Colin Klein

What insects can tell us about the origins of consciousness

How, why, and when consciousness evolved remain hotly debated topics. Addressing these issues requires considering the distribution of consciousness across the animal phylogenetic tree. Here we propose that at least one invertebrate clade, the insects, has a capacity for the most basic aspect of consciousness: subjective experience. In vertebrates the capacity for subjective experience is supported by integrated structures in the midbrain that create a neural simulation of the state of the mobile animal in space. This integrated and egocentric representation of the world from the animal’s perspective is sufficient for subjective experience. Structures in the insect brain perform analogous functions. Therefore, we argue the insect brain also supports a capacity for subjective experience. In both vertebrates and insects this form of behavioral control system evolved as an efficient solution to basic problems of sensory reafference and true navigation. The brain structures that support subjective experience in vertebrates and insects are very different from each other, but in both cases they are basal to each clade. Hence we propose the origins of subjective experience can be traced to the Cambrian.

Cognitive Ontology and Region- versus Network-Oriented Analyses

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in a context-sensitive manner. I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential.

Events as Changes in the Layout of Affordances

In a target article that appeared in Ecological Psychology, Stoffregen (2000a) questioned the possibility of ecological event perception research. This article describes experiments performed to examine the perception of the disappearance of gap-crossing affordances, a variety of event as defined by Chemero (2000).We found that participants reliably perceive both gap-crossing affordances and the disappearance of gap-crossing affordances. Our findings provide empirical evidence in favor of understanding events as changes in the layout of affordances, shoring up event perception research in ecological psychology.

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1. Introduction2. A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1. What is multivariate pattern analysis?2.2. The informational benefits of multivariate pattern analysis3. Why the Decoder’s Dictum Is False 3.1. We don’t know what information is decoded3.2. The theoretical basis for the dictum3.3. Undermining the theoretical basis4. Objections and Replies 4.1. Does anyone really believe the dictum?4.2. Good decoding is not enough4.3. Predicting behaviour is not enough5. Moving beyond the Dictum6. Conclusion Introduction A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1. What is multivariate pattern analysis?2.2. The informational benefits of multivariate pattern analysis What is multivariate pattern analysis? The informational benefits of multivariate pattern analysis Why the Decoder’s Dictum Is False 3.1. We don’t know what information is decoded3.2. The theoretical basis for the dictum3.3. Undermining the theoretical basis We don’t know what information is decoded The theoretical basis for the dictum Undermining the theoretical basis Objections and Replies 4.1. Does anyone really believe the dictum?4.2. Good decoding is not enough4.3. Predicting behaviour is not enough Does anyone really believe the dictum? Good decoding is not enough Predicting behaviour is not enough Moving beyond the Dictum Conclusion

Imperatives, phantom pains, and hallucination by presupposition

Several authors have recently argued that the content of pains (and bodily sensations more generally) is imperative rather than descriptive. I show that such an account can help resolve competing intuitions about phantom limb pain. As imperatives, phantom pains are neither true nor false. However, phantom limb pains presuppose falsehoods, in the same way that any imperative which demands something impossible presupposes a falsehood. Phantom pains, like many chronic pains, are thus commands that cannot be satisfied. I conclude by showing that some of the negative psychological consequences of chronic pain are a direct consequence of their imperative nature.

What do predictive coders want

The so-called “dark room problem” makes vivd the challenges that purely predictive models face in accounting for motivation. I argue that the problem is a serious one. Proposals for solving the dark room problem via predictive coding architectures are either empirically inadequate or computationally intractable. The Free Energy principle might avoid the problem, but only at the cost of setting itself up as a highly idealized model, which is then literally false to the world. I draw at least one optimistic conclusion, however. Real-world, real-time systems may embody motivational states in a variety of ways consistent with idealized principles like FEP, including ways that are intuitively embodied and extended. This may allow predictive coding theorists to reconcile their account with embodied principles, even if it ultimately undermines loftier ambitions.

