Effectivities for Reach-to-Grasp Actions

I just reviewed the affordance properties that produce the spatial structure in reach-to-grasp actions, and there's an unquestioned assumption lurking in that analysis. Luckily, Mon-Williams & Bingham (2011) actually did question it, so I wanted to cover that part of the paper here.

The assumption in the analyses I described last time is that the unit of action is the aperture between the finger and thumb, and not the fingers themselves. Bingham refers to this as an opposition vector (Iberall, Bingham, & Arbib, 1986; van Bergen et al, 2007). In some ways, this is a weird idea; the action system working to control a space between limbs, and not the limbs! Smeets & Brenner (1999) proposed that grasping is actually about the two limbs. Mon-Williams & Bingham tested these hypotheses and found evidence in favour of the opposition vector.

I want to walk through this in a little detail, though, as of course identifying the relevant elemental variables is part of an UCM analysis, and affordance research helps here too. The task analysis that reveals affordance property options also points to effectivity property options (at least it should - these are complementary after all!). But another part of the UCM approach is that it can, in principle, test hypotheses about elemental and performance variables, so I want to lay this out as well.

Affordances for the Spatial Structure of Reach-To-Grasp (Mon-Williams & Bingham, 2011)

I have reviewed the spatial and temporal structure of reach-to-grasp movements, and the task dynamical analysis that has connected affordance properties and reach-to-grasp effectivities. Now it's time to work through some papers exploring this task using this analysis.

The first paper is Mon-Williams & Bingham (2011). The goal of this paper was to investigate what target properties shape the spatial structure of the reach-to-grasp movement. This means the behaviour of the hand as it forms a grip aperture and then encloses and contacts the object. Specifically, we want to examine the maximum grip aperture (MGA), which occurs partway through the reach and is larger than the object, and the terminal grip aperture (TGA), which occurs when the hand has stopped moving and the hand encloses the object, but before the fingers are in contact with the object. The question is, what object properties determine how these apertures are scaled? 

Motor Abundance & the Affordances for Reaching-to-Grasp

Movements are never the same twice, even when you are trying to do that same thing over and over. Variability is an inescapable fact of trying to organise and run a complex system such as a human body. But there is more than one source of variability in movement; there's noise, and then there's redundancy, and these are not the same thing. 

Our movement systems are redundant; specifically, they always have more degrees of freedom available than are ever required to perform a given task. This means that there is always more than one way to perform any given task, and this can range from slight variations to complete reorganisations. 

Redundancy is a feature, not a bug. It means that we can reliably achieve a task goal in the face of perturbations that range from trial-to-trial fluctuations in execution up to surprises like tripping or the sudden appearance of an obstacle. However, it poses two related control problems. First, a problem of action selection: given that there are many functional organisations of degrees of freedom that could solve that task, which do we choose, and why? Second, a problem of action control: once we have our degrees of freedom organised, we still have some left over that need to be actively controlled; how do we do this, and why do we control them the way we do?

Ecological Mechanisms and Models of Mechanisms (#MechanismWeek 4)

Mechanistic models are great, but so far cognitive science doesn't have any. We have functional models (of, for example, memory or categorisation) and dynamical models (of, for example, neural networks) but none of these can support the kind of explanations mechanistic models can. Is that it for psychology, or can we do better?

Here we propose that it's possible to do psychology in a way that allows for the development of explanatory, mechanistic models. The trick, as we have discussed, is to identify the correct level of analysis at which to ground those models. These models will definitely end up being multi-level (Craver, 2007), but the form of these final models will be dictated and constrained by the nature of the real parts and operations at the grounding level.

The correct level of analysis, we propose, is the ecological level. Specifically, ecological information is going to be the real component whose nature will place the necessary constraints on both our empirical investigations of psychological mechanisms as well as the mechanistic models we develop.

Let's see how this might work.

