Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components - not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.

/pdf/perceptual-symbol-systems-4spsk97r61.pdf

Perceptual symbol systems.

In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A.G. Greenwald, D.E. McGhee, & J.L.K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.

/pdf/understanding-and-using-the-implicit-association-test-i-an-1sxb24sjrl.pdf

Understanding and using the Implicit Association Test: I. An improved scoring algorithm.

The power of feedback.

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

ions Keil, 1989 ; Rips, 1989)  For example, bats have the perceptual and behavioral characteristics of birds (they are similar to birds in this sense), but they are classified as mammals, because of important (though nonobvious) properties, such as giving birth to live young On the basis of examples like this, similarity's role in categorization has been challenged ; it has been argued that category membership judgments are theory based rather than similarity based (Keil, 1989 ; Murphy & Medin, 1985)  The process of alignment and mapping points the way to a reconciliation of similarity-based and theorybased accounts (see also Goldstone, 1994a)  If we focus purely on perceptual similarity among objects, we are led to conclude that bats should be categorized with birds  On this view, theory-based knowledge (such as why bats are mammals) must intervene from elsewhere to overrule this assignment  However, if the similarity computation is assumed to be that ofstructural alignment, then the similarity between two instances will be based riot only on object-level commonalities but also on common relations such as common causal relations and common origins  Assuming that our representations include information about theory-based relations, such as that bats bear live young, as well as information about features, then the schism between similarity-based and theory-based categorization may be more apparent than real  Developmentally, if we assume that theoretical knowledge is acquired gradually, this view would account for the characteristic-to-defining shift (Keil & Batterman, 1984) in children's interpretations of word meaning from local object features (eg , a taxi is bright yellow and has a checkered sign) to deeper relational commonalities (eg , a taxi is a vehicle that may be hired to transport people)  Choice and decision  Structural alignment also sheds light on the processes underlying choice behavior Medin, Goldstone, and Markman (1995) reviewed paral lels between phenomena in decision processing and phenomena in comparison processing that suggest an important role for structural alignment in decision making  Structural alignment influences which features to pay attention to in choice options  Research suggests that alignable differences are given more weight in choice situations than are nonalignable differences (Lindemann & Markman, 1996 ; Markman & Medin, 1995 ; Slovic & MacPhillamy, 1974)  For example, Markman and Medin (1995) asked participants to choose between video games and to justify their choices Their justifications were more likely to contain alignable differences than nonalignable differences  As another example, Kahneman and Z'versky (1984) described to participants a hypothetical store in which a jacket could be bought for $125 and a calculator for $15  They offered participants the opportunity to go to another store and save $5 on the total purchase Participants who were offered ajacket for $125 and a calculator for $10 were more willing to make the effort to go to another store than those offered a jacket for $120 and a calculator for $15  Even though the monetary reward for going to the other store was the same for both groups, participants were influenced by the alignable difference 

/pdf/structure-mapping-in-analogy-and-similarity-2sfelc2ufh.pdf

Structure mapping in analogy and similarity.

Knowledge Representation

Structural Alignment during Similarity Comparisons

Although diffusion models have been successfully used to predict the adoption patterns of new products and technologies, little research has examined the psychological processes underlying the individual consumer’s adoption decision. This research uses the knowledge transfer paradigm, studied often in the context of analogies, to demonstrate that both existing knowledge and innovation continuity are major factors influencing the consumer’s adoption process. In two experiments, the authors demonstrate that the relationship between expertise and adoption is relatively complex. Specifically, their findings indicate that, compared with novices, experts report higher comprehension, more net benefits, and therefore higher preferences for continuous innovations. However, for discontinuous innovations, experts’ entrenched knowledge is related to lower comprehension, fewer perceived net benefits, and lower preferences compared with that of novices. Only when this entrenched knowledge is accompanied by rel...

Entrenched Knowledge Structures and Consumer Response to New Products

Theories of similarity generally agree that the similarity of a pair increases with its commonalities and decreases with its differences. Recent research suggests that this comparison process involves an alignment of structured representations yielding commonalities, differences related to the commonalities, and differences unrelated to the commonalities. One counterintuitive prediction of this view is that it should be easier to find the differences between pairs of similar items than to find the differences between pairs of dissimilar items. This prediction is particularly strong for differences that are related to the commonalities. We tested this prediction in two experiments in which subjects listed a single difference for each of a number of word pairs. The results are consistent with the predictions of structural alignment. In light of these findings, we discuss the potential role of structural alignment in other cognitive processes that involve comparisons.

/pdf/structural-alignment-in-comparison-no-difference-without-4cja3al168.pdf

Arthur B. Markman

Papers

Structure mapping in analogy and similarity.

Knowledge Representation

Structural Alignment during Similarity Comparisons

Entrenched Knowledge Structures and Consumer Response to New Products

Structural Alignment in Comparison: No Difference Without Similarity: