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• What Is Deductive Reasoning? | Explanation & Examples

What Is Deductive Reasoning? | Explanation & Examples

Published on January 20, 2022 by Pritha Bhandari . Revised on June 22, 2023.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic or top-down reasoning.

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What is deductive reasoning, validity and soundness, deductive reasoning in research, deductive vs. inductive reasoning, other interesting articles, frequently asked questions about deductive reasoning.

In deductive reasoning, you’ll often make an argument for a certain idea. You make an inference, or come to a conclusion, by applying different premises.

A premise is a generally accepted idea, fact, or rule, and it’s a statement that lays the groundwork for a theory or general idea. Conclusions are statements supported by premises.

Deductive logic arguments

In a simple deductive logic argument, you’ll often begin with a premise, and add another premise. Then, you form a conclusion based on these two premises. This format is called “premise-premise-conclusion.”

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Validity and soundness are two criteria for assessing deductive reasoning arguments.

In this context, validity is about the way the premises relate to each other and the conclusion. This is a different concept from research validity .

An argument is valid if the premises logically support and relate to the conclusion. But the premises don’t need to be true for an argument to be valid.

• If there’s a rainbow, flights get canceled.
• There is a rainbow now.
• Therefore, flights are canceled.
• All chili peppers are spicy.
• Tomatoes are a chili pepper.
• Therefore, tomatoes are spicy.

In an invalid argument, your premises can be true but that doesn’t guarantee a true conclusion. Your conclusion may inadvertently be true, but your argument can still be invalid because your conclusion doesn’t logically follow from the relationship between the statements.

• All leopards have spots.
• My pet gecko has spots.
• Therefore, my pet gecko is a leopard.
• All US presidents live in the White House.
• Barack Obama lived in the White House.
• Therefore, Barack Obama was a US president.

An argument is sound only if it’s valid and the premises are true. All invalid arguments are unsound.

If you begin with true premises and a valid argument, you’re bound to come to a true conclusion.

• Flights get canceled when there are extreme weather conditions.
• There are extreme weather conditions right now.
• All fruits are grown from flowers and contain seeds.
• Tomatoes are grown from flowers and contain seeds.
• Therefore, tomatoes are fruits.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

This method is used for academic as well as non-academic research.

Here are the general steps for deductive research:

• Select a research problem and create a problem statement.
• Develop falsifiable hypotheses .
• Collect your data with appropriate measures.
• Analyze and test your data.
• Decide whether to reject your null hypothesis .

Importantly, your hypotheses should be falsifiable. If they aren’t, you won’t be able to determine whether your results support them or not.

You formulate your main hypothesis : Switching to a four-day work week will improve employee well-being. Your null hypothesis states that there’ll be no difference in employee well-being before and after the change.

You collect data on employee well-being through quantitative surveys on a monthly basis before and after the change. When analyzing the data, you note a 25% increase in employee well-being after the change in work week.

Deductive reasoning is a top-down approach, while inductive reasoning is a bottom-up approach.

In deductive reasoning, you start with general ideas and work toward specific conclusions through inferences. Based on theories, you form a hypothesis. Using empirical observations, you test that hypothesis using inferential statistics and form a conclusion.

Inductive reasoning is also called a hypothesis-generating approach, because you start with specific observations and build toward a theory. It’s an exploratory method that’s often applied before deductive research.

In practice, most research projects involve both inductive and deductive methods.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Deductive reasoning is also called deductive logic.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

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1.1 Introduction

Logic is one of the oldest intellectual disciplines in human history. It dates back to Aristotle. It has been studied through the centuries by people like Leibniz, Boole, Russell, Turing, and many others. And it is still a subject of active investigation today.

We use Logic in just about everything we do. We use the language of Logic to define concepts, to encode constraints, to express partial information. We use logical reasoning to derive conclusions from these bits of information. We use logical proofs to convince others of our conclusions.

And we are not alone! Logic is increasingly being used by computers - to prove mathematical theorems, to validate engineering designs, to diagnose failures, to encode and analyze laws and regulations and business rules.

Logic is also becoming more common at the interface between man and machine, in "logic-enabled" computer systems, where users can view and edit logical sentences. Think, for example, about email readers that allow users to write rules to manage incoming mail messages - deleting some, moving others to various mailboxes, and so forth based on properties of those messages. In the business world, eCommerce systems allow companies to encode price rules based on the product, the customer, the date, and so forth.

Moreover, Logic is sometimes used not just by users in communicating with computer systems but by software engineers in building those systems (using a programming methodology known as logic programming ).

This chapter is an overview of Logic as presented in this book. We start with a discussion of possible worlds and illustrate the notion in an application area known as Sorority World. We then give an informal introduction to the key elements of Logic - logical sentences, logical entailment, and logical proofs. We then talk about the value of using a formal language for expressing logical information instead of natural language. Finally, we discuss the automation of logical reasoning and some of the computer applications that this makes possible.

1.2 Possible Worlds

Consider the interpersonal relations of a small sorority. There are just four members - Abby, Bess, Cody, and Dana. Some of the girls like each other, but some do not.

The following figure shows one set of possibilities. The checkmark in the first row here means that Abby likes Cody, while the absence of a checkmark means that Abby does not like the other girls (including herself). Bess likes Cody too. Cody likes everyone but herself. And Dana also likes the popular Cody.

Of course, this is not the only possible state of affairs. The figure below shows another possible world. In this world, every girl likes exactly two other girls, and every girl is liked by just two girls.

As it turns out, there are quite a few possibilities. Given four girls, there are sixteen possible instances of the likes relation - Abby likes Abby, Abby likes Bess, Abby likes Cody, Abby likes Dana, Bess likes Abby, and so forth. Each of these sixteen can be either true or false. There are 2 16 (65,536) possible combinations of these true-false possibilities, and so there are 2 16 possible worlds.

Let's assume that we do not know the likes and dislikes of the girls ourselves but we have informants who are willing to tell us about them. Each informant knows a little about the likes and dislikes of the girls, but no one knows everything.

This is where Logic comes in. By writing logical sentences , each informant can express exactly what he or she knows - no more, no less. For our part, we can use the sentences we have been told to draw conclusions that are logically entailed by those sentences. And we can use logical proofs to explain our conclusions to others. Let's consider each of these elements in turn.

1.3 Logical Sentences

The following figure shows some logical sentences pertaining to our sorority world. The first sentence is straightforward; it tells us directly that Dana likes Cody. The second and third sentences tell us what is not true without saying what is true. The fourth sentence says that one condition holds or another but does not say which. The fifth sentence gives a general fact about the girls Abby likes. The sixth sentence expresses a general fact about Cody's likes. The last sentence says something about everyone.