Dispositional implementation solves the superfluous structure problem

Consciousness supervenes on activity; computation supervenes on structure. Because of this, some argue, conscious states cannot supervene on computational ones. If true, this would present serious difficulties for computationalist analyses of consciousness (or, indeed, of any domain with properties that supervene on actual activity). I argue that the computationalist can avoid the Superfluous Structure Problem (SSP) by moving to a dispositional theory of implementation. On a dispositional theory, the activity of computation depends entirely on changes in the intrinsic properties of implementing material. As extraneous structure is not required for computation, a system can implement a program running on some but not all possible inputs. Dispositional computationalism thus permits episodes of computational activity that correspond to potential episodes of conscious awareness. The SSP cannot be motivated against this account, and so computationalism may be preserved.

Kicking the Kohler Habit

Kohlers experiments with inverting goggles are often thought to support enactivism by showing that visual re-inversion occurs simultaneous with the return of sensorimotor skill. Closer examination reveals that Kohlers work does not show this. Recent work by Linden et al. shows that re-inversion, if it occurs at all, does not occur when the enactivist predicts. As such, the empirical evidence weighs against enactivism.

Consciousness, intention, and command-following in the vegetative state

Some vegetative state patients show fMRI responses similar to those of healthy controls when instructed to perform mental imagery tasks. Many authors have argued that this provides evidence that such patients are in fact conscious, as response to commands requires intentional agency. I argue for an alternative reading, on which responsive patients have a deficit similar to that seen in severe forms of akinetic mutism. Akinetic mutism is marked by the inability to form and maintain intentions to act. Responsive patients are likely still conscious. However, the route to this conclusion does not support attributions of intentional agency. I argue that aspects of consciousness, rather than broad diagnostic categories, are the more appropriate target of empirical investigation. Investigating aspects of consciousness provides a better method for investigating profound disorders of consciousness. 1 Introduction 2 Responses in the Vegetative State   2.1 The imaging evidence   2.2 The need for models 3 Responsive Patients and Akinetic Mutism   3.1 Akinetic mutism   3.2 Akinetic mutism as a deficit in intentions   3.3 Arguments for the link   3.4 Interim conclusion 4 Other Models 4.1 A deficit in ability? 4.2 Modular intentions? 5 Consciousness and Method 5.1 Are they conscious? 5.2 Aspects versus levels 1 Introduction 2 Responses in the Vegetative State   2.1 The imaging evidence   2.2 The need for models   2.1 The imaging evidence   2.2 The need for models 3 Responsive Patients and Akinetic Mutism   3.1 Akinetic mutism   3.2 Akinetic mutism as a deficit in intentions   3.3 Arguments for the link   3.4 Interim conclusion   3.1 Akinetic mutism   3.2 Akinetic mutism as a deficit in intentions   3.3 Arguments for the link   3.4 Interim conclusion 4 Other Models 4.1 A deficit in ability? 4.2 Modular intentions? 4.1 A deficit in ability? 4.2 Modular intentions? 5 Consciousness and Method 5.1 Are they conscious? 5.2 Aspects versus levels 5.1 Are they conscious? 5.2 Aspects versus levels

Ghosts in machine learning for cognitive neuroscience: Moving from data to theory

ABSTRACT The application of machine learning methods to neuroimaging data has fundamentally altered the field of cognitive neuroscience. Future progress in understanding brain function using these methods will require addressing a number of key methodological and interpretive challenges. Because these challenges often remain unseen and metaphorically “haunt” our efforts to use these methods to understand the brain, we refer to them as “ghosts”. In this paper, we describe three such ghosts, situate them within a more general framework from philosophy of science, and then describe steps to address them. The first ghost arises from difficulties in determining what information machine learning classifiers use for decoding. The second ghost arises from the interplay of experimental design and the structure of information in the brain – that is, our methods embody implicit assumptions about information processing in the brain, and it is often difficult to determine if those assumptions are satisfied. The third ghost emerges from our limited ability to distinguish information that is merely decodable from the brain from information that is represented and used by the brain. Each of the three ghosts place limits on the interpretability of decoding research in cognitive neuroscience. There are no easy solutions, but facing these issues squarely will provide a clearer path to understanding the nature of representation and computation in the human brain. HIGHLIGHTSProvides a philosophical framework for thinking about applications of machine learning to cognitive neuroscience datasets.Discussion of current challenges for neural decoding research.Gives suggestions about how to address contemporary challenges in decoding research.