Verbal Ability ...

Read the passage carefully and answer the questions that follow.

Predictive processing casts the brain as a ‘prediction engine’ – something that’s constantly attempting to predict the sensory signals it encounters in the world, and to minimise the discrepancy (called the ‘prediction error’) between those predictions and the incoming signal. Over time, such systems build up a ‘generative model’, a structured understanding of the statistical regularities in our environment that’s used to generate predictions. This generative model is essentially a mental model of our world, including both immediate, task-specific information, as well as longer-term information that constitutes our narrative sense of self. According to this framework, predictive systems go about minimising prediction errors in two ways: either they update the generative model to more accurately reflect the world, or they behave in ways that bring the world better in line with their prediction. In this way, the brain forms part of an embodied predictive system that’s always moving from uncertainty to certainty. By reducing potentially harmful surprises, it keeps us alive and well.

Consider the healthy and expected body temperature of 37°C for a human being. A shift in either direction registers as a spike in prediction error, signalling to the organism that it’s moving into an unexpected, and therefore potentially dangerous, state. This rise in prediction error is fed back to us as feelings of discomfort, stress and an inclination to do something to get a better predictive grip on reality. We could just sit there and come to terms with the changing temperature (update our generative model), or we might reach for a blanket or open a window. In these cases, what we’re doing is acting upon our environment, sampling the world and changing our relation to it, in order to bring ourselves back within acceptable bounds of uncertainty.

According to the emerging picture from predictive processing, cognition and affect are tightly interwoven aspects of the same predictive system. Prediction errors aren’t merely data points within a computational system. Rather, rising prediction errors feel bad to us, while resolving errors in line with expectation feels good. This means that, as predictive organisms, we actively seek out waves of manageable prediction error – manageable uncertainty – because resolving it results in our feeling good. The recent rise in jigsaw puzzle sales during the COVID-19 lockdown testifies to our love of manageable uncertainty. These feelings evolved to keep us well tuned to our environment, helping us to curiously feel out novel and successful strategies for survival, while also avoiding all of the stress and unpleasantness that comes with runaway uncertainty. This active, recursive and felt relationship with the environment is crucial to grasping how social media can be detrimental to our mental health, and why we often find it so hard to stop using it.

Read the passage carefully and answer the questions that follow.

Predictive processing casts the brain as a ‘prediction engine’ – something that’s constantly attempting to predict the sensory signals it encounters in the world, and to minimise the discrepancy (called the ‘prediction error’) between those predictions and the incoming signal. Over time, such systems build up a ‘generative model’, a structured understanding of the statistical regularities in our environment that’s used to generate predictions. This generative model is essentially a mental model of our world, including both immediate, task-specific information, as well as longer-term information that constitutes our narrative sense of self. According to this framework, predictive systems go about minimising prediction errors in two ways: either they update the generative model to more accurately reflect the world, or they behave in ways that bring the world better in line with their prediction. In this way, the brain forms part of an embodied predictive system that’s always moving from uncertainty to certainty. By reducing potentially harmful surprises, it keeps us alive and well.

Consider the healthy and expected body temperature of 37°C for a human being. A shift in either direction registers as a spike in prediction error, signalling to the organism that it’s moving into an unexpected, and therefore potentially dangerous, state. This rise in prediction error is fed back to us as feelings of discomfort, stress and an inclination to do something to get a better predictive grip on reality. We could just sit there and come to terms with the changing temperature (update our generative model), or we might reach for a blanket or open a window. In these cases, what we’re doing is acting upon our environment, sampling the world and changing our relation to it, in order to bring ourselves back within acceptable bounds of uncertainty.

According to the emerging picture from predictive processing, cognition and affect are tightly interwoven aspects of the same predictive system. Prediction errors aren’t merely data points within a computational system. Rather, rising prediction errors feel bad to us, while resolving errors in line with expectation feels good. This means that, as predictive organisms, we actively seek out waves of manageable prediction error – manageable uncertainty – because resolving it results in our feeling good. The recent rise in jigsaw puzzle sales during the COVID-19 lockdown testifies to our love of manageable uncertainty. These feelings evolved to keep us well tuned to our environment, helping us to curiously feel out novel and successful strategies for survival, while also avoiding all of the stress and unpleasantness that comes with runaway uncertainty. This active, recursive and felt relationship with the environment is crucial to grasping how social media can be detrimental to our mental health, and why we often find it so hard to stop using it.

Read the passage carefully and answer the questions that follow.