Sentences like these constrain the possible ways the world could be. Each sentence divides the set of possible worlds into two subsets, those in which the sentence is true and those in which the sentence is false, as suggested by the following figure. Believing a sentence is tantamount to believing that the world is in the first set.

Given two sentences, we know the world must be in the intersection of the set of worlds in which the first sentence is true and the set of worlds in which the second sentence is true. Ideally, when we have enough sentences, we know exactly how things stand.

Effective communication requires a language that allows us to express what we know, no more and no less. If we know the state of the world, then we should write enough sentences to communicate this to others. If we do not know which of various ways the world could be, we need a language that allows us to express only what we know. The beauty of Logic is that it gives us a means to express incomplete information when that is all we have and to express complete information when full information is available.

1.4 Logical Entailment

Logical sentences can sometimes pinpoint a specific world from among many possible worlds. However, this is not always the case. Sometimes, a collection of sentences only partially constrains the world. For example, there are four different worlds that satisfy the sentences in in the preceding section, viz. the ones shown below.

Even though a set of sentences does not determine a unique world, it is often the case that some sentences are true in every world that satisfies the given sentences. A sentence of this sort is said to be a logical conclusion from the given sentences. Said the other way around, a set of sentences logically entails a conclusion if and only if every world that satisfies the sentences also satisfies the conclusion.

What can we conclude from the bits of information in our sample logical sentences? Quite a bit, as it turns out. For example, it must be the case that Bess likes Cody. Also, Bess does not like Dana. There are also some general conclusions that must be true. For example, in this world with just four girls, we can conclude that everybody likes somebody. Also, everyone is liked by somebody.

One way to check whether a set of sentences logically entails a conclusion is to examine the set of all worlds in which the given sentences are true. For example, in our case, we notice that, in every world that satisfies our sentences, Bess likes Cody, so the statement that Bess likes Cody is a logical conclusion from our set of sentences. In every world that satisfies our sentences, Bess does not like Abby, so the statement that Bess likes Abby is not a logical conclusion from our set of sentences and the statement that Bess does not like Abby is a logical conclusion.

Note that, when there are multiple possible worlds, as in this case, there are sentences that we do not know to be true or false. For example, in the case above, there is a possible world in which Abby likes Bess and there is a possible world in which Abby does not like Bess. The upshot is that the statement Abby likes Bess is not a logical conclusion; and, at the same time, the statement that Abby does not likes Bess is not a logical conclusion either. Obviously, one of these statements is true in the real world, but we do not know which is true purely on the basis of the information we are given.

1.5 Logical Proofs

Unfortunately, determining logical entailment by checking all possible worlds is impractical in general. There are usually many, many possible worlds. Moreover, as we shall see, in some cases the number of possible worlds is infinite . The upshot is that case checking is not always practical.

Luckily there is an alternative that can work even when case checking fails. The answer is logical deduction , i.e. the application of rules of inference to derive logical conclusions and thereby produce logical proofs , i.e. sequences of reasoning steps that leads from premises to conclusions .

For example, we can use this sort of reasoning to conclude that Block C is next to block D in our Blocks World example without enumerating possible worlds. The line of argument goes as shown below.

The alternative is logical reasoning , viz. the application of reasoning rules to derive logical conclusions and produce logical proofs , i.e. sequences of reasoning steps that leads from premises to conclusions .

The concept of proof, in order to be meaningful, requires that we be able to recognize certain reasoning steps as immediately obvious. In other words, we need to be familiar with the reasoning "atoms" out of which complex proof "molecules" are built.

Formalizing this, we say that a conclusion is provable from a set of premises if and only if there is a finite sequence of sentences in which every element is either a premise or the result of applying a sound rule of inference to earlier members in the sequence.

As we shall see, for well-behaved logics, logical entailment and provability are identical - a set of premises logically entails a conclusion if and only if the conclusion is provable from the premises. This is a very big deal.

One of Aristotle's great contributions to philosophy was his recognition that what makes a step of a proof immediately obvious is its form rather than its content. It does not matter whether you are talking about people or buildings or numbers. What matters is the structure of the facts with which you are working. Such patterns are called rules of inference.

As an example, consider the reasoning step shown below. We know that all Accords are Hondas, and we know that all Hondas are Japanese cars. Consequently, we can conclude that all Accords are Japanese cars.

Now consider another example. We know that all borogoves are slithy toves, and we know that all slithy toves are mimsy. Consequently, we can conclude that all borogoves are mimsy. What's more, in order to reach this conclusion, we do not need to know anything about borogoves or slithy toves or what it means to be mimsy.

What is interesting about these examples is that they share the same reasoning structure, viz. the pattern shown below.

The existence of such reasoning patterns is fundamental in Logic but raises important questions. Which patterns are correct? Are there many such patterns or just a few?

Let us consider the first of these questions. Obviously, there are patterns that are just plain wrong in the sense that they can lead to incorrect conclusions. Consider, as an example, the (faulty) reasoning pattern shown below.

Now let us take a look at an instance of this pattern. If we replace x by Toyotas and y by cars and z by made in America , we get the following line of argument, leading to a conclusion that happens to be correct.

On the other hand, if we replace x by Toyotas and y by cars and z by Porsches , we get a line of argument leading to a conclusion that is questionable.

What distinguishes a correct pattern from one that is incorrect is that it must always lead to correct conclusions, i.e. they must be correct so long as the premises on which they are based are correct. As we will see, this is the defining criterion for what we call deduction .

Now, it is noteworthy that there are patterns of reasoning that are sometimes useful but do not satisfy this strict criterion. There is inductive reasoning, abductive reasoning, reasoning by analogy, and so forth.

Induction is reasoning from the particular to the general. The example shown below illustrates this. If we see enough cases in which something is true and we never see a case in which it is false, we tend to conclude that it is always true.

Abduction is reasoning from effects to possible causes. Many things can cause an observed result. We often tend to infer a cause even when our enumeration of possible causes is incomplete.

Reasoning by analogy is reasoning in which we infer a conclusion based on similarity of two situations, as in the following example.

Of all types of reasoning, deduction is the only one that guarantees its conclusions in all cases, it produces only those conclusions that are logically entailed by one's premises.

1.6 Formalization

So far, we have illustrated everything with sentences in English. While natural language works well in many circumstances, it is not without its problems. Natural language sentences can be complex; they can be ambiguous; and failing to understand the meaning of a sentence can lead to errors in reasoning.

Even very simple sentences can be troublesome. Here we see two grammatically legal sentences. They are the same in all but the last word, but their structure is entirely different. In the first, the main verb is blossoms , while in the second blossoms is a noun and the main verb is sank .

As another example of grammatical complexity, consider the following excerpt taken from the University of Michigan lease agreement. The sentence in this case is sufficiently long and the grammatical structure sufficiently complex that people must often read it several times to understand precisely what it says.

