechanisms involved in perspective taking, especially as it is triggered by narrative and/or fiction. c) Suggest a potential intervention to reduce stigma and increase empathy towards people suffering from mental disorders. d) Inform work in the philosophy of emotion, moral philosophy, and embodied cognition.

Fernanda Perez-Gay Juarez, PhD. Postdoctoral Scholar, Departments of Philosophy and Psychiatry, McGill University

Rodrigo Diaz, PhD. Postdoctoral Scholar, Center for Research in Ethics, University of Montreal

Kate Finley, PhD. Assistant Professor, Department of Philosophy , Hope College

Laura Matthews, PhD. Instructor, Department of Philosophy, Auburn University

Samantha Fede, PhD. Assistant Professor, Department of Psychological Sciences , Auburn University

Perceptual and Attentional Influences on Moral Agency

Amount awarded: $27,171

Moral agency describes individuals’ choices to engage in moral behavior, but it is unclear that moral judgment is solely a matter of cognitive, rational choice. Indeed, the scientific literature suggests that in some cases, the influence of emotion processes over cognition is necessary to make the morally acceptable choice. Other evidence suggests that some level of moral perception occurs in 300ms or less, much more quickly than a moral judgment, which is on the order of 2000ms. Taken together, this suggests that the role of the moral agent may be as an attentional director at a pre-conscious level, defined in part by trait characteristics of the individual. Given the continuing harm caused by antisocial behavior and lack of prosocial intervention in suffering, there remains a critical need for insight into the mechanisms underlying moral agency. Therefore, the overall objective for this project is to describe the features influencing moral perception and attention. The central hypothesis is that salient, morally relevant properties of visual content will influence moral deliberation, with individual differences influencing the tendency to detect and use these properties. This project has two specific aims: 1) To determine the extent to which attention to salient features of stimuli is adequate for moral deliberation; and 2) To identify the extent to which various individual differences correlate with moral sensitivity in attention. The first study will be an fMRI study wherein subjects (n = 30) will view moral, emotional, and neutral images for brief durations followed by a prompt to make a retrospective rating of moral content. This task will first be piloted (n = 100) behaviorally. We hypothesize that following visual presentations of moral stimuli too short to benefit from high-level cognitive influences on attention, individuals will have access to the relevant moral content (distinct from emotional content) to deliberate and make a moral evaluation. We expect this to be true for supraliminal stimuli, but not subliminal, providing important insight into the time course of moral perception. The second study will be an eye-tracking study wherein subjects (n = 50) will view moral and matched non-moral images and later will be asked to recall whether they had seen the image before. Subjects will also complete self-report measures of personality and will be given the opportunity to decide to donate a portion of their study compensation. We hypothesize that individuals who engage in greater prosocial behavior will also better attend to and remember cases of moral significance. Not only will completion of these two aims provide into the nature of moral agency, it will lay the groundwork for future development of training to leverage these individual characteristics to increase prosocial / reduce antisocial behavior.

Samantha Fede, PhD. Assistant Professor, Department of Psychological Sciences, Auburn University

Doug Addleman, PhD. Postdoctoral Scholar, Department of Psychological and Brain Sciences, Dartmouth College

Marshall Bierson, PhD. Assistant Professor, School of Philosophy , Catholic University of America

Ryan Daley, PhD. Assistant Professor, Department of Psychology, Gordon College

Laura Soter, PhD. Postdoctoral Scholar, Department of Philosophy, Duke University

Denis Buehler, PhD. Assistant Professor, Departments of Philosophy and Cognitive Science , Ecole Normale Supérieure, Paris

Incubation of Dreams with Positive Emotional Content as a Possible Clinical Tool for Depression

