There is a persistent worry that cognitive scientists may not be carving nature at its joints. This fear underlies debates over whether computational representations or dynamical systems best explain cognitive processing (e.g., Markman & Dietrich Reference Markman and Dietrich2000; Spivey Reference Spivey2007). It lies at the heart of critiques of the use of brain imaging to understand cognitive function (Uttal Reference Uttal2001). This issue is also central to the target book, Doing without Concepts (Machery Reference Machery2009).
This question is important, because cognitive scientists typically organize theories around tasks. Memory is explored by having people study items and then probing their memory for those items at some later time. Decision-making research involves presenting people with a set of options and having them select one. As Machery points out, categorization research often involves specific tasks such as classification and category-based induction. Theories then aim to explain performance in these tasks.
Machery takes the structure of the cognitive science literature on concepts as a given and then suggests that the notion of a concept is misleading. On his view, there are (at least) three distinct types of concepts: prototypes, exemplars, and theories. Using a single term – concepts – to refer to all of these is dangerous, because it fails to carve nature at its joints.
I suggest that the problem is even worse than Machery makes it out to be. Fundamentally, the set of tasks that we study involves a series of cross-cutting cognitive mechanisms. At present, cognitive science assumes that tasks like classification and category-based induction require an explanation that involves some set of representations and processes that are shared (to some degree) across different kinds of categorization tasks, but are relatively distinct from the kinds of representations and processes that are involved in decision-making, memory, or attention.
Ultimately, we need to reorient our theories to find the commonalities across tasks that are typically thought of as different. In the study of concepts, there are already some hints in the literature that this reorientation is starting to take place.
The most prominent shift in research on categorization comes from work relating categorization to memory. For example, the research by Ashby, Maddox, and colleagues draws parallels between behavioral and neurobiological research on categorization and memory (Ashby et al. Reference Ashby, Alfonso-Reese, Turken and Waldron1998; Maddox & Ashby Reference Maddox and Ashby2004). This work incorporates research from neural systems involved in memory to make predictions for performance in category learning experiments. Research on the kinds of categories that amnesics can learn is also inspired by the desire to create parallels between memory and categorization (Knowlton et al. Reference Knowlton, Squire and Gluck1994).
Research on the influences of learning tasks on category learning also forms parallels between categorization and memory (Markman & Ross Reference Markman and Ross2003). A growing body of research suggests that the tasks people perform while learning categories influences what people learn about those categories. For example, Yamauchi and Markman (Yamauchi & Markman Reference Yamauchi and Markman1998; Yamauchi et al. Reference Yamauchi, Love and Markman2002) found that people tend to learn about features that distinguish one category from another when learning by classifying examplars. In contrast, people tend to acquire category prototypes when learning by making predictive inferences about new category members that are missing some features (see also Chin-Parker & Ross Reference Chin-Parker and Ross2002). Markman and Ross (Reference Markman and Ross2003) drew on the memory literature and argued that this type of category acquisition involves transfer appropriate processing, which is also observed in memory (e.g., Morris et al. Reference Morris, Bransford and Franks1977).
The relationship between categorization and memory is also being driven forward by work on the influences of communication on category acquisition. Garrod and colleagues (Garrod & Anderson Reference Garrod and Anderson1987; Garrod & Doherty Reference Garrod and Doherty1994) found that people tend to arrive at a common method for referring to information in the environment over the course of conversing with others about those objects. Markman and Makin (Reference Markman and Makin1998) observed that the similarity of categories across people is made more uniform by communicating with others. The act of establishing a common reference influences memory by ensuring that category labels refer to a common set of properties.
These communicative factors have a significant influence on the ability to extract information about categories from memory. For example, Malt et al. (Reference Malt, Sloman and Gennari2003) found that it is hard to predict the labels people give to a common set of objects (such as jars, bottles, boxes, and containers) based solely on similarities among the items. Furthermore, there is no clear relationship between the labels given to objects in different languages. Instead, the labels given to objects reflect communicative history of those objects in the language. The labels people learn for objects, then, are determined in part by the utility of those labels for communication. We use a particular word for an object, because we know that others will know what we are talking about when we use that label.
I am not suggesting that we abandon theoretical devices like prototypes, exemplars, and theories as constructs for helping us to understand categorization behavior. Instead, the theoretical basis of research on categorization needs to be modified.
We must recognize that the tasks that we use to study psychology in the lab do not cut nature at its joints. Instead, we must look for the cross-cutting psychological mechanisms that are involved in a variety of different tasks. That means that theoretical constructs from memory, communication, attention, and motivation are all relevant to the study of category acquisition.
In addition, the valuable constructs from research on categorization must be exported to the study of other cognitive processes where they may be valuable. Undoubtedly, prototypes, exemplars, and theories have value in understanding a variety of aspects of cognitive processing beyond categorization. For example, exemplars play an increasingly important role in theories of automaticity (Logan Reference Logan1988; Reference Logan2002).
Ultimately, we must transition from a cognitive science in which psychological tasks organize our understanding of psychological mechanisms to one based on an understanding of the way a core set of psychological mechanisms permit us to perform those tasks.