The University may terminate this lease when the Lessee, having made application and executed this lease in advance of enrollment, is not eligible to enroll or fails to enroll in the University or leaves the University at any time prior to the expiration of this lease, or for violation of any provisions of this lease, or for violation of any University regulation relative to resident Halls, or for health reasons, by providing the student with written notice of this termination 30 days prior to the effective date of termination, unless life, limb, or property would be jeopardized, the Lessee engages in the sales of purchase of controlled substances in violation of federal, state or local law, or the Lessee is no longer enrolled as a student, or the Lessee engages in the use or possession of firearms, explosives, inflammable liquids, fireworks, or other dangerous weapons within the building, or turns in a false alarm, in which cases a maximum of 24 hours notice would be sufficient.

As an example of ambiguity, suppose I were to write the sentence There's a girl in the room with a telescope . See the following figure for two possible meanings of this sentence. Am I saying that there is a girl in a room containing a telescope? Or am I saying that there is a girl in the room and she is holding a telescope?

Such complexities and ambiguities can sometimes be humorous if they lead to interpretations the author did not intend. See the examples below for some infamous newspaper headlines with multiple interpretations. Using a formal language eliminates such unintentional ambiguities (and, for better or worse, avoids any unintentional humor as well).

As an illustration of errors that arise in reasoning with sentences in natural language, consider the following examples. In the first, we use the transitivity of the better relation to derive a conclusion about the relative quality of champagne and soda from the relative quality of champagne and beer and the relative quality or beer and soda. So far so good.

Now, consider what happens when we apply the same transitivity rule in the case illustrated below. The form of the argument is the same as before, but the conclusion is somewhat less believable. The problem in this case is that the use of nothing here is syntactically similar to the use of beer in the preceding example, but in English it means something entirely different.

Logic eliminates these difficulties through the use of a formal language for encoding information. Given the syntax and semantics of this formal language, we can give a precise definition for the notion of logical conclusion. Moreover, we can establish precise reasoning rules that produce all and only logical conclusions.

In this regard, there is a strong analogy between the methods of Formal Logic and those of high school algebra. To illustrate this analogy, consider the following algebra problem.

Xavier is three times as old as Yolanda. Xavier's age and Yolanda's age add up to twelve. How old are Xavier and Yolanda?

Typically, the first step in solving such a problem is to express the information in the form of equations. If we let x represent the age of Xavier and y represent the age of Yolanda, we can capture the essential information of the problem as shown below.

Using the methods of algebra, we can then manipulate these expressions to solve the problem. First we subtract the second equation from the first.

Next, we divide each side of the resulting equation by -4 to get a value for y . Then substituting back into one of the preceding equations, we get a value for x .

Now, consider the following logic problem.

If Mary loves Pat, then Mary loves Quincy. If it is Monday and raining, then Mary loves Pat or Quincy. If it is Monday and raining, does Mary love Quincy?

As with the algebra problem, the first step is formalization. Let p represent the possibility that Mary loves Pat; let q represent the possibility that Mary loves Quincy; let m represent the possibility that it is Monday; and let r represent the possibility that it is raining.

With these abbreviations, we can represent the essential information of this problem with the following logical sentences. The first says that p implies q , i.e. if Mary loves Pat, then Mary loves Quincy. The second says that m and r implies p or q , i.e. if it is Monday and raining, then Mary loves Pat or Mary loves Quincy.

As with Algebra, Formal Logic defines certain operations that we can use to manipulate expressions. The operation shown below is a variant of what is called Propositional Resolution . The expressions above the line are the premises of the rule, and the expression below is the conclusion.

There are two elaborations of this operation. (1) If a proposition on the left hand side of one sentence is the same as a proposition on the right hand side of the other sentence, it is okay to drop the two symbols, with the proviso that only one such pair may be dropped. (2) If a constant is repeated on the same side of a single sentence, all but one of the occurrences can be deleted.

We can use this operation to solve the problem of Mary's love life. Looking at the two premises above, we notice that p occurs on the left-hand side of one sentence and the right-hand side of the other. Consequently, we can cancel the p and thereby derive the conclusion that, if is Monday and raining, then Mary loves Quincy or Mary loves Quincy.

Dropping the repeated symbol on the right hand side, we arrive at the conclusion that, if it is Monday and raining, then Mary loves Quincy.

This example is interesting in that it showcases our formal language for encoding logical information. As with algebra, we use symbols to represent relevant aspects of the world in question, and we use operators to connect these symbols in order to express information about the things those symbols represent.

The example also introduces one of the most important operations in Formal Logic, viz. Resolution (in this case a restricted form of Resolution). Resolution has the property of being complete for an important class of logic problems, i.e. it is the only operation necessary to solve any problem in the class.

1.7 Automation

The existence of a formal language for representing information and the existence of a corresponding set of mechanical manipulation rules together have an important consequence, viz. the possibility of automated reasoning using digital computers.

The idea is simple. We use our formal representation to encode the premises of a problem as data structures in a computer, and we program the computer to apply our mechanical rules in a systematic way. The rules are applied until the desired conclusion is attained or until it is determined that the desired conclusion cannot be attained. (Unfortunately, in some cases, this determination cannot be made; and the procedure never halts. Nevertheless, as discussed in later chapters, the idea is basically sound.)

Although the prospect of automated reasoning has achieved practical realization only in the last few decades, it is interesting to note that the concept itself is not new. In fact, the idea of building machines capable of logical reasoning has a long tradition.

One of the first individuals to give voice to this idea was Leibnitz. He conceived of "a universal algebra by which all knowledge, including moral and metaphysical truths, can some day be brought within a single deductive system". Having already perfected a mechanical calculator for arithmetic, he argued that, with this universal algebra, it would be possible to build a machine capable of rendering the consequences of such a system mechanically.

Boole gave substance to this dream in the 1800s with the invention of Boolean algebra and with the creation of a machine capable of computing accordingly.

The early twentieth century brought additional advances in Logic, notably the invention of the predicate calculus by Russell and Whitehead and the proof of the corresponding completeness and incompleteness theorems by Godel in the 1930s.

The advent of the digital computer in the 1940s gave increased attention to the prospects for automated reasoning. Research in artificial intelligence led to the development of efficient algorithms for logical reasoning, highlighted by Robinson's invention of resolution theorem proving in the 1960s.

Today, the prospect of automated reasoning has moved from the realm of possibility to that of practicality, with the creation of logic technology in the form of automated reasoning systems, such as Vampire, Prover9, the Prolog Technology Theorem Prover, and others.

The emergence of this technology has led to the application of logic technology in a wide variety of areas. The following paragraphs outline some of these uses.

Mathematics. Automated reasoning programs can be used to check proofs and, in some cases, to produce proofs or portions of proofs.