Amount awarded: $28,000

Depression is one of the most prevalent mental disorders in the world (WHO, 2021) and despite the current advances in treatments, the rates of recurrence remain high affecting around 300 million people around the world (WHO, 2021). These patients show a bias towards encoding of negative information (Dillon & Pizzagalli 2018; Noworyta et al., 2021) and more negative dream content and nightmares than normal population (Steiger & Pawlowski 2019; Cartwright et al., 1998; Okorome Mume, 2009; Kaiser et al., 2015; Edge 2010; Luca et al., 2013; Akkaoui et al., 2020). We propose that this altered encoding as well as further memory processes that occur while we sleep could be sustaining their negative symptomatology. Normally, recently acquired information is reactivated and integrated during sleep, favoring its memory consolidation (Rasch & Born, 2013). These memory processes are responsible for what we know as dreams (Stickgold 2005; Wamsley & Stickgold, 2019). During dreams, we experience the reappearance not only of previous waking content reappears but also of our waking emotions (Hoss 2011; Hobson & Schredl 2011; Schredl 2017; Stocks et al., 2020). Furthermore, our dream emotions (mostly during REM dreams) could impact our waking life mood (Stocks et al., 2020). Taking all of these phenomena into account, we postulate that these memory processes act as a negative feedback loop for depressive patients, who, during wakefulness, preferentially encode negative information that will be later reactivated and integrated during sleep. These reactivations/integration processes could give rise to more negative dream content that directly affects their subsequent waking life mood, reinforcing their negative symptomatology. Consequently, we hypothesize that positively targeting their dream content could be used as a clinical intervention to improve patients mood and complement current treatments. Hypnagogia, the transitional state between wakefulness and sleep (Hori et al., 1993), is an ideal candidate for incubating dream content and emotions. During hypnagogia, dream-like reports frequency is high (80-90%) and includes vivid experiences but, in contrast to REM dreams, they are usually short (Hobson et al., 2000). Furthermore, previous research has shown that incubating dream-like content during hypnagogia leads to 67% of dream content related to the incubation instruction (Horowitz et al., 2020). We also know that brain activity is globally changed while we fall asleep, mainly due to the decreased activation in the prefrontal cortex (Tüshaus et al., 2017). This could lead to a reduction in self-monitoring, highly committed to sensory perception during wakefulness (Nir & Tononi, 2010). We propose that this deactivation could reduce depressive patients’ bias to encode negative information, and thus facilitate positive emotional dream content incubation. In the present project we will (a) design a new experimental protocol to incubate dreams with positive emotions during the hypnagogic period (objective I); (b) evaluate the effects of this protocol on dream content on subsequent REM sleep, as well as on positive mood in subsequent wakefulness (objective II); and (c) implement this protocol during one month on depressive patients to study its impact on their waking-life mood, their depressive symptoms, their dream reports and sleep quality as well as on their resting state functional connectivity (both commonly affected in these patients, Goldschmied et al., 2019; Kaiser et al., 2015) (objective III).

Cecilia Forcato, PhD. Associate Professor, Buenos Aires Institute of Technology

Marina Trakas, PhD. Postdoctoral Researcher, Institute of Philosophical Investigation.

Rodrigo Diaz, PhD. Postdoctoral Researcher, Center for Research in Ethics, University of Montreal

Fernanda Perez-Gay Juarez, PhD. Postdoctoral Research, Departments of Philosophy and Psychiatry, McGill University

Laura Kaczer, PhD. Associate Researcher, Institute of Philosophy, University of Buenos Aires

Ryan Daley, PhD. Assistant Professor, Department of Psychology, Gordon College

Melanie Rosen, PhD. Assistant Professor, Department of Philosophy, Trent University.