The Shrunken Finger Illusion

Ed Yong has a great write-up of an interesting little study in Current Biology (Ekroll, Sayim, Vander Hallen & Wagemans, 2016) that caught my eye. The study reports an illusion (the 'shrunken finger illusion') that shows how amodal volume completion can make you feel like your finger has shrunk, and everyone is very excited about how this shows our experience of the hidden back-sides of objects is "real".

In this post, I'll review the results, do a little ecological finger wagging about the breathless write-up (Ekroll's, not Ed's) and think about some studies the ecological reframing of the effect might motivate. Briefly, I think this effect is definitely real, and that we really do genuinely perceive hidden objects under certain circumstances. Of course, this has nothing to do with amodal mental representations of what we think is there and everything to do with the information the system is interacting with, but you know that of course because this is always the answer!

On "The poverty of embodied cognition" (Goldinger et al, in press)

A new paper in Psychonomic Bulletin and Review (Goldinger, Papesh, Barnhart, Hansen & Hout, 2015) has taken a swing at the field of embodied cognition, claiming that it is vague, trivial and unable to add anything scientific to the investigation of cognition.

...our goal is to zoom out from specific empirical debates, asking instead what EC offers to cognitive science in general. To preview, we argue that EC is theoretically vacuous with respect to nearly all cognitive phenomena. EC proponents selectively focus on a subset of domains that work, while ignoring nearly all the bedrock findings that define cognitive science. We also argue that the principles of EC are often (1) co-opted from other sources, such as evolution; (2) vague, such that model building is not feasible; (3) trivially true, offering little new insight; and, occasionally, (4) nonsensical. 

My basic take is a) I actually agree with a lot of the criticisms in the context of the kinds of 'embodied' cognition we critique for similar reasons, but b) there is nothing new to any of these critiques, none of them are compulsory failings of the field and nothing about them makes embodiment an intrinsically empty notion. 

Quantifying the Affordances for Throwing for Distance and Accuracy

I have a new paper in press at JEP:HPP (Wilson, Weightman, Bingham & Zhu, in press; supplemental material). It is the end result of five years work across two jobs, and it has involved kinematic data collection from expert throwers in Leeds and Wyoming, analysis of that data, then interpretation of that data in the context of detailed simulations we ran in order to identify the affordance property of the target structuring behaviour. This is my first paper on affordances, my first about my current favourite topic of throwing, and probably the heftiest empirical piece I have ever done, so getting it published in my journal of choice is pretty exciting!

I'm going to just lay out the basic framework of the paper here. I will leave the (very many) details to the paper. The paper consists of two experiments, a series of simulations, and a discussion of affordances as dispositional properties of tasks best described at the level of task dynamics. This last bit feeds into the argument in the (mostly philosophical) literature on the nature of affordances; bad news, people who think they are relations - they aren't, and I've got two experiments that back that up!

What Would It Take to Refute Radical Embodied Cognition?

People often send us papers and data via Twitter that they believe rule out a radical, non-representational theory of cognition. Because I have yet to agree about any of these studies, these people then often ask in exasperated tones 'well, what would you accept as evidence?'. 

My current best answer is "about 20 years of hard work". 

Learning the affordances for maximum distance throwing

Over the last couple of posts, I have reviewed data that shows people can perceive which object they can, in fact, throw the farthest ahead of time by hefting the object. Both the size and the weight of the object affect people's judgements and the distance thrown; however, only weight affects the dynamics of throwing (release angle and velocity are unaffected by changes in size). This rules out the smart perceptual mechanism proposed by Bingham et al (1989), which proposed that both size and weight changes affect hefting and throwing the same way. So how are people perceiving this affordance?

Hefting for a Maximum Distance Throw

From the task dynamic analysis of throwing for maximum distance, we've identified the fact that for a given release angle and maximum release velocity, there is an object whose size and weight optimises the distance it will travel when thrown. Can people perceive this combination ahead of time? More specifically, can people identify the object which affords throwing to a maximum distance, and if so, how?