Engineering. Engineers can use the language of Logic to write specifications for their products and to encode their designs. Automated reasoning tools can be used to simulate designs and in some cases validate that these designs meet their specification. Such tools can also be used to diagnose failures and to develop testing programs.

Database Systems. By conceptualizing database tables as sets of simple sentences, it is possible to use Logic in support of database systems. For example, the language of Logic can be used to define virtual views of data in terms of explicitly stored tables, and it can be used to encode constraints on databases. Automated reasoning techniques can be used to compute new tables, to detect problems, and to optimize queries.

Data Integration The language of Logic can be used to relate the vocabulary and structure of disparate data sources, and automated reasoning techniques can be used to integrate the data in these sources.

Law and Business. The language of Logic can be used to encode regulations and business rules, and automated reasoning techniques can be used to analyze such regulations for inconsistency and overlap.

1.8 Reading Guide

Although Logic is a single field of study, there is more than one logic in this field. In the three main units of this book, we look at three different types of logic, each more sophisticated than the one before.

Propositional Logic is the logic of propositions. Symbols in the language represent "conditions" in the world, and complex sentences in the language express interrelationships among these conditions. The primary operators are Boolean connectives, such as and , or , and not .

Relational Logic expands upon Propositional Logic by providing a means for explicitly talking about individual objects and their interrelationships (not just monolithic conditions). In order to do so, we expand our language to include object constants and relation constants, variables and quantifiers.

Functional Logic takes us one step further by providing a means for describing worlds with infinitely many objects. The resulting logic is much more powerful than Propositional Logic and Relational Logic. Unfortunately, as we shall see, some of the nice computational properties of the first two logics are lost as a result.

Despite their differences, there are many commonalities among these logics. In particular, in each case, there is a language with a formal syntax and a precise semantics; there is a notion of logical entailment; and there are legal rules for manipulating expressions in the language.

These similarities allow us to compare the logics and to gain an appreciation of the fundamental tradeoff between expressiveness and computational complexity. On the one hand, the introduction of additional linguistic complexity makes it possible to say things that cannot be said in more restricted languages. On the other hand, the introduction of additional linguistic flexibility has adverse effects on computability. As we proceed though the material, our attention will range from the completely computable case of Propositional Logic to a variant that is not at all computable.

One final comment. In the hopes of preventing difficulties, it is worth pointing out a potential source of confusion. This book exists in the meta world. It contains sentences about sentences; it contains proofs about proofs. In some places, we use similar mathematical symbology both for sentences in Logic and sentences about Logic. Wherever possible, we try to be clear about this distinction, but the potential for confusion remains. Unfortunately, this comes with the territory. We are using Logic to study Logic. It is our most powerful intellectual tool.

Logic is the study of information encoded in the form of logical sentences. Each logical sentence divides the set of all possible world into two subsets - the set of worlds in which the sentence is true and the set of worlds in which the set of sentences is false. A set of premises logically entails a conclusion if and only if the conclusion is true in every world in which all of the premises are true. Deduction is a form of symbolic reasoning that produces conclusions that are logically entailed by premises (distinguishing it from other forms of reasoning, such as induction , abduction , and analogical reasoning ). A proof is a sequence of simple, more-or-less obvious deductive steps that justifies a conclusion that may not be immediately obvious from given premises. In Logic, we usually encode logical information as sentences in formal languages; and we use rules of inference appropriate to these languages. Such formal representations and methods are useful for us to use ourselves. Moreover, they allow us to automate the process of deduction, though the computability of such implementations varies with the complexity of the sentences involved.

Exercise 1.1: Consider the state of the Sorority World depicted below.

For each of the following sentences, say whether or not it is true in this state of the world.

Exercise 1.2: Consider the state of the Sorority World depicted below.

Exercise 1.3: Consider the state of the Sorority World depicted below.

Exercise 1.4: Come up with a table of likes and dislikes for the Sorority World that makes all of the following sentences true. Note that there is more than one such table.

Exercise 1.5: Consider a set of Sorority World premises that are true in the four states of Sorority World shown in Section 1.4. For each of the following sentences, say whether or not it is logically entailed by these premises.

Exercise 1.6: Consider the sentences shown below.

Say whether each of the following sentences is logically entailed by these sentences.

Exercise 1.7: Say whether or not the following reasoning patterns are logically correct.

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Logical Fallacies

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This resource covers using logic within writing—logical vocabulary, logical fallacies, and other types of logos-based reasoning.

Fallacies are common errors in reasoning that will undermine the logic of your argument. Fallacies can be either illegitimate arguments or irrelevant points, and are often identified because they lack evidence that supports their claim. Avoid these common fallacies in your own arguments and watch for them in the arguments of others.

Slippery Slope: This is a conclusion based on the premise that if A happens, then eventually through a series of small steps, through B, C,..., X, Y, Z will happen, too, basically equating A and Z. So, if we don't want Z to occur, A must not be allowed to occur either. Example:

If we ban Hummers because they are bad for the environment eventually the government will ban all cars, so we should not ban Hummers.

In this example, the author is equating banning Hummers with banning all cars, which is not the same thing.

Hasty Generalization: This is a conclusion based on insufficient or biased evidence. In other words, you are rushing to a conclusion before you have all the relevant facts. Example:

Even though it's only the first day, I can tell this is going to be a boring course.

In this example, the author is basing his evaluation of the entire course on only the first day, which is notoriously boring and full of housekeeping tasks for most courses. To make a fair and reasonable evaluation the author must attend not one but several classes, and possibly even examine the textbook, talk to the professor, or talk to others who have previously finished the course in order to have sufficient evidence to base a conclusion on.

Post hoc ergo propter hoc: This is a conclusion that assumes that if 'A' occurred after 'B' then 'B' must have caused 'A.' Example:

I drank bottled water and now I am sick, so the water must have made me sick.

In this example, the author assumes that if one event chronologically follows another the first event must have caused the second. But the illness could have been caused by the burrito the night before, a flu bug that had been working on the body for days, or a chemical spill across campus. There is no reason, without more evidence, to assume the water caused the person to be sick.

Genetic Fallacy: This conclusion is based on an argument that the origins of a person, idea, institute, or theory determine its character, nature, or worth. Example:

The Volkswagen Beetle is an evil car because it was originally designed by Hitler's army.

In this example the author is equating the character of a car with the character of the people who built the car. However, the two are not inherently related.

Begging the Claim: The conclusion that the writer should prove is validated within the claim. Example:

Filthy and polluting coal should be banned.

Arguing that coal pollutes the earth and thus should be banned would be logical. But the very conclusion that should be proved, that coal causes enough pollution to warrant banning its use, is already assumed in the claim by referring to it as "filthy and polluting."

Circular Argument: This restates the argument rather than actually proving it. Example:

George Bush is a good communicator because he speaks effectively.