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There is a persistent worry that cognitive scientists may not be carving nature at its joints. This fear underlies debates over whether computational representations or dynamical systems best explain cognitive processing (e.g., Markman & Dietrich Reference Markman and Dietrich2000; Spivey Reference Spivey2007). It lies at the heart of critiques of the use of brain imaging to understand cognitive function (Uttal Reference Uttal2001). This issue is also central to the target book, Doing without Concepts (Machery Reference Machery2009).
This question is important, because cognitive scientists typically organize theories around tasks. Memory is explored by having people study items and then probing their memory for those items at some later time. Decision-making research involves presenting people with a set of options and having them select one. As Machery points out, categorization research often involves specific tasks such as classification and category-based induction. Theories then aim to explain performance in these tasks.
Machery takes the structure of the cognitive science literature on concepts as a given and then suggests that the notion of a concept is misleading. On his view, there are (at least) three distinct types of concepts: prototypes, exemplars, and theories. Using a single term – concepts – to refer to all of these is dangerous, because it fails to carve nature at its joints.
I suggest that the problem is even worse than Machery makes it out to be. Fundamentally, the set of tasks that we study involves a series of cross-cutting cognitive mechanisms. At present, cognitive science assumes that tasks like classification and category-based induction require an explanation that involves some set of representations and processes that are shared (to some degree) across different kinds of categorization tasks, but are relatively distinct from the kinds of representations and processes that are involved in decision-making, memory, or attention.
Ultimately, we need to reorient our theories to find the commonalities across tasks that are typically thought of as different. In the study of concepts, there are already some hints in the literature that this reorientation is starting to take place.
The most prominent shift in research on categorization comes from work relating categorization to memory. For example, the research by Ashby, Maddox, and colleagues draws parallels between behavioral and neurobiological research on categorization and memory (Ashby et al. Reference Ashby, Alfonso-Reese, Turken and Waldron1998; Maddox & Ashby Reference Maddox and Ashby2004). This work incorporates research from neural systems involved in memory to make predictions for performance in category learning experiments. Research on the kinds of categories that amnesics can learn is also inspired by the desire to create parallels between memory and categorization (Knowlton et al. Reference Knowlton, Squire and Gluck1994).
Research on the influences of learning tasks on category learning also forms parallels between categorization and memory (Markman & Ross Reference Markman and Ross2003). A growing body of research suggests that the tasks people perform while learning categories influences what people learn about those categories. For example, Yamauchi and Markman (Yamauchi & Markman Reference Yamauchi and Markman1998; Yamauchi et al. Reference Yamauchi, Love and Markman2002) found that people tend to learn about features that distinguish one category from another when learning by classifying examplars. In contrast, people tend to acquire category prototypes when learning by making predictive inferences about new category members that are missing some features (see also Chin-Parker & Ross Reference Chin-Parker and Ross2002). Markman and Ross (Reference Markman and Ross2003) drew on the memory literature and argued that this type of category acquisition involves transfer appropriate processing, which is also observed in memory (e.g., Morris et al. Reference Morris, Bransford and Franks1977).
The relationship between categorization and memory is also being driven forward by work on the influences of communication on category acquisition. Garrod and colleagues (Garrod & Anderson Reference Garrod and Anderson1987; Garrod & Doherty Reference Garrod and Doherty1994) found that people tend to arrive at a common method for referring to information in the environment over the course of conversing with others about those objects. Markman and Makin (Reference Markman and Makin1998) observed that the similarity of categories across people is made more uniform by communicating with others. The act of establishing a common reference influences memory by ensuring that category labels refer to a common set of properties.
These communicative factors have a significant influence on the ability to extract information about categories from memory. For example, Malt et al. (Reference Malt, Sloman and Gennari2003) found that it is hard to predict the labels people give to a common set of objects (such as jars, bottles, boxes, and containers) based solely on similarities among the items. Furthermore, there is no clear relationship between the labels given to objects in different languages. Instead, the labels given to objects reflect communicative history of those objects in the language. The labels people learn for objects, then, are determined in part by the utility of those labels for communication. We use a particular word for an object, because we know that others will know what we are talking about when we use that label.
I am not suggesting that we abandon theoretical devices like prototypes, exemplars, and theories as constructs for helping us to understand categorization behavior. Instead, the theoretical basis of research on categorization needs to be modified.
We must recognize that the tasks that we use to study psychology in the lab do not cut nature at its joints. Instead, we must look for the cross-cutting psychological mechanisms that are involved in a variety of different tasks. That means that theoretical constructs from memory, communication, attention, and motivation are all relevant to the study of category acquisition.
In addition, the valuable constructs from research on categorization must be exported to the study of other cognitive processes where they may be valuable. Undoubtedly, prototypes, exemplars, and theories have value in understanding a variety of aspects of cognitive processing beyond categorization. For example, exemplars play an increasingly important role in theories of automaticity (Logan Reference Logan1988; Reference Logan2002).
Ultimately, we must transition from a cognitive science in which psychological tasks organize our understanding of psychological mechanisms to one based on an understanding of the way a core set of psychological mechanisms permit us to perform those tasks.