Computation and Systematicity in the Brain

Amount Awarded: $17,720

A longstanding debate in cognitive science and the philosophy of mind concerns the extent to which human cognition can be characterized by systematic, symbolic, or rule-governed computation (Fodor & Pylyshyn, 1988; Pinker & Prince, 1988). Early research on neural networks emphasized that rule-like or symbolic behavior could emerge from sub-symbolic or distributed representations (Rumelhart & McClelland, 1987; Smolensky, 1987). However, Fodor and Pylyshyn (1988) famously argued that neural networks could not succeed in explaining human behavior on the cognitive level because they are incapable of capturing the systematicity of human language and thought. Cognitive theories, they argued, must account for the fact that any human that understands the sentence ‘John loves Mary’ will be able to understand certain systematically related sentences, like ‘Mary loves John,’ as a matter of necessity. This debate has resurfaced in cognitive science and artificial intelligence (AI) research in the wake of the resurgent popularity of neural networks in domains such as computer vision and natural language processing (LeCun et al., 2015), and as models of computation in the brain (Cadieu et al., 2014; Yamins et al., 2014; Yamins & DiCarlo, 2016). Although these connectionist systems have made impressive progress in these domains, recent studies have shown that they perform 2 poorly on problems requiring systematic generalization (Lake & Baroni, 2018), even when humans succeed at analogous tasks (Dekker et al., 2022; Lake, Linzen & Baroni, 2019). Philosophers were integral to earlier progress on this issue and, in some cases, primary drivers of the debate about its significance for connectionism as a theoretical framework in cognitive science. Furthermore, this debate also raises more general questions about the role computational models can play in explanations of cognition – a topic that has garnered much attention in both neuroscience (Bowers et al., 2022; Cisek & Hayden, 2022; Jonas & Kording, 2017; Krakauer, Ghazanfar, Gomez-Marin, MacIver, & Poeppel, 2017; Marom et al., 2009) and philosophy (Piccinini, 2015; Shagrir, 2022; Sprevak, 2018). Given the importance of philosophy in historical debates about systematicity and the relevance of these debates to contemporary philosophical questions, the recent studies of systematicity in neural networks present an opportunity for collaboration between neuroscientists and philosophers. However, the current iteration of the debate has been largely confined to machine learning and cognitive science, and has not received enough attention from philosophers of language and mind. The primary goal of this project is to reinvigorate open communication between philosophy and cognitive science about these issues. To this end, we will 1) conduct experiments on human systematicity informed by the history of relevant debates in the philosophy of mind, 2) inspired by (Favela and Machery, preprint), administer a survey examining how both philosophers and cognitive scientists think about computation and systematicity in order to investigate any terminological differences that may be contributing to a lack of communication between and within the fields, and 3) write a paper aimed at a philosophical audience that reviews recent machine learning advances within the broader context of the historical debate. Our proposed research program will aim to advance ongoing scientific inquiry into systematicity, laying the groundwork for future empirical research using additional methods such as neuroimaging, while facilitating dialogue with a broader philosophical audience that may contribute greater conceptual clarity and historical understanding.

Zoe Drayson, PhD. Associate Professor, Department of Philosophy, University of California, Davis

Lotem Elber-Dorozko, PhD. Postdoctoral Scholar, Humanities and Arts Department, Technion, Israel Institute of Technology

Sam McGrath. PhD Candidate, Department of Philosophy, Brown University

Jake Russin. PhD Candidate, Department of Psychology, University of California, Davis

Danielle Williams. PhD Candidate, Department of Philosophy, University of California, Davis

Luke Pistol. PhD Candidate, Department of Philosophy, Stanford University

Group Membership Modulation of Memory and Judgments for Morally Ambiguous Actions

Amount awarded: $26, 860

Group membership impacts how we remember and judge others, but this phenomenon is underexplored. Moreover, studies on memory and group membership typically present participants with others’ explicitly moral (e.g., helping) or immoral (e.g., harming) actions. Yet, a third category of behaviors often occurs - those that appear to be morally ambiguous - when one cannot tell whether the agent's motivations for an action are moral, immoral, or a mix. In these cases, the way that we remember and judge the agent and their actions may be impacted by their group membership (e.g. ingroup/outgroup status or relationship to other group members) and this in turn may result in practical and/or epistemic harms for those who are misremembered (known as mnemonic injustice). The present proposal investigates this phenomenon through pursuing three aims which examine how group membership can bias memory and evaluation of agents’ morally ambiguous actions and character. Our first two aims address whether group membership impacts memory prioritization and third-person evaluation of agents’ morally ambiguous actions in ecologically valid contexts, and through assigned minimal groups. Our third aim addresses how memory prioritization and third-person evaluations are impacted by the (in)consistency of agents’ morally ambiguous actions with the actions of other members of their ingroup. We will run separate online behavioral studies to evaluate each of these three AIMs. During all three studies, participants will be presented with twelve vignettes in which a different agent engages in a morally ambiguous action (i.e. the participant is presented with possible moral and immoral motivations for the action of the agent). In Study 1, the agent is either the same race or a different race from the participant; in Study 2, the agent is in either the minimal assigned ingroup or outgroup of the participant; and in Study 3, a sentence is inserted into the vignette to manipulate the (in)consistency of the agent’s actions with their ingroup. Then, in each of the Studies 1-3, following a delay, participants will be asked to recall as much as they can about each vignette and then make evaluations about the moral status of each agent’s action and character. We hypothesize that in all three studies: 1) the independent variable—group membership or action (in)consistency—will bias memory for the motivations of agents’ actions and 2) this explicitly retrieved memory content will impact moral evaluations of the agent. Completion of these three studies will help further illuminate the behavioral mechanisms involved in the relationship between memory and moral judgment in group membership contexts, and in so doing, will also provide crucial empirical support for the phenomenon of mnemonic injustice. This will in turn serve as a foundation for further investigation of the neural underpinnings of these behavioral mechanisms as well as the practical strategies for combating and remedying mnemonic injustice.