In this example, the conclusion that Bush is a "good communicator" and the evidence used to prove it "he speaks effectively" are basically the same idea. Specific evidence such as using everyday language, breaking down complex problems, or illustrating his points with humorous stories would be needed to prove either half of the sentence.

Either/or: This is a conclusion that oversimplifies the argument by reducing it to only two sides or choices. Example:

We can either stop using cars or destroy the earth.

In this example, the two choices are presented as the only options, yet the author ignores a range of choices in between such as developing cleaner technology, car-sharing systems for necessities and emergencies, or better community planning to discourage daily driving.

Ad hominem: This is an attack on the character of a person rather than his or her opinions or arguments. Example:

Green Peace's strategies aren't effective because they are all dirty, lazy hippies.

In this example, the author doesn't even name particular strategies Green Peace has suggested, much less evaluate those strategies on their merits. Instead, the author attacks the characters of the individuals in the group.

Ad populum/Bandwagon Appeal: This is an appeal that presents what most people, or a group of people think, in order to persuade one to think the same way. Getting on the bandwagon is one such instance of an ad populum appeal.

If you were a true American you would support the rights of people to choose whatever vehicle they want.

In this example, the author equates being a "true American," a concept that people want to be associated with, particularly in a time of war, with allowing people to buy any vehicle they want even though there is no inherent connection between the two.

Red Herring: This is a diversionary tactic that avoids the key issues, often by avoiding opposing arguments rather than addressing them. Example:

The level of mercury in seafood may be unsafe, but what will fishers do to support their families?

In this example, the author switches the discussion away from the safety of the food and talks instead about an economic issue, the livelihood of those catching fish. While one issue may affect the other it does not mean we should ignore possible safety issues because of possible economic consequences to a few individuals.

Straw Man: This move oversimplifies an opponent's viewpoint and then attacks that hollow argument.

People who don't support the proposed state minimum wage increase hate the poor.

In this example, the author attributes the worst possible motive to an opponent's position. In reality, however, the opposition probably has more complex and sympathetic arguments to support their point. By not addressing those arguments, the author is not treating the opposition with respect or refuting their position.

Moral Equivalence: This fallacy compares minor misdeeds with major atrocities, suggesting that both are equally immoral.

That parking attendant who gave me a ticket is as bad as Hitler.

In this example, the author is comparing the relatively harmless actions of a person doing their job with the horrific actions of Hitler. This comparison is unfair and inaccurate.

Premise Definition and Examples in Arguments

A Proposition Upon Which an Argument Is Based

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• Ph.D., Rhetoric and English, University of Georgia
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• B.A., English, State University of New York

A premise is a  proposition upon which an argument is based or from which a conclusion is drawn. Put another way, a premise includes the reasons and evidence behind a conclusion, says  Study.com .

A premise may be either the major or the minor proposition of a  syllogism —an argument in which two premises are made and a logical conclusion is drawn from them—in a deductive argument. Merriam-Webster gives this example of a major and minor premise (and conclusion):

"All mammals are warmblooded [ major premise ]; whales are mammals [ minor premise ]; therefore, whales are warmblooded [ conclusion ]."

The term premise comes from medieval Latin, meaning "things mentioned before." In philosophy as well as fiction and nonfiction writing, the premise follows largely the same pattern as that defined in Merriam-Webster. The premise—the thing or things that came before—lead (or fail to lead) to a logical resolution in an argument or story.

Premises in Philosophy

To understand what a premise is in philosophy, it helps to understand how the field defines an argument, says  Joshua May , an associate professor of philosophy at the University of Alabama, Birmingham. In philosophy, an argument is not concerned with disputes among people; it is a set of propositions that contain premises offered to support a conclusion, he says, adding:

"A premise is a proposition one offers in support of a conclusion. That is, one offers a premise as evidence for the truth of the conclusion, as justification for or a reason to believe the conclusion."

May offers this example of a major and minor premise, as well as a conclusion, that echoes the example from Merriam-Webster:

• All humans are mortal. [major premise]
• G.W. Bush is a human. [minor premise]
• Therefore, G.W. Bush is mortal. [conclusion]

May notes that the validity of an argument in philosophy (and in general) depends on the accuracy and truth of the premise or premises. For example, May gives this example of a bad (or inaccurate) premise:

• All women are Republican. [major premise: false]
• Hilary Clinton is a woman. [minor premise: true]
• Therefore, Hilary Clinton is a Republican. [conclusion: false]

The  Stanford Encyclopedia of Philosophy  says that an argument can be valid if it follows logically from its premises, but the conclusion can still be wrong if the premises are incorrect:

"However, if the premises are true, then the conclusion is also true, as a matter of logic."​​

In philosophy, then, the process of creating premises and carrying them through to a conclusion involves logic and deductive reasoning. Other areas provide a similar, but slightly different, take when defining and explaining premises.

Premises in Writing

For nonfiction writing, the term  premise  carries largely the same definition as in philosophy. Purdue OWL notes that a premise or premises are integral parts of constructing an argument. Indeed, says the language website operated by Purdue University, the very definition of an argument is that it is an "assertion of a conclusion based on logical premises."

Nonfiction writing uses the same terminology as in philosophy, such as  syllogism , which Purdue OWL describes as the "simplest sequence of logical premises and conclusions."

Nonfiction writers use a premise or premises as the backbone of a piece such as an editorial, opinion article, or even a letter to the editor of a newspaper. Premises are also useful for developing and writing an outline for a debate. Purdue gives this example:

• Nonrenewable resources do not exist in infinite supply. [premise 1]
• Coal is a nonrenewable resource. [premise 2]
• Coal does not exist in infinite supply. [conclusion]

The only difference in nonfiction writing versus the use of premises in philosophy is that nonfiction writing generally does not distinguish between major and minor premises.

Fiction writing also uses the concept of a premise but in a different way, and not one connected with making an argument. James M. Frey, as quoted on  Writer's Digest , notes:

"The premise is the foundation of your story—that single core statement of what happens to the characters as a result of the actions of a story.”

The writing website gives the example of the story "The Three Little Pigs," noting that the premise is: “Foolishness leads to death, and wisdom leads to happiness.” The well-known story does not seek to create an argument, as is the case in philosophy and nonfiction writing. Instead, the story itself is the argument, showing how and why the premise is accurate, says Writer's Digest:

"If you can establish what your premise is at the beginning of your project, you will have an easier time writing your story. That's because the fundamental concept you create in advance will drive the actions of your characters."

It's the characters—and to some degree, the plot—that prove or disprove the premise of the story.