Predictive processing casts the brain as a ‘prediction engine’ – something that’s constantly attempting to predict the sensory signals it encounters in the world, and to minimise the discrepancy (called the ‘prediction error’) between those predictions and the incoming signal. Over time, such systems build up a ‘generative model’, a structured understanding of the statistical regularities in our environment that’s used to generate predictions. This generative model is essentially a mental model of our world, including both immediate, task-specific information, as well as longer-term information that constitutes our narrative sense of self. According to this framework, predictive systems go about minimising prediction errors in two ways: either they update the generative model to more accurately reflect the world, or they behave in ways that bring the world better in line with their prediction. In this way, the brain forms part of an embodied predictive system that’s always moving from uncertainty to certainty. By reducing potentially harmful surprises, it keeps us alive and well.

Consider the healthy and expected body temperature of 37°C for a human being. A shift in either direction registers as a spike in prediction error, signalling to the organism that it’s moving into an unexpected, and therefore potentially dangerous, state. This rise in prediction error is fed back to us as feelings of discomfort, stress and an inclination to do something to get a better predictive grip on reality. We could just sit there and come to terms with the changing temperature (update our generative model), or we might reach for a blanket or open a window. In these cases, what we’re doing is acting upon our environment, sampling the world and changing our relation to it, in order to bring ourselves back within acceptable bounds of uncertainty.

According to the emerging picture from predictive processing, cognition and affect are tightly interwoven aspects of the same predictive system. Prediction errors aren’t merely data points within a computational system. Rather, rising prediction errors feel bad to us, while resolving errors in line with expectation feels good. This means that, as predictive organisms, we actively seek out waves of manageable prediction error – manageable uncertainty – because resolving it results in our feeling good. The recent rise in jigsaw puzzle sales during the COVID-19 lockdown testifies to our love of manageable uncertainty. These feelings evolved to keep us well tuned to our environment, helping us to curiously feel out novel and successful strategies for survival, while also avoiding all of the stress and unpleasantness that comes with runaway uncertainty. This active, recursive and felt relationship with the environment is crucial to grasping how social media can be detrimental to our mental health, and why we often find it so hard to stop using it.

Read the passage carefully and answer the questions that follow.

Predictive processing casts the brain as a ‘prediction engine’ – something that’s constantly attempting to predict the sensory signals it encounters in the world, and to minimise the discrepancy (called the ‘prediction error’) between those predictions and the incoming signal. Over time, such systems build up a ‘generative model’, a structured understanding of the statistical regularities in our environment that’s used to generate predictions. This generative model is essentially a mental model of our world, including both immediate, task-specific information, as well as longer-term information that constitutes our narrative sense of self. According to this framework, predictive systems go about minimising prediction errors in two ways: either they update the generative model to more accurately reflect the world, or they behave in ways that bring the world better in line with their prediction. In this way, the brain forms part of an embodied predictive system that’s always moving from uncertainty to certainty. By reducing potentially harmful surprises, it keeps us alive and well.

Consider the healthy and expected body temperature of 37°C for a human being. A shift in either direction registers as a spike in prediction error, signalling to the organism that it’s moving into an unexpected, and therefore potentially dangerous, state. This rise in prediction error is fed back to us as feelings of discomfort, stress and an inclination to do something to get a better predictive grip on reality. We could just sit there and come to terms with the changing temperature (update our generative model), or we might reach for a blanket or open a window. In these cases, what we’re doing is acting upon our environment, sampling the world and changing our relation to it, in order to bring ourselves back within acceptable bounds of uncertainty.

According to the emerging picture from predictive processing, cognition and affect are tightly interwoven aspects of the same predictive system. Prediction errors aren’t merely data points within a computational system. Rather, rising prediction errors feel bad to us, while resolving errors in line with expectation feels good. This means that, as predictive organisms, we actively seek out waves of manageable prediction error – manageable uncertainty – because resolving it results in our feeling good. The recent rise in jigsaw puzzle sales during the COVID-19 lockdown testifies to our love of manageable uncertainty. These feelings evolved to keep us well tuned to our environment, helping us to curiously feel out novel and successful strategies for survival, while also avoiding all of the stress and unpleasantness that comes with runaway uncertainty. This active, recursive and felt relationship with the environment is crucial to grasping how social media can be detrimental to our mental health, and why we often find it so hard to stop using it.

Read the passage carefully and answer the questions that follow.