Bingham, Schmidt & Rosenblum (1989) is the first paper investigating this question. It is a bear of a paper; I've stripped a lot of the methodological detail out in my summary so I can focus on the bigger picture. That bigger picture is this; Bingham et al first check whether people can identify objects that afford throwing to a maximum distance by hefting them ahead of time (they can). They then investigate the kinematics of hefting to identify an invariant relation in the timing of the wrist and elbow velocities and relate that invariant to the dynamics of throwing (specifically how it maximises the transfer of kinetic energy from the torso muscles to the projectile). They propose that using this invariant reflects a smart perceptual solution (Runeson, 1977) to the problem of selecting objects to throw to a maximum distance - future work (Zhu & Bingham, 2008) will actually show that this specific smart mechanism doesn't hold up, although the replacement is smart too.

How do we perceive which objects afford throwing the farthest?

Previous work has established that people with throwing experience can perceive the affordance of 'throwability'. If you let these people heft objects with a range of sizes and weights, they will confidently select the one they think they can throw the farthest, and they tend to be correct. It's a very natural task, one you have probably done yourself on a beach or lakeside looking for stones to throw into the water. 

This is only the first, and relatively easy step in any ecological task analysis. Once you've identified an affordance property and established that people are sensitive to it, you need to identify the information supporting this perception. For throwing, this has not been done, and while the paper I'm reviewing here doesn't solve the problem, it does rule out a highly likely contender for the source of the information that has implications for a lot of other research.

Giving children with movement problems a leg up with robots

Developmental coordination disorder (DCD) is a surprisingly common problem; it's thought that 6-8% of school aged children are diagnosable. DCD is a motor disorder, where children have great difficulty in producing skilled actions, especially anything requiring fine motor control. Handwriting, tying your shoelaces, sports of any kind are all huge problems for these children. 

One key question about DCD is why does it occur. Part of the problem in answering this is that it is a behavioural diagnosis; you get diagnosed if you have severe motor impairments that aren't a known side effect of something else. Regardless, there are two basic ways in which children might end up with such problems; crudely, they might have difficulties in producing movements, or they might have difficulty learning movements. My colleague and author on this paper, Mark Mon-Williams, uses the analogy that children with DCD may be bad drivers of perfectly working cars or good drivers of malfunctioning cars. It's obviously a little messier than that, but this is the essential idea, and the answer has implications for the kind of interventions you'll try and design.

The Small Effect Size Effect - Why Do We Put Up With Small Effects?

Small effects sometimes matter - but psychology can do better

One of the things that bugs me about 'embodied' cognition research is that the effects, while statistically significant, tend to be small. What this means is that the groups were indeed different in the direction the authors claim, but only slightly, and that the authors had enough people showing the effect to make it come out on average. 

The problem with small effect sizes is that they mean all you've done is nudge the system. The embodied nervous system is exquisitely sensitive to variations in the flow of information it is interacting with, and it's not clear to me that merely nudging such a system is all that great an achievement. What's really impressive is when you properly break it - If you can alter the information in a task and simply make it so that the task becomes impossible for an organism, then you have found something that the system considers really important. The reverse is also true, of course - if you find the right way to present the information the system needs, then performance should become trivially easy. 

Psychology has become enthralled by statistical significance (to the point that we're possibly gaming the system in order to cross this magical marker). If your effect comes with a p value of less than .05, it is interesting, regardless of how small the effect is in terms of function. This is a problem, and we don't have to put up with it. If you ask a question about the right thing, you should get an unambiguous answer. If your answer is ambiguous, you may not be asking about the right thing. 

I want to remind readers of a couple of examples of nuisance small effects I've covered here before, then talk a little about some work which either broke or fixed the right thing, to highlight that we don't actually have to suffer from the tyranny of the small effect effect.

Are babies super? Performance, competence and infant habituation

Are babies really more competent than we give them credit? (No.)

Developmental psychology is filled with studies that claim to show the hidden abilities of babies. The claim is that babies come pre-packaged with all kinds of knowledge and skills that provides them with the foot in the door they need to learn about the world. Babies are limited in their ability to demonstrate this knowledge, however, because of their immature bodies and inability to control these well. In the language of the field, there is hidden competence concealed by problems with performance, and researchers (such as Liz Spelke and Renee Baillargeon) are interested in finding ways to reveal this hidden competence.