Other Examples

The use of premises is not limited to philosophy and writing. The concept can also be useful in science, such as in the study of genetics or biology versus environment, which is also known as the nature-versus-nurture debate. In "Logic and Philosophy: A Modern Introduction," Alan Hausman, Howard Kahane, and Paul Tidman give this example:

"Identical twins often have different IQ test scores. Yet such twins inherit the same genes. So environment must play some part in determining IQ."

In this case, the argument consists of three statements:

• Identical twins often have different IQ scores. [premise]
• Identical twins inherit the same genes. [premise]
• The environment must play some part in determining IQ. [conclusion]

The use of the premise even reaches into religion and theological arguments.  Michigan State University  (MSU) gives this example:

• God exists, for the world is an organized system and all organized systems must have a creator. The creator of the world is God.

The statements provide reasons why God exists, says MSU. The argument of the statements can be organized into premises and a conclusion.

• Premise 1: The world is an organized system.
• Premise 2: Every organized system must have a creator.
• Conclusion: The creator of the world is God.

Consider the Conclusion

You can use the concept of the premise in countless areas, so long as each premise is true and relevant to the topic. The key to laying out a premise or premises (in essence, constructing an argument) is to remember that premises are assertions that, when joined together, will lead the reader or listener to a given conclusion, says the San Jose State University Writing Center, adding:

"The most important part of any premise is that your audience will accept it as true. If your audience rejects even one of your premises, they will likely also reject your conclusion, and your entire argument will fall apart.​"

Consider the following assertion: “Because greenhouse gases are causing the atmosphere to warm at a rapid rate...” The San Jose State writing lab notes that whether this is a solid premise depends on your audience:

"If your readers are members of an environmental group, they will accept this premise without qualms. If your readers are oil company executives, they may reject this premise and your conclusions."

When developing one or more premises, consider the rationales and beliefs not just of your audience but also of your opponents, says San Jose State. After all, your whole point in making an argument is not just to preach to a like-minded audience but to convince others of the correctness of your point of view.

Determine what "givens” you accept that your opponents do not, as well as where two sides of an argument can find common ground. That point is where you will find effective premises to reach your conclusion, the writing lab notes.

Hausman, Alan. "Logic and Philosophy: A Modern Introduction." Howard Kahane, Paul Tidman, 12th Edition, Cengage Learning, January 1, 2012.

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4 Main Types of Reasoning: Examples of Logic

Logic is a type of reasoning that involves making inferences and using evidence to support a conclusion. In this article, we will explore the four main types of reasoning used in logic and provide examples of each.

By understanding these four types of reasoning, you can improve your ability to make logical decisions and draw accurate conclusions.

Examples of Logic: 4 Main Types of Reasoning

Definition of Logic

Logic is a branch of philosophy that deals with the study of reasoning and argumentation. It is a systematic approach to reasoning that helps in distinguishing between valid and invalid arguments. In other words, logic is a tool that helps in evaluating the validity of an argument or statement.

The study of logic involves the examination of the principles and methods used in reasoning. It is concerned with the rules of inference and the principles of correct reasoning. Logic is a formal system that uses symbols and rules to represent and manipulate information.

There are different types of logic, including propositional logic, predicate logic, modal logic, and many others. Each type of logic has its own set of rules and symbols that are used to represent and manipulate information.

In logic, an argument is a set of statements that are used to support a conclusion. The conclusion is the statement that the argument is trying to prove. A valid argument is one where the conclusion follows logically from the premises. An invalid argument is one where the conclusion does not follow logically from the premises.

Types of Reasoning

When it comes to logic, there are four main types of reasoning: deductive reasoning, inductive reasoning, abductive reasoning, and analogical reasoning. Each type of reasoning has its own unique characteristics and applications.

Deductive Reasoning

Deductive reasoning is a type of reasoning that uses formal logic and observations to prove a theory or hypothesis. In deductive reasoning, you start with an assumption and then make observations or rational thoughts to validate or refute the assumption. Deductive reasoning is often used in mathematics and philosophy.

For example, if you know that all men are mortal and Socrates is a man, you can deduce that Socrates is mortal. Deductive reasoning is often represented in the form of syllogisms, which are logical arguments that use deductive reasoning to arrive at a conclusion.

Inductive Reasoning

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. In inductive reasoning, you start with specific observations or data and then make generalizations based on that information. Inductive reasoning is often used in science and research.

For example, if you observe that all the swans you have seen are white, you might conclude that all swans are white. However, this conclusion is not necessarily true, as there could be black swans that you have not observed. Inductive reasoning is often associated with probability and uncertainty.

Abductive Reasoning

Abductive reasoning is a type of reasoning that involves making an educated guess or hypothesis based on incomplete information. In abductive reasoning, you start with an observation or data and then make a hypothesis that explains that observation. Abductive reasoning is often used in medicine and detective work.

For example, if a patient presents with a certain set of symptoms, a doctor might use abductive reasoning to make a diagnosis based on those symptoms. However, the diagnosis is not necessarily true, as there could be other explanations for the symptoms.

Analogical Reasoning

Analogical reasoning is a type of reasoning that involves making comparisons between two or more things to draw a conclusion. In analogical reasoning, you start with a known situation or concept and then use that knowledge to understand a new situation or concept. Analogical reasoning is often used in problem-solving and decision-making.

For example, if you are trying to understand a new concept, you might use analogical reasoning to compare it to a similar concept that you already understand. However, the comparison is not necessarily accurate, as there could be differences between the two concepts.

Examples of Deductive Reasoning

In deductive reasoning, conclusions are drawn from premises that are assumed to be true. Deductive reasoning is often used in mathematics, science, and philosophy to prove theories and hypotheses. There are different types of deductive reasoning, such as propositional logic, categorical logic, and Boolean algebra. Here are some examples of deductive reasoning:

Syllogism: A syllogism is a deductive argument that consists of two premises and a conclusion. For example:

• Premise 1: All men are mortal.
• Premise 2: Socrates is a man.
• Conclusion: Therefore, Socrates is mortal.

Conditional reasoning: Conditional reasoning is a type of deductive reasoning that involves if-then statements. For example:

• If it rains, then the ground will be wet.
• Therefore, the ground is wet.

Mathematical proofs: Mathematical proofs are examples of deductive reasoning that use axioms, definitions, and logical rules to prove mathematical theorems. For example:

• The Pythagorean theorem states that in a right triangle, the square of the length of the hypotenuse is equal to the sum of the squares of the lengths of the other two sides.
• A mathematical proof of the Pythagorean theorem uses deductive reasoning to show that this statement is true for all right triangles.

Legal reasoning: Legal reasoning is a type of deductive reasoning that is used in the legal system to interpret laws and make decisions. For example:

• The Fourth Amendment of the United States Constitution protects citizens from unreasonable searches and seizures.
• The police conducted a search of John’s home without a warrant.
• Therefore, the search was unconstitutional.