Abstract art, emerging in the early 20th century, represents a pivotal shift away from representational art towards an exploration of the intrinsic qualities of the artwork itself: form, color, line, texture, and the process of art-making. Philosophically, it posits that art transcends mere representation of the external world, offering instead a visceral, immediate experience of reality as perceived and imagined by the artist. This mirrors the philosophical underpinnings of chaos theory in the sciences, which emerged in the mid-20th century as a framework for understanding the behavior of complex systems that are highly sensitive to initial conditions—a concept popularly known as the butterfly effect. Chaos theory suggests that within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization.

The similarity between abstract art and chaos theory lies in their mutual embrace of complexity and a non-linear approach to their respective fields. Abstract art does not seek to simplify reality into recognizable forms but rather to express its multifaceted nature through abstraction. Similarly, chaos theory does not attempt to reduce complex systems to linear, predictable models but instead explores the richness and unpredictability inherent in such systems.

Both abstract art and chaos theory challenge the traditional paradigms of their fields. Where classical art sought to mirror the visible world, abstract art seeks to invoke the unseen, the emotional, and the conceptual dimensions of human experience. Chaos theory, diverging from classical Newtonian physics, which emphasizes predictability and determinism, embraces the inherent unpredictability of certain systems and the limits of prediction. This philosophical departure underscores a shared belief in the deeper order that exists within apparent disorder, suggesting that what seems chaotic may follow principles and patterns that transcend conventional understanding.

Moreover, the process-oriented nature of abstract art, where the act of creation is as significant as the final product, parallels the dynamic, ever-evolving systems described by chaos theory. In both realms, the initial conditions—whether the first stroke on a canvas or the initial state of a weather system—play a crucial role in the unfolding of the process, yet the outcome remains inherently unpredictable and subject to an array of influences that can dramatically alter the final result.

The exploration of fractals serves as a potent symbol of the convergence between abstract art and chaos theory. Fractals, with their infinite complexity, self-similarity at different scales, and creation through simple, recursive processes, exemplify the principles of chaos theory and are often mirrored in the patterns and structures found in abstract art. This visual and conceptual similarity underscores the shared fascination with how simple rules can generate complex and beautiful outcomes, a core principle in both disciplines.

Read the passage carefully and answer the questions that follow.

Abstract art, emerging in the early 20th century, represents a pivotal shift away from representational art towards an exploration of the intrinsic qualities of the artwork itself: form, color, line, texture, and the process of art-making. Philosophically, it posits that art transcends mere representation of the external world, offering instead a visceral, immediate experience of reality as perceived and imagined by the artist. This mirrors the philosophical underpinnings of chaos theory in the sciences, which emerged in the mid-20th century as a framework for understanding the behavior of complex systems that are highly sensitive to initial conditions—a concept popularly known as the butterfly effect. Chaos theory suggests that within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization.

The similarity between abstract art and chaos theory lies in their mutual embrace of complexity and a non-linear approach to their respective fields. Abstract art does not seek to simplify reality into recognizable forms but rather to express its multifaceted nature through abstraction. Similarly, chaos theory does not attempt to reduce complex systems to linear, predictable models but instead explores the richness and unpredictability inherent in such systems.

Both abstract art and chaos theory challenge the traditional paradigms of their fields. Where classical art sought to mirror the visible world, abstract art seeks to invoke the unseen, the emotional, and the conceptual dimensions of human experience. Chaos theory, diverging from classical Newtonian physics, which emphasizes predictability and determinism, embraces the inherent unpredictability of certain systems and the limits of prediction. This philosophical departure underscores a shared belief in the deeper order that exists within apparent disorder, suggesting that what seems chaotic may follow principles and patterns that transcend conventional understanding.

Moreover, the process-oriented nature of abstract art, where the act of creation is as significant as the final product, parallels the dynamic, ever-evolving systems described by chaos theory. In both realms, the initial conditions—whether the first stroke on a canvas or the initial state of a weather system—play a crucial role in the unfolding of the process, yet the outcome remains inherently unpredictable and subject to an array of influences that can dramatically alter the final result.

The exploration of fractals serves as a potent symbol of the convergence between abstract art and chaos theory. Fractals, with their infinite complexity, self-similarity at different scales, and creation through simple, recursive processes, exemplify the principles of chaos theory and are often mirrored in the patterns and structures found in abstract art. This visual and conceptual similarity underscores the shared fascination with how simple rules can generate complex and beautiful outcomes, a core principle in both disciplines.

Read the passage carefully and answer the questions that follow.