At IU we referred to such studies as 'super baby' studies, because they purported to show that infants were remarkably competent and knowledgeable about the world. Besides the rampant dualism of 'mind' being concealed by 'body', these studies are good examples of a common problem (the psychologist's fallacy) in psychological research, one that a rigorous application of embodied cognition helps fix.

Coordination dynamics and relative speed

The Bingham model of coordinated rhythmic movement makes three predictions. First, it predicts that movement stability is a function of perceptual ability, and we confirmed this in two ways (by showing how people can move stably at non-0° with transformed visual feedback (Wilson et al, 2005) and by showing that perceptual learning of 90° led to improved movement stability without practice at the movement task; Wilson et al, 2010). This prediction is also supported by recent work by Kovacs and Shea, who are busy demonstrating that transformed, Lissajous feedback breaks the classic pattern of movement stability in coordination tasks. The second prediction is that relative phase is specified by the relative direction of motion; we confirmed this by selectively perturbing various components of motion and showing selective effects on performance (Wilson & Bingham, 2008). 

The third prediction was that the detection of relative direction was conditioned on the relative speed; the latter was simply a noise term. de Rugy, Oullier & Temprado (2008) tested this prediction by using an amplitude manipulation to alter the relative speeds. Their data did not support the model predictions, and they concluded that the approach taken by the Bingham model was flawed. We recently replicated their experiment (Snapp-Childs, Wilson & Bingham, in press as of Friday; download) and identified numerous critical flaws in their design and analysis which invalidated their criticism.

Task Specific Devices and the Perceptual Bottleneck

I've been wanting to blog this paper, Bingham (1988; download link), for some time, and I've had the excuse to be reading it this week as I develop a grant. There's a lot here, and many of these brief points are worth posts in and of themselves. My goal here was to create a walk through of the paper, and I hope to dive into some of these issues in more detail.

This paper comes from Geoff Bingham, my PhD advisor at IU. And, like most of the good things Geoff has taught me over the years, this paper is a gift that keeps giving as I come to grips with what's in it. What it does is lay out a methodological problem (the massive redundancy and complexity of the human action system), proposes a solution (studying task-specific devices) and firmly embeds the idea that these devices are intrinsically perception-action devices (by discussing the so-called perceptual bottleneck). In effect, it lays out a way to be a productive scientist studying a hugely complex system without shying away from the complexity. This paper blew my fragile little mind when I first read it, and I'm still pulling good ideas from it today.

This paper is what I think the science of perception-action should look like. It's the piece I think Chemero (2009) is missing for his radical embodied cognitive science, and it contains (oddly without a lot of specific references) all the key ideas that have come up on this blog in a single coherent frame work (e.g. Gibson & specification; Turvey et al on the symmetry principle). Frankly, if you want to study perception-action systems from a dynamical systems perspective, this is what you have to acknowledge is the lay of the land and these are the beginnings of the tool kit you'll need.

Perception, Action & Dynamical Systems

Over Easter I visited the Center of Functionally Integrative Neuroscience at Aarhus University in Denmark, courtesy of the Interacting Minds group. I gave a talk, got the tour, and met some of the faculty and students - some interesting opportunities for future collaborations, I hope - thanks for the hospitality!

I wanted to lay out the basics of the talk I gave. I took the opportunity to present some ideas that have been developing as I work on this blog, reading Chemero and working on coordination experiments. There is a core of people in Aarhus interested in things ecological, as well as dynamical systems, so it was a good audience to try these ideas out and they seemed to go over well. This is also the sketch of a paper Sabrina and I are going to work on over the summer.