Ryan Daley, PhD. Assistant Professor, Department of Psychology, Gordon College

Kate Finley, PhD. Assistant Professor, Department of Philosophy, Hope College

Marina Trakas, PhD. Postdoctoral Scholar, Institute of Philosophical Research

Laura Soter, PhD. Postdoctoral Scholar, Department of Philosophy, Duke University

Cecilia Forcato, PhD. Associate Professor, Buenos Aires Technological Institute

Laura Kaczer, PhD. Assistant Professor, Universidad of Buenos Aires

Hierarchical Representations of Two-Dimensional Shape

Amount Awarded: $23,565

Decades of research suggest that the visual system forms structured representations of object shape, compositionally building up an overall representation from a constrained set of primitives (Palmer, 1977; Marr & Nishihara, 1978; Kellman, Garrigan, & Erlikhman, 2013). Debate centers on three fundamental questions: (1) What are the primitives out of which shape representations are composed? (2) What are the ways in which these primitives can be combined to represent more complex shapes? (3) What sorts of processes affect the composition of these complex shape representations? The project at hand will investigate these questions as they apply to the visual perception of two-dimensional contours. While some experimental evidence supports the view that contour regions are perceptually segmented at curvature minima (i.e., points of maximum concavity along a contour) (Barenholtz & Feldman, 2003; De Winter & Wagemans, 2006), additional experimental evidence supports the view that the bounding contours of objects are coded as a relatively small sequence of constant curvature segments (Baker, Garrigan & Kellman, 2020). According to constant curvature theory, single parts of a contour segmented by curvature minima will typically be described by more than one constant curvature segment. Using a dot localization paradigm (Huttenlocher, Hedges & Duncan, 1991), we will investigate whether constant curvature parts are indeed a more basic component of contour shape representations. We predict that participants will show less sensitivity for detecting changes in a dot’s position on a contour when relocated to the same constant curvature part than when relocated to different parts. We will then investigate the way representations of constant curvature segments combine so as to represent higher-order parts (Aim 2). We hypothesize that representations of neighboring constant curvature segments that have the same curvature polarity (e.g., all are convex) are pieced together into higher-order representations of parts. Using a part-matching experiment (Palmer, 1977), we predict participants will be faster to identify the presence of a target constant-polarity part within a larger shape than a target mixed-polarity part. These constant-polarity parts approximate the parts defined by curvature minima theory. If our hypothesis is borne out, it will be a step toward bringing these seemingly distinct theories into alignment. Finally, we will explore the role of visual attention in the composition of hierarchical shape representations. Some work (e.g., Stankiewicz et al., 1998) suggests that attention modulates shape representations, while other work (e.g., Xu and Singh, 2002) indicates that shape parts are segmented preattentively. Using a visual priming paradigm (Stankiewicz et al., 1998), we will investigate whether attention modulates the level at which fine variations in the details of contours are encoded as essential parts of those contours. This proposal will elucidate the fundamental structure of representations of two-dimensional shape and the role of visual attention in their formation, while evaluating two major theories of shape representation.

