Clarifying status of DNNs as models of human vision.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature.

An experimental and simulation study of the impact of emotional information on analogical reasoning.

We investigated whether and how emotional information would affect analogical reasoning. We hypothesized that task-irrelevant emotional information would impair performance whereas task-relevant emotional information would enhance it. In Study 1, 233 undergraduates completed a novel version of the People Pieces Task (Emotional Faces People Task), an analogical reasoning task in which the task characters displayed emotional or neutral facial expressions (within-participants).

Deep problems with neural network models of human vision.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g.

A theory of relation learning and cross-domain generalization.

People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e.

Countering antivaccination attitudes.

Three times as many cases of measles were reported in the United States in 2014 as in 2013. The reemergence of measles has been linked to a dangerous trend: parents refusing vaccinations for their children. Efforts have been made to counter people's antivaccination attitudes by providing scientific evidence refuting vaccination myths, but these interventions have proven ineffective.

Revisiting Wittgenstein's puzzle: hierarchical encoding and comparison facilitate learning of probabilistic relational categories.

Kittur et al. (2004, 2006) and Jung and Hummel (2011, 2014) showed that people have great difficulty learning relation-based categories with a probabilistic (i.e.

Analogy, explanation, and proof.

People are habitual explanation generators. At its most mundane, our propensity to explain allows us to infer that we should not drink milk that smells sour; at the other extreme, it allows us to establish facts (e.g.

Making Probabilistic Relational Categories Learnable.

Theories of relational concept acquisition (e.g., schema induction) based on structured intersection discovery predict that relational concepts with a probabilistic (i.

Working memory for relations among objects.

Across many areas of study in cognition, the capacity of working memory (WM) is widely agreed to be roughly three to five items: three to five objects (i.e., bound collections of object features) in the literature on visual WM or three to five role bindings (i.

Comparison and mapping facilitate relation discovery and predication.

Relational concepts play a central role in human perception and cognition, but little is known about how they are acquired. For example, how do we come to understand that physical force is a higher-order multiplicative relation between mass and acceleration, or that two circles are the same-shape in the same way that two squares are? A recent model of relational learning, DORA (Discovery of Relations by Analogy; Doumas, Hummel & Sandhofer, 2008), predicts that comparison and analogical mapping play a central role in the discovery and predication of novel higher-order relations. We report two experiments testing and confirming this prediction.

Earlier development of analytical than holistic object recognition in adolescence.

Background: Previous research has shown that object recognition may develop well into late childhood and adolescence. The present study extends that research and reveals novel differences in holistic and analytic recognition performance in 7-12 year olds compared to that seen in adults. We interpret our data within a hybrid model of object recognition that proposes two parallel routes for recognition (analytic vs.

A neurocomputational system for relational reasoning.

The representation and manipulation of structured relations is central to human reasoning. Recent work in computational modeling and neuroscience has set the stage for developing more detailed neurocomputational models of these abilities. Several key neural findings appear to dovetail with computational constraints derived from a model of analogical processing, 'Learning and Inference with Schemas and Analogies' (LISA).

Children's use of analogy during collaborative reasoning.

This microgenetic study examined social influences on children's development of analogical reasoning during peer-led small-group discussions of stories about controversial issues. A total of 277 analogies were identified among 7,215 child turns for speaking during 54 discussions from 18 discussion groups in 6 fourth-grade classrooms (N = 120; age M=10.0, SD=0.

Symbolic versus associative learning.

Ramscar and colleagues (2010, this volume) describe the "feature-label-order" (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of "learning elements of a symbol system" (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of "solving the symbol grounding problem."

A computational account of the development of the generalization of shape information.

Abecassis, Sera, Yonas, and Schwade (2001) showed that young children represent shapes more metrically, and perhaps more holistically, than do older children and adults. How does a child transition from representing objects and events as undifferentiated wholes to representing them explicitly in terms of their attributes? According to RBC (Recognition-by-Components theory; Biederman, 1987), objects are represented as collections of categorical geometric parts ("geons") in particular categorical spatial relations. We propose that the transition from holistic to more categorical visual shape processing is a function of the development of geon-like representations via a process of progressive intersection discovery.

Visual priming of inverted and rotated objects.

Object images are identified more efficiently after prior exposure. Here, the authors investigated shape representations supporting object priming. The dependent measure in all experiments was the minimum exposure duration required to correctly identify an object image in a rapid serial visual presentation stream.

A theory of the discovery and predication of relational concepts.

Relational thinking plays a central role in human cognition. However, it is not known how children and adults acquire relational concepts and come to represent them in a form that is useful for the purposes of relational thinking (i.e.

Familiar interacting object pairs are perceptually grouped.

Identification of objects in a scene may be influenced by functional relations among those objects. In this study, observers indicated whether a target object matched a label. Each target was presented with a distractor object, and these were sometimes arranged to interact (as if being used together) and sometimes not to interact.

Role of gamma-band synchronization in priming of form discrimination for multiobject displays.

Previous research has shown that synchronized flicker can facilitate detection of a single Kanizsa square. The present study investigated the role of temporally structured priming in discrimination tasks involving perceptual relations between multiple Kanizsa-type figures. Results indicate that visual information presented as temporally structured flicker in the gamma band can modulate the perception of multiple objects in a subsequent display.

The One-to-One Constraint in Analogical Mapping and Inference.

Theories of analogical reasoning have assumed that a 1-to-1 constraint discourages reasoners from mapping a single element in 1 analog to multiple elements in another. Empirical evidence suggests that reasoners sometimes appear to violate the one-to-one constraint when asked to generate mappings, yet virtually never violate it when asked to generate analogical inferences. However, few studies have examined analogical inferences based on nonisomorphic analogs, and their conclusions are suspect due to methodological problems.

Comparison-induced decoy effects.

Extant theories of decoy effects on evaluations of attribute values were assessed with respect to their ability to account for a one-dimensional analogue of the asymmetric dominance effect. Parducci's (1965, 1995) range-frequency theory, Krumhansl's (1978) distance-density model, Tversky's (1977) diagnosticity principle, and reference point theories (e.g.

Relational integration, inhibition, and analogical reasoning in older adults.

The difficulty of reasoning tasks depends on their relational complexity, which increases with the number of relations that must be considered simultaneously to make an inference, and on the number of irrelevant items that must be inhibited. The authors examined the ability of younger and older adults to integrate multiple relations and inhibit irrelevant stimuli. Young adults performed well at all but the highest level of relational complexity, whereas older adults performed poorly even at a medium level of relational complexity, especially when irrelevant information was presented.

A neurocomputational model of analogical reasoning and its breakdown in frontotemporal lobar degeneration.

Analogy is important for learning and discovery and is considered a core component of intelligence. We present a computational account of analogical reasoning that is compatible with data we have collected from patients with cortical degeneration of either their frontal or anterior temporal cortices due to frontotemporal lobar degeneration (FTLD). These two patient groups showed different deficits in picture and verbal analogies: frontal lobe FTLD patients tended to make errors due to impairments in working memory and inhibitory abilities, whereas temporal lobe FTLD patients tended to make errors due to semantic memory loss.

Evidence for holistic representations of ignored images and analytic representations of attended images.

According to the hybrid theory of object recognition (J. E. Hummel, 2001), ignored object images are represented holistically, and attended images are represented both holistically and analytically.