403 research outputs found
retrocueing_RNN
No full textRNN data for the paper published as: Piwek, Stokes, & Summerfield (2023) A recurrent neural network model of prefrontal brain activity during a working memory task. PLOS Computational Biology (in press
Summerfield kindergarten school/community center
No full textThe thesis will examine the design of a kindergarten for the Summerfield area in Lubbock, Texas. Through knowledge gained from a literature review of Montessori and Piaget, this kindergarten is to be an environment that stimulates learning in children. Through the investigation of factors identified in the literature search and in case study evaluations, such as the use of appropriate scale and detail, an environment can be developed to excite spatial perception. Through an analysis of case studies, the author will identify issues and elements that could be incorporated into the design of the kindergarten
NEU902451 Supplemental Material - Supplemental material for Polyclonal lymphocytic infiltrate with arachnoiditis resulting from intrathecal stem cell transplantation
No full textSupplemental material, NEU902451 Supplemental Material for Polyclonal lymphocytic infiltrate with arachnoiditis resulting from intrathecal stem cell transplantation by Ajay A. Madhavan, Dan Summerfield, Christopher H. Hunt, Dong K. Kim, Karl N. Krecke, Aditya Raghunathan and John C. Benson in The Neuroradiology Journal</p
The Fin de Siècle Imagination in Australia, 1890–1914 (Book review)
No full textIn The Fin de Siècle Imagination in Australia, 1890–1914, Mark Hearn uses a biographical method to investigate the influence of ‘powerful movements’ and new ideas on seven select Australian writers, activists, and politicians, who are distinguished by their differences of race, class, and gender. The test subjects, in order, are the working-class writer Henry Lawson; the feminist activists Rose Summerfield and Vida Goldstein; the poet and academic Christopher Brennan; the journalist-turned-politician and, ultimately, prime minister Alfred Deakin; the First Nations writer and inventor David Unaipon; and the working-class activist John Dwyer. The book is organised into an introduction, the seven biographical chapters, and a brief conclusion.No Full Tex
A Man Comes from Someplace
No full textstory of a lost world, a story in history of a multi-generational Jewish family from a shtetl in Ukraine before WWI. As cultural study, the narrative draws upon the oral stories of the author?s father, family letters, eyewitness accounts, immigration papers, etc., and cultural research. The narrative becomes a transformative space to re-present story as performance, a meta-narrative, and an auto-ethnography for the author to reflect upon the effects of the stories on her own life, as daughter of a survivor, and as teacher/scholar. Summerfield raises questions about immigration, survival, resilience, place and identity, how story functions as antidote to trauma, a means of making sense of the world, and as resistance, the refusal to be silenced or erased, the insistence we know the past and remember those who came before. In 2011, she found her way back to the place her family came from in Ukraine. The book is now being read by students in their ESL classes in Novokoonstantinov, Ukraine
Dissociable sources of uncertainty in perceptual decision making
No full textThe natural world provides sensory systems with noisy and ambiguous information, which is often transformed into a more stable categorical percept. This thesis aims to investigate the nature of the neural representations in the visual system that support this transformation. To do so, we will employ a behavioural task that requires participants to average several independent sources of perceptual information. This task allows for the dissociation of two theoretically orthogonal sources of decision uncertainty: the mean distance of the perceptual information from a category boundary and the variability of the evidence under consideration. Behaviourally, both decreasing the mean distance to bound of information and increasing information variability are associated with increased errors and prolonged response times. We will present a computational model that can account for the independent behavioural effects of these two sources of uncertainty by assuming that categorical decisions are made on the basis of a probabilistic transformation of perceptual evidence. BOLD measurements demonstrate that these effects of mean and variability are supported by a partially dissociable network of brain regions. Electroencephalography demonstrates the differential influence of mean and variance in the pre- and post-decision period. Furthermore, we show that there is adaptation at the level of the perceptual representation to the information variance. Not only does this show that the visual system must represent information at the summary level, in addition to individual feature-based representation, but it also suggests that the costs associated with this form of perceptual uncertainty can be largely mitigated by the adoption of a more suitable representational range
Optimal decision agents? Biases during voluntary information sampling using eye-tracking
No full textThis thesis aims to understand the policy by which humans sample information, and how this policy influences perceptual decision-making. We will address this question using a variety of methods, including behavioural measures, computational modelling, and eye tracking. First, we will highlight the importance of considering an explicit information sampling policy as an integral component of the decision-making process. Secondly, we will focus on the determinants of this information sampling policy. Finally, we will address potential methodological concerns and provide a novel analysis approach to combat these concerns.</p
Context dependencies in decision making