Nick Baker, PhD. Assistant Professor, Department of Psychology, Loyal University

Kevin Lande, PhD. Assistant Professor, Department of Philosophy, York University

Denis Buehler, PhD. Assistant Professor, Departments of Philosophy and Cognitive Science, École Normale Supérieure, Paris

Doug Addleman, PhD. Postdoctoral Scholar, Department of Psychological and Brain Sciences, Dartmouth College

Neural Mechanisms Underlying Gender Disparities in Moral Judgements for Care Violations

Project Awarded: $30,000

Recent years have seen a burst of interest in sex and gender from both theoretical and empirical perspectives. Within the philosophical literature it has recently been argued that women, compared to men, are more strongly expected to provide certain moral goods including care and that these patriarchal gender norms are implicitly and explicitly enforced through moral judgements and punishments for perceived norm violations. Previous work in neuroscience suggests that expectancy violations should elicit norm prediction errors. However, the neuroscience literature on moral judgment has developed largely separately from philosophical theorizing about gendered social norms and has mostly centered on violations of fairness or reciprocity rather than on violations of care. Tying together research from neuroeconomics, organizational and social psychology, and philosophy, we hypothesize that men and women will be judged differently for failing to show care within close relationships, and that such gender disparities in moral judgment will be reflected in neural signaling of norm prediction errors. Using fMRI, we will look for two distinct patterns of activity described in the recent neuroscience literature: detection of a norm violation, associated with activity in the so-called salience network and dopaminergic frontostriatal circuits, and making moral judgements about the violation, associated primarily with activity in the mentalizing network. The behavioral and neural evidence to date suggests that norm violation and moral judgements are core mechanisms underlying actual punishment, and that norms for moralized social behavior and the engagement of the mentalizing network may vary systematically across male-and female-gendered transgressors. We plan to test these ideas using fMRI and adaptations of tasks traditionally used for measuring moral judgements.

Molly Crockett, PhD. Associate Professor,
Department of Psychology,
Yale University

Austin Baker, PhD. Postdoctoral Assistant Professor, Rutgers Center for Cognitive Science (RuCCS), Rutgers University, New Brunswick

Megha Chawla, Graduate Student. Department of Psychology, Yale University

Brian Earp, Associate Director, Yale-Hastings Program in Ethics and Health Policy, Yale University and The Hastings Center; Research Fellow, Uehiro Centre for Practical Ethics, University of Oxford

Dissociative Theories of Consciousness: A Precision Model Comparison

Project Awarded: $30,000

We will investigate the nature of visual representations in variants of the Sperling task by comparing two competing models of visual perception that make different predictions about the precision with which visual stimuli are represented (Ma, 2018; Zhang & Luck 2008). Our predictions about model fit are informed by arguments regarding dissociative theories of consciousness, which posit a distinction between phenomenal consciousness and access consciousness. The results of the classic Sperling experiment have been cited as evidence in favor of dissociative theories -specifically, as providing evidence that phenomenal consciousness overflows access (Block 1995, 2007, 2011). However, opponents of dissociative theories, who typically equate consciousness with access, claim that subjects simply experience a “degraded”, fragmented, or less determinate type of information during the Sperling task (Cohen and Dennett, 2011; Stazicker, 2011). Our experimental variants and model comparisons will allow us to estimate the precision of subjects’ perceptual representations and to assess whether participants were initially aware of all the items in the display, or whether they were never fully aware but perceived only a gist-like or degraded representation. Our results will adjudicate or at least narrow the space of possible empirical disagreement between proponents and opponents of dissociative theories of consciousness.

Trey Boone, PhD. Visiting Fellow, Duke University

Gerardo Viera, PhD. Lecturer, University of Sheffield

Jennifer Lee, PhD Candidate. Department of Psychology, New York University

Lara Krisst, PhD Candidate. Department of Psychology, University of California, Davis

