Neural Network Principles
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Based on our competences and preconceptions resulting from training and experience we predominantly see what we expect to see.
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If a hammer is all you have, every problem resembles a nail. This principle of compatibility in neural information processing implies a compulsion to be consistent with what we already know, think or have done, resulting in a tendency to ignore or overlook relevant information because it does not match with our current behavior or mindset. Other examples of this kind of biased reasoning are the curse of knowledge difficulty with taking the perspective of lesser-informed people: Kennedy, , the familiarity heuristic familiar items are favored over unfamiliar ones: Park and Lessig, and the sunk-cost fallacy tendency to consistently continue a chosen course with negative outcomes rather than alter it: Arkes and Ayton, Elaborating on the confirmation bias as an example: When people perceive information, they tend to selectively notice examples that are consistent with confirm their existing superstitious intuitions.
This may be explained by the fact that neural networks are more easily activated by stimulus patterns that are more congruent with their established connectionist properties or their current status. An example is priming the exposure to one stimulus influences the response to another: Meyer and Schvaneveldt, ; Bao et al. For example, a word is more quickly and easily recognized after the presentation of a semantically related word. When a stimulus is experienced, subsequent experiences of the same stimulus will be processed more quickly by the brain Forster and Davis, The situational context activates connected neural knowledge structures characteristics, patterns, stereotypes, similarities, and associations in an automatic and unconscious manner Bargh et al.
In general neural information processing is characterized by processes such as potentiation and facilitation Katz and Miledi, ; Bao et al.
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Neural facilitation means that a post-synaptic action potential evoked by an impulse is increased when that impulse closely follows a prior one. Hence, a cell is activated more easily its activation threshold is lower directly after a prior activation. These, and other short-term synaptic changes support a variety of computations Abbott and Regehr, ; Jackman and Regehr, Potentiation works on similar principles but on longer time scales tens of seconds to minutes: Bao et al. Long-term Hebbian forms of plasticity such as potentiation make the processing of incoming information more efficient and effective when this information complies with previous activations Destexhe and Marder, ; Wimmer and Shohamy, We suppose that these kinds of processes may form the basis for our tendency to interpret and focus on information that confirms previously established perceptions, interpretations or conclusions i.
So, whereas conventional repositories or hard disks can take up, process, and store information indifferently of its characteristics, in neural networks the selection and processing of inputs depends of the characteristics of the information. Compatible, conforming, or matching inputs are more easily selected, processed, and established, thus contributing to priming effects.
This may explain why we see what we expect to see and why we associate more value or importance to information that aligns with what is already represented in our brains. All stimuli entering the nervous system affect its physical—chemical structure and thereby its connectionist properties. So, unlike a computer program, once information has entered the brain, it cannot simply be ignored or put aside. It always has an effect.
Biased reasoning then occurs when irrelevant or misleading information associatively interferes with this process. The Hebbian principles of neural plasticity imply that the accumulation and processing of information necessarily causes synaptic changes, thereby altering the dynamics of neural networks Destexhe and Marder, When existing circuits are associatively activated by new related inputs, their processing characteristics and outcomes estimations, judgments, and decisions will also be affected. This may be elucidated by the hindsight and outcome biases.
Unlike conventional computer programs, the brain does not store new information independent and separately from old information. This neural reconsolidation of memory circuits, integrating new inputs with related existing representations will make the exact representation of the original information principally inaccessible for the brain. In behavioral terms: since hindsight or outcome knowledge is intrinsically connected to the memories about the original decision situation or event, new information received after the fact influences how the person remembers this original situation.
Because of this blending of the neural representations of initial situations and outcomes the original representations must be reconstructed, which may cause a bias toward the final state.
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This may easily result in the tendency to see past events as having been predictable at the time they occurred, and in the tendency to weigh the ultimate outcome in judging the quality of a previous course of events outcome bias. Likewise, the long-term possession of an item may result in more neural ingraining than something that is not yet owned. Loss of this property may then be more disruptive to the integrity of the associate neural circuitry than the idea of not acquiring this item.
