University Relations
http://www.umn.edu/urelate
612-624-6868
http://www.umn.edu/urelate
612-624-6868
Thursdays, 12:00 - 1:00 pm, Elliott Hall S204
Fall Semester 2017
September 14 - "Cognition does not affect perception: Evaluating the evidence for "top-down" effects,"
by Firestone and Scholl
Abstract
What determines what we see? In contrast to the traditional “modular” understanding of perception, according to which visual processing is encapsulated from higher-level cognition, a tidal wave of recent research alleges that states such as beliefs, desires, emotions, motivations, intentions, and linguistic representations exert direct, top-down influences on what we see. There is a growing consensus that such effects are ubiquitous, and that the distinction between perception and cognition may itself be unsustainable.We argue otherwise: None of these hundreds of studies — either individually or collectively — provides compelling evidence for true top-down effects on perception, or “cognitive penetrability.” In particular, and despite their variety, we suggest that these studies all fall prey to only a handful of pitfalls. And whereas abstract theoretical challenges have failed to resolve this debate in the past, our presentation of these pitfalls is empirically anchored: In each case, we show not only how certain studies could be susceptible to the pitfall (in principle), but also how several alleged top-down effects actually are explained by the pitfall (in practice). Moreover, these pitfalls are perfectly general, with each applying to dozens of other top-down effects.
We conclude by extracting the lessons provided by these pitfalls into a checklist that future work could use to convincingly demonstrate top-down effects on visual perception. The discovery of substantive top-down effects of cognition on perception would revolutionize our understanding of how the mind is organized; but without addressing these pitfalls, no such empirical report will license such exciting conclusions.
September 21 - "Unconscious influences on decision making: A critical review"
by Newell and Shanks
Abstract
To what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories assigning causally effective roles to unconscious influences.This article presents a novel framework for evaluating these claims and reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment, deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration.
The review highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of "landmark" results, and a tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future research should focus on tasks in which participants' attention is diverted away from the experimenter's hypothesis, rather than the highly reflective tasks that are currently often employed.
September 28 - "The Now-or-Never bottleneck: A fundamental constraint on language"
by Christiansen and Chater.
Abstract
Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this "Now-or-Never" bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must "eagerly" recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with "Right-First-Time"; once the original input is lost, there is no way for the language system to recover.This is "Chunk-and-Pass" processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution.
October 5 - "Precis on The Cognitive-Emotional Brain" by Pessoa
Abstract
In The Cognitive-Emotional Brain (Pessoa 2013), I describe the many ways that emotion and cognition interact and are integrated in the brain. The book summarizes five areas of research that support this integrative view and makes four arguments to organize each area.(1) Based on rodent and human data, I propose that the amygdala's functions go beyond emotion as traditionally conceived. Furthermore, the processing of emotion-laden information is capacity limited, thus not independent of attention and awareness.
(2) Cognitive-emotional interactions in the human prefrontal cortex (PFC) assume diverse forms and are not limited to mutual suppression. Particularly, the lateral PFC is a focal point for cognitive-emotional interactions.
(3) Interactions between motivation and cognition can be seen across a range of perceptual and cognitive tasks. Motivation shapes behavior in specific ways — for example, by reducing response conflict or via selective effects on working memory. Traditional accounts, by contrast, typically describe motivation as a global activation independent of particular control demands.
(4) Perception and cognition are directly influenced by information with affective or motivational content in powerful ways. A dual competition model outlines a framework for such interactions at the perceptual and executive levels. A specific neural architecture is proposed that embeds emotional and motivational signals into perception and cognition through multiple channels.
(5) A network perspective should supplant the strategy of understanding the brain in terms of individual regions. More broadly, in a network view of brain architecture, “emotion” and “cognition” may be used as labels of certain behaviors, but will not map cleanly into compartmentalized pieces of the brain.
October 12 - "How to undress the affective mind: An interview with Jaak Panksepp" by Shaun Gallagher.
October 26 - "Mental organs and the origins of mind" by Thomas S. Ray
November 9 - "Is AI riding a one-trick pony?" by James Somers.
Just about every AI advance you've heard of depends on a breakthrough that's three decades old. Keeping up the pace of progress will require confronting AI's serious limitations.
November 16th - "Cognitive effort: A neuroeconomic approach"
Abstract: Cognitive effort has been implicated in numerous theories regarding normal and aberrant behavior and the physiological response to engagement with demanding tasks. Yet, despite broad interest, no unifying, operational definition of cognitive effort itself has been proposed. Here, we argue that the most intuitive and epistemologically valuable treatment is in terms of effort-based decision-making, and advocate a neuroeconomics-focused research strategy. We first outline psychological and neuroscientific theories of cognitive effort. Then we describe the benefits of a neuroeconomic research strategy, highlighting how it affords greater inferential traction than do traditional markers of cognitive effort, including self-reports and physiologic markers of autonomic arousal. Finally, we sketch a future series of studies that can leverage the full potential of the neuroeconomic approach toward understanding the cognitive and neural mechanisms that give rise to phenomenal, subjective cognitive effort.
November 30 - "Big data and the industrialization of neuroscience: A safe roadmap for understanding the brain?"
by Yves Fregnac
Abstract: New technologies in neuroscience generate reams of data at an exponentially increasing rate, spurring the design of very-large-scale data-mining initiatives. Several supranational ventures are contemplating the possibility of achieving, within the next decade(s), full simulation of the human brain.
December 7 - "What is consciousness, and could machines have it?" by Dehaene et al.
Abstract:
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.