Making Sense of Probabilistic Visual Representations

Project Awarded: $30,000

All organisms must respond to properties of their environment that cannot be determined with certainty. The idea that they accomplish this by using probabilisticrepresentations has been irresistible to researchers across neuroscience, psychology, and philosophy. Understanding probabilistic representation promises to provideinsights into how humans and other organisms are able to cope with uncertainty, and also how we sometimes fail to behave adaptively. But theories of probabilisticrepresentation are very controversial, and researchers have different intuitions about how to define them. Because of this, it is not even clear what wouldcount asevidence for probabilistic representation, and some have claimed that probabilistic accounts are “justso” stories with no explanatory or predictive power. Itis essential that we develop a testable theory of probabilistic representation: doing so would lead to progress infields as diverse as perception, psychiatry and addiction studies, philosophy of mind, and economics. In this proposal, we will describe a theoretical framework that leads to empirical predictions as well as experiments for testing them. Our framework is based on the notion that probabilistic representations must be representations of uncertainty that are used in a way that is uniquely probabilistic: they should be marginalized over. This leads to two testable characteristics of a probabilistic representation: source invariance and probabilistic task transfer. Probabilistic representations should encode uncertainty in a manner that is invariant to multiple environmental causes, and they should be marginalized over in a way that can transfer to multiple downstream tasks. Aim 1 will provide a behavioral test of probabilistic representations by measuring behavioral generalizations in task settings which require marginalization. Aim 2will test that this generalization involves source invariant representations of uncertainty in the visual system, using cross decoding from functional magnetic resonance imaging. Aim 3will be to develop neural network models which possess source invariant representations of uncertainty and exhibit probabilistic task transfer. Taken together, this approach will use converging methods to test whether and how the visual system makes use of probabilistic representations. More broadly, it will provide a general framework for characterizing probabilistic representations that can help resolve longstanding debates that have impeded progress across a wide range of fields.

Raphael Gerraty, PhD. Postdoctoral Fellow, Columbia University

Gerardo Viera, PhD. Lecturer, University of Sheffield

Jessica Thompson, PhD. Postdoctoral Researcher, University of Oxford

A Beginner's Guide to Neural Mechanisms

Project Awarded: $30,000

There is a growing library of presentations, videos, and other resources about philosophy of neuroscience and neuroscience of philosophy available online, including SSNAP (ssnap.net), Neural Mechanisms Online (neuralmechanisms.org), and The Brains Blog (philosophyofbrains.com). Despite being freely accessible to a massive online audience, however most of the se resources do not have an introductory nature. Synchronous web-events organized by Neural Mechanisms Online, for instance, are entirely dedicated to advanced topics at the frontiers of the research in philosophy of neuroscience and neurophilosophy. There is little content for the uninitiated such as high school students and undergraduate majors in philosophy, psychology, and neuroscience, or for interested scholars with no specific training in philosophy of neuroscience. To fill this gap, our project will aim to deliver free, online, introductory video content for teachers and students of philosophy, neuroscience, and their intersections. This content will constitute the first introductory mini-course entirely devoted to philosophy of neuroscience and neurophilosophy. The course will cover six major topics. Each topic will include one main video lecture from a leading figure in philosophy and/or neuroscience introducing and discussing the research landscape. Additional learning material may include handouts an s suggested readings. To enhance outreach, we will also produce a short series of animated videos that direct viewers to the course. The requested funding will cover honoraria for scholars to record their footage and production costs (e.g., animations, video editing). The resulting material will be uploaded to a dedicated website of the project, as well as to popular venues for educational video content like YouTube in association with Neural Mechanisms Online and the Brains Blog. This will allow high school and college students to freely access neuroscience and philosophy earlier than they otherwise would find it and provide instructors with free, off-the-shelf material to create or update their own philosophy and neuroscience curricula. By providing a crash course for undergraduates and other scholars in the field, the project will expand the audience for online resources in the field. Lastly ,by fixing an interdisciplinary consensus on lexicon, concepts, and problems, the project seeks to establish a common ground for the interactions between philosophers and neuroscientists.

Zina Ward, PhD. Assistant Professor, Florida State University

Raphael Gerraty, PhD. Postdoctoral Fellow, Columbia University

Nick Byrd, PhD. Assistant Professor, Stevens Institute of Technology

Shadab Tabatabaeian, PhD Candidate, University of California, Merced

Fabrizio Calzavarini, PhD. Postdoctoral Fellow, University of Bergamo & LLC, Turin