In each of these examples, deductive reasoning is used to draw logical conclusions from premises that are assumed to be true. By using deductive reasoning, we can prove theories, solve problems, and make informed decisions based on logical principles.

Examples of Inductive Reasoning

Inductive reasoning is a type of reasoning that involves making generalizations based on specific observations. This type of reasoning is commonly used in scientific research and everyday life.

Example 1: Generalization

Suppose you observe that every time you eat seafood, you get a headache. You might use inductive reasoning to make a generalization that seafood causes headaches. This generalization is based on specific observations and is not necessarily true for everyone. However, it can provide a useful starting point for further investigation.

Example 2: Analogical Reasoning

Analogical reasoning is a type of inductive reasoning that involves comparing two things that are similar in some way. For example, if you know that caffeine makes you feel more alert, you might assume that other stimulants, such as nicotine or amphetamines, would have a similar effect. This type of reasoning can be useful, but it is important to recognize that similarities between two things do not necessarily mean that they will behave in the same way.

Example 3: Causal Inference

Causal inference is a type of inductive reasoning that involves making a causal connection between two events. For example, if you notice that every time it rains, the streets are wet, you might infer that rain causes the streets to be wet. This type of reasoning can be useful, but it is important to recognize that correlation does not necessarily imply causation.

Examples of Abductive Reasoning

Abductive reasoning is a type of reasoning that involves making an inference to the best explanation or hypothesis based on incomplete or limited information. In other words, it is a form of logical inference that seeks to find the most likely explanation for a particular phenomenon or set of observations.

Here are some examples of abductive reasoning:

• Dew on Morning Grass : When you go outside in the morning and see that the grass is completely covered with dew, you might infer that it rained last night. This inference is based on incomplete information, but it is the most likely explanation for the presence of dew on the grass.
• Medical Diagnosis : When a doctor sees a patient with a set of symptoms, they might use abductive reasoning to diagnose the underlying condition. For example, if a patient has a fever, cough, and chest pain, the doctor might infer that they have pneumonia, even though they have not yet conducted any tests.
• Crime Scene Investigation : When detectives investigate a crime scene, they might use abductive reasoning to identify the most likely suspect. For example, if a window is broken and a valuable item is missing, they might infer that a burglar broke in and stole the item, even though they have not yet identified any suspects.
• Product Design : When engineers design a new product, they might use abductive reasoning to identify the most likely cause of a problem. For example, if a product is malfunctioning, they might infer that a particular component is defective, even though they have not yet conducted any tests.

Examples of Analogical Reasoning

Analogical reasoning is a type of reasoning that involves comparing two things that are similar in some respects and drawing a conclusion about the second thing based on the similarity to the first thing. Analogies are used to explain complex ideas, to clarify concepts, and to persuade others. Here are some examples of analogical reasoning:

• Example 1 : “The human brain is like a computer. Just as a computer processes information, the brain processes information. And just as a computer can malfunction if it is overloaded with information, the brain can malfunction if it is overloaded with information.”
• Example 2 : “The internet is like a library. Just as a library contains many books and other materials, the internet contains many websites and other resources. And just as you can find information in a library by searching the catalog, you can find information on the internet by using a search engine.”
• Example 3 : “The human body is like a machine. Just as a machine has many parts that work together to perform a function, the body has many organs and systems that work together to keep us alive. And just as a machine can break down if one part fails, the body can become sick or injured if one system fails.”

Importance of Logic in Daily Life

Logic is a crucial part of our daily lives, whether we realize it or not. It underpins our ability to make sense of things and helps us to solve problems and make decisions. From simple tasks such as choosing what to wear to complex decision-making in our professional lives, logic is essential.

One of the key benefits of logic is that it helps us to think critically. By using logical reasoning, we can evaluate arguments and evidence, identify flaws in reasoning, and make informed decisions. This is particularly important in today’s world, where we are bombarded with information from a variety of sources and need to be able to distinguish between fact and fiction.

Another important aspect of logic is that it helps us to communicate effectively. By using clear and logical arguments, we can persuade others to see our point of view and make our ideas more convincing. This is particularly important in professional settings, where we may need to present arguments to colleagues, clients, or stakeholders.

In addition to these benefits, logic can also help us to improve our problem-solving skills. By breaking down complex problems into smaller, more manageable parts, we can identify the root cause of the problem and develop effective solutions. This can be particularly useful in fields such as engineering, science, and technology, where complex problems often require innovative solutions.

Logic in Different Fields

Logic is a fundamental concept that is used in various fields of study, including mathematics, philosophy, and computer science. Different fields use logic in different ways to solve problems and make decisions.

Logic in Mathematics

Mathematics is a field that heavily relies on logical reasoning. Mathematical proofs, for example, are based on logical arguments that are used to demonstrate the validity of mathematical statements. In mathematics, logic is used to identify patterns and relationships between numbers and to develop new mathematical concepts.

Mathematicians use different types of logic, including propositional logic and predicate logic, to analyze mathematical statements and to prove mathematical theorems. Propositional logic is used to study the logical relationships between propositions, while predicate logic is used to study the logical relationships between objects and their properties.

Logic in Philosophy

Philosophy is another field that makes extensive use of logic. Philosophers use logic to analyze arguments and to evaluate the validity of philosophical claims. In philosophy, logic is used to develop theories and to construct arguments that support or refute philosophical positions.

Philosophers use different types of logic, including deductive logic and inductive logic, to analyze arguments and to make inferences. Deductive logic is used to draw conclusions from premises that are known to be true, while inductive logic is used to draw general conclusions from specific observations.

Logic in Computer Science

Computer science is a field that heavily relies on logical reasoning and formal methods. Computer scientists use logic to design and analyze algorithms and to develop software systems. In computer science, logic is used to specify the behavior of computer programs and to verify their correctness.

Computer scientists use different types of logic, including propositional logic, predicate logic, and temporal logic, to analyze computer systems and to design algorithms. Propositional logic is used to study the logical relationships between propositions, while predicate logic is used to study the logical relationships between objects and their properties. Temporal logic is used to study the logical relationships between events and time.

Frequently Asked Questions

What are the 4 types of logical reasoning?

The four types of reasoning are deductive reasoning, inductive reasoning, abductive reasoning, and analogical reasoning. Deductive reasoning is when you start with a general statement and use it to make a specific conclusion. Inductive reasoning is when you start with specific observations and use them to make a general conclusion. Abductive reasoning is when you use the best explanation to make a conclusion. Analogical reasoning is when you use one situation to make a conclusion about another situation.

What is an example of logical reasoning in everyday life?

An example of logical reasoning in everyday life is when you use deductive reasoning to determine whether or not it will rain today. You know that when it rains, the ground gets wet. You observe that the ground is wet. Therefore, you can deduce that it rained.

What is an example of logic philosophy?

