To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
It is perhaps not inaccurate to say that much of the brain's machinery is, in essence, devoted to the detection of repetitions in the environment. Knowing that a particular entity is the same as the one seen on a previous occasion allows the organism to put into play a response appropriate to the reward contingencies associated with that entity. This entity can take many forms. It can be a temporally extended event, such as an acoustic phrase or a dance move, a location, such as a living room, or an individual object, such as a peacock or a lamp. Irrespective of what the precise entity is, the basic informationprocessing problem is the same – to discover how the complex sensory input can be carved into distinct entities and to recognize them on subsequent occasions. In this chapter, we examine different pieces of knowledge regarding visual object discovery and attempt to synthesize them into a coherent framework.
Let us examine the challenges inherent in this problem a little more closely. Consider Figure 16.1(a). This complex landscape appears to be just a random collection of peaks and valleys, with no clearly defined groups. Yet it represents an image that is easily parsed into distinct objects by the human visual system. Figure 16.1(b) shows the image that corresponds exactly to the plot shown in Figure 16.1(a), which simply represents pixel luminance by height. It is now trivial for us to notice that there are three children, and we can accurately delimit their extents as shown in Figure 16.1(c).
The most frequently used computational models in social psychology are probably various kinds of connectionist models, such as constraint satisfaction networks, feedforward pattern associators with delta-rule learning, and multilayer recurrent networks with learning. The chapter begins with work on causal learning, causal reasoning, and impression formation. A large number of central phenomena in social psychology can be captured by a fairly simple feedback or recurrent network with learning. Important findings on causal learning, causal reasoning, individual and group impression formation, and attitude change can all be captured within the same basic architecture. This suggests that we might be close to being able to provide an integrated theory or account of a wide range of social psychological phenomena. It also suggests that underlying the apparent high degree of complexity of social and personality phenomena may be more fundamental simplicity.