No full textIrrelevant information should not affect our decisions. Yet, our choices are often swayed by contextual input that is entirely unrelated to the decision at hand. This is true for perceptual judgments pertaining to the appearance of a visual stimulus, as well as for more abstract evaluations of economic prospects. My thesis examines three specific instances of context-dependent behavior across the realms of perception and economics. Across all studies, I charted the behavioral signatures of context-dependent choices via careful experimental manipulation. I combined this behavioral data with simulations of mathematical models to arbitrate between different theoretical accounts of the mechanisms underlying context-dependent decision making. My first study investigated how the context provided by an additional (decoy) alternative sways economic choice between two target options. It mapped decoy influence across the full attribute space of possible decoy definitions. My second study examined the influence that an irrelevant distractor stimulus wields on perceptual decisions. It combined psychophysical measurement with regression-based and reverse correlation analytic approaches to shed light on the functional form of distractor effects. Finally, my third study probed the mechanisms driving context-dependent categorization decisions. It measured the contributions of putative drivers of contextual influence combining novel analytic techniques with experimental design controlling for potential confounds. Across the three studies, normalization-based information processing schemes emerged as a common theme. Viewed through the normative lens of efficient neural coding, normalization provides a compelling account for the observed patterns of context-dependent choice behavior
On confidence in individual and group decision-making
No full textThis thesis is about the human ability to share and combine representations of the uncertainty associated with individual beliefs – an ability which is called metacognition and facilitates effective cooperation. We distinguish between two metacognitive representations: an implicit confidence variable for oneself and an explicit confidence report for sharing with others. Using visual psychophysics and computational modelling, we address the issues of optimality and flexibility in the formation and the utilisation of these representations. We show that people can compute the confidence variable in an optimal manner (the probability that a given belief is correct as per Bayesian inference). Further, we show that the mapping of this variable onto a confidence report can vary flexibly – with people adjusting their reports according to the history of reports given and feedback obtained. This optimality and flexibility is important for effective cooperation. Being a probability, the optimal confidence variable can be compared across people. However, to facilitate this comparison, people must adapt their confidence reports to each other and develop a common metric for reporting the probability that their belief is correct. We show that people solve this communication problem sub-optimally; they match each other’s mean confidence and confidence distributions, regardless of whether they are equally likely to be correct or not. In addition, we show that, while people can take into account differences in underlying competence to some extent, they fail to do so adequately; they exhibit an equality bias, weighting their partner’s beliefs as if they were as good or as bad as their own, regardless of true differences in their underlying competence. More generally, our results pose a problem for our current understanding of metacognition which assumes that confidence reports are stable over time. In addition, our results show that confidence reports are socially malleable, and thus raise the possibility that well-known biases, such as overconfidence, might reflect particular norms for social interaction
How are cognitive maps formed and encoded in humans?
No full textTo structure knowledge efficiently, we need to acquire and store it in a way that retains the relations between different pieces of content. A cognitive map is a way of organising knowledge in a relational manner that provides us with behavioural flexibility when goal demands change. If we needed a new cognitive map for every situation we encounter, however, this would be very computationally expensive. In Chapter 2, we investigate how humans share or reuse relational maps across different contexts when it is adaptive to do so and how this process is encoded neurally. Using a novel task in which the transition function is shared across one set of contexts but not the other, we find that the human medial temporal lobe encodes transition probabilities in an efficient way by representing them more similarly when they can be shared across contexts.
But what factors affect how well and robustly a novel cognitive map is learned in the first place? In Chapters 3 and 4 of this thesis we manipulate the training curriculum used during novel cognitive map learning and aim to elucidate the effect of training curriculum on map learning behaviourally (Chapter 3) and in terms of neural representations (Chapter 4). We investigate map learning using maps consisting of discrete images. We find that the spatial sampling of transitions during training affects participants’ retention as well as multi-step inference ability. Spatially disjoint sampling of transitions within a block of training (i.e. experiencing transitions from across the map) was beneficial compared to random walks along rows or columns of the map (trajectory training). We hypothesise that this is due to better spatial grounding in the disjoint condition as well as weaker encoding and compression of the map into separate rows and columns in the trajectory condition. In Chapter 4, we find evidence for the latter, with trajectory training resulting in neural encoding of the map that is more similar to separate transitive lines along rows and columns, the extent of which is related to behavioural accuracy. It therefore appears that the order in which we experience parts of a map during initial exposure is crucial for not only map retention but also the nature of the resulting map
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