Ned Block

The study of the brain’s representations of uncertainty is a central topic in neuroscience. Unlike most quantities of which the neural representation is studied, uncertainty is a property of an observer’s beliefs about the world, which poses specific methodological challenges. We analyze how the literature on the neural representations of uncertainty addresses those challenges and distinguish between ‘code-driven’ and ‘correlational’ approaches. Code-driven approaches make assumptions about the neural code for representing world states and the associated uncertainty. By contrast, correlational approaches search for relationships between uncertainty and neural activity without constraints on the neural representation of the world state that this uncertainty accompanies. To compare these two approaches, we apply several criteria for neural representations: sensitivity, specificity, invariance and functionality. Our analysis reveals that the two approaches lead to different but complementary findings, shaping new research questions and guiding future experiments.

Despite the centrality of the notion of representation in neuroscience, the field lacks a unified framework for the concepts used to characterize representation, leading to disparate use of both terminology and the measures associated with it. To offer clarification, we propose a core set of conceptual dimensions that characterize representations in neuroscience. These dimensions describe relations between a neural response, features that may be represented and downstream effects of the neural response. A neural response may be shown to be sensitive or specific to a feature, invariant to other features or functional (it is used downstream in the brain). We use information-theoretic measures to illustrate these conceptual dimensions and explain how they relate to data analysis methods such as correlational analyses, decoding and encoding models, representational similarity analysis, and tests of statistical dependence or adaptation. We consider several canonical examples, including models of the representation of orientation, numerosity and spatial location, which illustrate how the evidence put forth in support or criticism of these models is systematized by our framework. By offering a unified conceptual framework to characterize representation in neuroscience, we hope to aid the comparison and integration of results across studies and research groups and to help to determine when evidence for a neural representation is strong.

Experiments suggest that conscious decisions to act may be initiated by unconscious neural events that precede the decision. Some have concluded that unconscious neural events are sufficient to cause both the decision to act and the action, so consciousness has no causal efficacy in producing the action. Here, I explain why this reasoning is fallacious in terms that apply to all mental events, using a variety of examples in which the conscious aspect of a mental event has a different and even an “opposite” effect on behavior from the unconscious aspect. These cases provide vivid illustrations of the distinct causal contributions of conscious and unconscious aspects of a mental event to behavior. One conclusion is that the neuroscience of decision has been overly focused on binary “go/no go” contents, obscuring the ways in which conscious and unconscious contents can make distinct contributions to decision and to action.

This chapter criticizes the property dualism argument. It argues that one version of the argument conflates two different notions of mode of presentation: the “cognitive mode of presentation,” which is defined in terms of its role in determining reference and/or explaining cognitive significance; and the “metaphysical mode of presentation,” which is a property of the referent in virtue of which the cognitive mode of presentation plays its semantic and cognitive roles. It also examines John Perry's (2001) book, which discusses both Max Black's argument and the Knowledge Argument as well as some arguments drawn from Stephen White's (1986) essay on the topic and arguments inspired by unpublished papers by White.

Computational functionalism claims that executing certain computations is suf- cient for consciousness, regardless of the physical mechanisms implementing those computations. This view neglects a compelling alternative: that subcomputational biological mechanisms, which realize computational pro- cesses, are necessary for consciousness. By contrasting computational roles with their subcomputational biological realizers, I show that there is a systematic tension in our criteria for consciousness: prioritizing computational roles favors consciousness in AI, while prioritizing subcomputational biological realizers fa- vors consciousness in simpler animals. Current theories of consciousness are 'meat-neutral', but if speci c physical substrates are necessary, AI may never achieve consciousness. Understanding whether consciousness depends on computational roles, biological realizers, or both, is crucial for assessing the prospects of consciousness in AI and less complex animals.

qualia include the ways it feels to see, hear and smell, the way it feels to have a pain; more generally, what it's like to have mental states. Qualia are experiential properties of sensations, feelings, perceptions and, in my view, thoughts and desires as well. But, so defined, who could deny that qualia exist? Yet, the existence of qualia is controversial. Here is what is controversial: whether qualia, so defined, can be characterized in intentional, functional or purely cognitive terms. Opponents of qualia think that the content of experience is intentional content (like the content of thought), or that experiences are functionally definable, or that to have a qualitative state is to have a state that is monitored in a certain way or accompanied by a thought to the effect that I have that state (seecontent functionalism; intentionality). If we include the idea that experiential properties are not intentional or functional or purely cognitive in the definition of ‘qualia’, then it is controversial whether there are qualia.

Consciousness is a mongrel concept: there are a number of very different “consciousnesses.” Phenomenal consciousness is experience; the phenomenally conscious aspect of a state is what it is like to be in that state. The mark of access-consciousness, by contrast, is availability for use in reasoning and rationally guiding speech and action. These concepts are often partly or totally conflated, with bad results. This target article uses as an example a form of reasoning about a function of “consciousness” based on the phenomenon of blindsight. Some information about stimuli in the blind field is represented in the brains of blindsight patients, as shown by their correct “guesses.” They cannot harness this information in the service of action, however, and this is said to show that a function of phenomenal consciousness is somehow to enable information represented in the brain to guide action. But stimuli in the blind field arebothaccess-unconscious and phenomenally unconscious. The fallacy is: an obvious function of the machinery of accessconsciousness is illicitly transferred to phenomenal consciousness.

Classic change blindness is the phenomenon where seemingly obvious changes which coincide with visual disruptions (such as blinks or brief blanks) go unnoticed by an attentive observer. Some early work into the causes of classic change blindness suggested that any pre-change stimulus representation is overwritten by a representation of the altered post-change stimulus, preventing change detection. However, recent work revealed that even when observers do maintain memory representations of both the pre- and post-change stimulus states, they can still miss the change, suggesting that change blindness can also arise from a failure to compare the stored representations. Here we study slow change blindness, a different but related phenomenon that occurs even in the absence of visual disruptions when the change occurs sufficiently slowly, to determine whether it could be explained by conclusions from classic change blindness. Across three different slow change blindness experiments we found that observers, who consistently failed to notice the change, had access to at least two memory representations of the changing display. One representation was precise but short-lived: a relatively detailed representation of the more recent states of slow-change display, but fragile. The other representation lasted longer but was fairly general: stable but too coarse to differentiate the various stages of the changing display. These findings suggest that although multiple representations are formed, the failure to compare hypothesis might not explain slow change blindness; even if a comparison were made, the representations formed would be either too sparse (longer term stores) or too fragile (short-lived stores) for such a comparison to be informative about a change.