Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?


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Linguistics has almost entirely ignored this, which unfortunately makes most of it nonsense.

The concepts of representation and information in explanatory theories of human behavior

There are exceptions, of course. One of the last thing I did in AI before leaving the field was to try to model some bits of its insights. When talking about Fighting Games with my community members, we often physically act out the techniques we see on screen in order to communicate them to each other. This is very amusing when you have strings of impossible movement, such as animations canceling immediately into other animations. Seeing a person go from one action and as fast as possible into the next one, or imitating the action of flying in the air while being firmly on the ground.

They physically remember humanly impossible action, because that is the most expedient frame of reference for remembering what characters do in a fighting game! Delightfully weird! Or give a brief account? It involves a major paradigm shift, in which you realize that much of what you believe about minds is wrong. Most people need to come to understand how many of the individual bits are wrong before the whole picture flips over. Clearly things-in-the-head play some role, but they are often not sufficient.

And, generally, understanding things-in-the-head in terms of representations is misleading. In part, at least. His book Being-in-the-World is what flipped me; and unfortunately it might still be the best available account. I hope to do better eventually. Meaning, meaningfulness, and meaninglessness are all easily accounted for from a representational view. Actually not. Attempting to do this always runs into insoluble logical paradoxes. No one has found an answer to that, even in principle. To make the issue clearer, it may be better to think about a physical thing in a computer that supposedly represents this knowledge.

Understanding Representation in the Cognitive Sciences: Does Representation Need Reality?

What physical property could make a thing-in-a-computer represent that? There are exceptions, like John Searle. There, we have a direct causal link between two things temperature and angle. It has to work, because of physics. The same way a ball has to fall if dropped. A thermostat whose angle does not encode the temperature is broken, by definition.

There is no alternative explanation. I think if you read the Amazon blurb on it, you will recognize some of what you said there. This seems importantly right, and I think that book is a huge contribution, and I recommend it highly. What property of the computation makes it do that? Or, given a particular computation, how can we tell whether or not it is doing that? A note written on paper is a representation only because someone can read and interpret its meaning. Or, to radicalize the claim, there is no original intentionality.

All meaning is derived from interpretation potentially by someone else. This is potentially bad for science because it can lead to scientists ignoring issues that actually need to be challenged and researched more. Maybe I am wrong here, as I am not familiar with the current research being done, but it seems that epistemological humility is important for science. Who knows, by taking philosophy more seriously it could change how scientists view issues like these, and perhaps allow for someone to think of a hypothesis or some way to try to move towards empirically testing these now tenuous philosophical issues.

I find this argument confusing. A mind human or animal needs a model of the world around it in order to effect action. If it is to be useful, it needs to satisfy your criterion of causality. If the mind is basically a fancy computer no ghost in the machine , this model has to be coded in a certain way.


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Now, given a coded representation of the real world, a well-engineered system will tolerate modification and still yield sensible results, through extrapolation. Maybe an analogy to control theory: The classical AI position seems to be like naive open-loop control. You build a model of the system, invert it and then you can control it …NOT.

Heidegger strikes me as similar to simple closed-loop control. A PID controller does not involve a representation of the system it is trying to control. What I think happens in the brain and what AI needs to strive for is closer to things like more complex control approaches, which combine modelling with continuous feedback. Kalman filter You can nitpick the details but I think it is mathematically provable that such systems necessarily develop models of their surroundings. I think the correct model is that humans develop a representation of their environment which is continually updated as new information comes in.

Login to provide us with feedback. Share on. Bayne T. Some questions for neurophenomenology. Bitbol M. Bockelman P. Frontiers in Human Neuroscience 7: Brooks R. Artificial Intelligence 47 1—3 : — Chalmers D. Journal of Consciousness Studies 2 3 : — Chang H. Evidence, realism and pluralism.

Springer, New York. De Preester H. Psychoanalytische Perspectieven 20 4 : — Dennett D. In: Hookway C. Cambridge University Press, London: — Depraz N. Les Cahiers Philosophiques de Strasbourg 47— John Benjamins, Philadelphia. Dreyfus H. Artificial Intelligence — Dupuy J. Gallagher S. Second edition. Routledge, London.

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Originally published in As is well known, selective processes act on organisms through differential survival to modify gene frequencies genotype , which in turn leads to the evolution of certain body forms and behaviors extrinsic phenotype. Thus, the acquisition of information and creation of mental representations occurs in a two-step process.

Second, the validity of these representations must be gradually achieved by confronting them with the environment. The hypothesis discussed here is that the sophisticated psychological constructs classically associated with the concept of mental representation start from simple interactive behaviors. The capacity of using language and interacting in social groups allowed the gradual emergence of more complex human mental phenomena. This development can had occurred even by a relatively disorganized process of creation, modification, and correction of internal states in function of new inputs from external world.