The take home message of the talk was simple - dynamical systems is the right kind of mindset for cognitive science, but it is not a theory of behaviour. Dynamics merely provides the right kind of modelling tools - the form of the model must be based on hypotheses about the specific kind of dynamical systems we are or else they are merely an exercise in data-fitting. Ecological psychology is the right theory, and the Bingham model of coordinated rhythmic movement is currently the only example of a genuinely perception-action dynamical systems model. My thoughts here are largely from my response to Chapter 4 of Chemero (on 'the dynamical stance') and Chapter 5, his initial attempt to use dynamics to serve as a guide to discovery which I think fails and which Chemero then replaces with ecological psychology. The description of Bingham's model comes from here.

The Size-Weight Illusion is Functional, and It's About Throwing

My colleagues, Geoff Bingham and Qin Zhu, have recently published some fascinating data which has emerged from their work on the uniquely human skill, long-distance throwing. This is a novel and rich perception-action task which Bingham and Zhu (and recently, me) have been investigating for some time, with many interesting results. I'll get onto blogging about this project once I've caught up with the coordination studies and have had some time to get my head around the data I'm helping generate.

I wanted to blog about this new paper, though, because it's an exciting result which deserves all the attention it gets. The result is about the size-weight illusion, one of the most robust illusions around. As I've talked about before, illusions are a concern to ecological psychologists only in that they suggest the task has been incorrectly characterised. This paper presents data that suggests the size-weight illusion is actually functional, and that it reflects the readiness of the human perception-action system to throw objects long distances.

This paper has seen some activity in the popular press already (e.g. here and here): Geoff's hoping for the NYT Science section too! 

UPDATE: Geoff being interview on NPR

Identifying the Visual Information for Relative Phase

Bingham's model predicts that the information for relative phase is the relative direction of movement. The first direct test of this hypothesis was the experiment that followed on from my learning study, in which we systematically perturbed the various candidate information variables to see which affected performance in the perceptual judgement task.

I like this study a lot, if I do say so myself. It's a serious attempt to make a strong test of the model's predictions, and we invested a lot of time in the methodology. This is also that rare paper that benefited from a vigorous review process; the end result is, I think, a clear, careful, and detailed presentation of a critical result for the perception-action approach Geoff and I are developing.

Readers interested in the issue of how you can scientifically study information from an ecological perspective should certainly read the paper (Ken, that's you :); it's my go-to reference for how I believe this has to be done. The main lesson - it's hard to do this properly, but the rewards, in terms of unambiguous data, are clear.

Perceptual Learning Stabilises Action: A Test of the Bingham Model

Bingham's perception-action model was initially inspired by perceptual judgement studies (using vision and proprioception). The HKB phenomena are movement phenomena, however; simply noting that the same qualitative pattern is seen in different judgement and action studies is a good first step but only suggestive, at best. We therefore next took simultaneous judgement & action measures from a movement task where we manipulated the feedback display (Wilson et al, 2005a). For instance, when the display showed 0°, movement was stable, even when the movement was at, for example, 90°. Perception of relative phase was driving the stability of the movements.

On the basis of all this data, the model predicts that the reason 0° and 180° are easy is that the information specifying that you are moving this way is easily perceived. There is provisional evidence to support relative direction of motion as the specifying information (Bogaerts et al, 2003; Wilson et al, 2005b; Wimmers et al, 1992) with relative speed acting as a noise term. This variable certainly predicts the observed pattern, as the relative direction of motion is only stable at 0° and 180°. It is maximally variable at 90°, which would explain why movements here are also maximally unstable. The model is therefore explaining the problem with moving at 90° as a problem detecting the information required to maintain the coordination; as we saw in the case of friction, no information means unstable behaviour.

The model therefore makes a critical prediction. If we could improve people's ability to perceive 90°, they should gain the ability to move at 90° without any practice at the movement itself. All previous learning studies had entailed training people to move by having them move, with the help of various forms of transformed feedback methods (visual metronomes or Lissajous plots; more on this when I discuss feedback). The prediction, that movement stability should improve with improved perceptual ability, is a strong test of both the model and the modelling strategy in general, and the experiment to test it was the first half of my dissertation.