Abstract art, emerging in the early 20th century, represents a pivotal shift away from representational art towards an exploration of the intrinsic qualities of the artwork itself: form, color, line, texture, and the process of art-making. Philosophically, it posits that art transcends mere representation of the external world, offering instead a visceral, immediate experience of reality as perceived and imagined by the artist. This mirrors the philosophical underpinnings of chaos theory in the sciences, which emerged in the mid-20th century as a framework for understanding the behavior of complex systems that are highly sensitive to initial conditions—a concept popularly known as the butterfly effect. Chaos theory suggests that within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization.

The similarity between abstract art and chaos theory lies in their mutual embrace of complexity and a non-linear approach to their respective fields. Abstract art does not seek to simplify reality into recognizable forms but rather to express its multifaceted nature through abstraction. Similarly, chaos theory does not attempt to reduce complex systems to linear, predictable models but instead explores the richness and unpredictability inherent in such systems.

Both abstract art and chaos theory challenge the traditional paradigms of their fields. Where classical art sought to mirror the visible world, abstract art seeks to invoke the unseen, the emotional, and the conceptual dimensions of human experience. Chaos theory, diverging from classical Newtonian physics, which emphasizes predictability and determinism, embraces the inherent unpredictability of certain systems and the limits of prediction. This philosophical departure underscores a shared belief in the deeper order that exists within apparent disorder, suggesting that what seems chaotic may follow principles and patterns that transcend conventional understanding.

Moreover, the process-oriented nature of abstract art, where the act of creation is as significant as the final product, parallels the dynamic, ever-evolving systems described by chaos theory. In both realms, the initial conditions—whether the first stroke on a canvas or the initial state of a weather system—play a crucial role in the unfolding of the process, yet the outcome remains inherently unpredictable and subject to an array of influences that can dramatically alter the final result.

The exploration of fractals serves as a potent symbol of the convergence between abstract art and chaos theory. Fractals, with their infinite complexity, self-similarity at different scales, and creation through simple, recursive processes, exemplify the principles of chaos theory and are often mirrored in the patterns and structures found in abstract art. This visual and conceptual similarity underscores the shared fascination with how simple rules can generate complex and beautiful outcomes, a core principle in both disciplines.

Read the passage carefully and answer the questions that follow.

Abstract art, emerging in the early 20th century, represents a pivotal shift away from representational art towards an exploration of the intrinsic qualities of the artwork itself: form, color, line, texture, and the process of art-making. Philosophically, it posits that art transcends mere representation of the external world, offering instead a visceral, immediate experience of reality as perceived and imagined by the artist. This mirrors the philosophical underpinnings of chaos theory in the sciences, which emerged in the mid-20th century as a framework for understanding the behavior of complex systems that are highly sensitive to initial conditions—a concept popularly known as the butterfly effect. Chaos theory suggests that within the apparent randomness of chaotic complex systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization.

The similarity between abstract art and chaos theory lies in their mutual embrace of complexity and a non-linear approach to their respective fields. Abstract art does not seek to simplify reality into recognizable forms but rather to express its multifaceted nature through abstraction. Similarly, chaos theory does not attempt to reduce complex systems to linear, predictable models but instead explores the richness and unpredictability inherent in such systems.

Both abstract art and chaos theory challenge the traditional paradigms of their fields. Where classical art sought to mirror the visible world, abstract art seeks to invoke the unseen, the emotional, and the conceptual dimensions of human experience. Chaos theory, diverging from classical Newtonian physics, which emphasizes predictability and determinism, embraces the inherent unpredictability of certain systems and the limits of prediction. This philosophical departure underscores a shared belief in the deeper order that exists within apparent disorder, suggesting that what seems chaotic may follow principles and patterns that transcend conventional understanding.

Moreover, the process-oriented nature of abstract art, where the act of creation is as significant as the final product, parallels the dynamic, ever-evolving systems described by chaos theory. In both realms, the initial conditions—whether the first stroke on a canvas or the initial state of a weather system—play a crucial role in the unfolding of the process, yet the outcome remains inherently unpredictable and subject to an array of influences that can dramatically alter the final result.

The exploration of fractals serves as a potent symbol of the convergence between abstract art and chaos theory. Fractals, with their infinite complexity, self-similarity at different scales, and creation through simple, recursive processes, exemplify the principles of chaos theory and are often mirrored in the patterns and structures found in abstract art. This visual and conceptual similarity underscores the shared fascination with how simple rules can generate complex and beautiful outcomes, a core principle in both disciplines.

Read the passage carefully and answer the questions that follow.