Therefore, it is possible to admit that the mechanisms by which human cognition had developed are present in other classes of organisms.

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For example, an insect survives in its natural habitat because it can maintain a sufficiently accurate representation of external world. However, the quality and precision of this representation is the optimized result of a compromise between anatomo-physiological constraints and the necessity of providing information processing resources in the context of selective pressure in a specific niche.

Partial representations may be suited to improve survival chances because they are easier to be created and corrected and faster to be implemented in natural life situations. Another representational strategy that emerged along the evolution is the representation of the rules or patterns governing what happen in the external world. For example, conditioned behaviors in several animal species can be understood as a representation of external regularities.

In the human brain, similar mechanisms seem to work even in more complex activities. Noelle reviewed evidences that rule-guided behaviors in humans are associated with the functioning of the prefrontal cortex, the basal ganglia, and related brain structures. Also, statistical regularities in the experiences present during the development of prefrontal cortex can profoundly shape the kinds the explicit rules that can robustly be represented and applied. The process of information processing based on representation of rules can be further enhanced by the creation of subsets of a priori representations available for use in natural situations.

Innate behaviors, related to threat detection for example, require the pre-existence of relatively complex representations capable of enhancing fast protective actions. This characteristic is called preparedness of fear and phobias and it has been identified also in human behavior. Thus, fear-relevant stimuli lead to superior conditioning of aversive associations compared with fear-irrelevant stimuli.

Second, the module is automatically activated by fear-relevant stimuli, meaning that fear activation occurs before conscious cognitive analysis of the stimulus can occur. Third, the fear module is relatively impenetrable to conscious cognitive control, and fear conditioning with fear-relevant stimuli can occur even with subliminal conditioned stimuli. Fourth, the amygdala seems to be the central brain area dedicated to the fear module. The high velocity required by the process of identifying threats and implementing adequate responses imply in an increased probability of errors related to the simplification of external situations, misinterpretation of new events, and ultimately the creation of distorted representations.

This style of cognitive functioning can be understood under a biological perspective where, in natural situations, errors of commission wrongly reacting to a non-threat are more acceptable than errors of omission not reacting to a real threat. Other cognitive capacities like empathy and face recognizing also seem to be implemented by similar mechanisms of working with pre-prepared representations Regenbogen et al. Admitting that the same design strategy is used in the implementation of other cognitive functions, this mechanism of simplifying representations in order to facilitate stimuli responses may be hypothesized as playing a role in complex phenomena associated to partial or biased evaluations of external situations like folk psychological explanations and the occurrence of preconceptions in social contexts.

This concept does not depend either on the physical, biological, or linguistic nature of external object nor on the cognitive capacity of the receiver. Correlational information depends on the receiver capacity of modifying aspects of its internal states in function of changes occurring in the external environment. This strategy of adopting an information model based in correlations aims to emphasize the importance of the receiver component of in the general model of information system.


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Note that, in this model, how exactly this correspondence is physically implemented is not important. The central point is that the path blue, red, green, yellow in the external world correspond to the path a,b,c,d inside the organism. This figure illustrates different representational capacities of sensory organs.

A represents a light source emitting different colors. The sensory organ illustrated in B is capable of associating the series of different states a,b,c,d where each state is related to a different color blue, red, green, and yellow. C illustrate another kind of sensory organ not capable of distinguishing blue from green and red from yellow and, therefore, representing the changes occurring in the exterior world by simplified set of states f,g. According this model, the flux of correlational information along nervous system is the set of modifications gradually established along sensory cells, nerves, interneurons, and brain structures involved in behavior expression.

In experimental context, even physiological manifestations like, for example changes in autonomic functioning or postural control can be considered as part of the set of information that composes mental representations. The inclusion of these not purely cognitive elements is essential, for example, in the study of emotions where several experiential elements cannot be adequately described by language. This proposal does not imply in denying the existence of internally generated states. Although mental events can occur with a degree of independence from external influences for example, reflections, interpretations, and mathematical thinking the basic neural components that allowed the development of these sophisticated capacities are closely related to those working in other relatively more simple brain activities.

The human thinking process can run with a relative independence from external inputs like in mental fantasies.

What is MENTAL REPRESENTATION? What does MENTAL REPRESENTATION mean? MENTAL REPRESENTATION meaning

The correlational model proposes is that the ability of working at this level of abstraction was acquired by the gradual improvement of the capacity of using correlational information. Once acquired, this ability allows to the individual to work with independence from direct sensorial inputs and add new elements to mental contents. Although fantasies can be generated with large degree of freedom, the awareness that these contents are internally created is given by the capacity of confronting them with external inputs.

