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Attempts to identify the difference between humans and other primates are never ending. Other primates possess numerous characteristics in common with humans. For example, a comparison of the genomes shows that We are both bipedaloid; yet the mental gap between men and apes is immense. What is it that humans have that other primates do not? The two books currently under review take two different approaches to this question, and thereby offer two different — though complimentary — answers.
Professor Passingham was awarded a B. He did his Ph. George Ettlinger — His book is a search for the. The Heythrop Journal — Wiley. Enjoy affordable access to over 18 million articles from more than 15, peer-reviewed journals. Get unlimited, online access to over 18 million full-text articles from more than 15, scientific journals. See the journals in your area.
McCall, Bradford Pp. Anderson McCall, Bradford. Read Article.
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Share Full Text for Free beta. Web of Science. Where A is just a constant and d is the decay rate. Each time a memory was accessed, it added an increment to the odds that it would be needed again, with these increments all decaying according to a power function.
Thus, if an item occurred n times, the odds of it appearing again is.
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Where tk is the time since the kth practice of an item. Thus, the past history of memory use predicts the odds that the memory will be needed. But the context of the current situation is involved as well. It turns out that memory availability is adjusted as a function of context; e. Thus, human memory reflects the statistics of the environment and performs a triage on memories, devoting its limited resources to those that are most likely to be needed.
How is this fact realized in ACT-R? At any given moment, countless things are impinging on the human sensorium, of which we only remember a very small fraction. For instance, ambient sounds or things in the visual periphery certainly undergo processing in various brain regions, but they seldom attended to and thus often never make it into buffers. These chunks have activation values that govern the speed and success of their retrieval. Specifically, a given memory has an inherent, base-level activation, plus its strength of association to elements in the present context.
Since the odds of needing a memory can be considered the sum of a quantity that reflects the past history of that memory and the present context, we can represent this in Bayesian terms as. The main point is that memory is responding to two statistical effects in the environment: 1 the more often a memory is retrieved, the more likely it is to be retrieved in the future. Secondly, 2 the more memories associated with a particular element, the worse a predictor the element is of any particular memory. This is because when an element is associated with more memories, its appearance becomes a poorer predictor of any specific fact.
These results have been shown to affect all of our memories. In experimental illustration of this, Peterson and Potts had participants study 1 or 4 true facts about famous historical figures that they did not previously know, such as that Beethoven never married. Two weeks later, participants were tested on memory for three kinds of facts: 1 new facts they had learned about historical figures as part of the experiment, 2 known facts that they knew about the historical figures before the experiment eg, Beethoven was a musician , and 3 false facts that they had not learned for the experiment and that should be recognizable as very unlikely Beethoven was an famous athlete.
Participants were shown these types of statements and had to rate them as true or false, and their speed in doing so was recorded. First, it was found that the facts they knew before the experiment were recognized much more quickly than those they learned for the experiment, reflecting the greater practice and base-level activation of the prior facts. More importantly, the number of facts they had learned for the experiment 1 vs.
Anderson writes:. This relationship is also borne out in fMRI research. Using a fan-effect paradigm, it was found that greater fan more connections to a single memory resulted in decreased activation and therefore stronger fMRI respones Sohn , Here, we rely on memories rather than reasoning on the basis of general principles. This kind of instance-based reasoning may be far more common than has been traditionally thought. Given all of the above, we know how important a flexible declarative memory is to our ability to adapt to a changing environment; but once the relevant information has been retrieved, we have to act on it, using it to make inferences or predictions.
This often requires intensive, deliberative processing which is not appropriate when we have to act rapidly in stressful situations. Indeed, to the extent that one can anticipate how knowledge will be used, it makes sense to prepackage the application of that knowledge in a way that can be executed without planning. It turns out that there is a process by which frequently useful computations are identified and cached as cognitive reactions that can be elicited directly by the situation, bypassing laborious deliberation.
This shift is very visible in the debate between Tolman and Hull about the relative roles of mental reflection and mechanistic action in producing behavior. To illustrate the struggle between thought and action in the mind, Anderson has us consider the Stroop task, where you are instructed to quickly report the font color while reading a list like red yellow orange green blue black etc.
Note that these respectively correspond to the procedural module, the declarative module, and the goal module.
How Can the Human Mind Occur in the Physical Universe?
Fortunately, it appears they do just that! For example, Hikosaka et al. The monkeys practiced such sets over the course of several months, and telling differences emerged between performance during the early months and later months. Early on, the monkeys performed the same regardless of what order the grids were shown in, or of which hand they used; however, after months of practice, they had become much faster at completing the task but could not go out of order and could only use their favored hand to input the answer.
Thus, it seemed that the monkeys had switched from a flexible declarative representation of the task to a classic stimulus-response representation. Hikosaka et al. As expected, the task activated prefrontal regions early on, but after much practice the task primarily produced activity basal ganglia structures, which are thought to display a variant of reinforcement learning. Furthermore, temporarily inactivating basal ganglia structures disrupted only the highly practiced sequences not newly learned sequences.
The basal ganglia, then, is involved in producing automatic responses to stimuli.
The basal ganglia is involved in a dopamine-mediated process that learns to recognize favorable patterns of activity in the cortex Houk and Wise, That is, dopamine neurons provide information to the basal ganglia about how rewarding a behavior was, if it was more rewarding than expected, etc. Importantly, an element of time-travel is involved, because the rewards strengthen the salience of reward-producing contexual patterns.
This was all very refreshing to me. Classical and operant conditioning are often presented in psychology classrooms as museum curiosities or animal training procedures, when in fact they apply equally well to human learning. I wanted to share one final experimental demonstration of the difference between learning in the hippocampus versus the basal ganglia.
Rats are trained to go to food housed in the south arm. What will rats do if they are put in the maze on the east side? Have they learned the spatial location of the food, or have they merely learned a right-turning behavior?
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If the former is true, they should turn down the correct arm of the maze to find the food; if they latter is true, their response will lead them down the wrong arm. Early results yielded no clear choice pattern Restle, However, Packard and McGaugh trained all rats on the maze and then gave them injections that temporarily impaired either their hippocampus or their basal ganglia specifically, the caudate. A convincing follow-up study by Packard produced the same pattern of results, but this time by using memory-enhancing agents applied selectively to the hippocampus or the caudate.
https://econassinavp.ga This time, rats with hippocampal enhancements displayed behavior consistent with place-learning they chose the correct arm , while rats with enhanced caudates relied on a right-turn response and chose the incorrect arm. But where do these stimulus-response associations come from? This could entail retrieving a similar prior experience upon which to base present actions or retrieving general principles and reasoning from them. In such a situation,. However, this newly formed production requires multiple repetitions for it to acquire enough strength to be applicable in new situations.
Such rules are learned slowly, consistent with the view that procedural memories are acquired gradually. However, each time this rule is recreated its utility is increased. This rule is so persistent because half of the time, it produces the correct outcome and thus the same reward as the more limiting bottom-from-top rule.
ACT-R is used to model the acquisition of the correct rule, and I found it very compelling. Thus, an important part of cognition is the accumulation of production rules in long-term memory, which can then become activated by the contents of working memory, which can be composed into more complex production-rule chains when a particular problem is solved, the result of which can be cached and, if used above some some frequency threshold, will become a production rule in its own right.
Anderson points out that his and my discussion up to this point has actually concerned primate learning; nothing so far has been unique to humans.
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In chapter 5, he discusses learning from verbal directions and worked-out examples. Anderson goes on to discuss how human cognition can support a uniquely human skill: learning algebra from verbal directions and examples.