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This may lead to the endowment effect, i. The Focus Principle states that the brain focusses associatively on dominant information, i. The fact that other possible relevant information may exist beyond is insufficiently recognized or ignored like a blind spot. When making a decision, the brain is not a logical system that systematically and proportionally takes into account and weighs all relevant information. Instead, our brain works more like a magnifying glass. When making decisions, we tend to rely on conclusions that are based on limited amounts of readily available information rather than on larger bodies of less consistent data illusion of validity: Kahneman and Tversky, This ignorance of lacking information works in the opposite direction of the previously discussed Retainment Principle.
This gap in our awareness of what we do not know resembles the blind spot in our visual field: the unaware obscuration of the visual field due to the absence of light-detecting photoreceptor cells on the location where the optic nerve passes through the optic disk of the retina. As result the corresponding part of the visual field roughly 7.
This local blindness usually remains completely unnoticed until one is subjected to specific tests Wandell, This is the tendency to judge the frequency, importance or likelihood of an event by the ease with which relevant instances come to mind, which is highly determined by their imaginability or retrievability Tversky and Kahneman, , Also, we have a poor appreciation of the limits of our knowledge, and we usually tend to be overconfident about how much we already know.
According to Kahneman , the capacity of humans to believe the unbelievable on the basis of limited amounts of consistent information is in itself almost inconceivable i. We often tend to be overconfident about the accuracy of our estimations or predictions prognosis illusion and we pay limited attention to factors that hamper the accuracy of our predictions overconfidence effect: Moore and Healy, This is supposed to result from the fact that we do not see the unknowns Kahneman, and that we have more information about ourselves than about others Moore and Healy, Other examples of focus-biased reasoning are related to our overall tendency to focus on certain information while ignoring the rest, such as: the focusing illusion and focalism the tendency to place too much emphasis on one or a limited number of aspects of an event or situation , the survivorship bias a tendency to focus on the elements that survived a process and to forget about those that were eliminated: Brown et al.
An example of how the Focus principle in neural processes may explain our tendency to focus on and overvalue readily available information can be demonstrated by the way formerly established preconceptions may associatively enter a judgment and deliberation process and thus affect the resulting decision.
In general, when people think about their superstitious intuitions, they are likely to automatically remember examples that support these. And when a compatible experience recently has reinforced such a superstitious belief, according to the Hebb rule it may be more easily activated again compatibility. Moreover, these reconsolidating activations may also enhance inhibitory collateral outputs, which for example mediate the mechanism of lateral inhibition Isaacson and Scanziani, Lateral inhibition in neural circuits involves a mutual inhibition of competing neurons proportionally to their activation level.
In this way groups of neurons that are slightly more active can quickly become dominant. Lateral inhibition amplifies initially small differences e. These dominant associations may suppress the activation and retrieval of other possibly contradicting associations. It may also explain why we are unable to simultaneously perceive contradicting interpretations in ambiguous stimuli, like the Necker Cube.
This basic neural mechanism of contrast enhancement by lateral inhibition makes us base our decisions on a limited amount of consistent information while we remain unware of the fact that we fail to consider additional relevant data or alternative interpretations. This way limited amounts of relatively strong ideas, habits or intuitions may easily dominate our decision-making processes by suppressing alternative but weaker processes. Although our tendency to use heuristics that seem to violate the tenets of rationality generally leads to fairly acceptable outcomes with little experienced costs, it can sometimes result in suboptimal decisions.
This qualification depends on the context and on the chosen qualification standard. We chose not to go into the fundamental question whether human heuristic thinking should be considered as rational or irrational, but we focused on the origins and theoretical explanation of the pervasive and systematic character of heuristics and bias in human cognition. The result of this endeavor may provide axioms and principles to better explain and model cognitive processes in order to adequately understand human decision making.
The current three explanatory perspectives attribute biases in human decision making to cognitive capacity limitations and mismatches between available or deployed heuristics that are optimized for specific conditions and the conditions in which they are actually applied. As noted before, this does not explain why human decision making so universally and systematically violates the rules of logic and probability in relatively simple and obvious problem situations even when sufficient time is available, or why some heuristics and biases involve an increase in the amount of information processing Shafir and LeBoeuf, This universal and pervasive character of heuristics and biases calls for a more fundamental and generic explanation.