One example of internally generated state involving pre-prepared structures closely related with external events is the mirror neurons system. Originally found in macaque monkeys, in the ventral premotor cortex, area F5 and inferior parietal lobule, this group of neurons fire when the animal sees another animal or the experimenter performing actions similar to those pertaining to its natural repertoire of actions. Neuroimaging and electrophysiological studies indicate that mirror neurons may serve for action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey Oztop et al.

Although primarily of motor nature, mirror neurons have been associated with mental activities like intention understanding, emotions, empathy, and speech Acharya and Shukla, Another examples of mental representations based in brain-environment co-variant proprieties are those involved in the orientation and movement in the space. A high-definition representation is not necessary, all that is required is that it provides a stable framework to which detailed information, provided by the visual pathways through the occipital and temporal lobes, can be temporarily attached.

The creation and recording of mental representations involves the gradual recruiting of relatively distant but highly connected brain components with different time dynamics. Consequently, mental representations are not localized in specific brain regions but they gradually emerge along the entire neuronal processing. This idea is compatible with several neurobiological phenomena associated with conscious experience. Shen et al. The ability of processing complex concepts and rules governing external events is essential to the emergence of another property of human cognitive systems that is the possibility of anticipating future events.


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The capacity of preview the occurrence of a given stimulus can be identified even in simple organisms exhibiting conditioned behaviors. For example, the technique of olfactory conditioning of the sting extension response has been extensively used to yield new insights into the rules and mechanisms of aversive learning in insects Tedjakumala and Giurfa, This simple capacity of representing rules can be improved by the development of more complex neural resources.

In fact, this capacity vary from one species to other Seed et al. Moreover, there are also evidences that this representational capacity do not depend of neuronal mechanisms but also of adequate social and cultural influences Moriguchi, The next question, central for this discussion, is how simple mechanisms of correlation allow the emergence of complex abstractions in the human mind. A possible strategy for clarifying this point is to explore complex systems theories and its applicability at the several structural and organizational levels evolved in the genesis of human behavior.

The idea that complex patterns can spontaneously emerge from simpler components is largely discussed in natural sciences and a number of theoretical ideas have been proposed to explain their occurrence like, for example agent-based models and genetic algorithms Caticha and Vicente, ; Gros, One of these theoretical models in particular, known as self-organized criticality SOC , has received great attention as a possible explanation for the spontaneous emergence of complex patterns both at neural and behavioral levels. The concept of SOC was proposed by Bak et al.

Beggs and Plenz reported evidences of this phenomenon studying organotypic cultures from coronal slices of rat somatosensory cortex. They continuously recorded spontaneous local field potentials LFPs using a 60 channel multielectrode array and found that the propagation of synchronized LFPs activity was described by a power-law. The authors suggested the slope of this power-law, as well as its branching parameter, indicate the presence of SOC in these preparations. Beggs and Plenz, found evidence that the critical branching process optimizes information transmission while preserving stability in cortical networks.

Simulations showed that a branching parameter at value found in the experimental preparation optimizes information transmission in feed forward networks, while preventing runaway network excitation. Compatible with the ideas discussed here, the identification of such patterns of functioning seems to depend on the brain functioning in context. El Boustani et al. The authors found the recordings displayed power-law frequency scaling at high frequencies, with a fractional exponent dependent on the spatio-temporal statistics of the visual stimuli.

They also reported that this effect was reproduced in computational models of a recurrent network. The possibility of SOC being relevant for explaining complex human behavior was explored by Ramos et al. Although the behavior of each individual had been very different from other participants in absolute terms, the statistical description of the different groups individuals with depression, psychosis, mania, and normal controls showed identical patterns of behavioral variation.

In all groups, comparing the behavior of individuals with themselves, small changes of behavior were very frequent while large variations were rare. The characteristic of having the same variation pattern reproduced at different levels of human activity, suggests the presence of self-similarity Serrano et al. The curves describing the behavior of all clinical groups and controls showed the same aspect and fitted a power-law.

Understanding Representation in the Cognitive Sciences - Does Representation Need Reality? Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?
Understanding Representation in the Cognitive Sciences - Does Representation Need Reality? Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?
Understanding Representation in the Cognitive Sciences - Does Representation Need Reality? Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?
Understanding Representation in the Cognitive Sciences - Does Representation Need Reality? Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?
Understanding Representation in the Cognitive Sciences - Does Representation Need Reality? Understanding Representation in the Cognitive Sciences - Does Representation Need Reality?

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