The Positivists, anxious to stake out their claim for history as a science, contributed the weight of their influence to the cult of facts. First ascertain the facts, said the positivists, then draw your conclusions from them…..This is what may [be] called the common-sense view of history. History consists of a corpus of ascertained facts. The facts are available to the historian in documents, inscriptions, and so on…[Sir George Clark] contrasted the "hard core of facts" in history with the surrounding pulp of disputable interpretation forgetting perhaps that the pulpy part of the fruit is more rewarding than the hard core.….It recalls the favourite dictum of the great liberal journalist C. P. Scott: "Facts are sacred, opinion is free.". . .

What is a historical fact?..... According to the common-sense view, there are certain basic facts which are the same for all historians and which form, so to speak, the backbone of history—the fact, for example, that the Battle of Hastings was fought in 1066. But this view calls for two observations. In the first place, it is not with facts like these that the historian is primarily concerned. It is no doubt important to know that the great battle was fought in 1066 and not in 1065 or 1067, and that it was fought at Hastings and not at Eastbourne or Brighton. The historian must not get these things wrong. But [to] praise a historian for his accuracy is like praising an architect for using well-seasoned timber or properly mixed concrete in his building. It is a necessary condition of his work, but not his essential function. It is precisely for matters of this kind that the historian is entitled to rely on what have been called the "auxiliary sciences" of history—archaeology, epigraphy, numismatics, chronology, and so forth….

The second observation is that the necessity to establish these basic facts rests not on any quality in the facts themselves, but on an apriori decision of the historian. In spite of C. P. Scott's motto, every journalist knows today that the most effective way to influence opinion is by the selection and arrangement of the appropriate facts. It used to be said that facts speak for themselves. This is, of course, untrue. The facts speak only when the historian calls on them: it is he who decides to which facts to give the floor, and in what order or context. . . . The only reason why we are interested to know that the battle was fought at Hastings in 1066 is that historians regard it as a major historical event……... Professor Talcott Parsons once called [science] "a selective system of cognitive orientations to reality." It might perhaps have been put more simply. But history is, among other things, that. The historian is necessarily selective. The belief in a hard core of historical facts existing objectively and independently of the interpretation of the historian is a preposterous fallacy, but one which it is very hard to eradicate.

Read the passage carefully and answer the questions that follow.

The Positivists, anxious to stake out their claim for history as a science, contributed the weight of their influence to the cult of facts. First ascertain the facts, said the positivists, then draw your conclusions from them…..This is what may [be] called the common-sense view of history. History consists of a corpus of ascertained facts. The facts are available to the historian in documents, inscriptions, and so on…[Sir George Clark] contrasted the "hard core of facts" in history with the surrounding pulp of disputable interpretation forgetting perhaps that the pulpy part of the fruit is more rewarding than the hard core.….It recalls the favourite dictum of the great liberal journalist C. P. Scott: "Facts are sacred, opinion is free.". . .

What is a historical fact?..... According to the common-sense view, there are certain basic facts which are the same for all historians and which form, so to speak, the backbone of history—the fact, for example, that the Battle of Hastings was fought in 1066. But this view calls for two observations. In the first place, it is not with facts like these that the historian is primarily concerned. It is no doubt important to know that the great battle was fought in 1066 and not in 1065 or 1067, and that it was fought at Hastings and not at Eastbourne or Brighton. The historian must not get these things wrong. But [to] praise a historian for his accuracy is like praising an architect for using well-seasoned timber or properly mixed concrete in his building. It is a necessary condition of his work, but not his essential function. It is precisely for matters of this kind that the historian is entitled to rely on what have been called the "auxiliary sciences" of history—archaeology, epigraphy, numismatics, chronology, and so forth….

The second observation is that the necessity to establish these basic facts rests not on any quality in the facts themselves, but on an apriori decision of the historian. In spite of C. P. Scott's motto, every journalist knows today that the most effective way to influence opinion is by the selection and arrangement of the appropriate facts. It used to be said that facts speak for themselves. This is, of course, untrue. The facts speak only when the historian calls on them: it is he who decides to which facts to give the floor, and in what order or context. . . . The only reason why we are interested to know that the battle was fought at Hastings in 1066 is that historians regard it as a major historical event……... Professor Talcott Parsons once called [science] "a selective system of cognitive orientations to reality." It might perhaps have been put more simply. But history is, among other things, that. The historian is necessarily selective. The belief in a hard core of historical facts existing objectively and independently of the interpretation of the historian is a preposterous fallacy, but one which it is very hard to eradicate.