Based on biological and neuroscientific knowledge, we conjectured that cognitive heuristics and bias are inevitable tendencies linked to the inherent design characteristics of our brain. According to our framework, all neural networks typically include association as their most fundamental property. Our brain has a strong inclination or preference for coherence i. This tendency, which may be very efficient for the execution of perceptual-motor tasks, may lead to various distortions in the processing of cognitive information.
Next to association, as the most fundamental and pervasive principle of neural wetware, we describe three additional characteristics that are directly related to associative information processing and that may affect decision making i. The effects of the four discussed characteristics are additional and not mutually exclusive. So, the examples presented above should not suggest that there is a one-to-one relationship between underlying design principles of the brain and cognitive biases Poldrack, Some biases even may have common origins that mutually reinforce each other.
For example, on the basis of the strong preference of our brain for coherence i. On top of that, this tendency may also have had survival value for our ancestors because it made them careful of any possible harmful coincidences. So this tendency of neural wetware to detect superstitious relationships may have been evolutionary reinforced. All four principles may affect decision making and may contribute to cognitive biases, but the degree to which they do so may vary over different biases and situations.
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For example, the thoughts that come into our mind, or the information we consider when we make a decision availability bias always result from a continuing flow of Associations. Which relevant or irrelevant information is associatively included or preferred in our deliberations is affected by the characteristics of the given information in relation to our current neural state Compatibility.
The effects of irrelevant associative processes on our judgments or decisions cannot easily be suppressed or ignored Retainment , and we focus on those thoughts that associatively do pop up, while neglecting relevant information that is not directly available or known Focus. So, these intrinsic design characteristics form the basis for our inclinations to associate and combine unrelated information, to give priority to compatible information, to retain irrelevant information that should be ignored, and to focus on specific information, while ignoring possible relevant information that is not directly available or recognized.
Although the number of heuristics and biases that have been identified in the psychological and behavioral economics literature is large, closer inspection reveals many similarities and consistencies among them, the one often being a specific example of the other. This abundance of often quite similar bias phenomena may be readily simplified and explained by the aforementioned unifying principles of neural networks.
It should be noted, however, that it appeared not possible to relate the whole range over of bias phenomena to the four principles. The kind of biases we could not readily map onto the present four principles appeared to be those that are concerned with calculations and estimations on gain and loss and our poor abilities in statistical reasoning in general.
So, the present framework does not readily explain why people seem not very concerned with the outcomes of probability reasoning Kahneman and Tversky, Our focus on the generic characteristics of neural information processing is not in conflict with the conception of the brain as an anatomically differentiated organ whose individual regions are functionally specialized and make specific contributions to mind and cognition Finger, The notion that many parts and regions of the brain are associated with the expression of specific mental, behavioral, or cognitive operations is supported by a wealth of evidence from both anatomical and physiological studies, as well as from non-invasive neuroimaging.
Functional specialization is one of the enduring theoretical foundations of cognitive neuroscience van den Heuvel and Sporns, Our approach should be considered as complementary to the neuroscientific studies on functional specialization and interaction of brain regions. The intrinsic mechanisms and characteristics of neural processes that we propose are inherent to all neural networks and will therefore occur throughout the brain.
While we also link neural processes to biases and heuristics, this is done at level of connectionist properties of cell assemblies and the interaction between neurons in a network as this happens throughout the brain. It should thus be considered additional to the work on biases and heuristics in the functional brain anatomy sense. In general, it is likely that good choices are shaped by an interplay between cognitive and emotional processes, i.
However, according to our framework there is a strong, overall tendency to default to heuristic thinking that can be rather simply and readily explained by generic principles of neural wetware. Limbic—frontal interactions Type 2 may be involved in modulating this pervasive default. When heuristics and biases emerge from the basic characteristics of biological neural networks, it is not very surprising that comparable cognitive phenomena are observed in animal behavior e.
For example, hyperbolic discounting has been found in rats, pigeons, and monkeys Alexander and Brown, Also, in the domain of artificial neural networks, it has been found that applying standard machine learning to textual data results in human-like stereotyped biases that reflect everyday human culture and traditional semantic associations Caliskan et al.