An example of logic philosophy is the syllogism “All men are mortal. Socrates is a man. Therefore, Socrates is mortal.” This syllogism uses deductive reasoning to make a conclusion.

What is logic?

Logic is the study of reasoning, argumentation, and inference. It is concerned with the principles of correct reasoning and the evaluation of arguments.

Last Updated on September 5, 2023

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Apple rafforza Logic Pro con potenti strumenti per creare musica su Mac e iPad

Nuove funzioni in arrivo su Mac e iPad

Novità di Logic Pro per Mac

Novità di Logic Pro per iPad

• Logic Pro 10.8 è disponibile da oggi sul Mac App Store come aggiornamento gratuito per chi già usa l’app e a € 229,99 (IVA inclusa) per chi la scarica per la prima volta. Logic Pro per Mac richiede macOS Ventura 13.5 o successivo. Maggiori informazioni sono disponibili su apple.com/it/logic-pro .
• Logic Pro per iPad 1.1 è disponibile da oggi sull’App Store come aggiornamento gratuito per chi già usa l’app e a € 4,99 al mese o € 49 all’anno con un mese di prova gratuita per chi scarica l’app per la prima volta. Logic Pro per iPad richiede iPadOS 17 o successivo e un iPad con chip Apple A12 Bionic o successivo. Ulteriori informazioni sono disponibili su apple.com/it/logic-pro-for-ipad .

Testo dell’articolo

06 novembre 2023

AGGIORNAMENTI

Tra le novità ci sono Mastering Assistant, la registrazione a 32 bit, Sample Alchemy e Beat Breaker su Mac, la modalità Recorder di Quick Sampler Recorder su iPad, e altro ancora

Oggi Apple ha aggiornato Logic Pro con nuove potenti funzioni disponibili su Mac e iPad. Con il nuovo Mastering Assistant e la sua palette professionale di strumenti intuitivi per il sound shaping, chi crea musica può produrre in men che non si dica ottimi mix pronti per la pubblicazione. Su Logic Pro per Mac la manipolazione dei campioni fa un grande passo avanti con Sample Alchemy e le funzioni di reshaping e reshuffling di Beat Breaker. Logic Pro per iPad offre ancora più funzioni, tra cui la modalità Quick Sampler Recorder, che permette di acquisire suoni al volo usando il microfono integrato di iPad e creare all’istante strumenti personalizzati e pronti da suonare. Chi produce basi musicali con le proprie raccolte di campioni può creare velocemente dei kit o degli strumenti personalizzati usando le funzioni di multitasking migliorate, come Split View e Stage Manager. E può anche passare facilmente da Mac ad iPad e viceversa grazie al round tripping dei progetti, che permette di creare musica in mobilità o in studio.

Mastering Assistant offre un modo semplice e veloce di aggiungere ai mix quel tocco professionale che li rende pronti per la diffusione. Quando il mix è completato, Mastering Assistant analizza l’audio all’istante e rifinisce con maestria il suono, regolando elementi quali dinamica, bilanciamento della frequenza, timbro e volume. Chi crea musica può anche usare l’elaborazione iniziale di Mastering Assistant come punto di partenza per poi perfezionare le singole impostazioni con controlli intuitivi, in modo che ogni mix abbia un suono perfetto in auto così come in discoteca.

L’app Logic Pro ora supporta la registrazione a 32 bit float quando usata con interfacce audio compatibili. L’artista può quindi ridurre al minimo il clipping digitale che si verifica quando si registrano più performance o strumenti dinamici. Grazie al formato a 32 bit float, anche le registrazioni fatte a volumi più bassi possono essere amplificate con facilità senza alzare il rumore di fondo, rendendo possibili una qualità audio di livello professionale e maggiore flessibilità in post-produzione.

Introdotti in Logic Pro per iPad a inizio anno, Sample Alchemy e Beat Breaker sono ora disponibili anche nella versione per Mac. Sample Alchemy permette di trasformare un singolo campione audio in uno strumento pronto da suonare usando una serie di tecniche, tra cui la sintesi granulare, additiva e spettrale. L’artista può ora creare un’ampia gamma di basi sonore, pad, effetti e ritmi originali, portando al massimo la creatività.

Il plug-in multieffetto Beat Breaker permette di effettuare il reshaping e il reshuffling dell’audio in tempo reale per suddividerlo, riordinarlo e aggiungere effetti scratch. Alterando la velocità, la direzione, il volume e il numero di ripetizioni per ogni fetta, l’artista può facilmente definire pattern complessi modificabili al volo.

Il menu degli strumenti nell’area delle tracce e gli editor basati sul tempo includono ora le funzioni Slip e Rotate, che permettono di spostare i contenuti all’interno delle regioni di audio e strumenti software senza alterarne i confini.

L’aggiornamento di oggi di Logic Pro per Mac introduce due nuovi pacchetti di suoni: The Hybrid Texture che include una raccolta di 70 patch oltre a 80 Apple Loop con Sample Alchemy; e Vox Melodics che contiene una raccolta diversificata di oltre 475 frasi liriche, hook, armonie stratificate, effetti e campioni one-shot. L’utente può scaricare gratuitamente tutti i pacchetti dell’app attraverso la Libreria suoni.

Il plug-in Quick Sampler permette di creare al volo strumenti da virtualmente qualsiasi suono. Grazie alla nuova modalità Recorder è possibile creare strumenti e drum kit praticamente ovunque registrando i suoni con il microfono integrato o una sorgente audio collegata.

Ora ci si può spostare facilmente tra Logic Pro e altre app come Memo Vocali o File usando Split View e Stage Manager. Split View consente di aprire due app, o due finestre della stessa app, dividendo lo schermo in due parti ridimensionabili, mentre Stage Manager permette di ridimensionare le finestre, vedere più finestre sovrapposte in un’unica vista o passare da un’app all’altra con un tap, e molto altro. 

L’utente può trovare il sound perfetto nel Browser semplicemente trascinando in su o in giù con il dito per ascoltare in anteprima vari campioni, loop o strumenti. Inoltre, può selezionare e trascinare i propri campioni dall’app File a Logic Pro, per creare facilmente drum kit o aggiungere stem a un progetto.

Logic Pro arricchisce la sua apprezzata raccolta di lezioni in-app con un’ampia gamma di argomenti aggiuntivi, tra cui Mastering Assistant, Sample Alchemy, Beat Breaker, la creazione di beat, l’automazione, i pacchetti di suoni e Play Surfaces. Il pacchetto di suoni The Hybrid Textures è disponibile anche in Logic Pro per iPad, offrendo nuovo materiale per sperimentare la potenza e la portata di Sample Alchemy.

Prezzi e disponibilità

Contatti stampa

Tiziana Scanu

[email protected]

Helpline Apple per la stampa

[email protected]

02 27326227

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